librarian-bots/card_with_first_commit · Datasets at Hugging Face

modelId stringlengths 4 81	tags list	pipeline_tag stringclasses 17 values	config dict	downloads int64 0 59.7M	first_commit timestamp[ns, tz=UTC]	card stringlengths 51 438k
Chakita/gpt2_mwp	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	6	2022-11-21T17:59:15Z	--- license: bsd-3-clause datasets: - bookcorpus - wikipedia - openwebtext --- # FlexiBERT-Mini model Pretrained model on the English language using a macked language modeling (MLM) objective. It was found by executing a neural architecture search (NAS) over a design space of ~3.32 billion flexible and heterogeneous transformer architectures in [this paper](https://arxiv.org/abs/2205.11656). The model is case sensitive. # Model description The model consists of diverse attention heads including the traditional self-attention and the discrete cosine transform (DCT). The design space also supports weighted multiplicative attention (WMA), discrete Fourier transform (DFT), and convolution operations in the same transformer model along with different hidden dimensions for each encoder layer. # How to use This model should be finetuned on a downstream task. Other models within the FlexiBERT design space can be generated using a model dicsiontary. See this [github repo](https://github.com/JHA-Lab/txf_design-space) for more details. To instantiate a fresh FlexiBERT-Mini model (for pre-trainining using the MLM objective): ```python from transformers import FlexiBERTConfig, FlexiBERTModel, FlexiBERTForMaskedLM config = FlexiBERTConfig() model_dict = {'l': 4, 'o': ['sa', 'sa', 'l', 'l'], 'h': [256, 256, 128, 128], 'n': [2, 2, 4, 4], 'f': [[512, 512, 512], [512, 512, 512], [1024], [1024]], 'p': ['sdp', 'sdp', 'dct', 'dct']} config.from_model_dict(model_dict) model = FlexiBERTForMaskedLM(config) ``` # Developer [Shikhar Tuli](https://github.com/shikhartuli). For any questions, comments or suggestions, please reach me at [[email protected]](mailto:[email protected]). # Cite this work Cite our work using the following bitex entry: ``` @article{tuli2022jair, title={{FlexiBERT}: Are Current Transformer Architectures too Homogeneous and Rigid?}, author={Tuli, Shikhar and Dedhia, Bhishma and Tuli, Shreshth and Jha, Niraj K.}, year={2022}, eprint={2205.11656}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # License BSD-3-Clause. Copyright (c) 2022, Shikhar Tuli and Jha Lab. All rights reserved. See License file for more details.
Chan/distilroberta-base-finetuned-wikitext2	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-21T18:29:10Z	--- license: mit tags: - generated_from_trainer model-index: - name: BERiT_2000_2_layers_40_epochs results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERiT_2000_2_layers_40_epochs This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.8375 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - label_smoothing_factor: 0.2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 15.0851 \| 0.19 \| 500 \| 8.5468 \| \| 7.8971 \| 0.39 \| 1000 \| 7.3376 \| \| 7.3108 \| 0.58 \| 1500 \| 7.1632 \| \| 7.134 \| 0.77 \| 2000 \| 7.0700 \| \| 7.0956 \| 0.97 \| 2500 \| 7.0723 \| \| 7.0511 \| 1.16 \| 3000 \| 6.9560 \| \| 7.0313 \| 1.36 \| 3500 \| 6.9492 \| \| 7.0028 \| 1.55 \| 4000 \| 6.9048 \| \| 6.9563 \| 1.74 \| 4500 \| 6.8456 \| \| 6.9214 \| 1.94 \| 5000 \| 6.8019 \| \| 11.1596 \| 2.13 \| 5500 \| 7.5882 \| \| 7.5824 \| 2.32 \| 6000 \| 7.1291 \| \| 7.2581 \| 2.52 \| 6500 \| 7.1123 \| \| 7.2232 \| 2.71 \| 7000 \| 7.1059 \| \| 7.1734 \| 2.9 \| 7500 \| 7.1120 \| \| 7.1504 \| 3.1 \| 8000 \| 7.0946 \| \| 7.1314 \| 3.29 \| 8500 \| 7.0799 \| \| 7.1236 \| 3.49 \| 9000 \| 7.1175 \| \| 7.1275 \| 3.68 \| 9500 \| 7.0905 \| \| 7.1087 \| 3.87 \| 10000 \| 7.0839 \| \| 7.1212 \| 4.07 \| 10500 \| 7.0822 \| \| 7.1136 \| 4.26 \| 11000 \| 7.0703 \| \| 7.1025 \| 4.45 \| 11500 \| 7.1035 \| \| 7.0931 \| 4.65 \| 12000 \| 7.0759 \| \| 7.0899 \| 4.84 \| 12500 \| 7.0883 \| \| 7.0834 \| 5.03 \| 13000 \| 7.1307 \| \| 7.0761 \| 5.23 \| 13500 \| 7.0642 \| \| 7.0706 \| 5.42 \| 14000 \| 7.0324 \| \| 7.0678 \| 5.62 \| 14500 \| 7.0704 \| \| 7.0614 \| 5.81 \| 15000 \| 7.0317 \| \| 7.0569 \| 6.0 \| 15500 \| 7.0421 \| \| 7.057 \| 6.2 \| 16000 \| 7.0250 \| \| 7.0503 \| 6.39 \| 16500 \| 7.0129 \| \| 7.0529 \| 6.58 \| 17000 \| 7.0316 \| \| 7.0453 \| 6.78 \| 17500 \| 7.0436 \| \| 7.0218 \| 6.97 \| 18000 \| 7.0064 \| \| 7.0415 \| 7.16 \| 18500 \| 7.0385 \| \| 7.0338 \| 7.36 \| 19000 \| 6.9756 \| \| 7.0488 \| 7.55 \| 19500 \| 7.0054 \| \| 7.0347 \| 7.75 \| 20000 \| 6.9946 \| \| 7.0464 \| 7.94 \| 20500 \| 7.0055 \| \| 7.017 \| 8.13 \| 21000 \| 7.0158 \| \| 7.0159 \| 8.33 \| 21500 \| 7.0052 \| \| 7.0223 \| 8.52 \| 22000 \| 6.9925 \| \| 6.9989 \| 8.71 \| 22500 \| 7.0307 \| \| 7.0218 \| 8.91 \| 23000 \| 6.9767 \| \| 6.9998 \| 9.1 \| 23500 \| 7.0096 \| \| 7.01 \| 9.3 \| 24000 \| 6.9599 \| \| 6.9964 \| 9.49 \| 24500 \| 6.9896 \| \| 6.9906 \| 9.68 \| 25000 \| 6.9903 \| \| 7.0336 \| 9.88 \| 25500 \| 6.9807 \| \| 7.0053 \| 10.07 \| 26000 \| 6.9776 \| \| 6.9826 \| 10.26 \| 26500 \| 6.9836 \| \| 6.9897 \| 10.46 \| 27000 \| 6.9886 \| \| 6.9829 \| 10.65 \| 27500 \| 6.9991 \| \| 6.9849 \| 10.84 \| 28000 \| 6.9651 \| \| 6.9901 \| 11.04 \| 28500 \| 6.9822 \| \| 6.9852 \| 11.23 \| 29000 \| 6.9921 \| \| 6.9757 \| 11.43 \| 29500 \| 6.9636 \| \| 6.991 \| 11.62 \| 30000 \| 6.9952 \| \| 6.9818 \| 11.81 \| 30500 \| 6.9799 \| \| 6.9911 \| 12.01 \| 31000 \| 6.9725 \| \| 6.9423 \| 12.2 \| 31500 \| 6.9540 \| \| 6.9885 \| 12.39 \| 32000 \| 6.9771 \| \| 6.9636 \| 12.59 \| 32500 \| 6.9475 \| \| 6.9567 \| 12.78 \| 33000 \| 6.9653 \| \| 6.9749 \| 12.97 \| 33500 \| 6.9711 \| \| 6.9739 \| 13.17 \| 34000 \| 6.9691 \| \| 6.9651 \| 13.36 \| 34500 \| 6.9569 \| \| 6.9599 \| 13.56 \| 35000 \| 6.9608 \| \| 6.957 \| 13.75 \| 35500 \| 6.9531 \| \| 6.9539 \| 13.94 \| 36000 \| 6.9704 \| \| 6.958 \| 14.14 \| 36500 \| 6.9478 \| \| 6.9597 \| 14.33 \| 37000 \| 6.9510 \| \| 6.9466 \| 14.52 \| 37500 \| 6.9625 \| \| 6.9518 \| 14.72 \| 38000 \| 6.9787 \| \| 6.9509 \| 14.91 \| 38500 \| 6.9391 \| \| 6.9505 \| 15.1 \| 39000 \| 6.9694 \| \| 6.9311 \| 15.3 \| 39500 \| 6.9440 \| \| 6.9513 \| 15.49 \| 40000 \| 6.9425 \| \| 6.9268 \| 15.69 \| 40500 \| 6.9223 \| \| 6.9415 \| 15.88 \| 41000 \| 6.9435 \| \| 6.9308 \| 16.07 \| 41500 \| 6.9281 \| \| 6.9216 \| 16.27 \| 42000 \| 6.9415 \| \| 6.9265 \| 16.46 \| 42500 \| 6.9164 \| \| 6.9023 \| 16.65 \| 43000 \| 6.9237 \| \| 6.9407 \| 16.85 \| 43500 \| 6.9100 \| \| 6.9211 \| 17.04 \| 44000 \| 6.9295 \| \| 6.9147 \| 17.23 \| 44500 \| 6.9131 \| \| 6.9224 \| 17.43 \| 45000 \| 6.9188 \| \| 6.9215 \| 17.62 \| 45500 \| 6.9077 \| \| 6.915 \| 17.82 \| 46000 \| 6.9371 \| \| 6.906 \| 18.01 \| 46500 \| 6.8932 \| \| 6.91 \| 18.2 \| 47000 \| 6.9100 \| \| 6.8999 \| 18.4 \| 47500 \| 6.9251 \| \| 6.9113 \| 18.59 \| 48000 \| 6.9078 \| \| 6.9197 \| 18.78 \| 48500 \| 6.9099 \| \| 6.8985 \| 18.98 \| 49000 \| 6.9074 \| \| 6.9009 \| 19.17 \| 49500 \| 6.8971 \| \| 6.8937 \| 19.36 \| 50000 \| 6.8982 \| \| 6.9094 \| 19.56 \| 50500 \| 6.9077 \| \| 6.9069 \| 19.75 \| 51000 \| 6.9006 \| \| 6.8991 \| 19.95 \| 51500 \| 6.8912 \| \| 6.8924 \| 20.14 \| 52000 \| 6.8881 \| \| 6.899 \| 20.33 \| 52500 \| 6.8899 \| \| 6.9028 \| 20.53 \| 53000 \| 6.8938 \| \| 6.8997 \| 20.72 \| 53500 \| 6.8822 \| \| 6.8943 \| 20.91 \| 54000 \| 6.9005 \| \| 6.8804 \| 21.11 \| 54500 \| 6.9048 \| \| 6.8848 \| 21.3 \| 55000 \| 6.9062 \| \| 6.9072 \| 21.49 \| 55500 \| 6.9104 \| \| 6.8783 \| 21.69 \| 56000 \| 6.9069 \| \| 6.8879 \| 21.88 \| 56500 \| 6.8938 \| \| 6.8922 \| 22.08 \| 57000 \| 6.8797 \| \| 6.8892 \| 22.27 \| 57500 \| 6.9168 \| \| 6.8863 \| 22.46 \| 58000 \| 6.8820 \| \| 6.8822 \| 22.66 \| 58500 \| 6.9130 \| \| 6.8752 \| 22.85 \| 59000 \| 6.8973 \| \| 6.8823 \| 23.04 \| 59500 \| 6.8933 \| \| 6.8813 \| 23.24 \| 60000 \| 6.8919 \| \| 6.8787 \| 23.43 \| 60500 \| 6.8855 \| \| 6.8886 \| 23.63 \| 61000 \| 6.8956 \| \| 6.8744 \| 23.82 \| 61500 \| 6.9092 \| \| 6.8799 \| 24.01 \| 62000 \| 6.8944 \| \| 6.879 \| 24.21 \| 62500 \| 6.8850 \| \| 6.8797 \| 24.4 \| 63000 \| 6.8782 \| \| 6.8724 \| 24.59 \| 63500 \| 6.8691 \| \| 6.8803 \| 24.79 \| 64000 \| 6.8965 \| \| 6.8899 \| 24.98 \| 64500 \| 6.8986 \| \| 6.8873 \| 25.17 \| 65000 \| 6.9034 \| \| 6.8777 \| 25.37 \| 65500 \| 6.8658 \| \| 6.8784 \| 25.56 \| 66000 \| 6.8803 \| \| 6.8791 \| 25.76 \| 66500 \| 6.8727 \| \| 6.8736 \| 25.95 \| 67000 \| 6.8832 \| \| 6.8865 \| 26.14 \| 67500 \| 6.8811 \| \| 6.8668 \| 26.34 \| 68000 \| 6.8817 \| \| 6.8709 \| 26.53 \| 68500 \| 6.8945 \| \| 6.8755 \| 26.72 \| 69000 \| 6.8777 \| \| 6.8635 \| 26.92 \| 69500 \| 6.8747 \| \| 6.8752 \| 27.11 \| 70000 \| 6.8875 \| \| 6.8729 \| 27.3 \| 70500 \| 6.8696 \| \| 6.8728 \| 27.5 \| 71000 \| 6.8659 \| \| 6.8692 \| 27.69 \| 71500 \| 6.8856 \| \| 6.868 \| 27.89 \| 72000 \| 6.8689 \| \| 6.8668 \| 28.08 \| 72500 \| 6.8877 \| \| 6.8576 \| 28.27 \| 73000 \| 6.8783 \| \| 6.8633 \| 28.47 \| 73500 \| 6.8828 \| \| 6.8737 \| 28.66 \| 74000 \| 6.8717 \| \| 6.8702 \| 28.85 \| 74500 \| 6.8485 \| \| 6.8785 \| 29.05 \| 75000 \| 6.8771 \| \| 6.8818 \| 29.24 \| 75500 \| 6.8815 \| \| 6.8647 \| 29.43 \| 76000 \| 6.8877 \| \| 6.8574 \| 29.63 \| 76500 \| 6.8920 \| \| 6.8474 \| 29.82 \| 77000 \| 6.8936 \| \| 6.8558 \| 30.02 \| 77500 \| 6.8768 \| \| 6.8645 \| 30.21 \| 78000 \| 6.8921 \| \| 6.8786 \| 30.4 \| 78500 \| 6.8604 \| \| 6.8693 \| 30.6 \| 79000 \| 6.8603 \| \| 6.855 \| 30.79 \| 79500 \| 6.8559 \| \| 6.8429 \| 30.98 \| 80000 \| 6.8746 \| \| 6.8688 \| 31.18 \| 80500 \| 6.8774 \| \| 6.8735 \| 31.37 \| 81000 \| 6.8643 \| \| 6.8541 \| 31.56 \| 81500 \| 6.8767 \| \| 6.8695 \| 31.76 \| 82000 \| 6.8804 \| \| 6.8607 \| 31.95 \| 82500 \| 6.8674 \| \| 6.8538 \| 32.15 \| 83000 \| 6.8572 \| \| 6.8472 \| 32.34 \| 83500 \| 6.8683 \| \| 6.8763 \| 32.53 \| 84000 \| 6.8758 \| \| 6.8405 \| 32.73 \| 84500 \| 6.8764 \| \| 6.8658 \| 32.92 \| 85000 \| 6.8614 \| \| 6.8834 \| 33.11 \| 85500 \| 6.8641 \| \| 6.8554 \| 33.31 \| 86000 \| 6.8787 \| \| 6.8738 \| 33.5 \| 86500 \| 6.8747 \| \| 6.848 \| 33.69 \| 87000 \| 6.8699 \| \| 6.8621 \| 33.89 \| 87500 \| 6.8654 \| \| 6.8543 \| 34.08 \| 88000 \| 6.8639 \| \| 6.8606 \| 34.28 \| 88500 \| 6.8852 \| \| 6.8666 \| 34.47 \| 89000 \| 6.8840 \| \| 6.8717 \| 34.66 \| 89500 \| 6.8773 \| \| 6.854 \| 34.86 \| 90000 \| 6.8671 \| \| 6.8526 \| 35.05 \| 90500 \| 6.8762 \| \| 6.8592 \| 35.24 \| 91000 \| 6.8644 \| \| 6.8641 \| 35.44 \| 91500 \| 6.8599 \| \| 6.8655 \| 35.63 \| 92000 \| 6.8622 \| \| 6.8557 \| 35.82 \| 92500 \| 6.8671 \| \| 6.8546 \| 36.02 \| 93000 \| 6.8573 \| \| 6.853 \| 36.21 \| 93500 \| 6.8542 \| \| 6.8597 \| 36.41 \| 94000 \| 6.8518 \| \| 6.8576 \| 36.6 \| 94500 \| 6.8700 \| \| 6.8549 \| 36.79 \| 95000 \| 6.8628 \| \| 6.8576 \| 36.99 \| 95500 \| 6.8695 \| \| 6.8505 \| 37.18 \| 96000 \| 6.8870 \| \| 6.8564 \| 37.37 \| 96500 \| 6.8898 \| \| 6.8627 \| 37.57 \| 97000 \| 6.8619 \| \| 6.8502 \| 37.76 \| 97500 \| 6.8696 \| \| 6.8548 \| 37.96 \| 98000 \| 6.8663 \| \| 6.8512 \| 38.15 \| 98500 \| 6.8683 \| \| 6.8484 \| 38.34 \| 99000 \| 6.8605 \| \| 6.8581 \| 38.54 \| 99500 \| 6.8749 \| \| 6.8525 \| 38.73 \| 100000 \| 6.8849 \| \| 6.8375 \| 38.92 \| 100500 \| 6.8712 \| \| 6.8423 \| 39.12 \| 101000 \| 6.8905 \| \| 6.8559 \| 39.31 \| 101500 \| 6.8574 \| \| 6.8441 \| 39.5 \| 102000 \| 6.8722 \| \| 6.8467 \| 39.7 \| 102500 \| 6.8550 \| \| 6.8389 \| 39.89 \| 103000 \| 6.8375 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2
Chandanbhat/distilbert-base-uncased-finetuned-cola	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-21T18:56:14Z	--- license: mit tags: - generated_from_trainer - nlu - intent-classification - text-classification metrics: - accuracy - f1 model-index: - name: xlm-r-base-amazon-massive-intent-label_smoothing results: - task: name: intent-classification type: intent-classification dataset: name: MASSIVE type: AmazonScience/massive split: test metrics: - name: F1 type: f1 value: 0.8879 datasets: - AmazonScience/massive language: - en --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-r-base-amazon-massive-intent-label_smoothing This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset. It achieves the following results on the evaluation set: - Loss: 2.5148 - Accuracy: 0.8879 - F1: 0.8879 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - label_smoothing_factor: 0.4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 3.3945 \| 1.0 \| 720 \| 2.7175 \| 0.7900 \| 0.7900 \| \| 2.7629 \| 2.0 \| 1440 \| 2.5660 \| 0.8549 \| 0.8549 \| \| 2.5143 \| 3.0 \| 2160 \| 2.5389 \| 0.8711 \| 0.8711 \| \| 2.4678 \| 4.0 \| 2880 \| 2.5172 \| 0.8883 \| 0.8883 \| \| 2.4187 \| 5.0 \| 3600 \| 2.5148 \| 0.8879 \| 0.8879 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2
CharlieChen/feedback-bigbird	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-vanilla-mtop results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-vanilla-mtop This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1581 - Exact Match: 0.6331 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Exact Match \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------:\| \| 1.5981 \| 6.65 \| 200 \| 0.1598 \| 0.4940 \| \| 0.1335 \| 13.33 \| 400 \| 0.1155 \| 0.5884 \| \| 0.074 \| 19.98 \| 600 \| 0.1046 \| 0.6094 \| \| 0.0497 \| 26.65 \| 800 \| 0.1065 \| 0.6139 \| \| 0.0363 \| 33.33 \| 1000 \| 0.1134 \| 0.6255 \| \| 0.0278 \| 39.98 \| 1200 \| 0.1177 \| 0.6313 \| \| 0.022 \| 46.65 \| 1400 \| 0.1264 \| 0.6255 \| \| 0.0183 \| 53.33 \| 1600 \| 0.1260 \| 0.6304 \| \| 0.0151 \| 59.98 \| 1800 \| 0.1312 \| 0.6300 \| \| 0.0124 \| 66.65 \| 2000 \| 0.1421 \| 0.6277 \| \| 0.0111 \| 73.33 \| 2200 \| 0.1405 \| 0.6277 \| \| 0.0092 \| 79.98 \| 2400 \| 0.1466 \| 0.6331 \| \| 0.008 \| 86.65 \| 2600 \| 0.1522 \| 0.6340 \| \| 0.007 \| 93.33 \| 2800 \| 0.1590 \| 0.6295 \| \| 0.0064 \| 99.98 \| 3000 \| 0.1581 \| 0.6331 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
Charlotte77/model_test	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	Third model is Nightmare Wet Worms. Prompt being "NghtmrWrmFrk". It's more based on my models that are full of tentacles, worms, maggots, wet looking, drippy....etc. This model isn't perfect and alot of words don't seem to matter as much, but you can still get some amazing results if your into this type of look. Heck, just type a bunch of random words and you get weird images! Keep the CFG low, steps at any amount though. Samples can be anything. ![MODEL 4.png](https://s3.amazonaws.com/moonup/production/uploads/1669065138906-6333e639d58823d613336ee3.png)
ChaseBread/DialoGPT-small-harrypotter	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	9	2022-11-21T19:08:28Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-base-vanilla-mtop results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-vanilla-mtop This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2080 - Exact Match: 0.6394 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Exact Match \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------:\| \| 1.0516 \| 6.65 \| 200 \| 0.1173 \| 0.5875 \| \| 0.0541 \| 13.33 \| 400 \| 0.1130 \| 0.6331 \| \| 0.0468 \| 19.98 \| 600 \| 0.1290 \| 0.6036 \| \| 0.0241 \| 26.65 \| 800 \| 0.1306 \| 0.6273 \| \| 0.0125 \| 33.33 \| 1000 \| 0.1425 \| 0.6291 \| \| 0.0077 \| 39.98 \| 1200 \| 0.1518 \| 0.6345 \| \| 0.0054 \| 46.65 \| 1400 \| 0.1643 \| 0.6362 \| \| 0.004 \| 53.33 \| 1600 \| 0.1718 \| 0.6362 \| \| 0.0033 \| 59.98 \| 1800 \| 0.1803 \| 0.6336 \| \| 0.0026 \| 66.65 \| 2000 \| 0.1808 \| 0.6394 \| \| 0.0021 \| 73.33 \| 2200 \| 0.1915 \| 0.6371 \| \| 0.0017 \| 79.98 \| 2400 \| 0.1919 \| 0.6403 \| \| 0.0013 \| 86.65 \| 2600 \| 0.2024 \| 0.6358 \| \| 0.0011 \| 93.33 \| 2800 \| 0.2049 \| 0.6353 \| \| 0.0008 \| 99.98 \| 3000 \| 0.2080 \| 0.6394 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
Cheapestmedsshop/Buymodafinilus	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- tags: - generated_from_keras_callback model-index: - name: GeoBERT results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # GeoBERT_Analyzer GeoBERT_Analyzer is a Text Classification model that was fine-tuned from GeoBERT on the Geoscientific Corpus dataset. The model was trained on the Labeled Geoscientific & Non-Geosceintific Corpus dataset (21416 x 2 sentences). ## Intended uses The train aims to make the Language Model have the ability to distinguish between Geoscience and Non – Geoscience (General) corpus ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Framework versions - Transformers 4.22.1 - TensorFlow 2.10.0 - Datasets 2.4.0 - Tokenizers 0.12.1 ## Model performances (metric: seqeval) entity\|precision\|recall\|f1 -\|-\|-\|- General \|0.9976\|0.9980\|0.9978 Geoscience\|0.9980\|0.9984\|0.9982 ## How to use GeoBERT with HuggingFace ##### Load GeoBERT and its sub-word tokenizer : ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("botryan96/GeoBERT_analyzer") model = AutoModelForTokenClassification.from_pretrained("botryan96/GeoBERT_analyzer") #Define the pipeline from transformers import pipeline anlyze_machine=pipeline('text-classification',model = model_checkpoint2) #Define the sentences sentences = ['the average iron and sulfate concentrations were calculated to be 19 . 6 5 . 2 and 426 182 mg / l , respectively .', 'She first gained media attention as a friend and stylist of Paris Hilton'] #Deploy the machine anlyze_machine(sentences) ```
Cheatham/xlm-roberta-base-finetuned	[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "XLMRobertaForSequenceClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	20	null	--- language: - en thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg tags: - question-answering license: apache-2.0 datasets: - squad metrics: - squad --- # DistilBERT with a second step of distillation ## Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` ## Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` ## Training procedure ## Eval results \| \| Exact Match \| F1 \| \|------------------\|-------------\|------\| \| DistilBERT paper \| 79.1 \| 86.9 \| \| Ours \| 78.4 \| 86.5 \| The scores were calculated using the `squad` metric from `datasets`. ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Cheatham/xlm-roberta-large-finetuned-d1	[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "XLMRobertaForSequenceClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	20	2022-11-21T19:12:40Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### ataturkai Dreambooth model trained by thothai with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Cheatham/xlm-roberta-large-finetuned-r01	[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "XLMRobertaForSequenceClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	23	2022-11-21T19:21:21Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### Open Potion Bottle v2 Dreambooth model trained by [piEsposito](https://twitter.com/piesposi_to) with open weights, configs and prompts (as it should be) - Concept: `potionbottle` You can run this concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: ## Usage examples with `potionbottle` - Prompt: fantasy dragon inside a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_1.png" width=512/> - Prompt: potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_2.png" width=512/> - Prompt: green potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_3.png" width=512/> - Prompt: spiral galaxy inside a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_4.png" width=512/> - Prompt: lightning storm inside a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_5.png" width=512/> - Prompt: pomeranian as a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_6.png" width=512/> - Prompt: milkshake as potionbottle, perfectly ornated, intricate details, 3d render vray, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_7.png" width=512/> - Prompt: a square potionbottle full of fire. Art by smoose2. Caustic reflections, shadows - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_8.png" width=512/> #### By https://twitter.com/piesposi_to
Cheatham/xlm-roberta-large-finetuned3	[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "XLMRobertaForSequenceClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	22	2022-11-21T19:21:32Z	--- language: "en" thumbnail: tags: - speechbrain - embeddings - Speaker - Verification - Identification - pytorch - ECAPA-TDNN license: "apache-2.0" datasets: - voxceleb metrics: - EER - Accuracy inference: true widget: - example_title: VoxCeleb Speaker id10003 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav - example_title: VoxCeleb Speaker id10004 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav --- # Speaker Identification with ECAPA-TDNN embeddings on Voxceleb This repository provides a pretrained ECAPA-TDNN model using SpeechBrain. The system can be used to extract speaker embeddings as well. Since we can't find any resource that has SpeechBrain or HuggingFace compatible checkpoints that has only been trained on VoxCeleb2 development data, so we decide to pre-train an ECAPA-TDNN system from scratch. # Pipeline description This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. We use FBank (16kHz, 25ms frame length, 10ms hop length, 80 filter-bank channels) as the input features. It was trained using initial learning rate of 0.001 and batch size of 512 with cyclical learning rate policy (CLR) for 20 epochs on 4 A100 GPUs. We employ additive noises and reverberation from [MUSAN](http://www.openslr.org/17/) and [RIR](http://www.openslr.org/28/) datasets to enrich the supervised information. The pre-training progress takes approximately ten days for the ECAPA-TDNN model. # Performance VoxCeleb1-O is the original verification test set from VoxCeleb1 consisting of 40 speakers. All speakers with names starting with "E" are reserved for testing. VoxCeleb1-E uses the entire VoxCeleb1 dataset, covering 1251 speakers. VoxCeleb1-H is a hard version of evaluation set consisting of 552536 pairs with 1190 speakers with the same nationality and gender. There are 18 nationality-gender combinations each with at least 5 individuals. \| Splits \| Backend \| S-norm \| EER(%) \| minDCF(0.01) \| \|:-------------:\|:--------------:\|:--------------:\|:--------------:\|:--------------:\| \| VoxCeleb1-O \| cosine \| no \| 1.29 \| 0.13 \| \| VoxCeleb1-O \| cosine \| yes \| 1.19 \| 0.11 \| \| VoxCeleb1-E \| cosine \| no \| 1.42 \| 0.16 \| \| VoxCeleb1-E \| cosine \| yes \| 1.31 \| 0.14 \| \| VoxCeleb1-H \| cosine \| no \| 2.66 \| 0.26 \| \| VoxCeleb1-H \| cosine \| yes \| 2.48 \| 0.23 \| - VoxCeleb1-O: includes 37611 test pairs with 40 speakers. - VoxCeleb1-E: includes 579818 test pairs with 1251 speakers. - VoxCeleb1-H: includes 550894 test pairs with 1190 speakers. # Compute the speaker embeddings The system is trained with recordings sampled at 16kHz (single channel). ```python import torch import torchaudio from speechbrain.pretrained.interfaces import Pretrained from speechbrain.pretrained import EncoderClassifier class Encoder(Pretrained): MODULES_NEEDED = [ "compute_features", "mean_var_norm", "embedding_model" ] def __init__(self, args, kwargs): super().__init__(args, **kwargs) def encode_batch(self, wavs, wav_lens=None, normalize=False): # Manage single waveforms in input if len(wavs.shape) == 1: wavs = wavs.unsqueeze(0) # Assign full length if wav_lens is not assigned if wav_lens is None: wav_lens = torch.ones(wavs.shape[0], device=self.device) # Storing waveform in the specified device wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device) wavs = wavs.float() # Computing features and embeddings feats = self.mods.compute_features(wavs) feats = self.mods.mean_var_norm(feats, wav_lens) embeddings = self.mods.embedding_model(feats, wav_lens) if normalize: embeddings = self.hparams.mean_var_norm_emb( embeddings, torch.ones(embeddings.shape[0], device=self.device) ) return embeddings classifier = Encoder.from_hparams( source="yangwang825/ecapa-tdnn-vox2" ) signal, fs = torchaudio.load('spk1_snt1.wav') embeddings = classifier.encode_batch(signal) >>> torch.Size([1, 1, 192]) ``` We will release our training results (models, logs, etc) shortly. # References 1. Ravanelli et al., SpeechBrain: A General-Purpose Speech Toolkit, 2021 2. Desplanques et al., ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification, 2020
Check/vaw2tmp	[ "tensorboard" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-21T19:24:42Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus100 model-index: - name: t5-small-finetuned-ta-to-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-ta-to-en This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 3.6087 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 3.826 \| 1.0 \| 11351 \| 3.6087 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2
CheonggyeMountain-Sherpa/kogpt-trinity-poem	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	15	2022-11-21T19:28:21Z	--- license: creativeml-openrail-m tags: - text-to-image widget: - text: "food_crit " --- ### Jak's Creepy Critter Pack for Stable Diffusion Trained using TheLastBen Dreambooth colab notebook, using 95 training images, 5000 training steps. Use Prompt: "food_crit" in the beginning of your prompt followed by a food. No major prompt-crafting needed. Thanks to /u/Jak_TheAI_Artist for supplying training images! Sample pictures of this concept: prompt: "food_crit, spaghetti and meatballs" ![food 0](https://huggingface.co/plasmo/food-crit/resolve/main/concept_images/spag.jpg) prompt: "food_crit, snowcone" ![food 1](https://huggingface.co/plasmo/food-crit/resolve/main/concept_images/snow.jpg) prompt: "food_crit, cola cola, vibrant colors" Steps: 27, Sampler: Euler a, CFG scale: 6, Seed: 1195328763![food 2](https://huggingface.co/plasmo/food-crit/resolve/main/concept_images/cola.jpg) prompt: "shiny ceramic 3d painting, (mens's shoe creature) gum stuck to sole, high detail render, vibrant, cinematic lighting" Negative prompt: painting, photoshop, illustration, blurry, dull, drawing Steps: 40, Sampler: Euler a, CFG scale: 10, Seed: 1018346393![food 3](https://huggingface.co/plasmo/food-crit/resolve/main/concept_images/shoes.jpg) Prompt: "melting trippy zombie muscle car, smoking, with big eyes, hyperrealistic, intricate detail, high detail render, vibrant, cinematic lighting, shiny, ceramic, reflections" Negative prompt: "painting, photoshop, illustration, blurry, dull" Steps: 40, Sampler: Euler a, CFG scale: 10, Seed: 3713218290, Size: 960x512, Model hash: d9aa872b![food 4](https://huggingface.co/plasmo/food-crit/resolve/main/concept_images/car.jpg)
Chertilasus/main	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-21T19:28:59Z	--- language: - te license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - Chai_Bisket_Stories_16-08-2021_14-17 metrics: - wer model-index: - name: Whisper Small Telugu - Naga Budigam results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Chai_Bisket_Stories_16-08-2021_14-17 type: Chai_Bisket_Stories_16-08-2021_14-17 config: None split: None args: 'config: te, split: test' metrics: - name: Wer type: wer value: 77.48711850971065 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Telugu - Naga Budigam This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Chai_Bisket_Stories_16-08-2021_14-17 dataset. It achieves the following results on the evaluation set: - Loss: 0.7063 - Wer: 77.4871 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.2933 \| 2.62 \| 500 \| 0.3849 \| 86.6429 \| \| 0.0692 \| 5.24 \| 1000 \| 0.3943 \| 82.7190 \| \| 0.0251 \| 7.85 \| 1500 \| 0.4720 \| 82.4415 \| \| 0.0098 \| 10.47 \| 2000 \| 0.5359 \| 81.6092 \| \| 0.0061 \| 13.09 \| 2500 \| 0.5868 \| 75.9413 \| \| 0.0025 \| 15.71 \| 3000 \| 0.6235 \| 76.6944 \| \| 0.0009 \| 18.32 \| 3500 \| 0.6634 \| 78.3987 \| \| 0.0005 \| 20.94 \| 4000 \| 0.6776 \| 77.1700 \| \| 0.0002 \| 23.56 \| 4500 \| 0.6995 \| 78.2798 \| \| 0.0001 \| 26.18 \| 5000 \| 0.7063 \| 77.4871 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
Chester/traffic-rec	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-21T19:30:17Z	--- language: en datasets: - Dizex/InstaFoodSet widget: - text: "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!" example_title: "Food example 1" - text: "Tartufo Pasta with garlic flavoured butter and olive oil, egg yolk, parmigiano and pasta water." example_title: "Food example 2" tags: - Instagram - NER - Named Entity Recognition - Food Entity Extraction - Social Media - Informal text - RoBERTa license: mit --- # InstaFoodRoBERTa-NER ## Model description InstaFoodRoBERTa-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition of Food entities on informal text (social media like). It has been trained to recognize a single entity: food (FOOD). Specifically, this model is a roberta-base model that was fine-tuned on a dataset consisting of 400 English Instagram posts related to food. The [dataset](https://huggingface.co/datasets/Dizex/InstaFoodSet) is open source. ## Intended uses #### How to use You can use this model with Transformers pipeline for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Dizex/InstaFoodRoBERTa-NER") model = AutoModelForTokenClassification.from_pretrained("Dizex/InstaFoodRoBERTa-NER") pipe = pipeline("ner", model=model, tokenizer=tokenizer) example = "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!" ner_entity_results = pipe(example, aggregation_strategy="simple") print(ner_entity_results) ``` To get the extracted food entities as strings you can use the following code: ```python def convert_entities_to_list(text, entities: list[dict]) -> list[str]: ents = [] for ent in entities: e = {"start": ent["start"], "end": ent["end"], "label": ent["entity_group"]} if ents and -1 <= ent["start"] - ents[-1]["end"] <= 1 and ents[-1]["label"] == e["label"]: ents[-1]["end"] = e["end"] continue ents.append(e) return [text[e["start"]:e["end"]] for e in ents] print(convert_entities_to_list(example, ner_entity_results)) ``` This will result in the following output: ```python ['olive poké bowl', 'chia seeds'] ``` ## Performance on [InstaFoodSet](https://huggingface.co/datasets/Dizex/InstaFoodSet) metric\|val -\|- f1 \|0.91 precision \|0.89 recall \|0.93
Chikita1/www_stash_stock	[ "license:bsd-3-clause-clear" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-21T21:12:34Z	--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/gandr-collage.jpg" tags: - stable-diffusion - text-to-image - image-to-image --- # 4elements-diffusion ##### A StableDiffusion All-In-One Legend of Korra style + Korra character Dreambooth model created by AI-Characters #### For what tokens to use in your prompts to employ the desired effects scroll down to the following section of this page: "Tokens to use to prompt the artstyle as well as Korra's different outfits" ![Thumbnail](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/gandr-collage.jpg) Feel free to donate to my [KoFi](https://ko-fi.com/aicharacters) to help me fund renting GPU's for further model creation and experimentation! Follow me on [Twitter](https://twitter.com/ai_characters) and [Instagram](https://www.instagram.com/ai_characters/) for AI art posts and model updates! ## Quick Feature Overview - Create anyone and anything in the LoK artstyle! - Create Korra in any artstyle! - Mix and match all of Korra's outfits however you want to! - Give anyone Korra's outfits! - Give Korra any outfits! This model is much trickier to use than other models, but in return it is very flexible and has high likeness! I thus highly recommend checking out the "How to correctly use this model" section of this page! --- This model is not yet final! I will keep working on it and trying to improve it! I also welcome anyone to use my uploaded dataset (see at the bottom of this page) to create a better version! --- ## IMPORTANT INFORMATION BEFORE YOU USE THIS MODEL I highly recommend using img2img when using this model, either by converting photos into the Legend of Korra artstyle or by resizing your initial 512x512 txt2img Legend of Korra style generations up to 1024x1024 or higher resolutions. Your initial 512x512 txt2img generations using the Legend of Korra artstyle WILL ALWAYS look like crap if you generate shots of characters that are more zoomed out than just a closeup (e.g. half-body or full-shot). Resizing the initial 512x512 generations to 1024x1024 or bigger (full-shots will likely need 1536x1536 to look good) using img2img will drastically improve your experience using this model! The model is also infected, e.g. photos output with this model WILL look different from those output in the vanilla SD model! So I recommend generating people in the vanilla SD model using txt2img first and then sending them to img2img and switching the model to this one and then applying the style! This way your result is more true to vanilla SD but just with the style applied! For more useful information on how to correctly use this model, see the "How to correctly use this model" section of this page! ## Introduction Welcome to my first ever published StableDiffusion model and the first public model trained on the Legend of Korra artstyle! But not just the artstyle: *I have trained this model on Korra, including all* of her outfits, as well! In total this model was trained using a manually captioned dataset of 1142 images: screencaps from the show, fanart, and cosplay photos. I spent every day the last 4 weeks working on this project and spent hundreds of euros renting many many many GPU hours on VastAI to experiment with various parameters. I have created more than 50 ckpt's since then and learned a ton since then and got a ton of insight. ## Recommended samplers, steps, CFG values and denoising strength settings (for img2img) - Euler a at 20 steps for quick results - LMS at 100-150 steps for higher quality results that also follow your prompt more closely - DPM++ 2M Karras at 20 steps for an alternative to EulerA - CFG value from 7 to 4 (4 can look better in terms of image quality because it will have less of the overtraining effect, but it can also look less detailed) - denoising strength of 0.4-0.6 for general img2img, and up to around 0.8 for more harcore cases where the style needs more denoising to be correctly applied (thoughthat will change the image of course, consider also to just do multiple runs through 0.5-0.6) ## How to correctly use this model (it's not as simple as the other models floating around the web currently!) This model is not as easy to use as some of the other models you might be used to. For good results prompt engineering and img2img resizing is required. I highly recommend tinkering with the prompt weights, word order in the prompt, samplers, cfg and step values, etc! The results can be well worth it! My recommendation is to generate a photo in the vanilla SD model, send it to img2img, then switching the model to this one, and using the img2img function to transfer the style to the Legend of Korra style! Also consider inpainting (though this model isn't trained on the new base inpainting model yet)! I also recommend to keep prompts simple and the "zoom" closer to the character for better results! Though sometimes a highly complex prompt can also result in much better generations, e.g. "Emma Watson, tlok artstyle" will almost always produce much worse results than a more complex prompt! - The most important bit first: SD doesn't play well with the artstyle at the standard 512x512. So your initial 512x512 generations in the artstyle will need to be resized to 1024x1024 for half-body shots and 1536x1536 for full-body shots in order to look good. Closeups will look okay in 512x512 but I still recommend upscaling to 1024x1024. An example: Initial 512x512 generation ![Initial 512x512 generation](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/29103-3085544956-full-shot%20Emma%20Watson%20standing%20city%20street%20background%20day%20tlok%20artstyle.png) Upscaled to 1024x1024 (with an inpainted face) ![Upscaled to 1024x1024 (with an inpainted face)](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00405-773303902-facial%20closeup%20Emma%20Watson%20standing%20city%20street%20background%20day%20tlok%20artstyle.png) Upscaled to 1526x1536 (with an inpainted face) ![Upscaled to 1526x1536 (with an inpainted face)](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00410-2273971619-facial%20closeup%20big%20eyes%20Emma%20Watson%20standing%20city%20street%20background%20day%20tlok%20artstyle.png) - I highly recommend using the following negative prompt for all generations (no matter what style, aka it massively improves the tlok artstyle generations as well!): blur, vignette, instagram This will drastically reduce the "overtrained effect" of the generations, e.g. too bright, vignetted and fried images. I have no idea why that works. It just does. Examples: Without the negative prompt: ![Without the negative prompt](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0164.png) With the negative prompt: ![With the negative prompt)](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0165.png) - Only for photos: You can add "photo, tlok artstyle" to the negative prompt for a further reduction in the "overtrained effect"! Doesn't always work, but sometimes does! Having photo in both the positive and negative prompt may sound nonsensical, but it works! - Also consider going from a CFG value of 7 down to a CFG value of 4. This will make the image somewhat less detailed but it will also look much better in certain cases! Example: CFG value of 7: ![CFG7](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0399.png) CFG value of 4: ![CFG4](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0398.png) - Use "cosplay photo" and not just "photo" in your positive prompt as just "photo" is sometimes not strong enough to force through the photo style, while "cosplay photo" almost always is because the captions were trained on that! Example: Just "photo" ![Just "photo"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0170.png) "cosplay photo" !["cosplay photo"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0169.png) - The model was trained using captions such as "cosplay photo", "full-shot", "half-body", "closeup", "facial closeup", among others. So in case you are trying to force a different style but the tlok artstyle keeps popping up, try changing "full-shot" to "full-body" for example! - Alternatively, add "tlok artstyle" to the negative prompt if you find that the Legend of Korra style is influencing your prompt too strongly! Example: "19th century oil painting" !["19th century oil painting"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0173.png) "19th century oil painting (negative prompt "tlok artstyle")" !["19th century oil painting (negative prompt "tlok artstyle")"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0174.png) - Sometimes the photo generations of Korra will be too white, add "white skin" to the negative prompt in that case! ## Example generations using this model! empire state building tlok artstyle (using img2img) !["empire state building tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00416-1482461181-empire%20state%20building%20tlok%20artstyle.png) woman with long blue hair wearing a traditional Japanese kimono during golden hour lighting tlok artstyle (resized with img2img + face inpainted) !["woman with long blue hair wearing a traditional Japanese kimono during golden hour lighting tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00418-1248489237-closeup%20woman%20with%20long%20blue%20hair%20wearing%20a%20traditional%20Japanese%20kimono%20during%20golden%20hour%20lighting%20tlok%20artstyle.png) young woman with red hair wearing modern casual white tshirt and blue jeans standing in front of the Brandenburg Gate tlok artstyle (resized with img2img + face inpainted) !["young woman with red hair wearing modern casual white tshirt and blue jeans standing in front of the Brandenburg Gate tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00422-3495601497-closeup%20young%20woman%20with%20red%20hair%20wearing%20modern%20casual%20white%20tshirt%20and%20blue%20jeans%20standing%20in%20front%20of%20the%20Brandenburg%20Gate%20tl.png) written letter tlok artstyle (resized using img2img) !["written letter tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00424-2560151794-written%20letter%20tlok%20artstyle.png) Korra wearing business suit stada hairstyle tlok artstyle (resized with img2img + face inpainted) !["facial closeup Korra wearing business suit stada hairstyle tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00427-2247884288-facial%20closeup%20Korra%20wearing%20business%20suit%20stada%20hairstyle%20tlok%20artstyle.png) full-shot Korra wearing astronaut outfit stada hairstyle tlok artstyle (resized using img2img) !["full-shot Korra wearing astronaut outfit stada hairstyle tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00429-2606052026-full-shot%20Korra%20wearing%20astronaut%20outfit%20stada%20hairstyle%20tlok%20artstyle.png) Korra wearing defa outfit stada hairstyle as a cute pixar character (resized using img2img) !["Korra wearing defa outfit stada hairstyle as a cute pixar character"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00430-2716015699-Korra%20wearing%20defa%20outfit%20stada%20hairstyle%20as%20a%20cute%20pixar%20character.png) half-body Korra wearing Kimono taio hairstyle figurine (resized using img2img) !["half-body Korra wearing Kimono taio hairstyle figurine"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00432-1669236977-half-body%20Korra%20wearing%20Kimono%20taio%20hairstyle%20figurine.png) dog tlok artstyle !["dog tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/29467-1204970377-dog%20tlok%20artstyle.png) mountain river valley tlok artstyle !["mountain river valley tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/29496-782504688-mountain%20river%20valley%20tlok%20artstyle.png) Korra wearing bikini shoa hairstyle realistic detailed digital art by Greg Rutkowski !["Korra wearing bikini shoa hairstyle realistic detailed digital art by Greg Rutkowski"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/29506-3823816358-Korra%20wearing%20bikini%20shoa%20hairstyle%20realistic%20detailed%20digital%20art%20by%20Greg%20Rutkowski.png) Korra wearing rain jacket and jeans stada hairstyle cosplay photograph !["Korra wearing rain jacket and jeans stada hairstyle cosplay photograph"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/29548-1019120701-Korra%20wearing%20rain%20jacket%20and%20jeans%20stada%20hairstyle%20cosplay%20photograph.png) car on a road city street background tlok artstyle (resized using img2img) !["car on a road city street background tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00433-2844107839-car%20on%20a%20road%20city%20street%20background%20tlok%20artstyle.png) Emma Watson (wearing defa outfit:1.3) cosplay photograph (resized using img2img) !["Emma Watson (wearing defa outfit_1.3) cosplay photograph"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00435-172622402-Emma%20Watson%20(wearing%20defa%20outfit_1.3)%20cosplay%20photograph.png) Zendaya standing in a forest wearing runa outfit tlok artstyle (resized using img2img + face inpainted) !["Zendaya standing in a forest wearing runa outfit tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00438-2275197862-facial%20closeup%20Zendaya%20standing%20in%20a%20forest%20wearing%20runa%20outfit%20tlok%20artstyle.png) ## Tokens to use to prompt the artstyle as well as Korra's different outfits You can give Korra's outfits and hairstyles also to other people thanks to the token method! You can also mix and match outfits and hairstyles however you want to, though results may at times be worse than if you just pair the correct hairstyle to the correct outfit (aka as it was in the show)! Legend of Korra artstyle: - tlok artstyle Korra's hairstyles: - stada hairstyle (Default ponytail hair) - oped hairstyle (Opened hair) - loes hairstyle (Loose hair) - shoa hairstyle (Season4 short hair) - taio hairstyle (Traditional formal hair) - foha hairstyle (Season4 formal hair) - okch hairstyle (young child Korra hairstyle) Korra's outfits: "wearing X outfit" (the second words are the hairstyles, e.g. with "runa shoa" "runa" is the outfit and "shoa" the hairstyle; prompting the corresponding hairstyle alongside the outfit will give you better likeness, but you can also mix and match different hairstyles and outfits together as you see fit at the cost of likeness, though some outfits and hairstyles work better than others in this regard) - runa shoa (earth kingdom runaway) - saco stada (default parka) - aino stada (airnomad (makes her look like a child for some reason)) - fife stada (fireferrets probending uniform) - eqli stada (equalist disguise) - boez oped (season2 parka) - defa stada (default outfit) - alte stada (season2 outfit) - asai shoa (Asami's jacket (doesn't work so well)) - taso stada (Tarrlok's taskforce) - dava oped (dark avatar/season 3 finale) - seri foha (series finale gown) - fose shoa (season4 outfit) - proe stada (probending training attire) - tuwa shoa (turfwars finale gown from the comics (doesn't work so well)) - cidi stada (civilian disguise) - epgo taio (traditional dress) - bafo loes (bath/sleeping robe) - ektu shoa (earth kingdom tunic/hoodie) - pama loes (pajamas) - exci stada (firebending exercise (doesn't work so well)) - as chie, wearing yowi (child korra, winter outfit from the comics) - as chie, wearing suou (child kora, summer outfit) ## Current shortcomings of the model - the model is infected due to no regularization. This means better likeness but also means that you are better off using the original vanilla SD model for txt2img photo generations and then send them to img2img and switch the model over to this one for style transfer! - the model may struggle at times with more complex prompts - location tagging is very rudimentary for now (exterior, day, arctic) - Landscapes could look better - No tagging of unique locations, e.g. Republic City - Korra is the only trained character for now - a few of the outfits don't work that well because of low amount of training images or low resolution images. Generally some outfits, people, things, styles and prompts will work better than others - likeness was better for certain prompts in my older models ## Outlook into the future - Ideally I will be able to expand upon this model in the future by adding all the other characters from the show and maybe even ATLA characters! However, right now I am uncertain if that is possible, as the model is already heavily trained. - Generally I want to improve this models likeness and flexibility - Training this model on the new base inpainting model - I seek to produce more models in the future such as models for Ahsoka, Aloy, Owl House, Ghibli, Sadie Sink, She-Ra, various online artists... but that will take time (and money) ## How I created this model and the underlying dataset (+ dataset download link!) At first I wanted to create only a small Korra model with only her default outfit. In the first days I was experimenting with the standard class and token Dreambooth method using JoePennas repo. For that I manually downloaded 900 screenshots from the show of Korra in her default outfit from fancaps.net. I then manually cropped and resized those images. As I ran into walls I stopped trying to create this model and restarted trying to create a general style model using native finetuning instead. This time however I used the 40€ paid version of "Bulk Image Downloader" to automatically download around 30000 screencaps of the show from fancaps.net. I then used AntiDupl.NET to delete around half of the images which were found out by the program to be a duplicate. I then used ChaiNNer and IrfanView to bulk crop and resize the rest of the dataset to 512x512. I also downloaded around 200 high-quality fanarts and cosplay photos depicting Korra in her various outfits and some non-show outfits and used Irfanview to automatically resize them to 512x512 without cropping by adding black borders to the image (those do not show up in the final model output, luckily). I spent a lot of money on GPU renting for the native finetuning but results were worse than my Dreambooth experiments so I went back to Dreambooth and used a small fraction of the finetuning dataset to create a style model. I learned a lot this time around and improved my model results but still results were not to my liking. That is when I found out about the caption method in JoePennas repo. So I went ahead and spent an entire weekend, 12 hours each day, manually captioning around 1000 images. I used around 300 images from the former finetuning dataset for the style, 600 from the former 900 manually cropped and resized screencaps of Korra in her default outfit, then around 200 fanarts and cosplay photos and some additional screencaps and images of Korra in all her other outfits, to create my final dataset. I used "Bulk File Rename" for Windows 10 to bulk rename files aka add captions. [The captioned dataset can be found here!](https://huggingface.co/datasets/ai-characters/4elements-diffusion-captioned-dataset) [The 14000 show screencaps can be found for download here!](https://www.dropbox.com/s/406u0tv9xuttgku/14284%20images%2C%20512x512%2C%20automatically%20cropped%2C%20downscaled%20from%201080x1080.7z?dl=1)** I encourage everyone to try and do it better than me and create your own Legend of Korra model! Ultimately I spent the past two weeks experimenting with various different captions and training settings to reach my final model. My final model uses these training settings: - Repo: JoePenna's with captions (no class or regularization and only a fake token that will not be used during training) - Learning rate: 3e-6 (for 80 repeats) and 2e-6 (for 35 repeats) - Repeats/Steps: See above (1 repeat = one run through the entire dataset, so 1142 steps) I had to use such high learning rates because due to the nature of the size of the dataset and captions it required it to attain the likeness I wanted for both the style and all of Korra's outfits. There is much more to be said here regarding my workflow, experimentation, and the like, but I don't want to make this longer than necessary and this is already very long. ## Alternative Download Links [Alternative download link for the model](https://www.dropbox.com/s/ayyk6c039gux7zs/4elements-diffusion.ckpt?dl=1) [Alternative download link for the captioned dataset](https://www.dropbox.com/s/iobslrmyvdoi8oy/1142%20images%2C%20manually%20captioned%2C%20manual%20and%20automatic%20cropping%2C%20downscaled%20from%201024x1024.7z?dl=1) ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
Ching/negation_detector	[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "RobertaForQuestionAnswering" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	9	null	Access to model wmduggan41/kd-distilBERT-clinc is restricted and you are not in the authorized list. Visit https://huggingface.co/wmduggan41/kd-distilBERT-clinc to ask for access.
Chinmay/mlindia	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-21T19:37:03Z	--- license: creativeml-openrail-m --- Anything-V3.0 based StableDiffusion model with Dreambooth training based on the general artstyle of Daniel Conway. Trained for 2,400 steps using 30 total training images. ## Usage Can be used in StableDiffusion, including the extremely popular Web UI by Automatic1111, like any other model by placing the .CKPT file in the correct directory. Please consult the documentation for your installation of StableDiffusion for more specific instructions. Use the following tokens in your prompt to achieve the desired output. Token: ```"dconway"``` Class: ```"illustration style"``` I have generally found the best results from using the token and class together at the beginning of the prompt. You can also try using one or the other or mixing them in other ways to achieve different outputs. Example Prompt 1: ```"dconway illustration style, 1girl, pink hair, blue eyes, french braid, hair bun, single sidelock, adjusting hair, light smile, parted lips, looking at viewer, head tilt, atrium, bird cage, water, potted plant, clock, fountain, dappled sunlight, sunbeam, light rays, caustics, bloom, extremely detailed, intricate, masterpiece, best quality"``` Example Prompt 2: ```"dconway illustration style, a beautiful landscape with a river rushing towards a mountain range in the distance with clouds above, glacier, flower"``` For a more anime style try adding ```"3d model"``` to your negative prompt. ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
Chiuchiyin/DialoGPT-small-Donald	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	2022-11-21T19:40:36Z	--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image --- This is the fine-tuned Stable Diffusion model trained on screenshots from The Clone wars TV series. Use the tokens "Clonewars style" in your prompts for the effect. If you enjoy my work, please consider supporting me: [![Buy me a coffee](https://badgen.net/badge/buy/Coffee/F96854)](https://ko-fi.com/trystar) ## Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI run CloneDiffusion: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/akhaliq/CloneDiffusion) Star Wars Characters ![Star Wars Characters](https://huggingface.co/TryStar/CloneDiffusion/resolve/main/Starwars.jpg) How to use? Use prompt "clonewars style" before your full prompt. I recommend Steps: 50, Sampler: Euler a and CFG scale: 7 This model was trained using the diffusers based dreambooth training by [TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) created by TryStar
Chiuchiyin/Donald	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-23T11:04:11Z	--- license: mit tags: - generated_from_trainer model-index: - name: BERiT_2000_2_layers_300_epochs results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERiT_2000_2_layers_300_epochs This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.0648 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - label_smoothing_factor: 0.2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:------:\|:---------------:\| \| 14.9639 \| 0.19 \| 500 \| 8.4999 \| \| 7.8976 \| 0.39 \| 1000 \| 7.3944 \| \| 7.3281 \| 0.58 \| 1500 \| 7.1320 \| \| 7.1202 \| 0.77 \| 2000 \| 7.0376 \| \| 7.0738 \| 0.97 \| 2500 \| 7.0277 \| \| 7.0327 \| 1.16 \| 3000 \| 6.9313 \| \| 6.9775 \| 1.36 \| 3500 \| 6.8580 \| \| 6.9568 \| 1.55 \| 4000 \| 6.7909 \| \| 6.9242 \| 1.74 \| 4500 \| 6.7869 \| \| 6.8842 \| 1.94 \| 5000 \| 6.7403 \| \| 6.8904 \| 2.13 \| 5500 \| 6.7860 \| \| 6.8757 \| 2.32 \| 6000 \| 6.7235 \| \| 6.8164 \| 2.52 \| 6500 \| 6.7383 \| \| 6.8439 \| 2.71 \| 7000 \| 6.6904 \| \| 6.8074 \| 2.9 \| 7500 \| 6.7116 \| \| 6.79 \| 3.1 \| 8000 \| 6.6995 \| \| 6.7915 \| 3.29 \| 8500 \| 6.6930 \| \| 6.7664 \| 3.49 \| 9000 \| 6.6794 \| \| 6.7822 \| 3.68 \| 9500 \| 6.6467 \| \| 6.7585 \| 3.87 \| 10000 \| 6.6787 \| \| 6.7784 \| 4.07 \| 10500 \| 6.6596 \| \| 6.7344 \| 4.26 \| 11000 \| 6.6315 \| \| 6.7374 \| 4.45 \| 11500 \| 6.7104 \| \| 6.7309 \| 4.65 \| 12000 \| 6.6566 \| \| 6.728 \| 4.84 \| 12500 \| 6.6726 \| \| 6.7154 \| 5.03 \| 13000 \| 6.6502 \| \| 6.7159 \| 5.23 \| 13500 \| 6.6477 \| \| 6.7114 \| 5.42 \| 14000 \| 6.6440 \| \| 6.7111 \| 5.62 \| 14500 \| 6.6685 \| \| 6.7038 \| 5.81 \| 15000 \| 6.6363 \| \| 6.7037 \| 6.0 \| 15500 \| 6.6036 \| \| 6.7 \| 6.2 \| 16000 \| 6.6199 \| \| 6.6864 \| 6.39 \| 16500 \| 6.5995 \| \| 6.6944 \| 6.58 \| 17000 \| 6.6211 \| \| 6.6743 \| 6.78 \| 17500 \| 6.6274 \| \| 6.6519 \| 6.97 \| 18000 \| 6.5919 \| \| 6.6707 \| 7.16 \| 18500 \| 6.6141 \| \| 6.6722 \| 7.36 \| 19000 \| 6.5356 \| \| 6.6695 \| 7.55 \| 19500 \| 6.5895 \| \| 6.6699 \| 7.75 \| 20000 \| 6.5913 \| \| 6.6783 \| 7.94 \| 20500 \| 6.6037 \| \| 6.651 \| 8.13 \| 21000 \| 6.6032 \| \| 6.6415 \| 8.33 \| 21500 \| 6.5818 \| \| 6.6485 \| 8.52 \| 22000 \| 6.5829 \| \| 6.6232 \| 8.71 \| 22500 \| 6.6029 \| \| 6.6407 \| 8.91 \| 23000 \| 6.5676 \| \| 6.6265 \| 9.1 \| 23500 \| 6.6313 \| \| 6.6436 \| 9.3 \| 24000 \| 6.5415 \| \| 6.6196 \| 9.49 \| 24500 \| 6.5655 \| \| 6.6093 \| 9.68 \| 25000 \| 6.5663 \| \| 6.6354 \| 9.88 \| 25500 \| 6.5946 \| \| 6.6202 \| 10.07 \| 26000 \| 6.5805 \| \| 6.5849 \| 10.26 \| 26500 \| 6.5799 \| \| 6.6035 \| 10.46 \| 27000 \| 6.5763 \| \| 6.5922 \| 10.65 \| 27500 \| 6.5716 \| \| 6.5924 \| 10.84 \| 28000 \| 6.5744 \| \| 6.6083 \| 11.04 \| 28500 \| 6.5326 \| \| 6.5896 \| 11.23 \| 29000 \| 6.5797 \| \| 6.607 \| 11.43 \| 29500 \| 6.5312 \| \| 6.5942 \| 11.62 \| 30000 \| 6.5917 \| \| 6.5863 \| 11.81 \| 30500 \| 6.5619 \| \| 6.5841 \| 12.01 \| 31000 \| 6.5590 \| \| 6.548 \| 12.2 \| 31500 \| 6.4872 \| \| 6.5831 \| 12.39 \| 32000 \| 6.5914 \| \| 6.5577 \| 12.59 \| 32500 \| 6.5784 \| \| 6.5585 \| 12.78 \| 33000 \| 6.5267 \| \| 6.5722 \| 12.97 \| 33500 \| 6.5539 \| \| 6.5832 \| 13.17 \| 34000 \| 6.5535 \| \| 6.5704 \| 13.36 \| 34500 \| 6.5624 \| \| 6.5437 \| 13.56 \| 35000 \| 6.5531 \| \| 6.5492 \| 13.75 \| 35500 \| 6.5616 \| \| 6.5437 \| 13.94 \| 36000 \| 6.5502 \| \| 6.5652 \| 14.14 \| 36500 \| 6.4985 \| \| 6.5573 \| 14.33 \| 37000 \| 6.5386 \| \| 6.5523 \| 14.52 \| 37500 \| 6.4916 \| \| 6.5636 \| 14.72 \| 38000 \| 6.5613 \| \| 6.5485 \| 14.91 \| 38500 \| 6.5201 \| \| 6.5424 \| 15.1 \| 39000 \| 6.5921 \| \| 6.5429 \| 15.3 \| 39500 \| 6.5397 \| \| 6.5518 \| 15.49 \| 40000 \| 6.5255 \| \| 6.5362 \| 15.69 \| 40500 \| 6.5129 \| \| 6.5329 \| 15.88 \| 41000 \| 6.5395 \| \| 6.535 \| 16.07 \| 41500 \| 6.5706 \| \| 6.5367 \| 16.27 \| 42000 \| 6.5382 \| \| 6.5227 \| 16.46 \| 42500 \| 6.5180 \| \| 6.5019 \| 16.65 \| 43000 \| 6.5454 \| \| 6.5536 \| 16.85 \| 43500 \| 6.5399 \| \| 6.52 \| 17.04 \| 44000 \| 6.5285 \| \| 6.5117 \| 17.23 \| 44500 \| 6.5488 \| \| 6.5367 \| 17.43 \| 45000 \| 6.5246 \| \| 6.5167 \| 17.62 \| 45500 \| 6.5400 \| \| 6.531 \| 17.82 \| 46000 \| 6.5299 \| \| 6.5273 \| 18.01 \| 46500 \| 6.4898 \| \| 6.5035 \| 18.2 \| 47000 \| 6.5093 \| \| 6.4885 \| 18.4 \| 47500 \| 6.5586 \| \| 6.5234 \| 18.59 \| 48000 \| 6.5677 \| \| 6.5092 \| 18.78 \| 48500 \| 6.4785 \| \| 6.4866 \| 18.98 \| 49000 \| 6.4909 \| \| 6.4985 \| 19.17 \| 49500 \| 6.5219 \| \| 6.5003 \| 19.36 \| 50000 \| 6.4935 \| \| 6.5253 \| 19.56 \| 50500 \| 6.4785 \| \| 6.486 \| 19.75 \| 51000 \| 6.5521 \| \| 6.4977 \| 19.95 \| 51500 \| 6.5230 \| \| 6.4825 \| 20.14 \| 52000 \| 6.5060 \| \| 6.4925 \| 20.33 \| 52500 \| 6.4851 \| \| 6.5028 \| 20.53 \| 53000 \| 6.5300 \| \| 6.5019 \| 20.72 \| 53500 \| 6.5044 \| \| 6.4749 \| 20.91 \| 54000 \| 6.4900 \| \| 6.4724 \| 21.11 \| 54500 \| 6.5211 \| \| 6.4873 \| 21.3 \| 55000 \| 6.4883 \| \| 6.4979 \| 21.49 \| 55500 \| 6.4993 \| \| 6.4646 \| 21.69 \| 56000 \| 6.5576 \| \| 6.4888 \| 21.88 \| 56500 \| 6.4719 \| \| 6.4996 \| 22.08 \| 57000 \| 6.4848 \| \| 6.4694 \| 22.27 \| 57500 \| 6.5130 \| \| 6.4757 \| 22.46 \| 58000 \| 6.4858 \| \| 6.4744 \| 22.66 \| 58500 \| 6.5284 \| \| 6.4807 \| 22.85 \| 59000 \| 6.4736 \| \| 6.4873 \| 23.04 \| 59500 \| 6.4829 \| \| 6.4797 \| 23.24 \| 60000 \| 6.5185 \| \| 6.4675 \| 23.43 \| 60500 \| 6.4920 \| \| 6.4905 \| 23.63 \| 61000 \| 6.5365 \| \| 6.4659 \| 23.82 \| 61500 \| 6.4717 \| \| 6.4703 \| 24.01 \| 62000 \| 6.4980 \| \| 6.4654 \| 24.21 \| 62500 \| 6.4492 \| \| 6.4724 \| 24.4 \| 63000 \| 6.5132 \| \| 6.4939 \| 24.59 \| 63500 \| 6.4642 \| \| 6.4732 \| 24.79 \| 64000 \| 6.4902 \| \| 6.4781 \| 24.98 \| 64500 \| 6.5341 \| \| 6.4691 \| 25.17 \| 65000 \| 6.5106 \| \| 6.4644 \| 25.37 \| 65500 \| 6.4463 \| \| 6.4525 \| 25.56 \| 66000 \| 6.4763 \| \| 6.4423 \| 25.76 \| 66500 \| 6.5226 \| \| 6.4658 \| 25.95 \| 67000 \| 6.4581 \| \| 6.4624 \| 26.14 \| 67500 \| 6.4748 \| \| 6.4731 \| 26.34 \| 68000 \| 6.4762 \| \| 6.4381 \| 26.53 \| 68500 \| 6.5184 \| \| 6.4375 \| 26.72 \| 69000 \| 6.4998 \| \| 6.4559 \| 26.92 \| 69500 \| 6.4751 \| \| 6.4663 \| 27.11 \| 70000 \| 6.4946 \| \| 6.4551 \| 27.3 \| 70500 \| 6.4495 \| \| 6.4464 \| 27.5 \| 71000 \| 6.4861 \| \| 6.451 \| 27.69 \| 71500 \| 6.4741 \| \| 6.4491 \| 27.89 \| 72000 \| 6.4275 \| \| 6.4506 \| 28.08 \| 72500 \| 6.4864 \| \| 6.4262 \| 28.27 \| 73000 \| 6.4839 \| \| 6.4261 \| 28.47 \| 73500 \| 6.4835 \| \| 6.4408 \| 28.66 \| 74000 \| 6.5073 \| \| 6.4402 \| 28.85 \| 74500 \| 6.4586 \| \| 6.4414 \| 29.05 \| 75000 \| 6.4639 \| \| 6.453 \| 29.24 \| 75500 \| 6.4764 \| \| 6.4362 \| 29.43 \| 76000 \| 6.5098 \| \| 6.4262 \| 29.63 \| 76500 \| 6.5176 \| \| 6.4057 \| 29.82 \| 77000 \| 6.5080 \| \| 6.4393 \| 30.02 \| 77500 \| 6.5053 \| \| 6.4385 \| 30.21 \| 78000 \| 6.4954 \| \| 6.4592 \| 30.4 \| 78500 \| 6.4517 \| \| 6.4472 \| 30.6 \| 79000 \| 6.4609 \| \| 6.4099 \| 30.79 \| 79500 \| 6.4770 \| \| 6.3925 \| 30.98 \| 80000 \| 6.4189 \| \| 6.4423 \| 31.18 \| 80500 \| 6.4781 \| \| 6.4236 \| 31.37 \| 81000 \| 6.4723 \| \| 6.4315 \| 31.56 \| 81500 \| 6.4890 \| \| 6.4529 \| 31.76 \| 82000 \| 6.5073 \| \| 6.4292 \| 31.95 \| 82500 \| 6.4460 \| \| 6.4164 \| 32.15 \| 83000 \| 6.4271 \| \| 6.4124 \| 32.34 \| 83500 \| 6.4864 \| \| 6.4447 \| 32.53 \| 84000 \| 6.4518 \| \| 6.4161 \| 32.73 \| 84500 \| 6.4543 \| \| 6.4326 \| 32.92 \| 85000 \| 6.4600 \| \| 6.4209 \| 33.11 \| 85500 \| 6.4686 \| \| 6.4177 \| 33.31 \| 86000 \| 6.4313 \| \| 6.4317 \| 33.5 \| 86500 \| 6.4893 \| \| 6.4133 \| 33.69 \| 87000 \| 6.4604 \| \| 6.4331 \| 33.89 \| 87500 \| 6.4411 \| \| 6.4114 \| 34.08 \| 88000 \| 6.4409 \| \| 6.4202 \| 34.28 \| 88500 \| 6.4300 \| \| 6.4162 \| 34.47 \| 89000 \| 6.4780 \| \| 6.4305 \| 34.66 \| 89500 \| 6.4473 \| \| 6.412 \| 34.86 \| 90000 \| 6.4621 \| \| 6.4032 \| 35.05 \| 90500 \| 6.4874 \| \| 6.412 \| 35.24 \| 91000 \| 6.4883 \| \| 6.4088 \| 35.44 \| 91500 \| 6.4290 \| \| 6.4289 \| 35.63 \| 92000 \| 6.4539 \| \| 6.4101 \| 35.82 \| 92500 \| 6.4571 \| \| 6.3897 \| 36.02 \| 93000 \| 6.4450 \| \| 6.4122 \| 36.21 \| 93500 \| 6.4488 \| \| 6.412 \| 36.41 \| 94000 \| 6.3988 \| \| 6.4063 \| 36.6 \| 94500 \| 6.4681 \| \| 6.3905 \| 36.79 \| 95000 \| 6.4018 \| \| 6.3934 \| 36.99 \| 95500 \| 6.4391 \| \| 6.408 \| 37.18 \| 96000 \| 6.4483 \| \| 6.3968 \| 37.37 \| 96500 \| 6.4651 \| \| 6.3998 \| 37.57 \| 97000 \| 6.4358 \| \| 6.4061 \| 37.76 \| 97500 \| 6.4524 \| \| 6.4006 \| 37.96 \| 98000 \| 6.4354 \| \| 6.3871 \| 38.15 \| 98500 \| 6.4286 \| \| 6.3776 \| 38.34 \| 99000 \| 6.4578 \| \| 6.3997 \| 38.54 \| 99500 \| 6.4358 \| \| 6.3885 \| 38.73 \| 100000 \| 6.4644 \| \| 6.3923 \| 38.92 \| 100500 \| 6.3955 \| \| 6.3919 \| 39.12 \| 101000 \| 6.4924 \| \| 6.3814 \| 39.31 \| 101500 \| 6.4437 \| \| 6.3766 \| 39.5 \| 102000 \| 6.4097 \| \| 6.3889 \| 39.7 \| 102500 \| 6.4231 \| \| 6.3734 \| 39.89 \| 103000 \| 6.4379 \| \| 6.3926 \| 40.09 \| 103500 \| 6.4474 \| \| 6.3809 \| 40.28 \| 104000 \| 6.4393 \| \| 6.3738 \| 40.47 \| 104500 \| 6.4199 \| \| 6.3844 \| 40.67 \| 105000 \| 6.4535 \| \| 6.3654 \| 40.86 \| 105500 \| 6.4676 \| \| 6.3874 \| 41.05 \| 106000 \| 6.4541 \| \| 6.3622 \| 41.25 \| 106500 \| 6.4522 \| \| 6.3853 \| 41.44 \| 107000 \| 6.4509 \| \| 6.3858 \| 41.63 \| 107500 \| 6.4682 \| \| 6.3865 \| 41.83 \| 108000 \| 6.3627 \| \| 6.3838 \| 42.02 \| 108500 \| 6.4209 \| \| 6.3637 \| 42.22 \| 109000 \| 6.4610 \| \| 6.3836 \| 42.41 \| 109500 \| 6.3808 \| \| 6.3948 \| 42.6 \| 110000 \| 6.4302 \| \| 6.3619 \| 42.8 \| 110500 \| 6.3986 \| \| 6.3796 \| 42.99 \| 111000 \| 6.3878 \| \| 6.3881 \| 43.18 \| 111500 \| 6.4563 \| \| 6.3632 \| 43.38 \| 112000 \| 6.4063 \| \| 6.3509 \| 43.57 \| 112500 \| 6.4481 \| \| 6.3744 \| 43.76 \| 113000 \| 6.4299 \| \| 6.3418 \| 43.96 \| 113500 \| 6.4200 \| \| 6.3549 \| 44.15 \| 114000 \| 6.4137 \| \| 6.3534 \| 44.35 \| 114500 \| 6.4691 \| \| 6.3744 \| 44.54 \| 115000 \| 6.4370 \| \| 6.3637 \| 44.73 \| 115500 \| 6.4239 \| \| 6.3501 \| 44.93 \| 116000 \| 6.4384 \| \| 6.3738 \| 45.12 \| 116500 \| 6.4248 \| \| 6.3483 \| 45.31 \| 117000 \| 6.4041 \| \| 6.3908 \| 45.51 \| 117500 \| 6.3876 \| \| 6.3513 \| 45.7 \| 118000 \| 6.3860 \| \| 6.3587 \| 45.89 \| 118500 \| 6.4781 \| \| 6.3611 \| 46.09 \| 119000 \| 6.4386 \| \| 6.3418 \| 46.28 \| 119500 \| 6.4188 \| \| 6.3704 \| 46.48 \| 120000 \| 6.3844 \| \| 6.3775 \| 46.67 \| 120500 \| 6.4102 \| \| 6.3553 \| 46.86 \| 121000 \| 6.4203 \| \| 6.354 \| 47.06 \| 121500 \| 6.3956 \| \| 6.3586 \| 47.25 \| 122000 \| 6.4365 \| \| 6.3356 \| 47.44 \| 122500 \| 6.4153 \| \| 6.3627 \| 47.64 \| 123000 \| 6.3749 \| \| 6.3702 \| 47.83 \| 123500 \| 6.4489 \| \| 6.3356 \| 48.02 \| 124000 \| 6.3944 \| \| 6.3327 \| 48.22 \| 124500 \| 6.3973 \| \| 6.3545 \| 48.41 \| 125000 \| 6.4039 \| \| 6.358 \| 48.61 \| 125500 \| 6.3921 \| \| 6.3531 \| 48.8 \| 126000 \| 6.4135 \| \| 6.342 \| 48.99 \| 126500 \| 6.4222 \| \| 6.3625 \| 49.19 \| 127000 \| 6.3813 \| \| 6.3484 \| 49.38 \| 127500 \| 6.4016 \| \| 6.3492 \| 49.57 \| 128000 \| 6.3944 \| \| 6.3362 \| 49.77 \| 128500 \| 6.4191 \| \| 6.3495 \| 49.96 \| 129000 \| 6.4099 \| \| 6.3403 \| 50.15 \| 129500 \| 6.3868 \| \| 6.3231 \| 50.35 \| 130000 \| 6.4068 \| \| 6.3481 \| 50.54 \| 130500 \| 6.4302 \| \| 6.3641 \| 50.74 \| 131000 \| 6.4025 \| \| 6.3269 \| 50.93 \| 131500 \| 6.3723 \| \| 6.3605 \| 51.12 \| 132000 \| 6.3974 \| \| 6.3329 \| 51.32 \| 132500 \| 6.4281 \| \| 6.3783 \| 51.51 \| 133000 \| 6.3982 \| \| 6.3234 \| 51.7 \| 133500 \| 6.3957 \| \| 6.3497 \| 51.9 \| 134000 \| 6.3913 \| \| 6.3313 \| 52.09 \| 134500 \| 6.4325 \| \| 6.348 \| 52.29 \| 135000 \| 6.3923 \| \| 6.3291 \| 52.48 \| 135500 \| 6.3462 \| \| 6.3503 \| 52.67 \| 136000 \| 6.3498 \| \| 6.3202 \| 52.87 \| 136500 \| 6.4250 \| \| 6.3419 \| 53.06 \| 137000 \| 6.3549 \| \| 6.3375 \| 53.25 \| 137500 \| 6.3781 \| \| 6.3492 \| 53.45 \| 138000 \| 6.3718 \| \| 6.3237 \| 53.64 \| 138500 \| 6.3962 \| \| 6.328 \| 53.83 \| 139000 \| 6.3892 \| \| 6.3251 \| 54.03 \| 139500 \| 6.4056 \| \| 6.3297 \| 54.22 \| 140000 \| 6.3886 \| \| 6.328 \| 54.42 \| 140500 \| 6.4028 \| \| 6.3233 \| 54.61 \| 141000 \| 6.3649 \| \| 6.3379 \| 54.8 \| 141500 \| 6.4070 \| \| 6.3152 \| 55.0 \| 142000 \| 6.4084 \| \| 6.3409 \| 55.19 \| 142500 \| 6.3630 \| \| 6.3249 \| 55.38 \| 143000 \| 6.3896 \| \| 6.3148 \| 55.58 \| 143500 \| 6.3882 \| \| 6.3256 \| 55.77 \| 144000 \| 6.3662 \| \| 6.3176 \| 55.96 \| 144500 \| 6.3843 \| \| 6.295 \| 56.16 \| 145000 \| 6.3652 \| \| 6.3331 \| 56.35 \| 145500 \| 6.4390 \| \| 6.314 \| 56.55 \| 146000 \| 6.3578 \| \| 6.3305 \| 56.74 \| 146500 \| 6.3335 \| \| 6.3614 \| 56.93 \| 147000 \| 6.3514 \| \| 6.3556 \| 57.13 \| 147500 \| 6.3592 \| \| 6.3171 \| 57.32 \| 148000 \| 6.3760 \| \| 6.2904 \| 57.51 \| 148500 \| 6.3886 \| \| 6.3402 \| 57.71 \| 149000 \| 6.3818 \| \| 6.3265 \| 57.9 \| 149500 \| 6.3572 \| \| 6.3293 \| 58.09 \| 150000 \| 6.3144 \| \| 6.3169 \| 58.29 \| 150500 \| 6.3792 \| \| 6.3188 \| 58.48 \| 151000 \| 6.3777 \| \| 6.31 \| 58.68 \| 151500 \| 6.3524 \| \| 6.3091 \| 58.87 \| 152000 \| 6.3450 \| \| 6.2778 \| 59.06 \| 152500 \| 6.3745 \| \| 6.3019 \| 59.26 \| 153000 \| 6.3503 \| \| 6.293 \| 59.45 \| 153500 \| 6.3432 \| \| 6.3083 \| 59.64 \| 154000 \| 6.3699 \| \| 6.3324 \| 59.84 \| 154500 \| 6.3354 \| \| 6.3273 \| 60.03 \| 155000 \| 6.3313 \| \| 6.3186 \| 60.22 \| 155500 \| 6.3619 \| \| 6.296 \| 60.42 \| 156000 \| 6.3852 \| \| 6.3293 \| 60.61 \| 156500 \| 6.3197 \| \| 6.3143 \| 60.81 \| 157000 \| 6.3526 \| \| 6.3262 \| 61.0 \| 157500 \| 6.3637 \| \| 6.3045 \| 61.19 \| 158000 \| 6.3603 \| \| 6.2767 \| 61.39 \| 158500 \| 6.4061 \| \| 6.3032 \| 61.58 \| 159000 \| 6.3877 \| \| 6.2984 \| 61.77 \| 159500 \| 6.4006 \| \| 6.2887 \| 61.97 \| 160000 \| 6.3599 \| \| 6.2977 \| 62.16 \| 160500 \| 6.3598 \| \| 6.2865 \| 62.35 \| 161000 \| 6.3446 \| \| 6.3158 \| 62.55 \| 161500 \| 6.3216 \| \| 6.2867 \| 62.74 \| 162000 \| 6.3698 \| \| 6.2886 \| 62.94 \| 162500 \| 6.3701 \| \| 6.2752 \| 63.13 \| 163000 \| 6.3066 \| \| 6.2996 \| 63.32 \| 163500 \| 6.3188 \| \| 6.2919 \| 63.52 \| 164000 \| 6.3131 \| \| 6.3029 \| 63.71 \| 164500 \| 6.2848 \| \| 6.3074 \| 63.9 \| 165000 \| 6.3071 \| \| 6.2801 \| 64.1 \| 165500 \| 6.3065 \| \| 6.278 \| 64.29 \| 166000 \| 6.2901 \| \| 6.2701 \| 64.48 \| 166500 \| 6.3544 \| \| 6.2851 \| 64.68 \| 167000 \| 6.3970 \| \| 6.2829 \| 64.87 \| 167500 \| 6.3621 \| \| 6.2734 \| 65.07 \| 168000 \| 6.3246 \| \| 6.2982 \| 65.26 \| 168500 \| 6.3342 \| \| 6.2894 \| 65.45 \| 169000 \| 6.3202 \| \| 6.3093 \| 65.65 \| 169500 \| 6.2975 \| \| 6.2948 \| 65.84 \| 170000 \| 6.3127 \| \| 6.2872 \| 66.03 \| 170500 \| 6.3311 \| \| 6.267 \| 66.23 \| 171000 \| 6.3159 \| \| 6.2776 \| 66.42 \| 171500 \| 6.2875 \| \| 6.2794 \| 66.62 \| 172000 \| 6.3315 \| \| 6.2785 \| 66.81 \| 172500 \| 6.3520 \| \| 6.273 \| 67.0 \| 173000 \| 6.3275 \| \| 6.2821 \| 67.2 \| 173500 \| 6.3348 \| \| 6.2906 \| 67.39 \| 174000 \| 6.2945 \| \| 6.2839 \| 67.58 \| 174500 \| 6.3456 \| \| 6.272 \| 67.78 \| 175000 \| 6.2964 \| \| 6.2615 \| 67.97 \| 175500 \| 6.3155 \| \| 6.2838 \| 68.16 \| 176000 \| 6.2967 \| \| 6.2844 \| 68.36 \| 176500 \| 6.3465 \| \| 6.2554 \| 68.55 \| 177000 \| 6.2919 \| \| 6.3059 \| 68.75 \| 177500 \| 6.2598 \| \| 6.2793 \| 68.94 \| 178000 \| 6.3347 \| \| 6.2826 \| 69.13 \| 178500 \| 6.2848 \| \| 6.2609 \| 69.33 \| 179000 \| 6.3692 \| \| 6.2544 \| 69.52 \| 179500 \| 6.3168 \| \| 6.247 \| 69.71 \| 180000 \| 6.3294 \| \| 6.2493 \| 69.91 \| 180500 \| 6.3097 \| \| 6.2649 \| 70.1 \| 181000 \| 6.3144 \| \| 6.2606 \| 70.29 \| 181500 \| 6.2910 \| \| 6.2736 \| 70.49 \| 182000 \| 6.3298 \| \| 6.2425 \| 70.68 \| 182500 \| 6.2905 \| \| 6.25 \| 70.88 \| 183000 \| 6.3027 \| \| 6.2808 \| 71.07 \| 183500 \| 6.2956 \| \| 6.2782 \| 71.26 \| 184000 \| 6.2946 \| \| 6.2733 \| 71.46 \| 184500 \| 6.2950 \| \| 6.2669 \| 71.65 \| 185000 \| 6.3152 \| \| 6.2396 \| 71.84 \| 185500 \| 6.3045 \| \| 6.2881 \| 72.04 \| 186000 \| 6.2768 \| \| 6.2551 \| 72.23 \| 186500 \| 6.2618 \| \| 6.2352 \| 72.42 \| 187000 \| 6.2557 \| \| 6.2641 \| 72.62 \| 187500 \| 6.2660 \| \| 6.2432 \| 72.81 \| 188000 \| 6.2997 \| \| 6.2313 \| 73.01 \| 188500 \| 6.3202 \| \| 6.2562 \| 73.2 \| 189000 \| 6.2877 \| \| 6.2565 \| 73.39 \| 189500 \| 6.2659 \| \| 6.2728 \| 73.59 \| 190000 \| 6.2763 \| \| 6.2418 \| 73.78 \| 190500 \| 6.2567 \| \| 6.2704 \| 73.97 \| 191000 \| 6.2568 \| \| 6.2519 \| 74.17 \| 191500 \| 6.2518 \| \| 6.2794 \| 74.36 \| 192000 \| 6.2631 \| \| 6.2542 \| 74.55 \| 192500 \| 6.2913 \| \| 6.2501 \| 74.75 \| 193000 \| 6.2927 \| \| 6.2576 \| 74.94 \| 193500 \| 6.2690 \| \| 6.2661 \| 75.14 \| 194000 \| 6.2881 \| \| 6.2403 \| 75.33 \| 194500 \| 6.2597 \| \| 6.2379 \| 75.52 \| 195000 \| 6.2629 \| \| 6.2377 \| 75.72 \| 195500 \| 6.2682 \| \| 6.2115 \| 75.91 \| 196000 \| 6.3002 \| \| 6.226 \| 76.1 \| 196500 \| 6.2506 \| \| 6.2485 \| 76.3 \| 197000 \| 6.2723 \| \| 6.2326 \| 76.49 \| 197500 \| 6.3033 \| \| 6.2481 \| 76.68 \| 198000 \| 6.2514 \| \| 6.2526 \| 76.88 \| 198500 \| 6.2639 \| \| 6.2514 \| 77.07 \| 199000 \| 6.2670 \| \| 6.2308 \| 77.27 \| 199500 \| 6.2644 \| \| 6.2482 \| 77.46 \| 200000 \| 6.2931 \| \| 6.2278 \| 77.65 \| 200500 \| 6.2476 \| \| 6.2441 \| 77.85 \| 201000 \| 6.1998 \| \| 6.2328 \| 78.04 \| 201500 \| 6.2583 \| \| 6.241 \| 78.23 \| 202000 \| 6.2229 \| \| 6.2148 \| 78.43 \| 202500 \| 6.2684 \| \| 6.2262 \| 78.62 \| 203000 \| 6.2946 \| \| 6.2563 \| 78.81 \| 203500 \| 6.2377 \| \| 6.2019 \| 79.01 \| 204000 \| 6.2411 \| \| 6.2158 \| 79.2 \| 204500 \| 6.2526 \| \| 6.2382 \| 79.4 \| 205000 \| 6.2308 \| \| 6.2263 \| 79.59 \| 205500 \| 6.2544 \| \| 6.2097 \| 79.78 \| 206000 \| 6.2356 \| \| 6.2072 \| 79.98 \| 206500 \| 6.2554 \| \| 6.216 \| 80.17 \| 207000 \| 6.2388 \| \| 6.2019 \| 80.36 \| 207500 \| 6.2589 \| \| 6.2537 \| 80.56 \| 208000 \| 6.2347 \| \| 6.2253 \| 80.75 \| 208500 \| 6.2654 \| \| 6.2352 \| 80.95 \| 209000 \| 6.1939 \| \| 6.2309 \| 81.14 \| 209500 \| 6.2902 \| \| 6.1946 \| 81.33 \| 210000 \| 6.2101 \| \| 6.2189 \| 81.53 \| 210500 \| 6.2582 \| \| 6.2307 \| 81.72 \| 211000 \| 6.2035 \| \| 6.2137 \| 81.91 \| 211500 \| 6.2357 \| \| 6.2442 \| 82.11 \| 212000 \| 6.2110 \| \| 6.2493 \| 82.3 \| 212500 \| 6.1889 \| \| 6.2164 \| 82.49 \| 213000 \| 6.2404 \| \| 6.1968 \| 82.69 \| 213500 \| 6.2383 \| \| 6.2159 \| 82.88 \| 214000 \| 6.2831 \| \| 6.2115 \| 83.08 \| 214500 \| 6.1869 \| \| 6.2043 \| 83.27 \| 215000 \| 6.2010 \| \| 6.2163 \| 83.46 \| 215500 \| 6.2458 \| \| 6.1923 \| 83.66 \| 216000 \| 6.1991 \| \| 6.193 \| 83.85 \| 216500 \| 6.2134 \| \| 6.1885 \| 84.04 \| 217000 \| 6.2060 \| \| 6.1987 \| 84.24 \| 217500 \| 6.2167 \| \| 6.2178 \| 84.43 \| 218000 \| 6.2093 \| \| 6.1902 \| 84.62 \| 218500 \| 6.1998 \| \| 6.1993 \| 84.82 \| 219000 \| 6.2215 \| \| 6.1846 \| 85.01 \| 219500 \| 6.2175 \| \| 6.1994 \| 85.21 \| 220000 \| 6.1620 \| \| 6.2197 \| 85.4 \| 220500 \| 6.1733 \| \| 6.1873 \| 85.59 \| 221000 \| 6.2190 \| \| 6.2143 \| 85.79 \| 221500 \| 6.1990 \| \| 6.1939 \| 85.98 \| 222000 \| 6.1844 \| \| 6.2026 \| 86.17 \| 222500 \| 6.1697 \| \| 6.2153 \| 86.37 \| 223000 \| 6.1711 \| \| 6.179 \| 86.56 \| 223500 \| 6.1625 \| \| 6.1904 \| 86.75 \| 224000 \| 6.1856 \| \| 6.1703 \| 86.95 \| 224500 \| 6.1340 \| \| 6.1766 \| 87.14 \| 225000 \| 6.2077 \| \| 6.1807 \| 87.34 \| 225500 \| 6.2494 \| \| 6.1677 \| 87.53 \| 226000 \| 6.1723 \| \| 6.1902 \| 87.72 \| 226500 \| 6.1880 \| \| 6.2089 \| 87.92 \| 227000 \| 6.1989 \| \| 6.1794 \| 88.11 \| 227500 \| 6.1637 \| \| 6.1819 \| 88.3 \| 228000 \| 6.1616 \| \| 6.2141 \| 88.5 \| 228500 \| 6.1359 \| \| 6.181 \| 88.69 \| 229000 \| 6.1380 \| \| 6.1806 \| 88.88 \| 229500 \| 6.1295 \| \| 6.1877 \| 89.08 \| 230000 \| 6.1433 \| \| 6.1691 \| 89.27 \| 230500 \| 6.1871 \| \| 6.1444 \| 89.47 \| 231000 \| 6.1767 \| \| 6.1818 \| 89.66 \| 231500 \| 6.1645 \| \| 6.1764 \| 89.85 \| 232000 \| 6.1641 \| \| 6.216 \| 90.05 \| 232500 \| 6.1159 \| \| 6.1565 \| 90.24 \| 233000 \| 6.1216 \| \| 6.1665 \| 90.43 \| 233500 \| 6.1386 \| \| 6.1926 \| 90.63 \| 234000 \| 6.1475 \| \| 6.1786 \| 90.82 \| 234500 \| 6.1157 \| \| 6.193 \| 91.01 \| 235000 \| 6.1285 \| \| 6.1893 \| 91.21 \| 235500 \| 6.1640 \| \| 6.1677 \| 91.4 \| 236000 \| 6.1405 \| \| 6.1872 \| 91.6 \| 236500 \| 6.0972 \| \| 6.153 \| 91.79 \| 237000 \| 6.1382 \| \| 6.1652 \| 91.98 \| 237500 \| 6.1195 \| \| 6.1636 \| 92.18 \| 238000 \| 6.0942 \| \| 6.1589 \| 92.37 \| 238500 \| 6.1100 \| \| 6.1431 \| 92.56 \| 239000 \| 6.1309 \| \| 6.157 \| 92.76 \| 239500 \| 6.1527 \| \| 6.1698 \| 92.95 \| 240000 \| 6.1463 \| \| 6.1726 \| 93.14 \| 240500 \| 6.1063 \| \| 6.1638 \| 93.34 \| 241000 \| 6.0897 \| \| 6.1587 \| 93.53 \| 241500 \| 6.1265 \| \| 6.1723 \| 93.73 \| 242000 \| 6.1383 \| \| 6.1472 \| 93.92 \| 242500 \| 6.0735 \| \| 6.1774 \| 94.11 \| 243000 \| 6.1021 \| \| 6.1205 \| 94.31 \| 243500 \| 6.1257 \| \| 6.1624 \| 94.5 \| 244000 \| 6.0797 \| \| 6.1438 \| 94.69 \| 244500 \| 6.1059 \| \| 6.1722 \| 94.89 \| 245000 \| 6.1110 \| \| 6.1602 \| 95.08 \| 245500 \| 6.0810 \| \| 6.1423 \| 95.27 \| 246000 \| 6.0668 \| \| 6.1424 \| 95.47 \| 246500 \| 6.1259 \| \| 6.1472 \| 95.66 \| 247000 \| 6.1133 \| \| 6.1721 \| 95.86 \| 247500 \| 6.0732 \| \| 6.1389 \| 96.05 \| 248000 \| 6.1028 \| \| 6.1246 \| 96.24 \| 248500 \| 6.1174 \| \| 6.1285 \| 96.44 \| 249000 \| 6.1167 \| \| 6.1481 \| 96.63 \| 249500 \| 6.0627 \| \| 6.14 \| 96.82 \| 250000 \| 6.0413 \| \| 6.1426 \| 97.02 \| 250500 \| 6.1137 \| \| 6.1138 \| 97.21 \| 251000 \| 6.0706 \| \| 6.1153 \| 97.41 \| 251500 \| 6.0864 \| \| 6.1662 \| 97.6 \| 252000 \| 6.0970 \| \| 6.1157 \| 97.79 \| 252500 \| 6.0543 \| \| 6.129 \| 97.99 \| 253000 \| 6.0617 \| \| 6.1257 \| 98.18 \| 253500 \| 6.0196 \| \| 6.1188 \| 98.37 \| 254000 \| 6.0871 \| \| 6.1077 \| 98.57 \| 254500 \| 6.0634 \| \| 6.1202 \| 98.76 \| 255000 \| 6.0254 \| \| 6.1276 \| 98.95 \| 255500 \| 6.1073 \| \| 6.1105 \| 99.15 \| 256000 \| 6.0030 \| \| 6.105 \| 99.34 \| 256500 \| 6.0244 \| \| 6.1072 \| 99.54 \| 257000 \| 6.0833 \| \| 6.1061 \| 99.73 \| 257500 \| 6.0157 \| \| 6.1076 \| 99.92 \| 258000 \| 6.0297 \| \| 6.1397 \| 100.12 \| 258500 \| 6.0709 \| \| 6.1106 \| 100.31 \| 259000 \| 6.0028 \| \| 6.1141 \| 100.5 \| 259500 \| 6.0651 \| \| 6.1342 \| 100.7 \| 260000 \| 6.0409 \| \| 6.1062 \| 100.89 \| 260500 \| 5.9981 \| \| 6.1108 \| 101.08 \| 261000 \| 5.9928 \| \| 6.1198 \| 101.28 \| 261500 \| 6.0348 \| \| 6.1311 \| 101.47 \| 262000 \| 6.0392 \| \| 6.1215 \| 101.67 \| 262500 \| 6.0286 \| \| 6.0773 \| 101.86 \| 263000 \| 6.0042 \| \| 6.1002 \| 102.05 \| 263500 \| 6.0400 \| \| 6.084 \| 102.25 \| 264000 \| 6.0476 \| \| 6.1023 \| 102.44 \| 264500 \| 6.0125 \| \| 6.1006 \| 102.63 \| 265000 \| 6.0086 \| \| 6.1284 \| 102.83 \| 265500 \| 5.9758 \| \| 6.1001 \| 103.02 \| 266000 \| 6.0136 \| \| 6.1029 \| 103.21 \| 266500 \| 5.9535 \| \| 6.085 \| 103.41 \| 267000 \| 5.9307 \| \| 6.085 \| 103.6 \| 267500 \| 5.9810 \| \| 6.0918 \| 103.8 \| 268000 \| 5.9972 \| \| 6.0899 \| 103.99 \| 268500 \| 6.0040 \| \| 6.108 \| 104.18 \| 269000 \| 5.9606 \| \| 6.0835 \| 104.38 \| 269500 \| 6.0150 \| \| 6.0984 \| 104.57 \| 270000 \| 5.9414 \| \| 6.0727 \| 104.76 \| 270500 \| 5.9904 \| \| 6.0962 \| 104.96 \| 271000 \| 5.9662 \| \| 6.0813 \| 105.15 \| 271500 \| 5.9947 \| \| 6.105 \| 105.34 \| 272000 \| 5.9831 \| \| 6.0765 \| 105.54 \| 272500 \| 6.0098 \| \| 6.0748 \| 105.73 \| 273000 \| 5.9466 \| \| 6.0643 \| 105.93 \| 273500 \| 5.9434 \| \| 6.0818 \| 106.12 \| 274000 \| 5.9881 \| \| 6.0775 \| 106.31 \| 274500 \| 6.0043 \| \| 6.088 \| 106.51 \| 275000 \| 5.9833 \| \| 6.0981 \| 106.7 \| 275500 \| 5.9426 \| \| 6.0565 \| 106.89 \| 276000 \| 5.9937 \| \| 6.0769 \| 107.09 \| 276500 \| 5.9498 \| \| 6.0615 \| 107.28 \| 277000 \| 5.9442 \| \| 6.0802 \| 107.47 \| 277500 \| 5.9181 \| \| 6.0732 \| 107.67 \| 278000 \| 5.9088 \| \| 6.0626 \| 107.86 \| 278500 \| 5.9383 \| \| 6.0914 \| 108.06 \| 279000 \| 5.9347 \| \| 6.0359 \| 108.25 \| 279500 \| 5.9666 \| \| 6.0672 \| 108.44 \| 280000 \| 5.9783 \| \| 6.0726 \| 108.64 \| 280500 \| 5.8990 \| \| 6.0677 \| 108.83 \| 281000 \| 5.9633 \| \| 6.0641 \| 109.02 \| 281500 \| 5.9010 \| \| 6.0415 \| 109.22 \| 282000 \| 5.9579 \| \| 6.0544 \| 109.41 \| 282500 \| 5.9360 \| \| 6.0775 \| 109.6 \| 283000 \| 5.9221 \| \| 6.0786 \| 109.8 \| 283500 \| 5.8871 \| \| 6.0598 \| 109.99 \| 284000 \| 5.9277 \| \| 6.0783 \| 110.19 \| 284500 \| 5.9164 \| \| 6.0499 \| 110.38 \| 285000 \| 5.9539 \| \| 6.0655 \| 110.57 \| 285500 \| 5.8884 \| \| 6.054 \| 110.77 \| 286000 \| 5.8377 \| \| 6.0548 \| 110.96 \| 286500 \| 5.8962 \| \| 6.0543 \| 111.15 \| 287000 \| 5.9042 \| \| 6.0446 \| 111.35 \| 287500 \| 5.9362 \| \| 6.0429 \| 111.54 \| 288000 \| 5.9378 \| \| 6.0564 \| 111.74 \| 288500 \| 5.9262 \| \| 6.0559 \| 111.93 \| 289000 \| 5.8897 \| \| 6.0267 \| 112.12 \| 289500 \| 5.8988 \| \| 6.0402 \| 112.32 \| 290000 \| 5.8629 \| \| 6.0353 \| 112.51 \| 290500 \| 5.8836 \| \| 6.0337 \| 112.7 \| 291000 \| 5.9077 \| \| 6.0556 \| 112.9 \| 291500 \| 5.8944 \| \| 6.006 \| 113.09 \| 292000 \| 5.8421 \| \| 6.0253 \| 113.28 \| 292500 \| 5.8627 \| \| 6.0431 \| 113.48 \| 293000 \| 5.8871 \| \| 6.0452 \| 113.67 \| 293500 \| 5.9370 \| \| 6.0406 \| 113.87 \| 294000 \| 5.8726 \| \| 6.0383 \| 114.06 \| 294500 \| 5.8940 \| \| 6.0168 \| 114.25 \| 295000 \| 5.9241 \| \| 6.0144 \| 114.45 \| 295500 \| 5.8618 \| \| 6.0422 \| 114.64 \| 296000 \| 5.8867 \| \| 6.0353 \| 114.83 \| 296500 \| 5.8656 \| \| 6.0176 \| 115.03 \| 297000 \| 5.8710 \| \| 6.0351 \| 115.22 \| 297500 \| 5.8750 \| \| 6.0387 \| 115.41 \| 298000 \| 5.8251 \| \| 6.0369 \| 115.61 \| 298500 \| 5.8821 \| \| 5.9935 \| 115.8 \| 299000 \| 5.8763 \| \| 6.0324 \| 116.0 \| 299500 \| 5.8195 \| \| 6.016 \| 116.19 \| 300000 \| 5.9093 \| \| 6.0085 \| 116.38 \| 300500 \| 5.8991 \| \| 6.0163 \| 116.58 \| 301000 \| 5.8530 \| \| 5.9794 \| 116.77 \| 301500 \| 5.8573 \| \| 6.0053 \| 116.96 \| 302000 \| 5.8403 \| \| 5.9691 \| 117.16 \| 302500 \| 5.8189 \| \| 6.0235 \| 117.35 \| 303000 \| 5.8071 \| \| 6.0432 \| 117.54 \| 303500 \| 5.7983 \| \| 6.0167 \| 117.74 \| 304000 \| 5.8640 \| \| 5.9905 \| 117.93 \| 304500 \| 5.8887 \| \| 5.9941 \| 118.13 \| 305000 \| 5.8196 \| \| 6.0021 \| 118.32 \| 305500 \| 5.8368 \| \| 5.9802 \| 118.51 \| 306000 \| 5.8229 \| \| 5.9773 \| 118.71 \| 306500 \| 5.8570 \| \| 5.9757 \| 118.9 \| 307000 \| 5.7777 \| \| 6.0091 \| 119.09 \| 307500 \| 5.7950 \| \| 5.9971 \| 119.29 \| 308000 \| 5.8058 \| \| 5.9846 \| 119.48 \| 308500 \| 5.8305 \| \| 5.988 \| 119.67 \| 309000 \| 5.7729 \| \| 5.9825 \| 119.87 \| 309500 \| 5.7965 \| \| 6.0092 \| 120.06 \| 310000 \| 5.7714 \| \| 6.0 \| 120.26 \| 310500 \| 5.8226 \| \| 5.9562 \| 120.45 \| 311000 \| 5.7942 \| \| 5.9945 \| 120.64 \| 311500 \| 5.7819 \| \| 5.9627 \| 120.84 \| 312000 \| 5.8089 \| \| 5.9931 \| 121.03 \| 312500 \| 5.8007 \| \| 5.9671 \| 121.22 \| 313000 \| 5.8015 \| \| 6.001 \| 121.42 \| 313500 \| 5.7838 \| \| 5.983 \| 121.61 \| 314000 \| 5.8071 \| \| 5.9861 \| 121.8 \| 314500 \| 5.8000 \| \| 5.9767 \| 122.0 \| 315000 \| 5.7423 \| \| 5.9704 \| 122.19 \| 315500 \| 5.7823 \| \| 5.9561 \| 122.39 \| 316000 \| 5.7528 \| \| 5.9631 \| 122.58 \| 316500 \| 5.7772 \| \| 5.9732 \| 122.77 \| 317000 \| 5.7773 \| \| 5.9914 \| 122.97 \| 317500 \| 5.7848 \| \| 5.987 \| 123.16 \| 318000 \| 5.7447 \| \| 5.9451 \| 123.35 \| 318500 \| 5.7845 \| \| 5.9494 \| 123.55 \| 319000 \| 5.7627 \| \| 5.9717 \| 123.74 \| 319500 \| 5.7585 \| \| 5.9437 \| 123.93 \| 320000 \| 5.7714 \| \| 5.9714 \| 124.13 \| 320500 \| 5.7679 \| \| 5.9405 \| 124.32 \| 321000 \| 5.7276 \| \| 5.9532 \| 124.52 \| 321500 \| 5.7943 \| \| 5.9563 \| 124.71 \| 322000 \| 5.7375 \| \| 5.956 \| 124.9 \| 322500 \| 5.7355 \| \| 5.9469 \| 125.1 \| 323000 \| 5.7351 \| \| 5.9721 \| 125.29 \| 323500 \| 5.7592 \| \| 5.9573 \| 125.48 \| 324000 \| 5.7352 \| \| 5.9558 \| 125.68 \| 324500 \| 5.7532 \| \| 5.9481 \| 125.87 \| 325000 \| 5.7344 \| \| 5.962 \| 126.07 \| 325500 \| 5.7352 \| \| 5.9668 \| 126.26 \| 326000 \| 5.7034 \| \| 5.9436 \| 126.45 \| 326500 \| 5.7157 \| \| 5.9579 \| 126.65 \| 327000 \| 5.7318 \| \| 5.924 \| 126.84 \| 327500 \| 5.6861 \| \| 5.9429 \| 127.03 \| 328000 \| 5.7517 \| \| 5.9263 \| 127.23 \| 328500 \| 5.7812 \| \| 5.9501 \| 127.42 \| 329000 \| 5.7444 \| \| 5.9481 \| 127.61 \| 329500 \| 5.6990 \| \| 5.9563 \| 127.81 \| 330000 \| 5.7232 \| \| 5.9362 \| 128.0 \| 330500 \| 5.7270 \| \| 5.9223 \| 128.2 \| 331000 \| 5.7522 \| \| 5.9314 \| 128.39 \| 331500 \| 5.7059 \| \| 5.9335 \| 128.58 \| 332000 \| 5.7011 \| \| 5.9314 \| 128.78 \| 332500 \| 5.7114 \| \| 5.9476 \| 128.97 \| 333000 \| 5.6984 \| \| 5.9133 \| 129.16 \| 333500 \| 5.7490 \| \| 5.9616 \| 129.36 \| 334000 \| 5.7261 \| \| 5.9224 \| 129.55 \| 334500 \| 5.6712 \| \| 5.9301 \| 129.74 \| 335000 \| 5.7070 \| \| 5.9273 \| 129.94 \| 335500 \| 5.6583 \| \| 5.9176 \| 130.13 \| 336000 \| 5.6984 \| \| 5.9181 \| 130.33 \| 336500 \| 5.6638 \| \| 5.9331 \| 130.52 \| 337000 \| 5.6596 \| \| 5.9161 \| 130.71 \| 337500 \| 5.6462 \| \| 5.8896 \| 130.91 \| 338000 \| 5.7193 \| \| 5.906 \| 131.1 \| 338500 \| 5.6919 \| \| 5.9277 \| 131.29 \| 339000 \| 5.7109 \| \| 5.917 \| 131.49 \| 339500 \| 5.7309 \| \| 5.9208 \| 131.68 \| 340000 \| 5.6484 \| \| 5.9108 \| 131.87 \| 340500 \| 5.7129 \| \| 5.9192 \| 132.07 \| 341000 \| 5.6477 \| \| 5.9108 \| 132.26 \| 341500 \| 5.6546 \| \| 5.8858 \| 132.46 \| 342000 \| 5.6823 \| \| 5.9272 \| 132.65 \| 342500 \| 5.6619 \| \| 5.9104 \| 132.84 \| 343000 \| 5.6446 \| \| 5.8863 \| 133.04 \| 343500 \| 5.6903 \| \| 5.9221 \| 133.23 \| 344000 \| 5.6717 \| \| 5.9181 \| 133.42 \| 344500 \| 5.6931 \| \| 5.8639 \| 133.62 \| 345000 \| 5.6886 \| \| 5.9569 \| 133.81 \| 345500 \| 5.6852 \| \| 5.9086 \| 134.0 \| 346000 \| 5.6531 \| \| 5.9009 \| 134.2 \| 346500 \| 5.6950 \| \| 5.9131 \| 134.39 \| 347000 \| 5.6686 \| \| 5.9135 \| 134.59 \| 347500 \| 5.6983 \| \| 5.9059 \| 134.78 \| 348000 \| 5.6516 \| \| 5.8808 \| 134.97 \| 348500 \| 5.6244 \| \| 5.8817 \| 135.17 \| 349000 \| 5.6266 \| \| 5.8753 \| 135.36 \| 349500 \| 5.6479 \| \| 5.8801 \| 135.55 \| 350000 \| 5.6431 \| \| 5.8649 \| 135.75 \| 350500 \| 5.6959 \| \| 5.8893 \| 135.94 \| 351000 \| 5.6552 \| \| 5.8809 \| 136.13 \| 351500 \| 5.6294 \| \| 5.8763 \| 136.33 \| 352000 \| 5.5950 \| \| 5.8668 \| 136.52 \| 352500 \| 5.6509 \| \| 5.8815 \| 136.72 \| 353000 \| 5.6334 \| \| 5.884 \| 136.91 \| 353500 \| 5.6059 \| \| 5.8801 \| 137.1 \| 354000 \| 5.6690 \| \| 5.8969 \| 137.3 \| 354500 \| 5.5998 \| \| 5.8768 \| 137.49 \| 355000 \| 5.6211 \| \| 5.8703 \| 137.68 \| 355500 \| 5.6612 \| \| 5.8759 \| 137.88 \| 356000 \| 5.5840 \| \| 5.8714 \| 138.07 \| 356500 \| 5.5737 \| \| 5.8848 \| 138.26 \| 357000 \| 5.6426 \| \| 5.8477 \| 138.46 \| 357500 \| 5.6164 \| \| 5.8549 \| 138.65 \| 358000 \| 5.6253 \| \| 5.863 \| 138.85 \| 358500 \| 5.6246 \| \| 5.8729 \| 139.04 \| 359000 \| 5.6626 \| \| 5.8503 \| 139.23 \| 359500 \| 5.6267 \| \| 5.844 \| 139.43 \| 360000 \| 5.6095 \| \| 5.8388 \| 139.62 \| 360500 \| 5.6281 \| \| 5.846 \| 139.81 \| 361000 \| 5.6648 \| \| 5.8621 \| 140.01 \| 361500 \| 5.6222 \| \| 5.8595 \| 140.2 \| 362000 \| 5.5792 \| \| 5.8632 \| 140.4 \| 362500 \| 5.5882 \| \| 5.8598 \| 140.59 \| 363000 \| 5.5988 \| \| 5.8528 \| 140.78 \| 363500 \| 5.5913 \| \| 5.8632 \| 140.98 \| 364000 \| 5.5803 \| \| 5.8408 \| 141.17 \| 364500 \| 5.5976 \| \| 5.8687 \| 141.36 \| 365000 \| 5.5876 \| \| 5.8236 \| 141.56 \| 365500 \| 5.6500 \| \| 5.8713 \| 141.75 \| 366000 \| 5.5915 \| \| 5.8684 \| 141.94 \| 366500 \| 5.6197 \| \| 5.8592 \| 142.14 \| 367000 \| 5.5516 \| \| 5.8548 \| 142.33 \| 367500 \| 5.5978 \| \| 5.8483 \| 142.53 \| 368000 \| 5.5751 \| \| 5.8428 \| 142.72 \| 368500 \| 5.6102 \| \| 5.8305 \| 142.91 \| 369000 \| 5.5387 \| \| 5.8211 \| 143.11 \| 369500 \| 5.5782 \| \| 5.8425 \| 143.3 \| 370000 \| 5.5443 \| \| 5.8089 \| 143.49 \| 370500 \| 5.5261 \| \| 5.818 \| 143.69 \| 371000 \| 5.5743 \| \| 5.874 \| 143.88 \| 371500 \| 5.5478 \| \| 5.7944 \| 144.07 \| 372000 \| 5.5818 \| \| 5.8595 \| 144.27 \| 372500 \| 5.5393 \| \| 5.8456 \| 144.46 \| 373000 \| 5.5713 \| \| 5.8278 \| 144.66 \| 373500 \| 5.5661 \| \| 5.8337 \| 144.85 \| 374000 \| 5.5628 \| \| 5.8421 \| 145.04 \| 374500 \| 5.6046 \| \| 5.8462 \| 145.24 \| 375000 \| 5.5581 \| \| 5.8205 \| 145.43 \| 375500 \| 5.5547 \| \| 5.8076 \| 145.62 \| 376000 \| 5.5323 \| \| 5.8244 \| 145.82 \| 376500 \| 5.5266 \| \| 5.8509 \| 146.01 \| 377000 \| 5.5014 \| \| 5.815 \| 146.2 \| 377500 \| 5.5106 \| \| 5.8371 \| 146.4 \| 378000 \| 5.5998 \| \| 5.8157 \| 146.59 \| 378500 \| 5.5538 \| \| 5.8436 \| 146.79 \| 379000 \| 5.5187 \| \| 5.8205 \| 146.98 \| 379500 \| 5.5724 \| \| 5.8312 \| 147.17 \| 380000 \| 5.5023 \| \| 5.8223 \| 147.37 \| 380500 \| 5.5392 \| \| 5.8202 \| 147.56 \| 381000 \| 5.5574 \| \| 5.7997 \| 147.75 \| 381500 \| 5.5587 \| \| 5.824 \| 147.95 \| 382000 \| 5.5293 \| \| 5.8008 \| 148.14 \| 382500 \| 5.5805 \| \| 5.8229 \| 148.33 \| 383000 \| 5.5611 \| \| 5.8047 \| 148.53 \| 383500 \| 5.5052 \| \| 5.8054 \| 148.72 \| 384000 \| 5.6634 \| \| 5.805 \| 148.92 \| 384500 \| 5.5414 \| \| 5.8054 \| 149.11 \| 385000 \| 5.5301 \| \| 5.8028 \| 149.3 \| 385500 \| 5.5031 \| \| 5.822 \| 149.5 \| 386000 \| 5.5315 \| \| 5.7946 \| 149.69 \| 386500 \| 5.5576 \| \| 5.7915 \| 149.88 \| 387000 \| 5.5596 \| \| 5.8203 \| 150.08 \| 387500 \| 5.5502 \| \| 5.7824 \| 150.27 \| 388000 \| 5.5722 \| \| 5.7706 \| 150.46 \| 388500 \| 5.5451 \| \| 5.8074 \| 150.66 \| 389000 \| 5.5307 \| \| 5.8216 \| 150.85 \| 389500 \| 5.5555 \| \| 5.7996 \| 151.05 \| 390000 \| 5.5039 \| \| 5.8076 \| 151.24 \| 390500 \| 5.5535 \| \| 5.7969 \| 151.43 \| 391000 \| 5.5254 \| \| 5.7884 \| 151.63 \| 391500 \| 5.5390 \| \| 5.7691 \| 151.82 \| 392000 \| 5.5186 \| \| 5.7964 \| 152.01 \| 392500 \| 5.5439 \| \| 5.7907 \| 152.21 \| 393000 \| 5.5262 \| \| 5.7896 \| 152.4 \| 393500 \| 5.5059 \| \| 5.7943 \| 152.59 \| 394000 \| 5.5126 \| \| 5.81 \| 152.79 \| 394500 \| 5.4547 \| \| 5.7981 \| 152.98 \| 395000 \| 5.5141 \| \| 5.7845 \| 153.18 \| 395500 \| 5.5964 \| \| 5.7919 \| 153.37 \| 396000 \| 5.4650 \| \| 5.8165 \| 153.56 \| 396500 \| 5.5123 \| \| 5.7675 \| 153.76 \| 397000 \| 5.5191 \| \| 5.7473 \| 153.95 \| 397500 \| 5.5018 \| \| 5.7774 \| 154.14 \| 398000 \| 5.4447 \| \| 5.7875 \| 154.34 \| 398500 \| 5.4997 \| \| 5.7614 \| 154.53 \| 399000 \| 5.5125 \| \| 5.7704 \| 154.73 \| 399500 \| 5.5306 \| \| 5.8041 \| 154.92 \| 400000 \| 5.4993 \| \| 5.7729 \| 155.11 \| 400500 \| 5.5061 \| \| 5.7782 \| 155.31 \| 401000 \| 5.4924 \| \| 5.7788 \| 155.5 \| 401500 \| 5.5045 \| \| 5.7867 \| 155.69 \| 402000 \| 5.5064 \| \| 5.7453 \| 155.89 \| 402500 \| 5.4588 \| \| 5.7694 \| 156.08 \| 403000 \| 5.4874 \| \| 5.7495 \| 156.27 \| 403500 \| 5.4519 \| \| 5.7981 \| 156.47 \| 404000 \| 5.5117 \| \| 5.7725 \| 156.66 \| 404500 \| 5.4655 \| \| 5.7646 \| 156.86 \| 405000 \| 5.4456 \| \| 5.7733 \| 157.05 \| 405500 \| 5.4685 \| \| 5.7618 \| 157.24 \| 406000 \| 5.4861 \| \| 5.7747 \| 157.44 \| 406500 \| 5.4771 \| \| 5.742 \| 157.63 \| 407000 \| 5.4824 \| \| 5.7884 \| 157.82 \| 407500 \| 5.4122 \| \| 5.7312 \| 158.02 \| 408000 \| 5.4824 \| \| 5.7584 \| 158.21 \| 408500 \| 5.5168 \| \| 5.7494 \| 158.4 \| 409000 \| 5.4527 \| \| 5.7351 \| 158.6 \| 409500 \| 5.4517 \| \| 5.7571 \| 158.79 \| 410000 \| 5.4462 \| \| 5.7646 \| 158.99 \| 410500 \| 5.4827 \| \| 5.7448 \| 159.18 \| 411000 \| 5.4191 \| \| 5.7008 \| 159.37 \| 411500 \| 5.5147 \| \| 5.7455 \| 159.57 \| 412000 \| 5.4602 \| \| 5.7352 \| 159.76 \| 412500 \| 5.4281 \| \| 5.7438 \| 159.95 \| 413000 \| 5.4478 \| \| 5.7111 \| 160.15 \| 413500 \| 5.4608 \| \| 5.742 \| 160.34 \| 414000 \| 5.4418 \| \| 5.7541 \| 160.53 \| 414500 \| 5.4423 \| \| 5.7397 \| 160.73 \| 415000 \| 5.4406 \| \| 5.7393 \| 160.92 \| 415500 \| 5.4741 \| \| 5.7342 \| 161.12 \| 416000 \| 5.4575 \| \| 5.7198 \| 161.31 \| 416500 \| 5.3906 \| \| 5.691 \| 161.5 \| 417000 \| 5.4405 \| \| 5.7585 \| 161.7 \| 417500 \| 5.4259 \| \| 5.7279 \| 161.89 \| 418000 \| 5.5081 \| \| 5.7217 \| 162.08 \| 418500 \| 5.3794 \| \| 5.7452 \| 162.28 \| 419000 \| 5.4250 \| \| 5.7226 \| 162.47 \| 419500 \| 5.4700 \| \| 5.7482 \| 162.66 \| 420000 \| 5.4034 \| \| 5.7095 \| 162.86 \| 420500 \| 5.4118 \| \| 5.6917 \| 163.05 \| 421000 \| 5.4417 \| \| 5.7282 \| 163.25 \| 421500 \| 5.4055 \| \| 5.7171 \| 163.44 \| 422000 \| 5.4351 \| \| 5.7424 \| 163.63 \| 422500 \| 5.4415 \| \| 5.6961 \| 163.83 \| 423000 \| 5.4633 \| \| 5.7231 \| 164.02 \| 423500 \| 5.4643 \| \| 5.7365 \| 164.21 \| 424000 \| 5.4110 \| \| 5.7358 \| 164.41 \| 424500 \| 5.4220 \| \| 5.7008 \| 164.6 \| 425000 \| 5.4246 \| \| 5.7353 \| 164.79 \| 425500 \| 5.3805 \| \| 5.7047 \| 164.99 \| 426000 \| 5.3864 \| \| 5.701 \| 165.18 \| 426500 \| 5.4106 \| \| 5.7117 \| 165.38 \| 427000 \| 5.4074 \| \| 5.7173 \| 165.57 \| 427500 \| 5.4123 \| \| 5.7192 \| 165.76 \| 428000 \| 5.3903 \| \| 5.709 \| 165.96 \| 428500 \| 5.4557 \| \| 5.7064 \| 166.15 \| 429000 \| 5.3853 \| \| 5.6831 \| 166.34 \| 429500 \| 5.4376 \| \| 5.6873 \| 166.54 \| 430000 \| 5.4053 \| \| 5.6988 \| 166.73 \| 430500 \| 5.4159 \| \| 5.7169 \| 166.92 \| 431000 \| 5.4370 \| \| 5.7118 \| 167.12 \| 431500 \| 5.3915 \| \| 5.6992 \| 167.31 \| 432000 \| 5.4012 \| \| 5.6984 \| 167.51 \| 432500 \| 5.3864 \| \| 5.6991 \| 167.7 \| 433000 \| 5.3968 \| \| 5.7088 \| 167.89 \| 433500 \| 5.4048 \| \| 5.6914 \| 168.09 \| 434000 \| 5.3965 \| \| 5.6985 \| 168.28 \| 434500 \| 5.4305 \| \| 5.716 \| 168.47 \| 435000 \| 5.4073 \| \| 5.7114 \| 168.67 \| 435500 \| 5.3939 \| \| 5.6991 \| 168.86 \| 436000 \| 5.4275 \| \| 5.6844 \| 169.05 \| 436500 \| 5.4270 \| \| 5.6609 \| 169.25 \| 437000 \| 5.3867 \| \| 5.6984 \| 169.44 \| 437500 \| 5.4050 \| \| 5.6937 \| 169.64 \| 438000 \| 5.3821 \| \| 5.7043 \| 169.83 \| 438500 \| 5.4297 \| \| 5.7031 \| 170.02 \| 439000 \| 5.4376 \| \| 5.6958 \| 170.22 \| 439500 \| 5.3795 \| \| 5.658 \| 170.41 \| 440000 \| 5.4534 \| \| 5.6807 \| 170.6 \| 440500 \| 5.4420 \| \| 5.6979 \| 170.8 \| 441000 \| 5.4005 \| \| 5.6782 \| 170.99 \| 441500 \| 5.3995 \| \| 5.6872 \| 171.19 \| 442000 \| 5.3994 \| \| 5.6786 \| 171.38 \| 442500 \| 5.3890 \| \| 5.6815 \| 171.57 \| 443000 \| 5.4163 \| \| 5.6832 \| 171.77 \| 443500 \| 5.4296 \| \| 5.6833 \| 171.96 \| 444000 \| 5.3816 \| \| 5.6773 \| 172.15 \| 444500 \| 5.3820 \| \| 5.6489 \| 172.35 \| 445000 \| 5.3720 \| \| 5.6826 \| 172.54 \| 445500 \| 5.3859 \| \| 5.675 \| 172.73 \| 446000 \| 5.3909 \| \| 5.6678 \| 172.93 \| 446500 \| 5.3636 \| \| 5.6802 \| 173.12 \| 447000 \| 5.3338 \| \| 5.6882 \| 173.32 \| 447500 \| 5.3822 \| \| 5.6817 \| 173.51 \| 448000 \| 5.3794 \| \| 5.6744 \| 173.7 \| 448500 \| 5.3187 \| \| 5.6407 \| 173.9 \| 449000 \| 5.3966 \| \| 5.6389 \| 174.09 \| 449500 \| 5.3547 \| \| 5.6648 \| 174.28 \| 450000 \| 5.3423 \| \| 5.6576 \| 174.48 \| 450500 \| 5.3684 \| \| 5.6484 \| 174.67 \| 451000 \| 5.3507 \| \| 5.6705 \| 174.86 \| 451500 \| 5.4060 \| \| 5.6877 \| 175.06 \| 452000 \| 5.3540 \| \| 5.6768 \| 175.25 \| 452500 \| 5.3535 \| \| 5.6693 \| 175.45 \| 453000 \| 5.3339 \| \| 5.6294 \| 175.64 \| 453500 \| 5.3484 \| \| 5.6398 \| 175.83 \| 454000 \| 5.3836 \| \| 5.6617 \| 176.03 \| 454500 \| 5.4004 \| \| 5.6628 \| 176.22 \| 455000 \| 5.3228 \| \| 5.6707 \| 176.41 \| 455500 \| 5.3083 \| \| 5.6593 \| 176.61 \| 456000 \| 5.3822 \| \| 5.6522 \| 176.8 \| 456500 \| 5.3683 \| \| 5.6483 \| 176.99 \| 457000 \| 5.3286 \| \| 5.6352 \| 177.19 \| 457500 \| 5.4293 \| \| 5.6528 \| 177.38 \| 458000 \| 5.3603 \| \| 5.6591 \| 177.58 \| 458500 \| 5.3808 \| \| 5.6799 \| 177.77 \| 459000 \| 5.4076 \| \| 5.6485 \| 177.96 \| 459500 \| 5.3092 \| \| 5.6645 \| 178.16 \| 460000 \| 5.3530 \| \| 5.6401 \| 178.35 \| 460500 \| 5.3411 \| \| 5.6307 \| 178.54 \| 461000 \| 5.3876 \| \| 5.6338 \| 178.74 \| 461500 \| 5.3084 \| \| 5.6684 \| 178.93 \| 462000 \| 5.3771 \| \| 5.6684 \| 179.12 \| 462500 \| 5.3206 \| \| 5.6373 \| 179.32 \| 463000 \| 5.3839 \| \| 5.6817 \| 179.51 \| 463500 \| 5.4119 \| \| 5.6499 \| 179.71 \| 464000 \| 5.3780 \| \| 5.6542 \| 179.9 \| 464500 \| 5.4049 \| \| 5.6648 \| 180.09 \| 465000 \| 5.2990 \| \| 5.6531 \| 180.29 \| 465500 \| 5.3401 \| \| 5.6586 \| 180.48 \| 466000 \| 5.4087 \| \| 5.6261 \| 180.67 \| 466500 \| 5.3383 \| \| 5.6128 \| 180.87 \| 467000 \| 5.3714 \| \| 5.6704 \| 181.06 \| 467500 \| 5.3260 \| \| 5.6429 \| 181.25 \| 468000 \| 5.3600 \| \| 5.638 \| 181.45 \| 468500 \| 5.3364 \| \| 5.651 \| 181.64 \| 469000 \| 5.4135 \| \| 5.6448 \| 181.84 \| 469500 \| 5.4075 \| \| 5.6273 \| 182.03 \| 470000 \| 5.3312 \| \| 5.6459 \| 182.22 \| 470500 \| 5.3315 \| \| 5.6487 \| 182.42 \| 471000 \| 5.3298 \| \| 5.6669 \| 182.61 \| 471500 \| 5.3472 \| \| 5.6473 \| 182.8 \| 472000 \| 5.3055 \| \| 5.6281 \| 183.0 \| 472500 \| 5.2734 \| \| 5.6327 \| 183.19 \| 473000 \| 5.3361 \| \| 5.614 \| 183.38 \| 473500 \| 5.3431 \| \| 5.6216 \| 183.58 \| 474000 \| 5.3655 \| \| 5.6307 \| 183.77 \| 474500 \| 5.3467 \| \| 5.6411 \| 183.97 \| 475000 \| 5.4350 \| \| 5.6219 \| 184.16 \| 475500 \| 5.3125 \| \| 5.6226 \| 184.35 \| 476000 \| 5.3687 \| \| 5.6078 \| 184.55 \| 476500 \| 5.3488 \| \| 5.6096 \| 184.74 \| 477000 \| 5.3533 \| \| 5.6246 \| 184.93 \| 477500 \| 5.3244 \| \| 5.618 \| 185.13 \| 478000 \| 5.3299 \| \| 5.6114 \| 185.32 \| 478500 \| 5.3263 \| \| 5.5982 \| 185.52 \| 479000 \| 5.3405 \| \| 5.6245 \| 185.71 \| 479500 \| 5.3282 \| \| 5.6172 \| 185.9 \| 480000 \| 5.3250 \| \| 5.5996 \| 186.1 \| 480500 \| 5.3614 \| \| 5.65 \| 186.29 \| 481000 \| 5.3115 \| \| 5.6313 \| 186.48 \| 481500 \| 5.3997 \| \| 5.6252 \| 186.68 \| 482000 \| 5.3107 \| \| 5.6152 \| 186.87 \| 482500 \| 5.2778 \| \| 5.6237 \| 187.06 \| 483000 \| 5.3143 \| \| 5.6066 \| 187.26 \| 483500 \| 5.2831 \| \| 5.6261 \| 187.45 \| 484000 \| 5.3489 \| \| 5.6369 \| 187.65 \| 484500 \| 5.3050 \| \| 5.5793 \| 187.84 \| 485000 \| 5.2617 \| \| 5.6006 \| 188.03 \| 485500 \| 5.2924 \| \| 5.5963 \| 188.23 \| 486000 \| 5.2961 \| \| 5.6163 \| 188.42 \| 486500 \| 5.3068 \| \| 5.5976 \| 188.61 \| 487000 \| 5.3241 \| \| 5.6247 \| 188.81 \| 487500 \| 5.3540 \| \| 5.6252 \| 189.0 \| 488000 \| 5.2798 \| \| 5.5877 \| 189.19 \| 488500 \| 5.3412 \| \| 5.6068 \| 189.39 \| 489000 \| 5.3222 \| \| 5.6096 \| 189.58 \| 489500 \| 5.3245 \| \| 5.6141 \| 189.78 \| 490000 \| 5.4048 \| \| 5.6076 \| 189.97 \| 490500 \| 5.3013 \| \| 5.5593 \| 190.16 \| 491000 \| 5.2765 \| \| 5.5958 \| 190.36 \| 491500 \| 5.3411 \| \| 5.6028 \| 190.55 \| 492000 \| 5.3543 \| \| 5.5886 \| 190.74 \| 492500 \| 5.3400 \| \| 5.6006 \| 190.94 \| 493000 \| 5.2841 \| \| 5.5828 \| 191.13 \| 493500 \| 5.3125 \| \| 5.5995 \| 191.32 \| 494000 \| 5.2710 \| \| 5.585 \| 191.52 \| 494500 \| 5.3224 \| \| 5.6109 \| 191.71 \| 495000 \| 5.3154 \| \| 5.5949 \| 191.91 \| 495500 \| 5.3213 \| \| 5.5803 \| 192.1 \| 496000 \| 5.3214 \| \| 5.5996 \| 192.29 \| 496500 \| 5.2980 \| \| 5.5777 \| 192.49 \| 497000 \| 5.3015 \| \| 5.6193 \| 192.68 \| 497500 \| 5.3166 \| \| 5.624 \| 192.87 \| 498000 \| 5.2569 \| \| 5.5654 \| 193.07 \| 498500 \| 5.2981 \| \| 5.5593 \| 193.26 \| 499000 \| 5.2812 \| \| 5.5732 \| 193.45 \| 499500 \| 5.2912 \| \| 5.6158 \| 193.65 \| 500000 \| 5.3224 \| \| 5.6012 \| 193.84 \| 500500 \| 5.3529 \| \| 5.5906 \| 194.04 \| 501000 \| 5.2782 \| \| 5.5993 \| 194.23 \| 501500 \| 5.2995 \| \| 5.5731 \| 194.42 \| 502000 \| 5.2697 \| \| 5.5928 \| 194.62 \| 502500 \| 5.2955 \| \| 5.5777 \| 194.81 \| 503000 \| 5.2641 \| \| 5.5753 \| 195.0 \| 503500 \| 5.3061 \| \| 5.6029 \| 195.2 \| 504000 \| 5.3681 \| \| 5.563 \| 195.39 \| 504500 \| 5.3171 \| \| 5.6065 \| 195.58 \| 505000 \| 5.3106 \| \| 5.574 \| 195.78 \| 505500 \| 5.3547 \| \| 5.5759 \| 195.97 \| 506000 \| 5.2560 \| \| 5.5704 \| 196.17 \| 506500 \| 5.3061 \| \| 5.5619 \| 196.36 \| 507000 \| 5.3233 \| \| 5.5876 \| 196.55 \| 507500 \| 5.2826 \| \| 5.5849 \| 196.75 \| 508000 \| 5.3096 \| \| 5.5938 \| 196.94 \| 508500 \| 5.2849 \| \| 5.5666 \| 197.13 \| 509000 \| 5.3538 \| \| 5.5784 \| 197.33 \| 509500 \| 5.2532 \| \| 5.5893 \| 197.52 \| 510000 \| 5.2387 \| \| 5.5556 \| 197.71 \| 510500 \| 5.2909 \| \| 5.5741 \| 197.91 \| 511000 \| 5.4365 \| \| 5.5713 \| 198.1 \| 511500 \| 5.2402 \| \| 5.5583 \| 198.3 \| 512000 \| 5.3146 \| \| 5.5669 \| 198.49 \| 512500 \| 5.2166 \| \| 5.5523 \| 198.68 \| 513000 \| 5.3176 \| \| 5.5626 \| 198.88 \| 513500 \| 5.3053 \| \| 5.5788 \| 199.07 \| 514000 \| 5.2880 \| \| 5.5682 \| 199.26 \| 514500 \| 5.2790 \| \| 5.5499 \| 199.46 \| 515000 \| 5.2771 \| \| 5.5783 \| 199.65 \| 515500 \| 5.2516 \| \| 5.5425 \| 199.85 \| 516000 \| 5.3402 \| \| 5.5472 \| 200.04 \| 516500 \| 5.2679 \| \| 5.5628 \| 200.23 \| 517000 \| 5.2623 \| \| 5.5635 \| 200.43 \| 517500 \| 5.2496 \| \| 5.5645 \| 200.62 \| 518000 \| 5.2267 \| \| 5.5567 \| 200.81 \| 518500 \| 5.3454 \| \| 5.5591 \| 201.01 \| 519000 \| 5.2430 \| \| 5.5729 \| 201.2 \| 519500 \| 5.2992 \| \| 5.582 \| 201.39 \| 520000 \| 5.2823 \| \| 5.5528 \| 201.59 \| 520500 \| 5.3184 \| \| 5.5392 \| 201.78 \| 521000 \| 5.2932 \| \| 5.5632 \| 201.98 \| 521500 \| 5.2308 \| \| 5.5294 \| 202.17 \| 522000 \| 5.2836 \| \| 5.5385 \| 202.36 \| 522500 \| 5.2770 \| \| 5.5388 \| 202.56 \| 523000 \| 5.2804 \| \| 5.5681 \| 202.75 \| 523500 \| 5.2253 \| \| 5.5716 \| 202.94 \| 524000 \| 5.2818 \| \| 5.5572 \| 203.14 \| 524500 \| 5.2616 \| \| 5.5505 \| 203.33 \| 525000 \| 5.2558 \| \| 5.5573 \| 203.52 \| 525500 \| 5.3141 \| \| 5.545 \| 203.72 \| 526000 \| 5.2502 \| \| 5.5549 \| 203.91 \| 526500 \| 5.2166 \| \| 5.5498 \| 204.11 \| 527000 \| 5.2486 \| \| 5.5372 \| 204.3 \| 527500 \| 5.2524 \| \| 5.5337 \| 204.49 \| 528000 \| 5.2573 \| \| 5.5462 \| 204.69 \| 528500 \| 5.2399 \| \| 5.5371 \| 204.88 \| 529000 \| 5.2402 \| \| 5.5804 \| 205.07 \| 529500 \| 5.2804 \| \| 5.5265 \| 205.27 \| 530000 \| 5.2506 \| \| 5.5631 \| 205.46 \| 530500 \| 5.2290 \| \| 5.5643 \| 205.65 \| 531000 \| 5.2431 \| \| 5.5289 \| 205.85 \| 531500 \| 5.2717 \| \| 5.5462 \| 206.04 \| 532000 \| 5.2784 \| \| 5.5364 \| 206.24 \| 532500 \| 5.3275 \| \| 5.5203 \| 206.43 \| 533000 \| 5.3078 \| \| 5.5612 \| 206.62 \| 533500 \| 5.2713 \| \| 5.5461 \| 206.82 \| 534000 \| 5.2105 \| \| 5.4844 \| 207.01 \| 534500 \| 5.2427 \| \| 5.5281 \| 207.2 \| 535000 \| 5.2753 \| \| 5.5524 \| 207.4 \| 535500 \| 5.2430 \| \| 5.5413 \| 207.59 \| 536000 \| 5.2350 \| \| 5.5157 \| 207.78 \| 536500 \| 5.2656 \| \| 5.538 \| 207.98 \| 537000 \| 5.2013 \| \| 5.5398 \| 208.17 \| 537500 \| 5.2710 \| \| 5.536 \| 208.37 \| 538000 \| 5.2514 \| \| 5.5077 \| 208.56 \| 538500 \| 5.2851 \| \| 5.5267 \| 208.75 \| 539000 \| 5.2317 \| \| 5.5379 \| 208.95 \| 539500 \| 5.2661 \| \| 5.5261 \| 209.14 \| 540000 \| 5.2653 \| \| 5.5028 \| 209.33 \| 540500 \| 5.2561 \| \| 5.5209 \| 209.53 \| 541000 \| 5.2058 \| \| 5.4972 \| 209.72 \| 541500 \| 5.2360 \| \| 5.5079 \| 209.91 \| 542000 \| 5.1901 \| \| 5.4981 \| 210.11 \| 542500 \| 5.2492 \| \| 5.542 \| 210.3 \| 543000 \| 5.2457 \| \| 5.5527 \| 210.5 \| 543500 \| 5.2126 \| \| 5.5133 \| 210.69 \| 544000 \| 5.2157 \| \| 5.5217 \| 210.88 \| 544500 \| 5.2405 \| \| 5.5288 \| 211.08 \| 545000 \| 5.2562 \| \| 5.5165 \| 211.27 \| 545500 \| 5.2422 \| \| 5.524 \| 211.46 \| 546000 \| 5.2168 \| \| 5.5541 \| 211.66 \| 546500 \| 5.1961 \| \| 5.514 \| 211.85 \| 547000 \| 5.2531 \| \| 5.5246 \| 212.04 \| 547500 \| 5.2418 \| \| 5.4989 \| 212.24 \| 548000 \| 5.2581 \| \| 5.4825 \| 212.43 \| 548500 \| 5.1648 \| \| 5.5009 \| 212.63 \| 549000 \| 5.1800 \| \| 5.5621 \| 212.82 \| 549500 \| 5.2023 \| \| 5.5356 \| 213.01 \| 550000 \| 5.2142 \| \| 5.4894 \| 213.21 \| 550500 \| 5.2415 \| \| 5.5265 \| 213.4 \| 551000 \| 5.1678 \| \| 5.5408 \| 213.59 \| 551500 \| 5.1895 \| \| 5.5226 \| 213.79 \| 552000 \| 5.2287 \| \| 5.5282 \| 213.98 \| 552500 \| 5.2413 \| \| 5.4997 \| 214.18 \| 553000 \| 5.2408 \| \| 5.5177 \| 214.37 \| 553500 \| 5.1881 \| \| 5.5186 \| 214.56 \| 554000 \| 5.2222 \| \| 5.5227 \| 214.76 \| 554500 \| 5.2009 \| \| 5.5002 \| 214.95 \| 555000 \| 5.2383 \| \| 5.5174 \| 215.14 \| 555500 \| 5.2386 \| \| 5.5308 \| 215.34 \| 556000 \| 5.1832 \| \| 5.4914 \| 215.53 \| 556500 \| 5.2360 \| \| 5.4864 \| 215.72 \| 557000 \| 5.1961 \| \| 5.5116 \| 215.92 \| 557500 \| 5.2403 \| \| 5.5065 \| 216.11 \| 558000 \| 5.2019 \| \| 5.4919 \| 216.31 \| 558500 \| 5.2194 \| \| 5.519 \| 216.5 \| 559000 \| 5.2472 \| \| 5.5075 \| 216.69 \| 559500 \| 5.2192 \| \| 5.5181 \| 216.89 \| 560000 \| 5.2218 \| \| 5.5015 \| 217.08 \| 560500 \| 5.2167 \| \| 5.487 \| 217.27 \| 561000 \| 5.2329 \| \| 5.5179 \| 217.47 \| 561500 \| 5.2464 \| \| 5.4807 \| 217.66 \| 562000 \| 5.2115 \| \| 5.4998 \| 217.85 \| 562500 \| 5.2462 \| \| 5.5032 \| 218.05 \| 563000 \| 5.2216 \| \| 5.5031 \| 218.24 \| 563500 \| 5.2147 \| \| 5.5083 \| 218.44 \| 564000 \| 5.2162 \| \| 5.5038 \| 218.63 \| 564500 \| 5.1412 \| \| 5.4659 \| 218.82 \| 565000 \| 5.2629 \| \| 5.4794 \| 219.02 \| 565500 \| 5.2163 \| \| 5.4744 \| 219.21 \| 566000 \| 5.1878 \| \| 5.5054 \| 219.4 \| 566500 \| 5.2107 \| \| 5.4841 \| 219.6 \| 567000 \| 5.2308 \| \| 5.4891 \| 219.79 \| 567500 \| 5.2575 \| \| 5.4531 \| 219.98 \| 568000 \| 5.1906 \| \| 5.4901 \| 220.18 \| 568500 \| 5.1901 \| \| 5.4622 \| 220.37 \| 569000 \| 5.2440 \| \| 5.4799 \| 220.57 \| 569500 \| 5.2478 \| \| 5.4893 \| 220.76 \| 570000 \| 5.1878 \| \| 5.4961 \| 220.95 \| 570500 \| 5.2147 \| \| 5.508 \| 221.15 \| 571000 \| 5.2494 \| \| 5.4665 \| 221.34 \| 571500 \| 5.2317 \| \| 5.473 \| 221.53 \| 572000 \| 5.2471 \| \| 5.4754 \| 221.73 \| 572500 \| 5.2230 \| \| 5.4629 \| 221.92 \| 573000 \| 5.2310 \| \| 5.4941 \| 222.11 \| 573500 \| 5.2487 \| \| 5.5063 \| 222.31 \| 574000 \| 5.1748 \| \| 5.5031 \| 222.5 \| 574500 \| 5.2017 \| \| 5.4775 \| 222.7 \| 575000 \| 5.1819 \| \| 5.477 \| 222.89 \| 575500 \| 5.2201 \| \| 5.4974 \| 223.08 \| 576000 \| 5.1915 \| \| 5.471 \| 223.28 \| 576500 \| 5.1601 \| \| 5.4968 \| 223.47 \| 577000 \| 5.1940 \| \| 5.4802 \| 223.66 \| 577500 \| 5.2094 \| \| 5.4807 \| 223.86 \| 578000 \| 5.2069 \| \| 5.4802 \| 224.05 \| 578500 \| 5.2246 \| \| 5.4408 \| 224.24 \| 579000 \| 5.1933 \| \| 5.4635 \| 224.44 \| 579500 \| 5.2526 \| \| 5.4835 \| 224.63 \| 580000 \| 5.1989 \| \| 5.4697 \| 224.83 \| 580500 \| 5.2130 \| \| 5.4673 \| 225.02 \| 581000 \| 5.2051 \| \| 5.4653 \| 225.21 \| 581500 \| 5.1684 \| \| 5.4683 \| 225.41 \| 582000 \| 5.2201 \| \| 5.4597 \| 225.6 \| 582500 \| 5.1634 \| \| 5.4624 \| 225.79 \| 583000 \| 5.1864 \| \| 5.4818 \| 225.99 \| 583500 \| 5.1758 \| \| 5.4521 \| 226.18 \| 584000 \| 5.2370 \| \| 5.4829 \| 226.37 \| 584500 \| 5.2197 \| \| 5.4561 \| 226.57 \| 585000 \| 5.1673 \| \| 5.4604 \| 226.76 \| 585500 \| 5.1525 \| \| 5.4836 \| 226.96 \| 586000 \| 5.2036 \| \| 5.4556 \| 227.15 \| 586500 \| 5.1597 \| \| 5.4375 \| 227.34 \| 587000 \| 5.1354 \| \| 5.4542 \| 227.54 \| 587500 \| 5.2094 \| \| 5.4633 \| 227.73 \| 588000 \| 5.1696 \| \| 5.4631 \| 227.92 \| 588500 \| 5.1048 \| \| 5.4789 \| 228.12 \| 589000 \| 5.1532 \| \| 5.4708 \| 228.31 \| 589500 \| 5.1899 \| \| 5.4747 \| 228.51 \| 590000 \| 5.2007 \| \| 5.4562 \| 228.7 \| 590500 \| 5.1649 \| \| 5.4412 \| 228.89 \| 591000 \| 5.1794 \| \| 5.477 \| 229.09 \| 591500 \| 5.1865 \| \| 5.4415 \| 229.28 \| 592000 \| 5.1394 \| \| 5.4898 \| 229.47 \| 592500 \| 5.1865 \| \| 5.4986 \| 229.67 \| 593000 \| 5.1977 \| \| 5.4623 \| 229.86 \| 593500 \| 5.1879 \| \| 5.444 \| 230.05 \| 594000 \| 5.1844 \| \| 5.4514 \| 230.25 \| 594500 \| 5.2079 \| \| 5.4847 \| 230.44 \| 595000 \| 5.2058 \| \| 5.4936 \| 230.64 \| 595500 \| 5.2204 \| \| 5.4266 \| 230.83 \| 596000 \| 5.1847 \| \| 5.4596 \| 231.02 \| 596500 \| 5.1775 \| \| 5.4662 \| 231.22 \| 597000 \| 5.2368 \| \| 5.4447 \| 231.41 \| 597500 \| 5.1629 \| \| 5.4276 \| 231.6 \| 598000 \| 5.0777 \| \| 5.4758 \| 231.8 \| 598500 \| 5.1242 \| \| 5.4492 \| 231.99 \| 599000 \| 5.1298 \| \| 5.4386 \| 232.18 \| 599500 \| 5.1472 \| \| 5.4425 \| 232.38 \| 600000 \| 5.1869 \| \| 5.4525 \| 232.57 \| 600500 \| 5.1746 \| \| 5.4361 \| 232.77 \| 601000 \| 5.1657 \| \| 5.4606 \| 232.96 \| 601500 \| 5.1502 \| \| 5.4587 \| 233.15 \| 602000 \| 5.1334 \| \| 5.4491 \| 233.35 \| 602500 \| 5.1452 \| \| 5.4599 \| 233.54 \| 603000 \| 5.1541 \| \| 5.4692 \| 233.73 \| 603500 \| 5.1343 \| \| 5.4423 \| 233.93 \| 604000 \| 5.1430 \| \| 5.4387 \| 234.12 \| 604500 \| 5.1566 \| \| 5.4616 \| 234.31 \| 605000 \| 5.1718 \| \| 5.4678 \| 234.51 \| 605500 \| 5.1338 \| \| 5.3934 \| 234.7 \| 606000 \| 5.1227 \| \| 5.4454 \| 234.9 \| 606500 \| 5.1688 \| \| 5.4402 \| 235.09 \| 607000 \| 5.1094 \| \| 5.4294 \| 235.28 \| 607500 \| 5.1227 \| \| 5.448 \| 235.48 \| 608000 \| 5.1407 \| \| 5.4416 \| 235.67 \| 608500 \| 5.1410 \| \| 5.4617 \| 235.86 \| 609000 \| 5.1206 \| \| 5.4332 \| 236.06 \| 609500 \| 5.1739 \| \| 5.4195 \| 236.25 \| 610000 \| 5.1671 \| \| 5.4506 \| 236.44 \| 610500 \| 5.1708 \| \| 5.4235 \| 236.64 \| 611000 \| 5.1622 \| \| 5.4558 \| 236.83 \| 611500 \| 5.1731 \| \| 5.4344 \| 237.03 \| 612000 \| 5.1368 \| \| 5.4159 \| 237.22 \| 612500 \| 5.1689 \| \| 5.435 \| 237.41 \| 613000 \| 5.1383 \| \| 5.4408 \| 237.61 \| 613500 \| 5.1235 \| \| 5.416 \| 237.8 \| 614000 \| 5.1519 \| \| 5.4317 \| 237.99 \| 614500 \| 5.1538 \| \| 5.4444 \| 238.19 \| 615000 \| 5.1710 \| \| 5.4177 \| 238.38 \| 615500 \| 5.1571 \| \| 5.4352 \| 238.57 \| 616000 \| 5.1401 \| \| 5.4216 \| 238.77 \| 616500 \| 5.1795 \| \| 5.4412 \| 238.96 \| 617000 \| 5.1101 \| \| 5.4403 \| 239.16 \| 617500 \| 5.1405 \| \| 5.4694 \| 239.35 \| 618000 \| 5.1463 \| \| 5.4101 \| 239.54 \| 618500 \| 5.1289 \| \| 5.4316 \| 239.74 \| 619000 \| 5.1274 \| \| 5.4291 \| 239.93 \| 619500 \| 5.1681 \| \| 5.4204 \| 240.12 \| 620000 \| 5.1824 \| \| 5.4092 \| 240.32 \| 620500 \| 5.1620 \| \| 5.4151 \| 240.51 \| 621000 \| 5.1428 \| \| 5.4235 \| 240.7 \| 621500 \| 5.1342 \| \| 5.4342 \| 240.9 \| 622000 \| 5.1091 \| \| 5.4166 \| 241.09 \| 622500 \| 5.1483 \| \| 5.4166 \| 241.29 \| 623000 \| 5.1497 \| \| 5.3939 \| 241.48 \| 623500 \| 5.1323 \| \| 5.4253 \| 241.67 \| 624000 \| 5.1281 \| \| 5.3985 \| 241.87 \| 624500 \| 5.1087 \| \| 5.4103 \| 242.06 \| 625000 \| 5.1538 \| \| 5.4106 \| 242.25 \| 625500 \| 5.1367 \| \| 5.4258 \| 242.45 \| 626000 \| 5.0969 \| \| 5.434 \| 242.64 \| 626500 \| 5.1474 \| \| 5.4158 \| 242.84 \| 627000 \| 5.0803 \| \| 5.4053 \| 243.03 \| 627500 \| 5.1300 \| \| 5.4355 \| 243.22 \| 628000 \| 5.1774 \| \| 5.4214 \| 243.42 \| 628500 \| 5.1289 \| \| 5.3964 \| 243.61 \| 629000 \| 5.1782 \| \| 5.4092 \| 243.8 \| 629500 \| 5.1291 \| \| 5.3865 \| 244.0 \| 630000 \| 5.2033 \| \| 5.415 \| 244.19 \| 630500 \| 5.1307 \| \| 5.4053 \| 244.38 \| 631000 \| 5.1285 \| \| 5.4083 \| 244.58 \| 631500 \| 5.1260 \| \| 5.4308 \| 244.77 \| 632000 \| 5.1111 \| \| 5.4088 \| 244.97 \| 632500 \| 5.1473 \| \| 5.404 \| 245.16 \| 633000 \| 5.1695 \| \| 5.4006 \| 245.35 \| 633500 \| 5.1438 \| \| 5.3848 \| 245.55 \| 634000 \| 5.1529 \| \| 5.4202 \| 245.74 \| 634500 \| 5.1223 \| \| 5.4029 \| 245.93 \| 635000 \| 5.0946 \| \| 5.3855 \| 246.13 \| 635500 \| 5.1392 \| \| 5.4303 \| 246.32 \| 636000 \| 5.1367 \| \| 5.4033 \| 246.51 \| 636500 \| 5.1017 \| \| 5.4325 \| 246.71 \| 637000 \| 5.1393 \| \| 5.4134 \| 246.9 \| 637500 \| 5.1543 \| \| 5.3986 \| 247.1 \| 638000 \| 5.1309 \| \| 5.3746 \| 247.29 \| 638500 \| 5.1322 \| \| 5.4197 \| 247.48 \| 639000 \| 5.1160 \| \| 5.4235 \| 247.68 \| 639500 \| 5.1321 \| \| 5.3706 \| 247.87 \| 640000 \| 5.1676 \| \| 5.4018 \| 248.06 \| 640500 \| 5.1096 \| \| 5.3822 \| 248.26 \| 641000 \| 5.0967 \| \| 5.4332 \| 248.45 \| 641500 \| 5.1486 \| \| 5.3951 \| 248.64 \| 642000 \| 5.1048 \| \| 5.3899 \| 248.84 \| 642500 \| 5.1297 \| \| 5.3887 \| 249.03 \| 643000 \| 5.1264 \| \| 5.3808 \| 249.23 \| 643500 \| 5.1108 \| \| 5.3934 \| 249.42 \| 644000 \| 5.1363 \| \| 5.4008 \| 249.61 \| 644500 \| 5.1109 \| \| 5.4168 \| 249.81 \| 645000 \| 5.1005 \| \| 5.3844 \| 250.0 \| 645500 \| 5.1302 \| \| 5.396 \| 250.19 \| 646000 \| 5.1385 \| \| 5.4019 \| 250.39 \| 646500 \| 5.1112 \| \| 5.3883 \| 250.58 \| 647000 \| 5.1359 \| \| 5.3982 \| 250.77 \| 647500 \| 5.1295 \| \| 5.3858 \| 250.97 \| 648000 \| 5.1397 \| \| 5.4064 \| 251.16 \| 648500 \| 5.1076 \| \| 5.3845 \| 251.36 \| 649000 \| 5.1030 \| \| 5.3977 \| 251.55 \| 649500 \| 5.1283 \| \| 5.3936 \| 251.74 \| 650000 \| 5.0607 \| \| 5.3917 \| 251.94 \| 650500 \| 5.1286 \| \| 5.3857 \| 252.13 \| 651000 \| 5.1203 \| \| 5.4092 \| 252.32 \| 651500 \| 5.0867 \| \| 5.3949 \| 252.52 \| 652000 \| 5.0936 \| \| 5.3909 \| 252.71 \| 652500 \| 5.1033 \| \| 5.3748 \| 252.9 \| 653000 \| 5.1448 \| \| 5.36 \| 253.1 \| 653500 \| 5.1007 \| \| 5.4047 \| 253.29 \| 654000 \| 5.1083 \| \| 5.3664 \| 253.49 \| 654500 \| 5.1111 \| \| 5.3728 \| 253.68 \| 655000 \| 5.1023 \| \| 5.3863 \| 253.87 \| 655500 \| 5.0889 \| \| 5.3781 \| 254.07 \| 656000 \| 5.0758 \| \| 5.384 \| 254.26 \| 656500 \| 5.0883 \| \| 5.3748 \| 254.45 \| 657000 \| 5.1066 \| \| 5.4297 \| 254.65 \| 657500 \| 5.0840 \| \| 5.3763 \| 254.84 \| 658000 \| 5.0740 \| \| 5.3915 \| 255.03 \| 658500 \| 5.0531 \| \| 5.401 \| 255.23 \| 659000 \| 5.1152 \| \| 5.4052 \| 255.42 \| 659500 \| 5.1129 \| \| 5.4131 \| 255.62 \| 660000 \| 5.1075 \| \| 5.3829 \| 255.81 \| 660500 \| 5.1153 \| \| 5.3764 \| 256.0 \| 661000 \| 5.1075 \| \| 5.3757 \| 256.2 \| 661500 \| 5.1077 \| \| 5.3944 \| 256.39 \| 662000 \| 5.1051 \| \| 5.3688 \| 256.58 \| 662500 \| 5.0953 \| \| 5.4085 \| 256.78 \| 663000 \| 5.1339 \| \| 5.3561 \| 256.97 \| 663500 \| 5.0772 \| \| 5.3754 \| 257.16 \| 664000 \| 5.1090 \| \| 5.407 \| 257.36 \| 664500 \| 5.1180 \| \| 5.3627 \| 257.55 \| 665000 \| 5.1054 \| \| 5.3866 \| 257.75 \| 665500 \| 5.1373 \| \| 5.3599 \| 257.94 \| 666000 \| 5.0439 \| \| 5.3825 \| 258.13 \| 666500 \| 5.0759 \| \| 5.3584 \| 258.33 \| 667000 \| 5.1097 \| \| 5.3478 \| 258.52 \| 667500 \| 5.1463 \| \| 5.3608 \| 258.71 \| 668000 \| 5.1012 \| \| 5.4128 \| 258.91 \| 668500 \| 5.1192 \| \| 5.378 \| 259.1 \| 669000 \| 5.0897 \| \| 5.3831 \| 259.3 \| 669500 \| 5.1095 \| \| 5.3687 \| 259.49 \| 670000 \| 5.0835 \| \| 5.3658 \| 259.68 \| 670500 \| 5.0947 \| \| 5.3531 \| 259.88 \| 671000 \| 5.0795 \| \| 5.3745 \| 260.07 \| 671500 \| 5.1075 \| \| 5.4171 \| 260.26 \| 672000 \| 5.1051 \| \| 5.3669 \| 260.46 \| 672500 \| 5.1055 \| \| 5.4015 \| 260.65 \| 673000 \| 5.1121 \| \| 5.3423 \| 260.84 \| 673500 \| 5.1391 \| \| 5.3811 \| 261.04 \| 674000 \| 5.0921 \| \| 5.3607 \| 261.23 \| 674500 \| 5.1021 \| \| 5.3556 \| 261.43 \| 675000 \| 5.0886 \| \| 5.3887 \| 261.62 \| 675500 \| 5.0489 \| \| 5.3793 \| 261.81 \| 676000 \| 5.1188 \| \| 5.3871 \| 262.01 \| 676500 \| 5.1047 \| \| 5.3597 \| 262.2 \| 677000 \| 5.1699 \| \| 5.3839 \| 262.39 \| 677500 \| 5.0961 \| \| 5.3735 \| 262.59 \| 678000 \| 5.1041 \| \| 5.3725 \| 262.78 \| 678500 \| 5.0690 \| \| 5.3593 \| 262.97 \| 679000 \| 5.0925 \| \| 5.3571 \| 263.17 \| 679500 \| 5.0774 \| \| 5.3717 \| 263.36 \| 680000 \| 5.1172 \| \| 5.3609 \| 263.56 \| 680500 \| 5.0873 \| \| 5.3773 \| 263.75 \| 681000 \| 5.1073 \| \| 5.381 \| 263.94 \| 681500 \| 5.0893 \| \| 5.3406 \| 264.14 \| 682000 \| 5.0634 \| \| 5.383 \| 264.33 \| 682500 \| 5.0769 \| \| 5.3703 \| 264.52 \| 683000 \| 5.0812 \| \| 5.3568 \| 264.72 \| 683500 \| 5.0918 \| \| 5.3321 \| 264.91 \| 684000 \| 5.1248 \| \| 5.3735 \| 265.1 \| 684500 \| 5.0733 \| \| 5.3796 \| 265.3 \| 685000 \| 5.0809 \| \| 5.3352 \| 265.49 \| 685500 \| 5.1017 \| \| 5.3727 \| 265.69 \| 686000 \| 5.0930 \| \| 5.3333 \| 265.88 \| 686500 \| 5.0893 \| \| 5.3516 \| 266.07 \| 687000 \| 5.1134 \| \| 5.3768 \| 266.27 \| 687500 \| 5.0761 \| \| 5.3685 \| 266.46 \| 688000 \| 5.0557 \| \| 5.3604 \| 266.65 \| 688500 \| 5.0616 \| \| 5.3663 \| 266.85 \| 689000 \| 5.0996 \| \| 5.3756 \| 267.04 \| 689500 \| 5.0806 \| \| 5.3703 \| 267.23 \| 690000 \| 5.0482 \| \| 5.3772 \| 267.43 \| 690500 \| 5.0874 \| \| 5.3504 \| 267.62 \| 691000 \| 5.0664 \| \| 5.3695 \| 267.82 \| 691500 \| 5.0752 \| \| 5.3701 \| 268.01 \| 692000 \| 5.0659 \| \| 5.3811 \| 268.2 \| 692500 \| 5.1069 \| \| 5.3568 \| 268.4 \| 693000 \| 5.0801 \| \| 5.3752 \| 268.59 \| 693500 \| 5.0727 \| \| 5.3718 \| 268.78 \| 694000 \| 5.0704 \| \| 5.3419 \| 268.98 \| 694500 \| 5.0735 \| \| 5.3343 \| 269.17 \| 695000 \| 5.0845 \| \| 5.3348 \| 269.36 \| 695500 \| 5.0549 \| \| 5.3558 \| 269.56 \| 696000 \| 5.0596 \| \| 5.3729 \| 269.75 \| 696500 \| 5.0374 \| \| 5.3514 \| 269.95 \| 697000 \| 5.0976 \| \| 5.36 \| 270.14 \| 697500 \| 5.0621 \| \| 5.3763 \| 270.33 \| 698000 \| 5.0889 \| \| 5.3516 \| 270.53 \| 698500 \| 5.0927 \| \| 5.3824 \| 270.72 \| 699000 \| 5.0988 \| \| 5.3635 \| 270.91 \| 699500 \| 5.0921 \| \| 5.3366 \| 271.11 \| 700000 \| 5.0688 \| \| 5.358 \| 271.3 \| 700500 \| 5.0585 \| \| 5.37 \| 271.49 \| 701000 \| 5.0990 \| \| 5.3629 \| 271.69 \| 701500 \| 5.1258 \| \| 5.347 \| 271.88 \| 702000 \| 5.0644 \| \| 5.3331 \| 272.08 \| 702500 \| 5.0988 \| \| 5.3516 \| 272.27 \| 703000 \| 5.0773 \| \| 5.3345 \| 272.46 \| 703500 \| 5.0567 \| \| 5.3495 \| 272.66 \| 704000 \| 5.1025 \| \| 5.3315 \| 272.85 \| 704500 \| 5.0231 \| \| 5.3698 \| 273.04 \| 705000 \| 5.0677 \| \| 5.347 \| 273.24 \| 705500 \| 5.0602 \| \| 5.3708 \| 273.43 \| 706000 \| 5.0575 \| \| 5.3065 \| 273.63 \| 706500 \| 5.0442 \| \| 5.3453 \| 273.82 \| 707000 \| 5.0758 \| \| 5.3408 \| 274.01 \| 707500 \| 5.0838 \| \| 5.3429 \| 274.21 \| 708000 \| 5.0919 \| \| 5.342 \| 274.4 \| 708500 \| 5.0556 \| \| 5.3612 \| 274.59 \| 709000 \| 5.0716 \| \| 5.3666 \| 274.79 \| 709500 \| 5.0837 \| \| 5.3473 \| 274.98 \| 710000 \| 5.0536 \| \| 5.3684 \| 275.17 \| 710500 \| 5.0759 \| \| 5.3545 \| 275.37 \| 711000 \| 5.0618 \| \| 5.3424 \| 275.56 \| 711500 \| 5.0807 \| \| 5.3489 \| 275.76 \| 712000 \| 5.0750 \| \| 5.3409 \| 275.95 \| 712500 \| 5.0264 \| \| 5.3136 \| 276.14 \| 713000 \| 5.0516 \| \| 5.3393 \| 276.34 \| 713500 \| 5.0836 \| \| 5.3348 \| 276.53 \| 714000 \| 5.0567 \| \| 5.3743 \| 276.72 \| 714500 \| 5.0857 \| \| 5.3356 \| 276.92 \| 715000 \| 5.0667 \| \| 5.3431 \| 277.11 \| 715500 \| 5.0481 \| \| 5.3539 \| 277.3 \| 716000 \| 5.0604 \| \| 5.3587 \| 277.5 \| 716500 \| 5.0900 \| \| 5.3671 \| 277.69 \| 717000 \| 5.0950 \| \| 5.3414 \| 277.89 \| 717500 \| 5.0792 \| \| 5.3247 \| 278.08 \| 718000 \| 5.0677 \| \| 5.348 \| 278.27 \| 718500 \| 5.0357 \| \| 5.3521 \| 278.47 \| 719000 \| 5.0454 \| \| 5.3353 \| 278.66 \| 719500 \| 5.0591 \| \| 5.3691 \| 278.85 \| 720000 \| 5.0540 \| \| 5.3516 \| 279.05 \| 720500 \| 5.0605 \| \| 5.3626 \| 279.24 \| 721000 \| 5.0448 \| \| 5.3586 \| 279.43 \| 721500 \| 5.0610 \| \| 5.3456 \| 279.63 \| 722000 \| 5.0509 \| \| 5.3334 \| 279.82 \| 722500 \| 5.0505 \| \| 5.3487 \| 280.02 \| 723000 \| 5.0647 \| \| 5.3585 \| 280.21 \| 723500 \| 5.0700 \| \| 5.3031 \| 280.4 \| 724000 \| 5.0509 \| \| 5.3425 \| 280.6 \| 724500 \| 5.0527 \| \| 5.3564 \| 280.79 \| 725000 \| 5.0422 \| \| 5.3275 \| 280.98 \| 725500 \| 5.0818 \| \| 5.3389 \| 281.18 \| 726000 \| 5.0567 \| \| 5.3327 \| 281.37 \| 726500 \| 5.0413 \| \| 5.3321 \| 281.56 \| 727000 \| 5.0821 \| \| 5.3523 \| 281.76 \| 727500 \| 5.0261 \| \| 5.3471 \| 281.95 \| 728000 \| 5.0301 \| \| 5.3497 \| 282.15 \| 728500 \| 5.0944 \| \| 5.3607 \| 282.34 \| 729000 \| 5.0698 \| \| 5.3229 \| 282.53 \| 729500 \| 5.0782 \| \| 5.3291 \| 282.73 \| 730000 \| 5.0224 \| \| 5.3465 \| 282.92 \| 730500 \| 5.0285 \| \| 5.3333 \| 283.11 \| 731000 \| 5.0422 \| \| 5.3303 \| 283.31 \| 731500 \| 5.0738 \| \| 5.344 \| 283.5 \| 732000 \| 5.0664 \| \| 5.3354 \| 283.69 \| 732500 \| 5.0302 \| \| 5.3657 \| 283.89 \| 733000 \| 5.0333 \| \| 5.3483 \| 284.08 \| 733500 \| 5.0612 \| \| 5.336 \| 284.28 \| 734000 \| 5.0713 \| \| 5.3131 \| 284.47 \| 734500 \| 5.0794 \| \| 5.3473 \| 284.66 \| 735000 \| 5.0451 \| \| 5.3139 \| 284.86 \| 735500 \| 5.0408 \| \| 5.3561 \| 285.05 \| 736000 \| 5.0525 \| \| 5.3515 \| 285.24 \| 736500 \| 5.0468 \| \| 5.3405 \| 285.44 \| 737000 \| 5.0607 \| \| 5.3363 \| 285.63 \| 737500 \| 5.0528 \| \| 5.3144 \| 285.82 \| 738000 \| 5.0766 \| \| 5.3563 \| 286.02 \| 738500 \| 5.0321 \| \| 5.3151 \| 286.21 \| 739000 \| 5.0005 \| \| 5.3374 \| 286.41 \| 739500 \| 5.0595 \| \| 5.3336 \| 286.6 \| 740000 \| 5.0523 \| \| 5.3383 \| 286.79 \| 740500 \| 5.0394 \| \| 5.3445 \| 286.99 \| 741000 \| 5.0588 \| \| 5.3431 \| 287.18 \| 741500 \| 5.0369 \| \| 5.3277 \| 287.37 \| 742000 \| 5.0628 \| \| 5.3357 \| 287.57 \| 742500 \| 5.0469 \| \| 5.3348 \| 287.76 \| 743000 \| 5.0368 \| \| 5.3445 \| 287.96 \| 743500 \| 5.0085 \| \| 5.3292 \| 288.15 \| 744000 \| 5.0724 \| \| 5.3213 \| 288.34 \| 744500 \| 5.0137 \| \| 5.3251 \| 288.54 \| 745000 \| 5.0576 \| \| 5.3222 \| 288.73 \| 745500 \| 5.0740 \| \| 5.3121 \| 288.92 \| 746000 \| 5.0114 \| \| 5.3232 \| 289.12 \| 746500 \| 5.0531 \| \| 5.3315 \| 289.31 \| 747000 \| 5.0426 \| \| 5.3392 \| 289.5 \| 747500 \| 5.0531 \| \| 5.3187 \| 289.7 \| 748000 \| 5.0661 \| \| 5.3701 \| 289.89 \| 748500 \| 5.0260 \| \| 5.3446 \| 290.09 \| 749000 \| 5.0125 \| \| 5.3465 \| 290.28 \| 749500 \| 5.0423 \| \| 5.3283 \| 290.47 \| 750000 \| 5.0366 \| \| 5.338 \| 290.67 \| 750500 \| 5.0667 \| \| 5.2954 \| 290.86 \| 751000 \| 5.0613 \| \| 5.3194 \| 291.05 \| 751500 \| 5.0521 \| \| 5.3367 \| 291.25 \| 752000 \| 5.0795 \| \| 5.3469 \| 291.44 \| 752500 \| 5.0709 \| \| 5.3262 \| 291.63 \| 753000 \| 5.0545 \| \| 5.3107 \| 291.83 \| 753500 \| 5.0195 \| \| 5.3104 \| 292.02 \| 754000 \| 5.0633 \| \| 5.343 \| 292.22 \| 754500 \| 5.0673 \| \| 5.3171 \| 292.41 \| 755000 \| 5.0391 \| \| 5.344 \| 292.6 \| 755500 \| 5.0445 \| \| 5.3257 \| 292.8 \| 756000 \| 5.0666 \| \| 5.3102 \| 292.99 \| 756500 \| 5.0197 \| \| 5.3254 \| 293.18 \| 757000 \| 5.0403 \| \| 5.3494 \| 293.38 \| 757500 \| 5.0233 \| \| 5.3615 \| 293.57 \| 758000 \| 5.0868 \| \| 5.2934 \| 293.76 \| 758500 \| 5.0730 \| \| 5.3434 \| 293.96 \| 759000 \| 5.0714 \| \| 5.3512 \| 294.15 \| 759500 \| 5.0396 \| \| 5.3311 \| 294.35 \| 760000 \| 5.0887 \| \| 5.3422 \| 294.54 \| 760500 \| 5.0571 \| \| 5.3067 \| 294.73 \| 761000 \| 5.0656 \| \| 5.3382 \| 294.93 \| 761500 \| 5.0728 \| \| 5.3367 \| 295.12 \| 762000 \| 5.0628 \| \| 5.3343 \| 295.31 \| 762500 \| 5.0472 \| \| 5.3154 \| 295.51 \| 763000 \| 5.0429 \| \| 5.3099 \| 295.7 \| 763500 \| 5.0384 \| \| 5.3299 \| 295.89 \| 764000 \| 5.0563 \| \| 5.312 \| 296.09 \| 764500 \| 5.0682 \| \| 5.3282 \| 296.28 \| 765000 \| 5.0360 \| \| 5.3336 \| 296.48 \| 765500 \| 5.0175 \| \| 5.3495 \| 296.67 \| 766000 \| 5.0728 \| \| 5.3393 \| 296.86 \| 766500 \| 5.0527 \| \| 5.3478 \| 297.06 \| 767000 \| 5.0398 \| \| 5.3249 \| 297.25 \| 767500 \| 5.0344 \| \| 5.3217 \| 297.44 \| 768000 \| 5.0458 \| \| 5.3291 \| 297.64 \| 768500 \| 5.1057 \| \| 5.3253 \| 297.83 \| 769000 \| 5.0360 \| \| 5.3124 \| 298.02 \| 769500 \| 5.0854 \| \| 5.3029 \| 298.22 \| 770000 \| 5.0250 \| \| 5.3263 \| 298.41 \| 770500 \| 5.0399 \| \| 5.325 \| 298.61 \| 771000 \| 5.0587 \| \| 5.3315 \| 298.8 \| 771500 \| 5.0548 \| \| 5.2862 \| 298.99 \| 772000 \| 5.0644 \| \| 5.3218 \| 299.19 \| 772500 \| 5.0562 \| \| 5.3233 \| 299.38 \| 773000 \| 5.0442 \| \| 5.3001 \| 299.57 \| 773500 \| 5.0263 \| \| 5.334 \| 299.77 \| 774000 \| 5.0736 \| \| 5.327 \| 299.96 \| 774500 \| 5.0648 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2
ChoboAvenger/DialoGPT-small-DocBot	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-21T19:49:18Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-12k - wit-400m --- # Model card for vit_base_patch16_clip_224.openai_ft_in12k A Vision Transformer (ViT) image classification model. Pretrained on WIT-400M image-text pairs by OpenAI using CLIP. Fine-tuned on ImageNet-12k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143). ## Model Details - Model Type: Image classification / feature backbone - Model Stats: - Params (M): 94.9 - GMACs: 16.9 - Activations (M): 16.5 - Image size: 224 x 224 - Papers: - Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020 - Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - Dataset: ImageNet-12k - Pretrain Dataset: - WIT-400M ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('vit_base_patch16_clip_224.openai_ft_in12k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(*data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_base_patch16_clip_224.openai_ft_in12k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 197, 768) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` ```bibtex @article{cherti2022reproducible, title={Reproducible scaling laws for contrastive language-image learning}, author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia}, journal={arXiv preprint arXiv:2212.07143}, year={2022} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
ChoboAvenger/DialoGPT-small-joshua	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-21T20:03:02Z	--- license: other tags: - generated_from_keras_callback model-index: - name: nateraw/mit-b0-finetuned-sidewalks-v2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # nateraw/mit-b0-finetuned-sidewalks-v2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1508 - Validation Loss: 0.4301 - Validation Mean Iou: 0.3689 - Validation Mean Accuracy: 0.4261 - Validation Overall Accuracy: 0.8878 - Validation Per Category Iou: [0. 0.82155443 0.88837272 0.80869927 0.84681809 0.50445633 nan 0.5062558 0.58202362 0.09694114 0.86506226 0.10300594 0. 0.03122511 0. 0.55651564 0. 0. 0.76493797 0.04021662 0.40453306 0.56038987 0.34382567 nan 0.02428609 0.30885576 0.28811326 0. 0.87087236 0.74857511 0.94321046 0.02300712 0.03721037 0.20366003 0. ] - Validation Per Category Accuracy: [0. 0.88109026 0.95044945 0.85142397 0.95993416 0.6370042 nan 0.65971511 0.81045852 0.11321606 0.95401169 0.10670369 0. 0.04042904 0. 0.66801313 0. 0. 0.90595882 0.04265001 0.5292762 0.61230561 0.4092219 nan 0.0283755 0.37721503 0.3266398 0. 0.950358 0.87250445 0.96996696 0.02583519 0.09486859 0.28234463 0. ] - Epoch: 49 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Validation Mean Iou \| Validation Mean Accuracy \| Validation Overall Accuracy \| Validation Per Category Iou \| Validation Per Category Accuracy \| Epoch \| \|:----------:\|:---------------:\|:-------------------:\|:------------------------:\|:---------------------------:\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\|:-----:\| \| 1.4089 \| 0.8220 \| 0.1975 \| 0.2427 \| 0.7701 \| [0. 0.58353931 0.7655921 0.04209491 0.53135026 0.11779776 nan 0.07709853 0.15950712 0. 0.69634813 0. 0. 0. 0. 0. 0. 0. 0.61456822 0. 0.24971248 0.27129675 0. nan 0. 0.07697324 0. 0. 0.78576516 0.61267064 0.84564576 0. 0. 0.08904216 0. ] \| [0. 0.88026971 0.93475302 0.04216372 0.5484085 0.13285614 nan 0.08669707 0.19044773 0. 0.90089024 0. 0. 0. 0. 0. 0. 0. 0.76783975 0. 0.42102101 0.28659817 0. nan 0. 0.08671771 0. 0. 0.89590301 0.74932576 0.9434814 0. 0. 0.14245566 0. ] \| 0 \| \| 0.8462 \| 0.6135 \| 0.2551 \| 0.2960 \| 0.8200 \| [0. 0.66967645 0.80571406 0.56416239 0.66692248 0.24744912 nan 0.23994505 0.28962463 0. 0.76504783 0. 0. 0. 0. 0.14111353 0. 0. 0.6924468 0. 0.27988701 0.41876094 0. nan 0. 0.14755829 0. 0. 0.81614463 0.68429711 0.87710938 0. 0. 0.11234171 0. ] \| [0. 0.83805933 0.94928385 0.59586511 0.72913519 0.30595504 nan 0.3128234 0.34805831 0. 0.87847495 0. 0. 0. 0. 0.14205167 0. 0. 0.87543619 0. 0.36001144 0.49498574 0. nan 0. 0.18179115 0. 0. 0.92867923 0.7496178 0.92220166 0. 0. 0.15398549 0. ] \| 1 \| \| 0.7134 \| 0.5660 \| 0.2780 \| 0.3320 \| 0.8286 \| [0. 0.64791461 0.83800512 0.67301044 0.68120631 0.27361472 nan 0.26715802 0.43596999 0. 0.78649287 0. 0. 0. 0. 0.41256964 0. 0. 0.71114766 0. 0.31646321 0.44682442 0. nan 0. 0.17132551 0. 0. 0.81845697 0.67536699 0.88940936 0. 0. 0.1304862 0. ] \| [0. 0.85958877 0.92084269 0.82341633 0.74725972 0.33495972 nan 0.40755277 0.56591531 0. 0.90641721 0. 0. 0. 0. 0.48144408 0. 0. 0.88294811 0. 0.46962078 0.47517397 0. nan 0. 0.20631607 0. 0. 0.90956851 0.85856042 0.94107052 0. 0. 0.16669713 0. ] \| 2 \| \| 0.6320 \| 0.5173 \| 0.2894 \| 0.3454 \| 0.8435 \| [0. 0.70789146 0.84902296 0.65266358 0.76099965 0.32934391 nan 0.29576422 0.43988204 0. 0.79276447 0. 0. 0. 0. 0.42668367 0. 0. 0.71717911 0. 0.32151249 0.50084444 0. nan 0. 0.18711455 0. 0. 0.82903803 0.68990498 0.8990059 0. 0.00213015 0.14819771 0. ] \| [0. 0.84048763 0.93514369 0.68355212 0.88302113 0.458816 nan 0.38623272 0.69456442 0. 0.92379471 0. 0. 0. 0. 0.50677438 0. 0. 0.90362965 0. 0.4662386 0.57368294 0. nan 0. 0.23281768 0. 0. 0.9001526 0.86786434 0.95195314 0. 0.00333751 0.18532191 0. ] \| 3 \| \| 0.5609 \| 0.5099 \| 0.2920 \| 0.3599 \| 0.8385 \| [0. 0.70817583 0.84131144 0.66573523 0.81449696 0.38891117 nan 0.28124784 0.42659255 0. 0.80855146 0. 0. 0. 0. 0.46011866 0. 0. 0.65458792 0. 0.28411565 0.46758138 0. nan 0. 0.21849067 0. 0. 0.83829062 0.71207623 0.89929169 0. 0.02846127 0.13782635 0. ] \| [0. 0.88632871 0.91269832 0.79044294 0.88368528 0.57405218 nan 0.35035973 0.77610775 0. 0.8889696 0. 0. 0. 0. 0.6020786 0. 0. 0.74586521 0. 0.61602403 0.54519561 0. nan 0. 0.28447396 0. 0. 0.94520232 0.85544414 0.95994042 0. 0.04680851 0.21407134 0. ] \| 4 \| \| 0.5256 \| 0.4741 \| 0.3045 \| 0.3598 \| 0.8558 \| [0.00000000e+00 7.50159008e-01 8.53654462e-01 6.44928131e-01 7.90455244e-01 4.33599913e-01 nan 3.33472954e-01 4.74502513e-01 0.00000000e+00 8.01366017e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.67653814e-01 0.00000000e+00 0.00000000e+00 7.27412479e-01 0.00000000e+00 4.18946113e-01 5.04714837e-01 0.00000000e+00 nan 0.00000000e+00 2.00373855e-01 0.00000000e+00 0.00000000e+00 8.50200795e-01 7.41636173e-01 9.08320534e-01 2.77259907e-04 0.00000000e+00 1.45430716e-01 0.00000000e+00] \| [0.00000000e+00 8.86487233e-01 9.05201886e-01 7.23139265e-01 8.91929263e-01 7.26675641e-01 nan 4.36386295e-01 6.64378543e-01 0.00000000e+00 8.89056843e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.65450644e-01 0.00000000e+00 0.00000000e+00 9.27446136e-01 0.00000000e+00 5.36031025e-01 5.84198054e-01 0.00000000e+00 nan 0.00000000e+00 2.42514534e-01 0.00000000e+00 0.00000000e+00 9.31954754e-01 8.26849708e-01 9.59880377e-01 2.79039335e-04 0.00000000e+00 1.77106051e-01 0.00000000e+00] \| 5 \| \| 0.4761 \| 0.4922 \| 0.3036 \| 0.3754 \| 0.8517 \| [0.00000000e+00 7.18490241e-01 8.54701589e-01 5.90903088e-01 8.21902743e-01 4.76229883e-01 nan 3.32447673e-01 4.80642540e-01 0.00000000e+00 8.02904449e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.73285636e-01 0.00000000e+00 0.00000000e+00 7.16608930e-01 0.00000000e+00 3.16598081e-01 5.12540924e-01 0.00000000e+00 nan 0.00000000e+00 2.27702968e-01 0.00000000e+00 0.00000000e+00 8.51831675e-01 7.39827330e-01 9.07152231e-01 5.59070700e-04 3.70370370e-02 1.56538301e-01 0.00000000e+00] \| [0.00000000e+00 9.20834531e-01 8.92075255e-01 7.48664032e-01 9.03709011e-01 7.40703529e-01 nan 4.40828188e-01 7.92719139e-01 0.00000000e+00 9.21593374e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.90292855e-01 0.00000000e+00 0.00000000e+00 8.42229041e-01 0.00000000e+00 4.75170857e-01 6.72591473e-01 0.00000000e+00 nan 0.00000000e+00 2.94713089e-01 0.00000000e+00 0.00000000e+00 9.26034809e-01 8.39522012e-01 9.66679296e-01 6.06188900e-04 1.12807676e-01 2.07280968e-01 0.00000000e+00] \| 6 \| \| 0.4495 \| 0.4797 \| 0.3035 \| 0.3702 \| 0.8468 \| [0.00000000e+00 7.52163526e-01 8.46563375e-01 7.16396797e-01 7.38850637e-01 3.93073019e-01 nan 3.31795957e-01 4.92991567e-01 0.00000000e+00 8.11302090e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.16059849e-01 0.00000000e+00 0.00000000e+00 6.56058294e-01 1.25948501e-02 2.66942435e-01 5.34406894e-01 0.00000000e+00 nan 0.00000000e+00 2.27750085e-01 4.86381323e-04 0.00000000e+00 8.48618960e-01 7.25828093e-01 9.17747637e-01 8.28380212e-03 6.74590297e-02 1.51281596e-01 0.00000000e+00] \| [0.00000000e+00 8.75360044e-01 9.43650850e-01 8.78658645e-01 7.76578096e-01 4.85757596e-01 nan 4.30901582e-01 7.54126335e-01 0.00000000e+00 9.30112537e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.42914247e-01 0.00000000e+00 0.00000000e+00 7.57605356e-01 1.27102686e-02 6.50888458e-01 6.94757080e-01 0.00000000e+00 nan 0.00000000e+00 2.91727649e-01 4.86381323e-04 0.00000000e+00 9.42251577e-01 8.60753175e-01 9.56778008e-01 8.51551074e-03 1.38756779e-01 1.83583708e-01 0.00000000e+00] \| 7 \| \| 0.4193 \| 0.4487 \| 0.3073 \| 0.3633 \| 0.8594 \| [0. 0.77081114 0.86089485 0.64464211 0.82962632 0.36186873 nan 0.39092332 0.5399988 0. 0.81734925 0. 0. 0. 0. 0.50271555 0. 0. 0.70239658 0. 0.30875695 0.52195319 0. nan 0. 0.20124517 0.00696273 0. 0.84526591 0.72563399 0.91703372 0. 0.03526147 0.15693635 0. ] \| [0. 0.8654775 0.95711297 0.70665759 0.93130714 0.42436958 nan 0.52892143 0.69243377 0. 0.91682626 0. 0. 0. 0. 0.62315913 0. 0. 0.86251114 0. 0.5607807 0.70416055 0. nan 0. 0.24483525 0.00698305 0. 0.921099 0.81848055 0.96789871 0. 0.06891948 0.18778302 0. ] \| 8 \| \| 0.3883 \| 0.4824 \| 0.3086 \| 0.3690 \| 0.8527 \| [0. 0.76454291 0.86544951 0.70501066 0.77912256 0.39088976 nan 0.40275725 0.53334923 0. 0.82777802 0. 0. 0. 0. 0.49916177 0. 0. 0.68780083 0.01500768 0.31589145 0.53805504 0. nan 0. 0.22450413 0.03544121 0. 0.82663975 0.60689445 0.91513911 0.12702194 0.0163284 0.10604071 0. ] \| [0. 0.86846682 0.93345513 0.77258597 0.90365389 0.54440067 nan 0.51997559 0.73323435 0. 0.92499729 0. 0. 0. 0. 0.62015064 0. 0. 0.8190305 0.01503264 0.61258781 0.62514291 0. nan 0. 0.28141855 0.03574903 0. 0.95838638 0.66828866 0.96505306 0.19804095 0.04463913 0.1315269 0. ] \| 9 \| \| 0.3736 \| 0.4515 \| 0.3180 \| 0.3859 \| 0.8600 \| [0. 0.77296038 0.8679117 0.60122746 0.84573808 0.42877201 nan 0.40372521 0.5356554 0. 0.82057963 0. 0. 0. 0. 0.48309209 0. 0. 0.70156487 0.07165346 0.31172072 0.45383525 0. nan 0. 0.26337213 0.07457255 0. 0.85227381 0.7079085 0.92271657 0.20363628 0.03853875 0.13249146 0. ] \| [0. 0.90081404 0.93156248 0.71723323 0.91251575 0.57187527 nan 0.53665381 0.74547838 0. 0.93718616 0. 0. 0. 0. 0.6410839 0. 0. 0.80529967 0.07249561 0.6074764 0.5775282 0. nan 0. 0.34898163 0.07545859 0. 0.95221746 0.80297775 0.96768443 0.26155608 0.19382562 0.17354842 0. ] \| 10 \| \| 0.3487 \| 0.4486 \| 0.3181 \| 0.3898 \| 0.8637 \| [0. 0.79416982 0.87767891 0.70942695 0.81634288 0.46749785 nan 0.42873013 0.48671464 0. 0.82752704 0. 0. 0. 0. 0.50844774 0. 0. 0.68070149 0.03976498 0.29304387 0.46322705 0. nan 0. 0.24856882 0.12795031 0. 0.84646906 0.71781094 0.92550642 0.04810685 0.04610752 0.14423047 0. ] \| [0. 0.86951324 0.95247608 0.82408892 0.90393017 0.59760857 nan 0.5760741 0.83602638 0. 0.93420702 0. 0. 0. 0. 0.63502483 0. 0. 0.76902695 0.04024918 0.57179186 0.75842139 0. nan 0. 0.30837498 0.13239994 0. 0.95283514 0.78607095 0.96594744 0.05354669 0.18906967 0.2060098 0. ] \| 11 \| \| 0.3460 \| 0.4342 \| 0.3234 \| 0.3852 \| 0.8669 \| [0. 0.76828673 0.86958873 0.66044471 0.84588115 0.46323947 nan 0.41208499 0.54202812 0. 0.82543751 0. 0. 0. 0. 0.50071248 0. 0. 0.72333932 0.0173886 0.36535728 0.5284402 0. nan 0. 0.24239821 0.13456635 0. 0.86084123 0.73217705 0.92386442 0.09545854 0.04193608 0.11945951 0. ] \| [0. 0.92666259 0.91906703 0.74134089 0.92518489 0.60022437 nan 0.56316038 0.77045814 0. 0.93600314 0. 0. 0. 0. 0.61358664 0. 0. 0.87835072 0.01757469 0.57608316 0.64108174 0. nan 0. 0.30432247 0.13750695 0. 0.93332326 0.85806371 0.96442783 0.10753599 0.15152274 0.14552189 0. ] \| 12 \| \| 0.3146 \| 0.4175 \| 0.3339 \| 0.3995 \| 0.8745 \| [0. 0.81054591 0.88286867 0.68551149 0.86089895 0.4562385 nan 0.4522713 0.55496016 0.01456189 0.83576109 0. 0. 0. 0. 0.50709788 0. 0. 0.73464008 0.00175153 0.35021502 0.57263292 0. nan 0. 0.25185222 0.14419755 0. 0.85952374 0.70281003 0.9270307 0.17660456 0.04867831 0.18762581 0. ] \| [0. 0.9092016 0.94168672 0.86545289 0.89611216 0.55273728 nan 0.61409823 0.76682349 0.01569689 0.92776282 0. 0. 0. 0. 0.59972229 0. 0. 0.86700656 0.00175747 0.54181633 0.67419762 0. nan 0. 0.3252672 0.14789466 0. 0.9316378 0.88743565 0.97060047 0.33277846 0.15319149 0.25967892 0. ] \| 13 \| \| 0.3000 \| 0.4196 \| 0.3263 \| 0.3833 \| 0.8720 \| [0.00000000e+00 8.02547730e-01 8.74182776e-01 6.55641045e-01 8.69918767e-01 4.12920686e-01 nan 4.34054109e-01 5.54604573e-01 3.14830157e-03 8.29634841e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.98619437e-01 0.00000000e+00 0.00000000e+00 7.20371619e-01 1.62799781e-02 3.73295478e-01 5.20323501e-01 0.00000000e+00 nan 3.48000087e-04 2.41829304e-01 1.50045164e-01 0.00000000e+00 8.67415087e-01 7.31957881e-01 9.29791719e-01 1.28032094e-01 2.77808135e-02 1.25956544e-01 0.00000000e+00] \| [0.00000000e+00 9.10809038e-01 9.53614030e-01 6.91330346e-01 9.25106631e-01 4.73740259e-01 nan 5.64222160e-01 7.49045544e-01 3.42805593e-03 9.38335743e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.77484642e-01 0.00000000e+00 0.00000000e+00 8.68434883e-01 1.63507406e-02 5.76763406e-01 7.07811962e-01 0.00000000e+00 nan 3.51671539e-04 3.02660657e-01 1.55815731e-01 0.00000000e+00 9.39832349e-01 8.43146236e-01 9.70195728e-01 2.11579170e-01 1.06049228e-01 1.61502816e-01 0.00000000e+00] \| 14 \| \| 0.3000 \| 0.4375 \| 0.3296 \| 0.4004 \| 0.8666 \| [0. 0.78266617 0.87516084 0.70472612 0.86490176 0.45228049 nan 0.42625351 0.54739354 0. 0.82459025 0. 0. 0. 0. 0.51809119 0. 0. 0.69081711 0.12347692 0.35720113 0.50921058 0. nan 0.00489936 0.24630062 0.14805039 0. 0.86169724 0.71926146 0.92796331 0.08257639 0.06410606 0.14539247 0. ] \| [0. 0.9075929 0.9264549 0.93787289 0.92618179 0.57743083 nan 0.55003982 0.78286607 0. 0.94643176 0. 0. 0. 0. 0.62538921 0. 0. 0.80130182 0.13309691 0.69176706 0.69506169 0. nan 0.00507726 0.30979772 0.15393969 0. 0.93923901 0.84161243 0.9636732 0.1240378 0.17630371 0.19733096 0. ] \| 15 \| \| 0.2958 \| 0.4558 \| 0.3321 \| 0.3960 \| 0.8649 \| [0.00000000e+00 7.61108709e-01 8.60621205e-01 6.66132134e-01 8.52805958e-01 4.61529893e-01 nan 4.08367412e-01 5.31449716e-01 0.00000000e+00 8.35699926e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.07030790e-01 0.00000000e+00 0.00000000e+00 7.07129610e-01 6.48353710e-02 3.15606022e-01 5.11721371e-01 0.00000000e+00 nan 6.30311903e-04 2.73874288e-01 2.03863944e-01 0.00000000e+00 8.66259515e-01 7.58237242e-01 9.29139752e-01 2.50199629e-01 3.09762934e-02 1.61355571e-01 0.00000000e+00] \| [0.00000000e+00 9.26534567e-01 9.27389090e-01 7.04037518e-01 9.24733729e-01 5.57765301e-01 nan 5.03121563e-01 8.16946898e-01 0.00000000e+00 9.33051726e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.15985021e-01 0.00000000e+00 0.00000000e+00 8.13389275e-01 6.50734371e-02 6.70053085e-01 6.00421712e-01 0.00000000e+00 nan 6.81363606e-04 3.40217727e-01 2.25090328e-01 0.00000000e+00 9.48289295e-01 8.67999126e-01 9.71413074e-01 3.64800631e-01 8.85273258e-02 2.01834862e-01 0.00000000e+00] \| 16 \| \| 0.2734 \| 0.4359 \| 0.3324 \| 0.3921 \| 0.8700 \| [0.00000000e+00 7.69753150e-01 8.66375446e-01 7.12734804e-01 8.60891290e-01 4.75851982e-01 nan 4.34175617e-01 5.46685274e-01 3.24503811e-02 8.38429371e-01 6.02288697e-04 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.86131744e-01 0.00000000e+00 0.00000000e+00 7.27408322e-01 8.04817093e-02 3.54733831e-01 5.42941315e-01 0.00000000e+00 nan 2.71841412e-04 2.58488818e-01 1.79625596e-01 0.00000000e+00 8.66870562e-01 7.48169480e-01 9.27856685e-01 7.49529761e-02 5.06564228e-02 1.31117070e-01 0.00000000e+00] \| [0.00000000e+00 9.25702577e-01 9.27332221e-01 7.66360378e-01 9.04612005e-01 5.93608265e-01 nan 6.05902185e-01 7.70458139e-01 3.87911592e-02 9.33542542e-01 1.19000397e-03 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.85184718e-01 0.00000000e+00 0.00000000e+00 8.77167736e-01 9.19689932e-02 5.50758130e-01 6.18987337e-01 0.00000000e+00 nan 2.85733125e-04 3.36777474e-01 1.97679266e-01 0.00000000e+00 9.34506301e-01 8.56945493e-01 9.73108663e-01 8.09021630e-02 2.00250313e-01 1.67017963e-01 0.00000000e+00] \| 17 \| \| 0.2616 \| 0.4510 \| 0.3349 \| 0.4009 \| 0.8726 \| [0. 0.79136177 0.88249795 0.78534299 0.86229779 0.47690438 nan 0.43122561 0.54187893 0.01320356 0.83487865 0.01648963 0. 0.00144389 0. 0.51996092 0. 0. 0.70356624 0.09487574 0.37358034 0.43221057 0. nan 0.05030614 0.28552575 0.19602978 0. 0.86214818 0.75003141 0.92402054 0.03367055 0.0295635 0.15961857 0. ] \| [0. 0.9308775 0.94322916 0.85211162 0.90509242 0.57554177 nan 0.5785717 0.8193957 0.0157871 0.93896272 0.03232844 0. 0.00144389 0. 0.6718842 0. 0. 0.80146551 0.09782199 0.57800629 0.60986079 0. nan 0.05345407 0.40116852 0.21407727 0. 0.93948742 0.87904777 0.97272876 0.03561119 0.11522737 0.2649179 0. ] \| 18 \| \| 0.2570 \| 0.4381 \| 0.3378 \| 0.4040 \| 0.8691 \| [0. 0.78633412 0.8781239 0.70951789 0.85768155 0.49725305 nan 0.4385802 0.5419402 0.01325455 0.84049064 0.03469167 0. 0. 0. 0.52032603 0. 0. 0.68820155 0.07929718 0.30712852 0.51640481 0. nan 0.01769049 0.26803817 0.21887178 0. 0.85998636 0.71539146 0.93235425 0.24885785 0.05621853 0.11969413 0. ] \| [0. 0.91321796 0.93586512 0.7493935 0.91472526 0.63834931 nan 0.58292224 0.81417994 0.01497519 0.94252235 0.05394685 0. 0. 0. 0.64331398 0. 0. 0.82029437 0.08115742 0.56811405 0.59644195 0. nan 0.01995736 0.34179208 0.24586576 0. 0.94413845 0.83304234 0.96807676 0.34801978 0.20125156 0.15898892 0. ] \| 19 \| \| 0.2617 \| 0.4168 \| 0.3396 \| 0.3963 \| 0.8781 \| [0.00000000e+00 7.94986290e-01 8.78321279e-01 7.49897343e-01 8.49326301e-01 5.23130579e-01 nan 4.50929207e-01 5.51662857e-01 2.18050542e-02 8.41160082e-01 1.61248710e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.99800580e-01 0.00000000e+00 0.00000000e+00 7.33030551e-01 3.70162822e-02 3.87012787e-01 5.37036435e-01 0.00000000e+00 nan 2.52828519e-04 2.58401363e-01 2.18729726e-01 0.00000000e+00 8.68371051e-01 7.68056025e-01 9.33727233e-01 9.82409932e-02 3.83513478e-02 1.51214616e-01 0.00000000e+00] \| [0.00000000e+00 9.07468689e-01 9.33071883e-01 8.06640187e-01 9.49407168e-01 6.81786840e-01 nan 5.64532420e-01 8.05049940e-01 2.72440235e-02 9.42797113e-01 2.47917493e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.80009257e-01 0.00000000e+00 0.00000000e+00 8.80850860e-01 3.73148381e-02 5.59445628e-01 5.88173859e-01 0.00000000e+00 nan 2.63753654e-04 3.24654954e-01 2.34262090e-01 0.00000000e+00 9.43142842e-01 8.79414683e-01 9.68600549e-01 1.42839273e-01 9.82895286e-02 1.98829554e-01 0.00000000e+00] \| 20 \| \| 0.2444 \| 0.4463 \| 0.3361 \| 0.3952 \| 0.8747 \| [0.00000000e+00 8.09356188e-01 8.73758666e-01 7.03484664e-01 8.50663613e-01 4.40395666e-01 nan 4.70255723e-01 5.66815751e-01 5.21693766e-04 8.47186281e-01 1.10941303e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.16683571e-01 0.00000000e+00 0.00000000e+00 7.12799896e-01 5.64981182e-02 3.71929696e-01 5.03181014e-01 0.00000000e+00 nan 1.16099071e-03 2.60644571e-01 2.24766447e-01 0.00000000e+00 8.67722068e-01 7.66105359e-01 9.35288074e-01 1.30229066e-01 4.53205481e-02 1.26864531e-01 0.00000000e+00] \| [0.00000000e+00 9.19159433e-01 9.48361579e-01 7.32578878e-01 9.32163864e-01 5.01168899e-01 nan 5.99960700e-01 7.73417917e-01 5.41271989e-04 9.21135986e-01 1.94367315e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.32331903e-01 0.00000000e+00 0.00000000e+00 8.67884025e-01 5.84201607e-02 5.18153787e-01 7.57499634e-01 0.00000000e+00 nan 1.25282986e-03 3.28637485e-01 2.39056420e-01 0.00000000e+00 9.39480431e-01 8.67690868e-01 9.70610826e-01 2.31997152e-01 1.16729245e-01 1.65141676e-01 0.00000000e+00] \| 21 \| \| 0.2327 \| 0.4708 \| 0.3333 \| 0.3991 \| 0.8674 \| [0.00000000e+00 8.01482811e-01 8.67112634e-01 7.26469941e-01 8.47789494e-01 4.26344060e-01 nan 4.59877772e-01 5.61767489e-01 3.08784808e-02 8.50980045e-01 8.21140639e-04 0.00000000e+00 5.56009812e-02 0.00000000e+00 5.19460186e-01 0.00000000e+00 0.00000000e+00 6.66718429e-01 1.00749376e-01 3.06011822e-01 4.73609191e-01 0.00000000e+00 nan 5.21670878e-02 2.74661980e-01 2.16300138e-01 0.00000000e+00 8.70513680e-01 7.58257933e-01 9.31855744e-01 4.02310154e-04 5.41590367e-02 1.45066810e-01 0.00000000e+00] \| [0.00000000e+00 8.96213795e-01 9.66916102e-01 7.73539637e-01 8.80813661e-01 4.61368313e-01 nan 6.05202376e-01 7.84196522e-01 3.49120433e-02 9.33452042e-01 1.09083697e-03 0.00000000e+00 5.61056106e-02 0.00000000e+00 6.42177901e-01 0.00000000e+00 0.00000000e+00 7.70254403e-01 1.04428195e-01 7.01999428e-01 6.97985197e-01 0.00000000e+00 nan 5.98940589e-02 3.48124479e-01 2.34748471e-01 0.00000000e+00 9.37775670e-01 8.78224080e-01 9.72937862e-01 4.32992071e-04 2.44305382e-01 1.83551218e-01 0.00000000e+00] \| 22 \| \| 0.2307 \| 0.4395 \| 0.3472 \| 0.4131 \| 0.8740 \| [0. 0.78921013 0.87836164 0.7238651 0.85405051 0.48305222 nan 0.46174517 0.55335413 0.01711339 0.84971971 0.07427615 0. 0.19647651 0. 0.52684296 0. 0. 0.70735442 0.11184526 0.39826268 0.50387815 0. nan 0.05343915 0.27936942 0.23151827 0. 0.87119512 0.76032244 0.93287485 0.00244547 0.02821955 0.16774339 0. ] \| [0. 0.92820839 0.93256894 0.76932845 0.93401204 0.61981657 nan 0.61634072 0.81936678 0.01975643 0.94590099 0.1012495 0. 0.2415429 0. 0.6241269 0. 0. 0.81714715 0.1280285 0.64358053 0.67924551 0. nan 0.06437787 0.34337226 0.26928155 0. 0.94337279 0.8780219 0.97322357 0.00300208 0.10171047 0.23578672 0. ] \| 23 \| \| 0.2314 \| 0.4319 \| 0.3499 \| 0.4182 \| 0.8767 \| [0. 0.81268914 0.88090286 0.77985058 0.85570746 0.45828635 nan 0.47299524 0.53277529 0.04378213 0.84593985 0.03478787 0. 0.1971561 0. 0.54043589 0. 0. 0.7359562 0.05923714 0.36875898 0.54834606 0. nan 0.0091682 0.2642792 0.23962131 0. 0.86379601 0.6984222 0.9344155 0.19722481 0.0095982 0.16301427 0. ] \| [0. 0.89305484 0.93568687 0.88020793 0.94148981 0.58722529 nan 0.61994664 0.81367861 0.04916554 0.95151603 0.06277271 0. 0.25453795 0. 0.66359505 0. 0. 0.86745813 0.05975395 0.56066626 0.69054391 0. nan 0.01118755 0.32281238 0.27171345 0. 0.9460712 0.8195451 0.97224534 0.37103572 0.02778473 0.2253981 0. ] \| 24 \| \| 0.2280 \| 0.4165 \| 0.3413 \| 0.4040 \| 0.8751 \| [0. 0.80627187 0.88428648 0.68558419 0.86189479 0.47772167 nan 0.46152926 0.58013249 0.03674413 0.85526933 0.00621328 0. 0.00391914 0. 0.51552043 0. 0. 0.7219038 0.11633219 0.37078391 0.48759114 0. nan 0.04097689 0.25837403 0.23783935 0. 0.86459819 0.69037029 0.93378787 0.16489662 0.01161391 0.18831714 0. ] \| [0. 0.90036072 0.93838536 0.72247672 0.95316477 0.6161249 nan 0.61164372 0.77422776 0.05448805 0.94020362 0.00912336 0. 0.00391914 0. 0.64499705 0. 0. 0.8629953 0.12306992 0.55344734 0.64075339 0. nan 0.04945381 0.31623508 0.26601584 0. 0.93210162 0.89534679 0.97086444 0.24202332 0.02736754 0.28242179 0. ] \| 25 \| \| 0.2332 \| 0.4404 \| 0.3516 \| 0.4245 \| 0.8748 \| [0. 0.79554848 0.87301633 0.83834717 0.85459995 0.51844513 nan 0.47147093 0.51906075 0.07904743 0.85844229 0.11856842 0. 0.20806579 0. 0.49652457 0. 0. 0.71537872 0.12687546 0.36603458 0.48369829 0. nan 0.01636133 0.26809207 0.24211655 0. 0.86537401 0.74525303 0.93088227 0.00370986 0.03710524 0.17143043 0. ] \| [0. 0.90971751 0.92232534 0.87320268 0.9481476 0.68155153 nan 0.66184212 0.82753268 0.10690122 0.9341899 0.13962713 0. 0.38098185 0. 0.59963814 0. 0. 0.82473535 0.13202988 0.64751899 0.62376621 0. nan 0.01872651 0.32878909 0.28248332 0. 0.9485047 0.86660759 0.97579904 0.00397391 0.12273675 0.24574891 0. ] \| 26 \| \| 0.2146 \| 0.4515 \| 0.3472 \| 0.4129 \| 0.8760 \| [0. 0.77768215 0.87939478 0.75655563 0.85872515 0.51912277 nan 0.47242163 0.57270343 0.04912978 0.84772409 0.04133148 0. 0.28216704 0. 0.50142357 0. 0. 0.72646668 0.08708308 0.41888468 0.46264328 0. nan 0.00952928 0.24906863 0.23188316 0. 0.86985544 0.75426408 0.93570207 0. 0.00161353 0.15174018 0. ] \| [0. 0.91082524 0.9223558 0.86526048 0.93459861 0.70221741 nan 0.61752143 0.76844318 0.064682 0.94790176 0.04432765 0. 0.38675743 0. 0.58171337 0. 0. 0.86150488 0.08919156 0.64534791 0.76292334 0. nan 0.01094578 0.29978017 0.26035297 0. 0.9390376 0.84793111 0.96900737 0. 0.00383813 0.18862775 0. ] \| 27 \| \| 0.2245 \| 0.4819 \| 0.3481 \| 0.4183 \| 0.8677 \| [0. 0.74754716 0.87589221 0.7595096 0.75176585 0.46424109 nan 0.43492805 0.55661905 0.04973311 0.85506372 0.1407866 0. 0.15455217 0. 0.4863142 0. 0. 0.72532016 0.16110796 0.37871237 0.54738549 0. nan 0.03596414 0.27015132 0.27383189 0. 0.87155837 0.74696253 0.93097913 0.03859201 0.03974808 0.19051036 0. ] \| [0. 0.92496748 0.93198417 0.91066332 0.76595448 0.59939476 nan 0.57168392 0.76530022 0.06892197 0.94636898 0.19843316 0. 0.30218647 0. 0.5681646 0. 0. 0.83417317 0.18737447 0.68308592 0.66512645 0. nan 0.03932127 0.32249168 0.30377988 0. 0.94566641 0.86854326 0.97023299 0.04389577 0.11848144 0.26846741 0. ] \| 28 \| \| 0.2265 \| 0.4246 \| 0.3481 \| 0.4050 \| 0.8802 \| [0. 0.81690969 0.8780207 0.77104697 0.84868605 0.47437381 nan 0.4670048 0.56430385 0.0503272 0.85498949 0.10595414 0. 0.11204925 0. 0.53524176 0. 0. 0.74013112 0.04461066 0.39307836 0.51529041 0. nan 0.01267638 0.28575942 0.24784411 0. 0.87024598 0.76078812 0.93874897 0.00447477 0.03791316 0.15759319 0. ] \| [0. 0.90897891 0.95223635 0.79356326 0.91900275 0.59798835 nan 0.59817326 0.81432455 0.05827695 0.94238457 0.11364538 0. 0.13139439 0. 0.65023563 0. 0. 0.88394096 0.04698092 0.61423758 0.59820238 0. nan 0.01437457 0.35860267 0.28456782 0. 0.93586082 0.86061465 0.96934172 0.00557116 0.10738423 0.20431221 0. ] \| 29 \| \| 0.2067 \| 0.4302 \| 0.3582 \| 0.4204 \| 0.8782 \| [0. 0.80612851 0.87374492 0.77399105 0.80074838 0.5069675 nan 0.46617634 0.55019827 0.13081984 0.85635853 0.13375531 0. 0.27468398 0. 0.54140892 0. 0. 0.7462191 0.05761157 0.39168026 0.5446341 0. nan 0.01506284 0.29841736 0.22899218 0. 0.87061226 0.74041425 0.93770948 0.13239158 0.00821369 0.13209342 0. ] \| [0. 0.90557948 0.95169451 0.78927679 0.84910476 0.62275304 nan 0.60393893 0.82533454 0.18655841 0.94690627 0.1592622 0. 0.33168317 0. 0.68313978 0. 0. 0.88132122 0.06502636 0.62727359 0.66106362 0. nan 0.01804515 0.38855037 0.25931073 0. 0.94157236 0.8393111 0.97364591 0.18567662 0.01985816 0.15624759 0. ] \| 30 \| \| 0.1993 \| 0.4191 \| 0.3525 \| 0.4026 \| 0.8855 \| [0.00000000e+00 8.12143438e-01 8.82519501e-01 8.32421151e-01 8.74313051e-01 4.81253823e-01 nan 4.87073361e-01 5.86132068e-01 9.62771937e-02 8.59982957e-01 8.85474149e-02 0.00000000e+00 2.01491034e-04 0.00000000e+00 5.35389616e-01 0.00000000e+00 0.00000000e+00 7.53505814e-01 3.69389833e-02 3.98315791e-01 5.86352445e-01 0.00000000e+00 nan 3.86967641e-02 2.99523304e-01 2.23544639e-01 0.00000000e+00 8.66545952e-01 7.59345221e-01 9.36605085e-01 5.82816319e-04 7.04036476e-04 1.95231882e-01 0.00000000e+00] \| [0.00000000e+00 9.18027876e-01 9.52593301e-01 8.60861913e-01 9.17933036e-01 5.59645609e-01 nan 6.69381444e-01 7.81217462e-01 1.31348669e-01 9.42924301e-01 1.03431178e-01 0.00000000e+00 2.06270627e-04 0.00000000e+00 6.30669865e-01 0.00000000e+00 0.00000000e+00 8.97879175e-01 3.70010043e-02 4.89103277e-01 6.59469339e-01 0.00000000e+00 nan 4.58931358e-02 3.79932245e-01 2.46004725e-01 0.00000000e+00 9.44760882e-01 8.53162772e-01 9.75178979e-01 5.96566854e-04 1.75219024e-03 2.87445529e-01 0.00000000e+00] \| 31 \| \| 0.2068 \| 0.4805 \| 0.3370 \| 0.3952 \| 0.8643 \| [0. 0.77056757 0.8601312 0.79546358 0.80826542 0.46090981 nan 0.47734482 0.58905088 0.03181978 0.85901467 0.01694625 0. 0.00549451 0. 0.48326241 0. 0. 0.71413255 0.08548594 0.355285 0.56037404 0. nan 0.11377479 0.28155688 0.23155416 0. 0.84077004 0.62872483 0.94074387 0.04323906 0.00477968 0.16213294 0. ] \| [0. 0.86625295 0.93033124 0.83741848 0.95175277 0.58905634 nan 0.60932022 0.77904824 0.04077582 0.93578138 0.01963507 0. 0.00556931 0. 0.57342422 0. 0. 0.8469629 0.09113733 0.63002638 0.69225687 0. nan 0.12851397 0.34756471 0.25621873 0. 0.94994706 0.68278681 0.96967504 0.05913709 0.01677096 0.23138435 0. ] \| 32 \| \| 0.2026 \| 0.4072 \| 0.3583 \| 0.4178 \| 0.8851 \| [0. 0.82427491 0.88959049 0.80744875 0.87290087 0.48819667 nan 0.49479206 0.5689031 0.01115143 0.86655557 0.12424298 0. 0.08815858 0. 0.54211487 0. 0. 0.73966336 0.07793422 0.39875788 0.55704844 0. nan 0.12136758 0.29085268 0.26794571 0. 0.86907912 0.7468531 0.94141202 0.04189236 0.01138124 0.18175892 0. ] \| [0. 0.90820792 0.94843014 0.86590797 0.935603 0.6234152 nan 0.65185001 0.7529887 0.01136671 0.9412309 0.13427211 0. 0.10457921 0. 0.6997812 0. 0. 0.87681775 0.08373086 0.56962713 0.71690686 0. nan 0.15183419 0.36895259 0.31343802 0. 0.93965024 0.8725032 0.97198241 0.05008275 0.04046725 0.25251491 0. ] \| 33 \| \| 0.1890 \| 0.4580 \| 0.3568 \| 0.4229 \| 0.8760 \| [0. 0.81909166 0.87945582 0.84022719 0.85181051 0.44449375 nan 0.46587584 0.56531144 0.05849796 0.85834655 0.13038109 0. 0.23667513 0. 0.53010343 0. 0. 0.70286385 0.08324543 0.31813212 0.48792893 0. nan 0.08722779 0.30648587 0.25211314 0. 0.870058 0.73406627 0.93603361 0.09757433 0.04511995 0.17383064 0. ] \| [0. 0.89810904 0.95241393 0.90661944 0.91497772 0.51961956 nan 0.65114676 0.83394393 0.06865133 0.95007864 0.14656882 0. 0.30775578 0. 0.61461752 0. 0. 0.81590436 0.09039982 0.61695222 0.59357779 0. nan 0.09587445 0.39630552 0.2932532 0. 0.94689981 0.84862376 0.97537462 0.14406127 0.14217772 0.23057617 0. ] \| 34 \| \| 0.1856 \| 0.4192 \| 0.3656 \| 0.4327 \| 0.8810 \| [0. 0.80138932 0.88262901 0.81089302 0.86535724 0.47775953 nan 0.47998869 0.57502219 0.0555217 0.8547197 0.09356223 0. 0.29941446 0. 0.4994726 0. 0. 0.74427779 0.10476547 0.39876117 0.530613 0. nan 0.06260014 0.28825048 0.26307467 0. 0.86958647 0.76353802 0.93445836 0.20078295 0.02415195 0.18310195 0. ] \| [0. 0.92397697 0.93724033 0.85106091 0.92750395 0.5475243 nan 0.64661523 0.82444757 0.07397384 0.94253458 0.12971043 0. 0.39026403 0. 0.56784903 0. 0. 0.88627818 0.11856641 0.54413363 0.67935232 0. nan 0.0841594 0.35805457 0.30635075 0. 0.93943008 0.852706 0.97181886 0.43921754 0.0738423 0.26218876 0. ] \| 35 \| \| 0.1823 \| 0.4526 \| 0.3522 \| 0.4102 \| 0.8767 \| [0. 0.78316685 0.87642881 0.75047304 0.86249292 0.46791957 nan 0.49549382 0.57114384 0.08703693 0.86072555 0.10211813 0. 0.18376371 0. 0.49874928 0. 0. 0.73435033 0.05303611 0.39974749 0.45439447 0. nan 0.03187949 0.28929847 0.252677 0. 0.8723413 0.74111546 0.93814337 0.0911524 0.01717172 0.20816307 0. ] \| [0. 0.92816869 0.93137636 0.78767061 0.92675339 0.59854763 nan 0.62024483 0.71829085 0.14009923 0.95105293 0.11761206 0. 0.21431518 0. 0.56629218 0. 0. 0.87048153 0.05749435 0.53241044 0.64072965 0. nan 0.03600237 0.35849772 0.27463174 0. 0.93890724 0.88137406 0.97292233 0.14287776 0.04397163 0.28736837 0. ] \| 36 \| \| 0.1828 \| 0.4314 \| 0.3540 \| 0.4137 \| 0.8837 \| [0.00000000e+00 8.08817183e-01 8.86533437e-01 8.29367464e-01 8.66921982e-01 5.02412424e-01 nan 4.88635866e-01 5.60640323e-01 8.39031061e-02 8.56029524e-01 1.48001648e-01 0.00000000e+00 2.88729590e-02 0.00000000e+00 5.27888135e-01 0.00000000e+00 0.00000000e+00 7.40145106e-01 5.94355934e-02 3.83677842e-01 5.51371204e-01 0.00000000e+00 nan 2.54484244e-02 2.99810052e-01 2.57164681e-01 0.00000000e+00 8.66461858e-01 7.59758000e-01 9.39794819e-01 8.55545803e-03 2.93707321e-04 2.00986041e-01 0.00000000e+00] \| [0.00000000e+00 9.20986697e-01 9.42897048e-01 8.79794678e-01 9.16674152e-01 6.25797872e-01 nan 6.53392696e-01 7.86385022e-01 1.54984213e-01 9.51069237e-01 1.78103927e-01 0.00000000e+00 2.99092409e-02 0.00000000e+00 6.40158209e-01 0.00000000e+00 0.00000000e+00 8.70579667e-01 6.04130053e-02 5.57868972e-01 7.35243038e-01 0.00000000e+00 nan 3.09031365e-02 3.74579444e-01 2.92419400e-01 0.00000000e+00 9.45130703e-01 8.41614926e-01 9.69892426e-01 8.91001463e-03 6.67501043e-04 2.82612669e-01 0.00000000e+00] \| 37 \| \| 0.1824 \| 0.4277 \| 0.3516 \| 0.4128 \| 0.8808 \| [0. 0.80850849 0.8835188 0.81832156 0.87084804 0.52909381 nan 0.48544633 0.57416469 0.06544565 0.86014741 0.09572506 0. 0.04364361 0. 0.53546177 0. 0. 0.72880369 0.07815572 0.36619794 0.45105441 0. nan 0.02904442 0.31295304 0.268757 0. 0.87009835 0.77016379 0.93780115 0.02053909 0.00775031 0.19176263 0. ] \| [0. 0.93462002 0.91830503 0.88981486 0.94415116 0.74919228 nan 0.63542345 0.80433651 0.09896256 0.95110348 0.11324871 0. 0.04971122 0. 0.61501725 0. 0. 0.85270915 0.08732425 0.54361868 0.58993825 0. nan 0.03250764 0.39146001 0.30016676 0. 0.9446708 0.87850239 0.97614375 0.02966477 0.01535252 0.27686197 0. ] \| 38 \| \| 0.1853 \| 0.4315 \| 0.3703 \| 0.4396 \| 0.8843 \| [0. 0.82385333 0.88384746 0.82923402 0.87047461 0.44946715 nan 0.50195066 0.5526193 0.13775167 0.85419626 0.11663244 0. 0.24123441 0. 0.50296284 0. 0. 0.75525625 0.01028213 0.42676119 0.62702595 0.06111111 nan 0.01903464 0.32208879 0.27514231 0. 0.86733642 0.75225439 0.93747993 0.18324185 0.01936191 0.19849636 0. ] \| [0. 0.90365119 0.95252047 0.86895636 0.95780426 0.51196152 nan 0.66021153 0.84474181 0.20306721 0.91545736 0.16898056 0. 0.37891914 0. 0.57321383 0. 0. 0.86912221 0.01029375 0.54801488 0.73707468 0.06340058 nan 0.02149592 0.43258561 0.31736381 0. 0.94644158 0.84006614 0.97630627 0.4108325 0.04714226 0.34773038 0. ] \| 39 \| \| 0.1750 \| 0.4506 \| 0.3588 \| 0.4247 \| 0.8777 \| [0. 0.8085732 0.87850952 0.80281118 0.80588458 0.43532471 nan 0.49235495 0.56953178 0.04144004 0.86063777 0.1253508 0. 0.29125623 0. 0.56867524 0. 0. 0.72796854 0.03398534 0.34825502 0.58349517 0.04278075 nan 0.02356575 0.3145996 0.25091045 0. 0.86821676 0.7616297 0.94167999 0.0760155 0.01097801 0.17674597 0. ] \| [0. 0.8587409 0.96059527 0.84914689 0.95691624 0.49065318 nan 0.64206647 0.80599476 0.04330176 0.94759276 0.13288378 0. 0.49401815 0. 0.68717916 0. 0. 0.83929688 0.03427065 0.62310627 0.69531488 0.04610951 nan 0.02716663 0.42729112 0.28005142 0. 0.94607512 0.87723316 0.97355068 0.11214495 0.02653317 0.23750462 0. ] \| 40 \| \| 0.1856 \| 0.4630 \| 0.3468 \| 0.4169 \| 0.8702 \| [0. 0.79680775 0.87408906 0.80508886 0.7836208 0.54111501 nan 0.46906575 0.51571781 0.05906675 0.84646062 0.12752339 0. 0.02326951 0. 0.49396166 0. 0. 0.70301274 0.10455404 0.36326525 0.45432265 0.00288184 nan 0.06241517 0.29796314 0.27257073 0. 0.85824781 0.70264792 0.93738763 0.10944047 0.07468448 0.16378606 0. ] \| [0. 0.92066362 0.93733642 0.8457505 0.80518061 0.72000372 nan 0.61879522 0.83036713 0.090212 0.93724404 0.17978977 0. 0.03259076 0. 0.52749727 0. 0. 0.80827055 0.12335237 0.6834038 0.55220964 0.00288184 nan 0.08086247 0.38878944 0.30794886 0. 0.93395437 0.8773043 0.97470902 0.14777538 0.20984564 0.21967583 0. ] \| 41 \| \| 0.1886 \| 0.4251 \| 0.3676 \| 0.4456 \| 0.8827 \| [0. 0.81869055 0.88872516 0.83047488 0.84276443 0.50458334 nan 0.50154127 0.55514409 0.11767963 0.8593002 0.18142472 0. 0.17150681 0. 0.52765579 0. 0. 0.72523268 0.11768383 0.38784377 0.55537055 0.03125 nan 0.04156492 0.2927821 0.28836848 0. 0.87210633 0.77489312 0.94023927 0.01637101 0.07383087 0.2126436 0. ] \| [0. 0.91039924 0.94692984 0.8782822 0.93049806 0.63547194 nan 0.64587233 0.81659018 0.1533604 0.95128203 0.25357001 0. 0.5554868 0. 0.59909114 0. 0. 0.81739862 0.14772157 0.70659589 0.64127558 0.03170029 nan 0.05387168 0.38205471 0.34779739 0. 0.9516963 0.85983713 0.97204659 0.02417058 0.18481435 0.30708968 0. ] \| 42 \| \| 0.1649 \| 0.4242 \| 0.3717 \| 0.4383 \| 0.8829 \| [0. 0.8245252 0.88278 0.781006 0.85842353 0.51259623 nan 0.51245584 0.58046843 0.1180867 0.86495296 0.18057803 0. 0.19290237 0. 0.57019005 0. 0. 0.73953555 0.08630286 0.40811401 0.47419294 0.14364641 nan 0.03056729 0.30467132 0.2893461 0. 0.86773127 0.73354145 0.94299225 0.12116164 0.05836818 0.18823529 0. ] \| [0. 0.89896041 0.94310935 0.79709015 0.94969977 0.67147032 nan 0.65138807 0.79877367 0.14253496 0.94740116 0.23234827 0. 0.27021452 0. 0.66277455 0. 0. 0.86192885 0.09854381 0.60905623 0.64588831 0.14985591 nan 0.03674968 0.38943084 0.3317468 0. 0.93877576 0.88623506 0.97329085 0.17635286 0.14092616 0.25900882 0. ] \| 43 \| \| 0.1721 \| 0.4380 \| 0.3659 \| 0.4303 \| 0.8830 \| [0. 0.79204009 0.88761045 0.81838271 0.87599756 0.55268285 nan 0.51243018 0.57342413 0.1328 0.86370891 0.12697056 0. 0.01482085 0. 0.54979459 0. 0. 0.7440432 0.07280754 0.43229119 0.48547786 0.1754386 nan 0.04705945 0.29268443 0.28866261 0. 0.86370303 0.73106053 0.94208474 0.03836815 0.07654387 0.18451303 0. ] \| [0. 0.91791605 0.9276758 0.9021757 0.9262825 0.70668615 nan 0.6749058 0.79231422 0.1871899 0.94815532 0.13258628 0. 0.01877063 0. 0.65320205 0. 0. 0.86297038 0.08255398 0.62974983 0.64313491 0.20172911 nan 0.05861925 0.36101085 0.32994024 0. 0.9582507 0.8229035 0.97011706 0.05883881 0.16712557 0.26603474 0. ] \| 44 \| \| 0.1781 \| 0.4529 \| 0.3574 \| 0.4191 \| 0.8774 \| [0. 0.80590718 0.86988106 0.71453937 0.86614986 0.4249264 nan 0.47975456 0.54781776 0.09453882 0.84344987 0.10822898 0. 0.05507559 0. 0.52010855 0. 0. 0.74685485 0.08247778 0.43946981 0.52154191 0.24444444 nan 0.03570312 0.29856758 0.28932541 0. 0.86943451 0.76210843 0.93975656 0.04628113 0.00666137 0.17955183 0. ] \| [0. 0.90777016 0.93538454 0.74532453 0.96011721 0.53476928 nan 0.67329934 0.81065134 0.10744249 0.95209245 0.16798889 0. 0.06311881 0. 0.58061937 0. 0. 0.86390726 0.09013307 0.63941956 0.64314678 0.28530259 nan 0.04266215 0.38685357 0.33004447 0. 0.94754825 0.87771988 0.97640668 0.04758102 0.0090947 0.25304287 0. ] \| 45 \| \| 0.1684 \| 0.4189 \| 0.3690 \| 0.4293 \| 0.8874 \| [0. 0.84396057 0.88758961 0.81114547 0.88917721 0.45907522 nan 0.49766899 0.57141046 0.13114032 0.86950251 0.16812566 0. 0.05402798 0. 0.55671436 0. 0. 0.75187936 0.06411064 0.38664923 0.50159138 0.2393736 nan 0.03612036 0.31872514 0.30305254 0. 0.86860485 0.76230745 0.94340831 0.00953726 0.07691803 0.17518119 0. ] \| [0. 0.91434158 0.94889397 0.87210345 0.95322397 0.59465787 nan 0.66608234 0.78205623 0.16111863 0.93973807 0.20378818 0. 0.06930693 0. 0.66147017 0. 0. 0.89656164 0.06706628 0.53578308 0.5492624 0.30835735 nan 0.04147526 0.40549508 0.35870623 0. 0.94735747 0.87335684 0.97257866 0.01137326 0.19582812 0.23648119 0. ] \| 46 \| \| 0.1720 \| 0.4344 \| 0.3638 \| 0.4240 \| 0.8838 \| [0. 0.83634649 0.88252909 0.81745832 0.86977854 0.47517537 nan 0.5031844 0.59798619 0.1079865 0.85554815 0.12994482 0. 0.04696466 0. 0.53919303 0. 0. 0.75014771 0.0744804 0.3882156 0.59230036 0.10471204 nan 0.04368721 0.31787732 0.29543235 0. 0.86536823 0.72760094 0.94104705 0.03982311 0.02227327 0.18113074 0. ] \| [0. 0.8989315 0.94316465 0.90119359 0.9497093 0.61772174 nan 0.62712399 0.73975165 0.12124493 0.94667146 0.16580722 0. 0.06415017 0. 0.68711605 0. 0. 0.87653714 0.07776801 0.5226676 0.71570028 0.11527378 nan 0.05734444 0.42000828 0.35728182 0. 0.94526778 0.87853858 0.96476425 0.06568009 0.06391322 0.27004723 0. ] \| 47 \| \| 0.1621 \| 0.4094 \| 0.3770 \| 0.4361 \| 0.8898 \| [0. 0.82399384 0.89066451 0.819736 0.87845688 0.52339167 nan 0.51547818 0.58811266 0.11202084 0.86668833 0.16280099 0. 0.08182504 0. 0.55484888 0. 0. 0.76325378 0.05293637 0.40960729 0.5817725 0.34567901 nan 0.03438815 0.31336815 0.27887594 0. 0.87318663 0.74216819 0.94257309 0.02714967 0.0518505 0.20559452 0. ] \| [0. 0.92314847 0.9443915 0.90050762 0.92923985 0.63544129 nan 0.66654946 0.76154024 0.13576906 0.95223513 0.18236811 0. 0.13428218 0. 0.62953379 0. 0. 0.89400131 0.06196648 0.56801551 0.65125268 0.40345821 nan 0.04132141 0.3929294 0.31270845 0. 0.94826999 0.87554959 0.97042967 0.03051151 0.14226116 0.3023746 0. ] \| 48 \| \| 0.1508 \| 0.4301 \| 0.3689 \| 0.4261 \| 0.8878 \| [0. 0.82155443 0.88837272 0.80869927 0.84681809 0.50445633 nan 0.5062558 0.58202362 0.09694114 0.86506226 0.10300594 0. 0.03122511 0. 0.55651564 0. 0. 0.76493797 0.04021662 0.40453306 0.56038987 0.34382567 nan 0.02428609 0.30885576 0.28811326 0. 0.87087236 0.74857511 0.94321046 0.02300712 0.03721037 0.20366003 0. ] \| [0. 0.88109026 0.95044945 0.85142397 0.95993416 0.6370042 nan 0.65971511 0.81045852 0.11321606 0.95401169 0.10670369 0. 0.04042904 0. 0.66801313 0. 0. 0.90595882 0.04265001 0.5292762 0.61230561 0.4092219 nan 0.0283755 0.37721503 0.3266398 0. 0.950358 0.87250445 0.96996696 0.02583519 0.09486859 0.28234463 0. ] \| 49 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.0 - Tokenizers 0.13.2
ChrisP/xlm-roberta-base-finetuned-marc-en	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-21T19:56:44Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k - wit-400m - imagenet-12k --- # Model card for vit_large_patch14_clip_336.openai_ft_in12k_in1k A Vision Transformer (ViT) image classification model. Pretrained on WIT-400M image-text pairs by OpenAI using CLIP. Fine-tuned on ImageNet-12k and then ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143). ## Model Details - Model Type: Image classification / feature backbone - Model Stats: - Params (M): 304.5 - GMACs: 174.7 - Activations (M): 128.2 - Image size: 336 x 336 - Papers: - Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020 - Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - Dataset: ImageNet-1k - Pretrain Dataset: - WIT-400M - ImageNet-12k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('vit_large_patch14_clip_336.openai_ft_in12k_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(*data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_large_patch14_clip_336.openai_ft_in12k_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 577, 1024) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` ```bibtex @article{cherti2022reproducible, title={Reproducible scaling laws for contrastive language-image learning}, author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia}, journal={arXiv preprint arXiv:2212.07143}, year={2022} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
ChrisVCB/DialoGPT-medium-ej	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	13	2022-11-21T20:00:09Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### stevediffusion_v2 Dreambooth model trained by daniel-comet with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:
ChristopherA08/IndoELECTRA	[ "pytorch", "electra", "pretraining", "id", "dataset:oscar", "transformers" ]	null	{ "architectures": [ "ElectraForPreTraining" ], "model_type": "electra", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	4	2022-11-21T20:06:38Z	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-finetuned-transcriptSteve results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-finetuned-transcriptSteve This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6308 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 18 \| 2.6415 \| \| No log \| 2.0 \| 36 \| 2.6353 \| \| No log \| 3.0 \| 54 \| 2.6308 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2
Chun/DialoGPT-large-dailydialog	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	6	2022-11-21T20:16:51Z	--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/Avrik/abstract-anim-spritesheets/resolve/main/AnimationGrid.gif" tags: - stable-diffusion - text-to-image - image-to-image --- # Abstract Animation Sprite Sheets An experimental Dreambooth model trained on individual frames of looping 3D animations that were then laid out on a 4x4 grid. Generates sprite sheets that can create very interesting abstract animations. Use the token AbstrAnm spritesheet. Size must be set at 512x512 or your outputs may not work properly. Example prompt: <i>AbstrAnm spritesheet, animation of a red glowing orb in the sky, highly detailed, fog, atmosphere, glow, sprites, animated, abstract</i> <br> Negative prompt: <i>high contrast, text, overlay</i> <br> Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 8 Feel free to experiment with other types of prompts and/or model merges. ![Sample Generations](https://huggingface.co/Avrik/abstract-anim-spritesheets/resolve/main/AnimationGrid.gif) You can also upscale it 4x to produce 512x512 animations. Used SD Upscale from AUTOMATIC1111's web UI to add more sharpness and detail. ![Upscaled](https://huggingface.co/Avrik/abstract-anim-spritesheets/resolve/main/AnimationGridUpscale.gif) Discovered it's actually quite flexible and could even animate less abstract concepts. ![New Animations](https://huggingface.co/Avrik/abstract-anim-spritesheets/resolve/main/natureanims.gif) Prompt 1: <i>AbstrAnm spritesheet, animation of magical swirling clouds in the clear blue sky, floating in crystal clear water, circular, sunny, timelapse, lens flare, nature, 35mm lens shot, photorealistic, sprites, animated, art by Greg Rutkowski</i> <br> Negative prompt: <i>text, overlay, abstract, boring, empty, barren, simple background</i> <br> Steps: 25, Sampler: DPM++ 2S a, CFG scale: 10 Prompt 2: <i>AbstrAnm spritesheet, animation of a beautiful flower blowing in the wind, serene, pink, sunny, timelapse, lens flare, nature, 35mm lens shot, photorealistic, sprites, animated, art by Greg Rutkowski</i> Negative prompt: <i>text, overlay, abstract, boring, empty, barren, simple background</i> <br> Steps: 25, Sampler: DPM++ 2S a, CFG scale: 10 Some issues with this model: - May not loop seamlessly - Tends to be too noisy - Sprites aren't usually perfect squares - Small size and short animation (could experiment with training on larger resolutions in the future)
Chun/DialoGPT-medium-dailydialog	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	15	2022-11-21T20:17:36Z	--- language: en thumbnail: http://www.huggingtweets.com/adamscochran-fehrsam-taschalabs/1669062033978/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1504547300416364550/rFebXP9K_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1513406762904612866/-haRj3pk_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1593745112844144641/Q2zhPcdt_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tascha & Fred Ehrsam & Adam Cochran (adamscochran.eth)</div> <div style="text-align: center; font-size: 14px;">@adamscochran-fehrsam-taschalabs</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Tascha & Fred Ehrsam & Adam Cochran (adamscochran.eth). \| Data \| Tascha \| Fred Ehrsam \| Adam Cochran (adamscochran.eth) \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3244 \| 1674 \| 3242 \| \| Retweets \| 215 \| 188 \| 555 \| \| Short tweets \| 210 \| 150 \| 150 \| \| Tweets kept \| 2819 \| 1336 \| 2537 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/35tvoqtp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @adamscochran-fehrsam-taschalabs's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fv0c31k5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fv0c31k5/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/adamscochran-fehrsam-taschalabs') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Chun/w-en2zh-hsk	[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1	2022-11-21T20:21:16Z	This model is Nightmare XXX. Prompt being "NghtmrXxxFrk". To note this isn't actual porn or anything. However this takes my popular pictures that combine horrific, gross, nightmarish stuff with weird things like some minor nudity or even adult toy realm. In other words its semi-adult stuff that makes you say "WTF is this. Someone bleach my eyes!". ![Model 5.png](https://s3.amazonaws.com/moonup/production/uploads/1669084844054-6333e639d58823d613336ee3.png)
Chun/w-zh2en-hsk	[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-misinfo-model-700-Zhaohui results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-misinfo-model-700-Zhaohui This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5297 - Accuracy: 0.8857 - F1: 0.8889 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2
Chun/w-zh2en-mtm	[ "pytorch", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MBartForConditionalGeneration" ], "model_type": "mbart", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	8	null	--- language: en thumbnail: http://www.huggingtweets.com/adamscochran-fehrsam-taschalabs/1669062033978/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1504547300416364550/rFebXP9K_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1513406762904612866/-haRj3pk_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1593745112844144641/Q2zhPcdt_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tascha & Fred Ehrsam & Adam Cochran (adamscochran.eth)</div> <div style="text-align: center; font-size: 14px;">@adamscochran-fehrsam-taschalabs</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Tascha & Fred Ehrsam & Adam Cochran (adamscochran.eth). \| Data \| Tascha \| Fred Ehrsam \| Adam Cochran (adamscochran.eth) \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3244 \| 1674 \| 3242 \| \| Retweets \| 215 \| 188 \| 555 \| \| Short tweets \| 210 \| 150 \| 150 \| \| Tweets kept \| 2819 \| 1336 \| 2537 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/35tvoqtp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @adamscochran-fehrsam-taschalabs's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fv0c31k5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fv0c31k5/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/adamscochran-fehrsam-taschalabs') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Chun/w-zh2en-mto	[ "pytorch", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MBartForConditionalGeneration" ], "model_type": "mbart", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- license: cc0-1.0 --- Drop anneface.pt into your stable-diffusion-webui/embeddings folder and use prompt <anneface> to get this upset gal. ![anneface](https://huggingface.co/sd-concepts-library/anneface/resolve/main/1.png)
Cinnamon/electra-small-japanese-discriminator	[ "pytorch", "electra", "pretraining", "ja", "transformers", "license:apache-2.0" ]	null	{ "architectures": [ "ElectraForPreTraining" ], "model_type": "electra", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	419	null	--- inference: true language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m --- # Stable Diffusion v1.5 fine tuned on Waltz with Bashir screencaps Use prompt: 'wltzwthbshr' [Waltz with Bashir on IMDB](https://www.imdb.com/title/tt1185616) ### Output Samples: Settings used: "wltzwthbshr SUBJECT", euler a, 35 steps, cfg 7, 1024x1024, high res fix on, sd-vae-ft-mse-original (AUTOMATIC1111 webui) <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000797-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669066999379-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067001297-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067002574-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067002737-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000480-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669066999949-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067002829-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000524-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669066998455-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067001216-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000265-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000984-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000421-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067003066-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000476-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067002688-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067001859-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067002184-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067003006-637bef89ca8542a0ba8cd54b.png" width="100%"/> ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at [Stable diffusion Pipelines](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "mikesmodels/Waltz_with_Bashir_Diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "wltzwthbshr dwayne johnson" image = pipe(prompt).images[0] image.save("./dwayne_johnson.png") ```
Ciruzzo/DialoGPT-small-harrypotter	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	9	null	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### stevefussion_v3 Dreambooth model trained by daniel-comet with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:
Ciruzzo/DialoGPT-small-hattypotter	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # arinze/address-match-abp-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 64 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('arinze/address-match-abp-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=arinze/address-match-abp-v2) ## Training The model was trained with the parameters: DataLoader: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 3125 with parameters: ``` {'batch_size': 32} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 157, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 384, 'out_features': 64, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ClaudeYang/awesome_fb_model	[ "pytorch", "bart", "text-classification", "dataset:multi_nli", "transformers", "zero-shot-classification" ]	zero-shot-classification	{ "architectures": [ "BartForSequenceClassification" ], "model_type": "bart", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	26	null	# Lingala Text-to-Speech This model was trained on the OpenSLR's 71.6 hours aligned lingala bible dataset. ## Model description A Conditional Variational Autoencoder with Adversarial Learning(VITS), which is an end-to-end approach to the text-to-speech task. To train the model, we used the espnet2 toolkit. ## Usage First install espnet2 ``` sh pip install espnet ``` Download the model and the config files from this repo. To generate a wav file using this model, run the following: ``` sh from espnet2.bin.tts_inference import Text2Speech import soundfile as sf text2speech = Text2Speech(train_config="config.yaml",model_file="train.total_count.best.pth") wav = text2speech("oyo kati na Ye ozwi lisiko mpe bolimbisi ya masumu")["wav"] sf.write("outfile.wav", wav.numpy(), text2speech.fs, "PCM_16") ```
CleveGreen/FieldClassifier_v2	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	46	null	--- license: creativeml-openrail-m tags: - text-to-image --- ### 2d-art-sprites Dreambooth model trained by ana-tamais with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook You can test this model using this [Colab Notebook for Inference](https://colab.research.google.com/drive/1pFaEJHa7mxFruBfm2hDnR8S6aEo7sAFx?usp=sharing) Sample pictures of 2dart concept: <img src="https://huggingface.co/ana-tamais/2d-art-sprites/resolve/main/concept_images/2dart_1.png" width=256></img> <img src="https://huggingface.co/ana-tamais/2d-art-sprites/resolve/main/concept_images/2dart_4.png" width=256></img> <img src="https://huggingface.co/ana-tamais/2d-art-sprites/resolve/main/concept_images/2dart_9.png" width=256></img> We saved the training data in `dataset.zip`, and some generated results in `results.zip`. ### Some recommendations We recommend to set the: - prompt as: `"[some wizard, paladin, healer, etc.], in the style of 2dart, white background, no background, full body"` - negative prompt as: `"deformed, mutilated limbs, background, multiple people"` - guidance scale between `9 and 10` - sampling method as `Euler` - sampling steps as `60` - batch size as `1` - avoid high batch size, unless you have a high memory GPU This set of hyperparameters lead us to stable and good results. This model was trained using images which the character has some human format or is a not deformed living being. So if you try to predict something like "sword, mirror, candle, etc" (non-living things), we saw the model doesn't perform so well. You need at least a Tesla T4 to be able to run the inference step using the given [notebook](https://colab.research.google.com/drive/1pFaEJHa7mxFruBfm2hDnR8S6aEo7sAFx?usp=sharing).
CoachCarter/distilbert-base-uncased	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: creativeml-openrail-m tags: - text-to-image --- ### Dan reynolds on Stable Diffusion via Dreambooth #### model by JuandaSuarez This your the Stable Diffusion model fine-tuned the Dan reynolds concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: a photo of sks dan reynolds You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/JuandaSuarez/dan-reynolds/resolve/main/concept_images/3.jpeg) ![image 1](https://huggingface.co/JuandaSuarez/dan-reynolds/resolve/main/concept_images/0.jpeg) ![image 2](https://huggingface.co/JuandaSuarez/dan-reynolds/resolve/main/concept_images/1.jpeg) ![image 3](https://huggingface.co/JuandaSuarez/dan-reynolds/resolve/main/concept_images/2.jpeg)
CodeMonkey98/distilroberta-base-finetuned-wikitext2	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: mit widget: - text: "François Dupont prends la direction générale du groupe IPD" tags: - generated_from_trainer metrics: - f1 model-index: - name: camembert-base-articles-ner-backup results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # camembert-base-articles-ner-backup This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6701 - F1: 0.8723 ## Model description This model identifies Name Entities : PERSON, ORGANISATION, JOB TITLE Another Model is being developped to predict relationships between these entities (nomination, départure) ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.9205 \| 1.0 \| 6 \| 1.7426 \| 0.0 \| \| 1.6476 \| 2.0 \| 12 \| 1.5415 \| 0.0 \| \| 1.4607 \| 3.0 \| 18 \| 1.3944 \| 0.0635 \| \| 1.3299 \| 4.0 \| 24 \| 1.2587 \| 0.4848 \| \| 1.1973 \| 5.0 \| 30 \| 1.1287 \| 0.6207 \| \| 1.0707 \| 6.0 \| 36 \| 1.0110 \| 0.8043 \| \| 0.972 \| 7.0 \| 42 \| 0.9266 \| 0.8696 \| \| 0.8877 \| 8.0 \| 48 \| 0.8632 \| 0.8602 \| \| 0.8231 \| 9.0 \| 54 \| 0.8279 \| 0.8511 \| \| 0.7723 \| 10.0 \| 60 \| 0.8001 \| 0.8511 \| \| 0.7309 \| 11.0 \| 66 \| 0.7617 \| 0.8602 \| \| 0.6902 \| 12.0 \| 72 \| 0.7364 \| 0.8602 \| \| 0.6601 \| 13.0 \| 78 \| 0.7104 \| 0.8723 \| \| 0.6306 \| 14.0 \| 84 \| 0.7062 \| 0.8723 \| \| 0.6127 \| 15.0 \| 90 \| 0.6896 \| 0.8602 \| \| 0.605 \| 16.0 \| 96 \| 0.6743 \| 0.8723 \| \| 0.5892 \| 17.0 \| 102 \| 0.6801 \| 0.8723 \| \| 0.5843 \| 18.0 \| 108 \| 0.6797 \| 0.8723 \| \| 0.5731 \| 19.0 \| 114 \| 0.6731 \| 0.8723 \| \| 0.5707 \| 20.0 \| 120 \| 0.6701 \| 0.8723 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
CodeNinja1126/bert-p-encoder	[ "pytorch" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- language: eng datasets: - banking77 --- # Social Media Sentiment Analysis Model This is a fine-tuned version of the Distilbert model. It's best suited for sentiment-analysis. ## Model Description Social Media Sentiment Analysis Model was trained on the [dataset consisting of tweets](https://www.kaggle.com/code/mohamednabill7/sentiment-analysis-of-twitter-data/data) obtained from Kaggle." ## Intended Uses and Limitations This model is meant for sentiment-analysis. Because it was trained on a corpus of tweets, it is familiar with social media jargons. ### How to use You can use this model directly with a pipeline for text generation: ```python >>>from transformers import pipeline >>> model_name = "Kwaku/social_media_sa" >>> generator = pipeline("sentiment-analysis", model=model_name) >>> result = generator("I like this model") >>> print(result) Generated output: [{'label': 'positive', 'score': 0.9494990110397339}] ``` ### Limitations and bias This model inherits the bias of its parent, [Distilbert](https://huggingface.co/models?other=distilbert). Besides that, it was trained on only 1000 randomly selected sequences, and thus does not achieve a high probability rate. It does fairly well nonetheless.
CodeNinja1126/xlm-roberta-large-kor-mrc	[ "pytorch", "xlm-roberta", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "XLMRobertaForQuestionAnswering" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	8	null	--- language: - ko tags: - trocr - image-to-text license: mit metrics: - wer - cer widget: - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/random_2.jpg example_title: 랜덤 문장 1 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/random_6.jpg example_title: 랜덤 문장 2 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/chatbot_3.jpg example_title: 챗봇 1 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/chatbot_5.jpg example_title: 챗봇 2 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_1.jpg example_title: 뉴스 1 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_3.jpg example_title: 뉴스 2 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/nsmc_1.jpg example_title: 영화 리뷰 1 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/nsmc_2.jpg example_title: 영화 리뷰 2 --- # TrOCR for Korean Language (PoC) ## Overview TrOCR has not yet released a multilingual model including Korean, so we trained a Korean model for PoC purpose. Based on this model, it is recommended to collect more data to additionally train the 1st stage or perform fine-tuning as the 2nd stage. ## Collecting data ### Text data We created training data by processing three types of datasets. - News summarization dataset: https://huggingface.co/datasets/daekeun-ml/naver-news-summarization-ko - Naver Movie Sentiment Classification: https://github.com/e9t/nsmc - Chatbot dataset: https://github.com/songys/Chatbot_data For efficient data collection, each sentence was separated by a sentence separator library (Kiwi Python wrapper; https://github.com/bab2min/kiwipiepy), and as a result, 637,401 samples were collected. ### Image Data Image data was generated with TextRecognitionDataGenerator (https://github.com/Belval/TextRecognitionDataGenerator) introduced in the TrOCR paper. Below is a code snippet for generating images. ```shell python3 ./trdg/run.py -i ocr_dataset_poc.txt -w 5 -t {num_cores} -f 64 -l ko -c {num_samples} -na 2 --output_dir {dataset_dir} ``` ## Training ### Base model The encoder model used `facebook/deit-base-distilled-patch16-384` and the decoder model used `klue/roberta-base`. It is easier than training by starting weights from `microsoft/trocr-base-stage1`. ### Parameters We used heuristic parameters without separate hyperparameter tuning. - learning_rate = 4e-5 - epochs = 25 - fp16 = True - max_length = 64 ## Usage ### inference.py ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel, AutoTokenizer import requests from io import BytesIO from PIL import Image processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") model = VisionEncoderDecoderModel.from_pretrained("daekeun-ml/ko-trocr-base-nsmc-news-chatbot") tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/ko-trocr-base-nsmc-news-chatbot") url = "https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_1.jpg" response = requests.get(url) img = Image.open(BytesIO(response.content)) pixel_values = processor(img, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values, max_length=64) generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(generated_text) ``` All the code required for data collection and model training has been published on the author's Github. - https://github.com/daekeun-ml/sm-kornlp-usecases/tree/main/trocr
CoderBoy432/DialoGPT-small-harrypotter	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	11	null	--- language: eng datasets: - banking77 --- # Social Media Sentiment Analysis Model (Finetuned) This is a fine-tuned version of the [Social Media Sentiment Analysis Model](https://huggingface.co/Kwaku/social_media_sa) which is a finetuned version of [Distilbert](https://huggingface.co/models?other=distilbert). It's best suited for sentiment-analysis. ## Model Description Social Media Sentiment Analysis Model was trained on the [dataset consisting of tweets](https://www.kaggle.com/code/mohamednabill7/sentiment-analysis-of-twitter-data/data) obtained from Kaggle." ## Intended Uses and Limitations This model is meant for sentiment-analysis. Because it was trained on a corpus of tweets, it is familiar with social media jargons. ### How to use You can use this model directly with a pipeline for text generation: ```python >>>from transformers import pipeline >>> model_name = "Kwaku/social_media_sa_finetuned_1" >>> generator = pipeline("sentiment-analysis", model=model_name) >>> result = generator("I like this model") >>> print(result) Generated output: [{'label': 'positive', 'score': 0.9494990110397339}] ``` ### Limitations and bias This model inherits the bias of its parent, [Distilbert](https://huggingface.co/models?other=distilbert). Besides that, it was trained on only 1000 randomly selected sequences, and thus does not achieve a high probability rate. It does fairly well nonetheless.
Venkatakrishnan-Ramesh/Text_gen	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	This is a wav2vec2-base model trained from selected bird songs in a birddb dataset. ``` import librosa import torch from transformers import Wav2Vec2ForPreTraining,Wav2Vec2Processor sound_file = 'sample.wav' sound_data,_ = librosa.load(sound_file, sr=16000) model_id = "kojima-r/wav2vec2-base-birddb-small" model = Wav2Vec2ForPreTraining.from_pretrained(model_id) result=model(torch.tensor([sound_data])) hidden_vecs=result.projected_states ``` ![UMAP](https://huggingface.co/kojima-r/wav2vec2-base-birddb-small/resolve/main/umap.png)
CoffeeAddict93/gpt2-medium-modest-proposal	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- license: openrail --- # MJv4 Hallucinations These are 3 models trained on a small (<2000) dataset of Midjourney v4 images with no particular style. <b> These models are nowhere near as good as Midjourney v4 </b>, and they all suffer from a lot of "language drift" but they do have an interesting style. They are the best of something like 60 different models I trained as part of a set of experiments aimed at replicating Midjourney v4's style with only a few, uncaptioned images. The models are: - <b>mjg-4000-model.ckpt</b>: trained on 250 MJv4 images with no regularization for 4000 steps, prompt: "mjg style" - <b>mjg-12000-model.ckpt</b>: trained on 250 MJv4 images with no regularization for 12000 steps, prompt: "mjg style" - <b>mjv-1200-model.ckpt</b>: trained on 7 MJv4 images with 1000 regularization images for 1200 steps, prompt: "mjv style" Models you can download are <b>bolded</b> <img src="https://github.com/Lewington-pitsos/mj4-hallucinations/blob/main/compare.png?raw=true" width="100%"/> In my subjective opinion, only <b>mjv-1200-model.ckpt<\b> is actually worth downloading. ## Credits: - [NitroSock](https://github.com/nitrosocke/dreambooth-training-guide) for the regularization images - [prompthero](https://huggingface.co/prompthero/openjourney) whose idea I copied ## Take Down As far as I can tell, uploading these models does not cause any person or corporate entity any harm, but if you think I am wrong about this please reach out.
CoffeeAddict93/gpt2-modest-proposal	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	12	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2
CogComp/bart-faithful-summary-detector	[ "pytorch", "jax", "bart", "text-classification", "en", "dataset:xsum", "transformers", "xsum", "license:cc-by-sa-4.0" ]	text-classification	{ "architectures": [ "BartForSequenceClassification" ], "model_type": "bart", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": 1, "max_length": 128, "min_length": 12, "no_repeat_ngram_size": null, "num_beams": 4, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	234	null	WnB run: https://wandb.ai/jellywibble/huggingface/runs/1yo5mgs4?workspace=user-jellywibble
CogComp/roberta-temporal-predictor	[ "pytorch", "roberta", "fill-mask", "arxiv:2202.00436", "transformers", "license:mit", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "RobertaForMaskedLM" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	14	null	--- tags: - generated_from_keras_callback model-index: - name: dung1308/dung_NT_model_save results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # dung1308/dung_NT_model_save This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.8144 - Validation Loss: 3.6030 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.4431 \| 3.9985 \| 0 \| \| 3.9986 \| 3.8016 \| 1 \| \| 3.8144 \| 3.6030 \| 2 \| ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.7.0 - Tokenizers 0.11.0
CohleM/bert-nepali-tokenizer	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 155.33 +/- 58.36 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
CohleM/mbert-nepali-tokenizer	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- language: - en tags: - vision-language - clip - vilt datasets: - lil-lab/kilogram-data --- KiloGram dataset and code repo: https://github.com/lil-lab/kilogram Preprocessed training and evaluation data: https://huggingface.co/datasets/lil-lab/kilogram-data
ComCom/gpt2-large	[ "pytorch", "gpt2", "feature-extraction", "transformers" ]	feature-extraction	{ "architectures": [ "GPT2Model" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1	null	--- license: apache-2.0 tags: - vision - depth-estimation - generated_from_trainer model-index: - name: glpn-nyu-finetuned-diode-221122-014502 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # glpn-nyu-finetuned-diode-221122-014502 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.3476 - Mae: 0.2763 - Rmse: 0.4088 - Abs Rel: 0.3308 - Log Mae: 0.1161 - Log Rmse: 0.1700 - Delta1: 0.5682 - Delta2: 0.8301 - Delta3: 0.9279 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mae \| Rmse \| Abs Rel \| Log Mae \| Log Rmse \| Delta1 \| Delta2 \| Delta3 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:-------:\|:-------:\|:--------:\|:------:\|:------:\|:------:\| \| 0.7598 \| 1.0 \| 72 \| 0.5809 \| 0.7606 \| 0.9281 \| 0.9834 \| 0.2597 \| 0.3064 \| 0.1320 \| 0.3250 \| 0.6234 \| \| 0.4481 \| 2.0 \| 144 \| 0.4013 \| 0.3507 \| 0.4879 \| 0.4181 \| 0.1415 \| 0.1950 \| 0.4427 \| 0.7602 \| 0.9021 \| \| 0.4066 \| 3.0 \| 216 \| 0.3706 \| 0.3081 \| 0.4484 \| 0.3675 \| 0.1269 \| 0.1823 \| 0.5187 \| 0.7977 \| 0.9148 \| \| 0.3965 \| 4.0 \| 288 \| 0.3641 \| 0.2987 \| 0.4336 \| 0.3607 \| 0.1239 \| 0.1787 \| 0.5294 \| 0.8072 \| 0.9205 \| \| 0.3942 \| 5.0 \| 360 \| 0.3582 \| 0.2903 \| 0.4251 \| 0.3490 \| 0.1207 \| 0.1753 \| 0.5466 \| 0.8165 \| 0.9232 \| \| 0.3575 \| 6.0 \| 432 \| 0.3568 \| 0.2898 \| 0.4184 \| 0.3569 \| 0.1211 \| 0.1753 \| 0.5390 \| 0.8171 \| 0.9265 \| \| 0.3418 \| 7.0 \| 504 \| 0.3490 \| 0.2771 \| 0.4178 \| 0.3248 \| 0.1156 \| 0.1707 \| 0.5783 \| 0.8312 \| 0.9259 \| \| 0.2916 \| 8.0 \| 576 \| 0.3512 \| 0.2819 \| 0.4172 \| 0.3373 \| 0.1178 \| 0.1725 \| 0.5620 \| 0.8253 \| 0.9262 \| \| 0.3055 \| 9.0 \| 648 \| 0.3506 \| 0.2808 \| 0.4091 \| 0.3422 \| 0.1180 \| 0.1718 \| 0.5537 \| 0.8248 \| 0.9292 \| \| 0.2932 \| 10.0 \| 720 \| 0.3518 \| 0.2809 \| 0.4110 \| 0.3441 \| 0.1182 \| 0.1724 \| 0.5548 \| 0.8239 \| 0.9290 \| \| 0.2518 \| 11.0 \| 792 \| 0.3476 \| 0.2756 \| 0.4115 \| 0.3265 \| 0.1155 \| 0.1700 \| 0.5741 \| 0.8326 \| 0.9264 \| \| 0.3177 \| 12.0 \| 864 \| 0.3491 \| 0.2784 \| 0.4104 \| 0.3333 \| 0.1169 \| 0.1706 \| 0.5620 \| 0.8290 \| 0.9283 \| \| 0.3038 \| 13.0 \| 936 \| 0.3503 \| 0.2795 \| 0.4094 \| 0.3410 \| 0.1175 \| 0.1717 \| 0.5596 \| 0.8275 \| 0.9283 \| \| 0.3299 \| 14.0 \| 1008 \| 0.3460 \| 0.2750 \| 0.4098 \| 0.3257 \| 0.1154 \| 0.1693 \| 0.5721 \| 0.8325 \| 0.9283 \| \| 0.3325 \| 15.0 \| 1080 \| 0.3476 \| 0.2763 \| 0.4088 \| 0.3308 \| 0.1161 \| 0.1700 \| 0.5682 \| 0.8301 \| 0.9279 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu116 - Tokenizers 0.13.2
Craig/paraphrase-MiniLM-L6-v2	[ "pytorch", "bert", "arxiv:1908.10084", "sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "license:apache-2.0" ]	feature-extraction	{ "architectures": [ "BertModel" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1,026	null	--- license: apache-2.0 tags: - vision - depth-estimation - generated_from_trainer model-index: - name: glpn-nyu-finetuned-diode-221122-044810 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # glpn-nyu-finetuned-diode-221122-044810 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.3690 - Mae: 0.2909 - Rmse: 0.4208 - Abs Rel: 0.3635 - Log Mae: 0.1224 - Log Rmse: 0.1793 - Delta1: 0.5323 - Delta2: 0.8179 - Delta3: 0.9258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mae \| Rmse \| Abs Rel \| Log Mae \| Log Rmse \| Delta1 \| Delta2 \| Delta3 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:-------:\|:-------:\|:--------:\|:------:\|:------:\|:------:\| \| 1.3864 \| 1.0 \| 72 \| 1.2016 \| 3.4656 \| 3.5204 \| 5.1101 \| 0.6881 \| 0.7346 \| 0.0 \| 0.0011 \| 0.0764 \| \| 1.0082 \| 2.0 \| 144 \| 0.4607 \| 0.4107 \| 0.5420 \| 0.5254 \| 0.1697 \| 0.2234 \| 0.3596 \| 0.6460 \| 0.8465 \| \| 0.4656 \| 3.0 \| 216 \| 0.4071 \| 0.3431 \| 0.4758 \| 0.4359 \| 0.1425 \| 0.1992 \| 0.4567 \| 0.7481 \| 0.8958 \| \| 0.4093 \| 4.0 \| 288 \| 0.3953 \| 0.3261 \| 0.4622 \| 0.4197 \| 0.1363 \| 0.1947 \| 0.4841 \| 0.7624 \| 0.9103 \| \| 0.392 \| 5.0 \| 360 \| 0.3916 \| 0.3211 \| 0.4463 \| 0.4116 \| 0.1338 \| 0.1896 \| 0.4810 \| 0.7756 \| 0.9176 \| \| 0.3466 \| 6.0 \| 432 \| 0.3807 \| 0.3075 \| 0.4451 \| 0.3658 \| 0.1293 \| 0.1839 \| 0.5026 \| 0.7921 \| 0.9180 \| \| 0.3297 \| 7.0 \| 504 \| 0.3811 \| 0.3047 \| 0.4448 \| 0.3534 \| 0.1290 \| 0.1835 \| 0.5066 \| 0.7920 \| 0.9137 \| \| 0.2768 \| 8.0 \| 576 \| 0.3779 \| 0.3057 \| 0.4283 \| 0.3894 \| 0.1280 \| 0.1832 \| 0.5046 \| 0.7996 \| 0.9256 \| \| 0.2849 \| 9.0 \| 648 \| 0.3753 \| 0.2978 \| 0.4341 \| 0.3496 \| 0.1259 \| 0.1806 \| 0.5149 \| 0.8041 \| 0.9184 \| \| 0.2571 \| 10.0 \| 720 \| 0.3825 \| 0.3068 \| 0.4305 \| 0.3896 \| 0.1289 \| 0.1849 \| 0.4998 \| 0.7974 \| 0.9206 \| \| 0.2246 \| 11.0 \| 792 \| 0.3718 \| 0.2951 \| 0.4235 \| 0.3678 \| 0.1240 \| 0.1803 \| 0.5249 \| 0.8105 \| 0.9248 \| \| 0.2703 \| 12.0 \| 864 \| 0.3716 \| 0.2945 \| 0.4317 \| 0.3593 \| 0.1235 \| 0.1808 \| 0.5324 \| 0.8122 \| 0.9215 \| \| 0.2596 \| 13.0 \| 936 \| 0.3692 \| 0.2921 \| 0.4185 \| 0.3690 \| 0.1229 \| 0.1798 \| 0.5294 \| 0.8167 \| 0.9264 \| \| 0.2604 \| 14.0 \| 1008 \| 0.3684 \| 0.2893 \| 0.4171 \| 0.3601 \| 0.1223 \| 0.1785 \| 0.5325 \| 0.8179 \| 0.9252 \| \| 0.2679 \| 15.0 \| 1080 \| 0.3690 \| 0.2909 \| 0.4208 \| 0.3635 \| 0.1224 \| 0.1793 \| 0.5323 \| 0.8179 \| 0.9258 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu116 - Tokenizers 0.13.2
Cthyllax/DialoGPT-medium-PaladinDanse	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	10	null	--- license: creativeml-openrail-m --- Made using highly curated best quality masterful artwork from an ancient indonesian stone carving website, with some help from their independent doodling connoisseur brothers in arms, 3000 pieces of their best work. Prompt used: aiseeic aisee_10000.ckpt was made with Anything v.3. aiseeic_15000.ckpt was made with SD 1.5. AIsee (Anything) examples ![00043-170289776-Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, facial featur.png](https://s3.amazonaws.com/moonup/production/uploads/1669095647555-637c5f1b9495870ef76f1472.png) ![00042-3508825484-Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, facial featur.png](https://s3.amazonaws.com/moonup/production/uploads/1669095647512-637c5f1b9495870ef76f1472.png) ![00045-2382850738-Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, facial featur.png](https://s3.amazonaws.com/moonup/production/uploads/1669095647489-637c5f1b9495870ef76f1472.png) AIsee SD examples ![00053-758175738-aiseeic, Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, faci.png](https://s3.amazonaws.com/moonup/production/uploads/1669095834760-637c5f1b9495870ef76f1472.png) ![00039-477119149-Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, facial featur.png](https://s3.amazonaws.com/moonup/production/uploads/1669095834642-637c5f1b9495870ef76f1472.png) ![00057-2487031390-aiseeic, Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, faci.png](https://s3.amazonaws.com/moonup/production/uploads/1669095834700-637c5f1b9495870ef76f1472.png) I own nothing and I will be happy.
DJSammy/bert-base-swedish-uncased_BotXO-ai	[ "pytorch", "transformers" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1	null	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6346626984126984 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32887700534759357 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3264094955489614 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47581989994441354 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.464 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37719298245614036 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36342592592592593 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7761036612927528 - name: F1 (macro) type: f1_macro value: 0.7415561766602355 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7328638497652582 - name: F1 (macro) type: f1_macro value: 0.47573763054929613 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5390032502708559 - name: F1 (macro) type: f1_macro value: 0.49194003623703636 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8753564721430062 - name: F1 (macro) type: f1_macro value: 0.7536524804914483 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8282670009401442 - name: F1 (macro) type: f1_macro value: 0.8236645741563291 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2 RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/analogy.json)): - Accuracy on SAT (full): 0.32887700534759357 - Accuracy on SAT: 0.3264094955489614 - Accuracy on BATS: 0.47581989994441354 - Accuracy on U2: 0.37719298245614036 - Accuracy on U4: 0.36342592592592593 - Accuracy on Google: 0.464 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/classification.json)): - Micro F1 score on BLESS: 0.7761036612927528 - Micro F1 score on CogALexV: 0.7328638497652582 - Micro F1 score on EVALution: 0.5390032502708559 - Micro F1 score on K&H+N: 0.8753564721430062 - Micro F1 score on ROOT09: 0.8282670009401442 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6346626984126984 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen	[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "BertForQuestionAnswering" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1,907	null	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7048015873015873 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37967914438502676 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3916913946587537 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5347415230683713 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.69 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41228070175438597 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3888888888888889 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.853246948922706 - name: F1 (macro) type: f1_macro value: 0.8485536876305343 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8044600938967136 - name: F1 (macro) type: f1_macro value: 0.5726819680585065 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5839653304442037 - name: F1 (macro) type: f1_macro value: 0.5524953070884607 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.934687347847256 - name: F1 (macro) type: f1_macro value: 0.8063588254058023 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8279536195549985 - name: F1 (macro) type: f1_macro value: 0.7955713493721125 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1 RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37967914438502676 - Accuracy on SAT: 0.3916913946587537 - Accuracy on BATS: 0.5347415230683713 - Accuracy on U2: 0.41228070175438597 - Accuracy on U4: 0.3888888888888889 - Accuracy on Google: 0.69 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/classification.json)): - Micro F1 score on BLESS: 0.853246948922706 - Micro F1 score on CogALexV: 0.8044600938967136 - Micro F1 score on EVALution: 0.5839653304442037 - Micro F1 score on K&H+N: 0.934687347847256 - Micro F1 score on ROOT09: 0.8279536195549985 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7048015873015873 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken	[ "pytorch", "tensorboard", "bert", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "BertForQuestionAnswering" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6670436507936508 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3770053475935829 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37388724035608306 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4802668148971651 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.558 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33771929824561403 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34953703703703703 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.893174627090553 - name: F1 (macro) type: f1_macro value: 0.8866591988732194 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7863849765258216 - name: F1 (macro) type: f1_macro value: 0.5308624907920565 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5704225352112676 - name: F1 (macro) type: f1_macro value: 0.5510856788391408 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9581275648605412 - name: F1 (macro) type: f1_macro value: 0.8644516035001516 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8523973675963648 - name: F1 (macro) type: f1_macro value: 0.8523947470987124 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2 RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3770053475935829 - Accuracy on SAT: 0.37388724035608306 - Accuracy on BATS: 0.4802668148971651 - Accuracy on U2: 0.33771929824561403 - Accuracy on U4: 0.34953703703703703 - Accuracy on Google: 0.558 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2/raw/main/classification.json)): - Micro F1 score on BLESS: 0.893174627090553 - Micro F1 score on CogALexV: 0.7863849765258216 - Micro F1 score on EVALution: 0.5704225352112676 - Micro F1 score on K&H+N: 0.9581275648605412 - Micro F1 score on ROOT09: 0.8523973675963648 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6670436507936508 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: nce_logout - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
DamolaMack/Classyfied	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8018650793650793 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3502673796791444 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35014836795252224 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5202890494719289 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.644 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.43287037037037035 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8461654361910502 - name: F1 (macro) type: f1_macro value: 0.8411664963735426 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8145539906103286 - name: F1 (macro) type: f1_macro value: 0.5873414064116238 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6505958829902492 - name: F1 (macro) type: f1_macro value: 0.6269958308732405 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9319051262433052 - name: F1 (macro) type: f1_macro value: 0.8393686548194149 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7511751801942964 - name: F1 (macro) type: f1_macro value: 0.6464435364634403 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1 RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3502673796791444 - Accuracy on SAT: 0.35014836795252224 - Accuracy on BATS: 0.5202890494719289 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.43287037037037035 - Accuracy on Google: 0.644 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8461654361910502 - Micro F1 score on CogALexV: 0.8145539906103286 - Micro F1 score on EVALution: 0.6505958829902492 - Micro F1 score on K&H+N: 0.9319051262433052 - Micro F1 score on ROOT09: 0.7511751801942964 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8018650793650793 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
Danbi/distilgpt2-finetuned-wikitext2	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: agpl-3.0 language: - gl - pt widget: - text: >- A miña amiga Rosa, de Lisboa, estudou en Montreal. Agora traballa en Nova Pescanova. --- # Named Entity Recognition (NER) model for Galician This is a NER model for Galician (ILG/RAG spelling) which uses the standard 'enamex' classes: LOC (geographical locations); PER (people); ORG (organizations); MISC (other entities). The model is based on [BERT-base-gl-cased](https://huggingface.co/marcosgg/bert-base-gl-cased), which has been fine-tuned using custom splits of the [SLI_NERC dataset](https://github.com/xavier-gz/SLI_Galician_Corpora). On the test split of this dataset (not used for training), the model obtained the following results (Precision/Recall/F-score): 87.69 / 89.7 / 88.68.
Davlan/xlm-roberta-base-finetuned-lingala	[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "XLMRobertaForMaskedLM" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	9	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-idrak-practice results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-idrak-practice This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3538 - Wer: 0.3209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.634 \| 0.87 \| 500 \| 2.6452 \| 1.0 \| \| 1.0497 \| 1.73 \| 1000 \| 0.5711 \| 0.5138 \| \| 0.4584 \| 2.6 \| 1500 \| 0.4421 \| 0.4492 \| \| 0.3198 \| 3.46 \| 2000 \| 0.3818 \| 0.3941 \| \| 0.2263 \| 4.33 \| 2500 \| 0.3653 \| 0.3767 \| \| 0.1845 \| 5.19 \| 3000 \| 0.3424 \| 0.3661 \| \| 0.1388 \| 6.06 \| 3500 \| 0.3702 \| 0.3519 \| \| 0.1214 \| 6.92 \| 4000 \| 0.3515 \| 0.3439 \| \| 0.1026 \| 7.79 \| 4500 \| 0.3585 \| 0.3292 \| \| 0.0834 \| 8.65 \| 5000 \| 0.3474 \| 0.3236 \| \| 0.0737 \| 9.52 \| 5500 \| 0.3538 \| 0.3209 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu116 - Datasets 1.18.3 - Tokenizers 0.12.1
Dazai/Ko	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 duplicated_from: hf-internal-testing/tiny-stable-diffusion-torch --- ```python from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") ```
Declan/Breitbart_model_v1	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	9	null	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m --- Use prompt: 'btdmnky' to get a monkey. You can use the categories in the game to generate a monkey based on that category, such as putting "btdmnky magic" will generate a monkey based on the magic monkeys in-game. You can use: - primary - military - magic - support (results won't be great) - hero Some examples: <font size="1">magic hero, godly, laser beams, god rays, dominant pose, monkey, cloak, super powers, Magic The Gathering, magical, fantasy, colorful, realistic Negative prompt: lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, deformed, mutated, extra limbs, Steps: 35, Sampler: Euler, CFG scale: 6.5, Seed: 235226828, Size: 512x512</font> ![Super Magic Monkey](https://huggingface.co/Junglerally/Stable-BTD6/resolve/main/SuperMagicMonkey.png) <font size="1">btdmnky hero magic style, high quality, digital art, monkey, godly, god, powerful Negative prompt: jagged, jaggy lines, poorly drawn, low quality, (((text))), pixelated, jpeg artifacts, messy, deformed, mutated, extra limbs, extra tails Steps: 40, Sampler: DPM++ 2S a, CFG scale: 7, Seed: 2116075235, Size: 512x512</font> ![Magic Hero](https://huggingface.co/Junglerally/Stable-BTD6/resolve/main/MagicHeroGen.png) <font size="1">btdmnky primary style, high quality, digital art, monkey Negative prompt: jagged, jaggy lines, poorly drawn, low quality, (((text))), pixelated, jpeg artifacts, messy, deformed, mutated, extra limbs, extra tails Steps: 40, Sampler: DPM++ 2S a, CFG scale: 7, Seed: 3160304320, Size: 512x512</font> ![Primary](https://huggingface.co/Junglerally/Stable-BTD6/resolve/main/PrimaryGen.png) <font size="1">btdmnky hero style, high quality, digital art, monkey Negative prompt: jagged, jaggy lines, poorly drawn, low quality, (((text))), pixelated, jpeg artifacts, messy, deformed, mutated, extra limbs, extra tails Steps: 40, Sampler: DPM++ 2S a, CFG scale: 7, Seed: 968959303, Size: 512x512</font> ![Hero](https://huggingface.co/Junglerally/Stable-BTD6/resolve/main/HeroGen.png) <font size="1">btdmnky magic style, high quality, cat Negative prompt: jagged, jaggy lines, poorly drawn, low quality, (((text))), pixelated, jpeg artifacts Steps: 40, Sampler: Euler, CFG scale: 7, Seed: 2591613767, Size: 512x512</font> ![Cat Monkey](https://huggingface.co/Junglerally/Stable-BTD6/resolve/main/CatMonkey.png)
Declan/ChicagoTribune_model_v7	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- license: mit tags: - generated_from_trainer model-index: - name: chile-gpt results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # chile-gpt This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 9.4320 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 10.6676 \| 0.98 \| 6 \| 9.5748 \| \| 9.6237 \| 1.98 \| 12 \| 9.2470 \| \| 9.2815 \| 2.98 \| 18 \| 8.8724 \| \| 8.8097 \| 3.98 \| 24 \| 8.3629 \| \| 8.2296 \| 4.98 \| 30 \| 7.8407 \| \| 7.6891 \| 5.98 \| 36 \| 7.4161 \| \| 7.3013 \| 6.98 \| 42 \| 7.1598 \| \| 7.0671 \| 7.98 \| 48 \| 7.0080 \| \| 6.9404 \| 8.98 \| 54 \| 6.9133 \| \| 6.7543 \| 9.98 \| 60 \| 6.7723 \| \| 6.5845 \| 10.98 \| 66 \| 6.6619 \| \| 6.4193 \| 11.98 \| 72 \| 6.5965 \| \| 6.2554 \| 12.98 \| 78 \| 6.5185 \| \| 6.0993 \| 13.98 \| 84 \| 6.4632 \| \| 5.93 \| 14.98 \| 90 \| 6.4155 \| \| 5.7684 \| 15.98 \| 96 \| 6.4183 \| \| 5.6242 \| 16.98 \| 102 \| 6.3981 \| \| 5.4577 \| 17.98 \| 108 \| 6.4609 \| \| 5.2898 \| 18.98 \| 114 \| 6.4577 \| \| 5.1113 \| 19.98 \| 120 \| 6.5617 \| \| 4.9319 \| 20.98 \| 126 \| 6.5827 \| \| 4.7464 \| 21.98 \| 132 \| 6.6961 \| \| 4.5505 \| 22.98 \| 138 \| 6.8359 \| \| 4.341 \| 23.98 \| 144 \| 6.9193 \| \| 4.1324 \| 24.98 \| 150 \| 7.0325 \| \| 3.8938 \| 25.98 \| 156 \| 7.1993 \| \| 3.6691 \| 26.98 \| 162 \| 7.3179 \| \| 3.4316 \| 27.98 \| 168 \| 7.4708 \| \| 3.2041 \| 28.98 \| 174 \| 7.5654 \| \| 2.9614 \| 29.98 \| 180 \| 7.7535 \| \| 2.7189 \| 30.98 \| 186 \| 7.8551 \| \| 2.4944 \| 31.98 \| 192 \| 8.0094 \| \| 2.2624 \| 32.98 \| 198 \| 8.0527 \| \| 2.0292 \| 33.98 \| 204 \| 8.1857 \| \| 1.809 \| 34.98 \| 210 \| 8.3468 \| \| 1.597 \| 35.98 \| 216 \| 8.4307 \| \| 1.3849 \| 36.98 \| 222 \| 8.6230 \| \| 1.2081 \| 37.98 \| 228 \| 8.6666 \| \| 1.0273 \| 38.98 \| 234 \| 8.7926 \| \| 0.8661 \| 39.98 \| 240 \| 8.8861 \| \| 0.7308 \| 40.98 \| 246 \| 8.9042 \| \| 0.6189 \| 41.98 \| 252 \| 8.9202 \| \| 0.5335 \| 42.98 \| 258 \| 9.0861 \| \| 0.459 \| 43.98 \| 264 \| 9.1198 \| \| 0.3958 \| 44.98 \| 270 \| 9.2129 \| \| 0.3587 \| 45.98 \| 276 \| 9.2434 \| \| 0.3222 \| 46.98 \| 282 \| 9.3005 \| \| 0.2948 \| 47.98 \| 288 \| 9.3961 \| \| 0.2677 \| 48.98 \| 294 \| 9.4605 \| \| 0.2348 \| 49.98 \| 300 \| 9.4320 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+rocm5.2 - Datasets 2.6.1 - Tokenizers 0.13.2
Declan/NPR_model_v5	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - LiveEvil/autotrain-data-copuml-la-beta-demo co2_eq_emissions: emissions: 1.2815143214785873 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2205770755 - CO2 Emissions (in grams): 1.2815 ## Validation Metrics - Loss: 1.085 - Accuracy: 0.747 - Macro F1: 0.513 - Micro F1: 0.747 - Weighted F1: 0.715 - Macro Precision: 0.533 - Micro Precision: 0.747 - Weighted Precision: 0.691 - Macro Recall: 0.515 - Micro Recall: 0.747 - Weighted Recall: 0.747 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/LiveEvil/autotrain-copuml-la-beta-demo-2205770755 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("LiveEvil/autotrain-copuml-la-beta-demo-2205770755", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("LiveEvil/autotrain-copuml-la-beta-demo-2205770755", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
Declan/NPR_model_v6	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- tags: - generated_from_trainer datasets: - ebiquity-v2 model-index: - name: enlmr-conll2003 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # enlmr-conll2003 This model is a fine-tuned version of [manirai91/enlm-roberta-final](https://huggingface.co/manirai91/enlm-roberta-final) on the ebiquity-v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0 - Datasets 2.7.0 - Tokenizers 0.13.2
Declan/Politico_model_v1	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	2022-11-22T17:53:53Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-the_verge-linustechtips-two_min results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-the_verge-linustechtips-two_min This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Declan/Politico_model_v5	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	2022-11-22T18:13:52Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Modified-Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 16.10 +/- 10.73 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
DeepPavlov/xlm-roberta-large-en-ru	[ "pytorch", "xlm-roberta", "feature-extraction", "en", "ru", "transformers" ]	feature-extraction	{ "architectures": [ "XLMRobertaModel" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	190	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-neo-125M-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt-neo-125M-wikitext2 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0325 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 259 \| 6.4308 \| \| 6.8563 \| 2.0 \| 518 \| 6.0898 \| \| 6.8563 \| 3.0 \| 777 \| 6.0325 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
Denny29/DialoGPT-medium-asunayuuki	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	9	2022-11-22T22:24:13Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-multilingual-cased-sv2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-multilingual-cased-sv2 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the squad_v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
DeskDown/MarianMixFT_en-fil	[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	2022-11-22T22:30:20Z	--- language: - pt thumbnail: "Portuguese BERT for the Legal Domain" pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - transformers datasets: - assin - assin2 - stjiris/portuguese-legal-sentences-v1.0 widget: - source_sentence: "O advogado apresentou as provas ao juíz." sentences: - "O juíz leu as provas." - "O juíz leu o recurso." - "O juíz atirou uma pedra." example_title: "Example 1" model-index: - name: BERTimbau results: - task: name: STS type: STS metrics: - name: Pearson Correlation - assin Dataset type: Pearson Correlation value: 0.7716333759993093 - name: Pearson Correlation - assin2 Dataset type: Pearson Correlation value: 0.8403302138785704 - name: Pearson Correlation - stsb_multi_mt pt Dataset type: Pearson Correlation value: 0.8249826985133595 --- # stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0 (Legal BERTimbau) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0 derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large. It was trained using the MLM technique with a learning rate 3e-5 [Legal Sentences from +-30000 documents](https://huggingface.co/datasets/stjiris/portuguese-legal-sentences-v1.0) 130k training steps (best performance for our semantic search system implementation) It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets. [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2) and [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) portuguese subdataset ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Isto é um exemplo", "Isto é um outro exemplo"] model = SentenceTransformer('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0') model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1028, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors If you use this work, please cite: ```bibtex @inproceedings{MeloSemantic, author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o}, title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a}, } @inproceedings{souza2020bertimbau, author = {F{\'a}bio Souza and Rodrigo Nogueira and Roberto Lotufo}, title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese}, booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)}, year = {2020} } @inproceedings{fonseca2016assin, title={ASSIN: Avaliacao de similaridade semantica e inferencia textual}, author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S}, booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal}, pages={13--15}, year={2016} } @inproceedings{real2020assin, title={The assin 2 shared task: a quick overview}, author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo}, booktitle={International Conference on Computational Processing of the Portuguese Language}, pages={406--412}, year={2020}, organization={Springer} } @InProceedings{huggingface:dataset:stsb_multi_mt, title = {Machine translated multilingual STS benchmark dataset.}, author={Philip May}, year={2021}, url={https://github.com/PhilipMay/stsb-multi-mt} } ```
DeskDown/MarianMixFT_en-id	[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	2022-11-22T22:35:19Z	--- license: mit tags: - generated_from_trainer datasets: - conll2003 model-index: - name: xlm-roberta-conll2003 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-conll2003 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0 - Datasets 2.7.0 - Tokenizers 0.13.2
DeskDown/MarianMixFT_en-ms	[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	2022-11-22T22:47:21Z	--- license: mit tags: - image-to-text - image-to-image - text-to-image - text-to-text - image-editing - image-variation - generation - vision datasets: - Laion2B-en widget: - text: "A high tech solarpunk utopia in the Amazon rainforest" example_title: Amazon rainforest --- # Versatile Diffusion V1.0 Model Card We built Versatile Diffusion (VD), the first unified multi-flow multimodal diffusion framework, as a step towards Universal Generative AI. Versatile Diffusion can natively support image-to-text, image-variation, text-to-image, and text-variation, and can be further extended to other applications such as semantic-style disentanglement, image-text dual-guided generation, latent image-to-text-to-image editing, and more. Future versions will support more modalities such as speech, music, video and 3D. Resources for more information: [GitHub](https://github.com/SHI-Labs/Versatile-Diffusion), [arXiv](https://arxiv.org/abs/2211.08332). # Model Details One single flow of Versatile Diffusion contains a VAE, a diffuser, and a context encoder, and thus handles one task (e.g., text-to-image) under one data type (e.g., image) and one context type (e.g., text). The multi-flow structure of Versatile Diffusion shows in the following diagram: <p align="center"> <img src="https://huggingface.co/shi-labs/versatile-diffusion-model/resolve/main/assets/figures/vd_combined.png" width="99%"> </p> - Developed by: Xingqian Xu, Atlas Wang, Eric Zhang, Kai Wang, and Humphrey Shi - Model type: Diffusion-based multimodal generation model - Language(s): English - License: MIT - Resources for more information: [GitHub Repository](https://github.com/SHI-Labs/Versatile-Diffusion), [Paper](https://arxiv.org/abs/2211.08332). - Cite as: ``` @article{xu2022versatile, title = {Versatile Diffusion: Text, Images and Variations All in One Diffusion Model}, author = {Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi}, year = 2022, url = {https://arxiv.org/abs/2211.08332}, eprint = {2211.08332}, archiveprefix = {arXiv}, primaryclass = {cs.CV} } ``` # Usage You can use the model both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [SHI-Labs Versatile Diffusion codebase](https://github.com/SHI-Labs/Versatile-Diffusion). ## 🧨 Diffusers Diffusers let's you both use a unified and more memory-efficient, task-specific pipelines. Make sure to install `transformers` from `"main"` in order to use this model.: ``` pip install git+https://github.com/huggingface/transformers ``` ## VersatileDiffusionPipeline To use Versatile Diffusion for all tasks, it is recommend to use the [`VersatileDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion#diffusers.VersatileDiffusionPipeline) ```py #! pip install git+https://github.com/huggingface/transformers diffusers torch from diffusers import VersatileDiffusionPipeline import torch import requests from io import BytesIO from PIL import Image pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe = pipe.to("cuda") # prompt prompt = "a red car" # initial image url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") # text to image image = pipe.text_to_image(prompt).images[0] # image variation image = pipe.image_variation(image).images[0] # image variation image = pipe.dual_guided(prompt, image).images[0] ``` ### Task Specific The task specific pipelines load only the weights that are needed onto GPU. You can find all task specific pipelines [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion#versatilediffusion). You can use them as follows: ### Text to Image ```py from diffusers import VersatileDiffusionTextToImagePipeline import torch pipe = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe.remove_unused_weights() pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) image = pipe("an astronaut riding on a horse on mars", generator=generator).images[0] image.save("./astronaut.png") ``` #### Image variations ```py from diffusers import VersatileDiffusionImageVariationPipeline import torch import requests from io import BytesIO from PIL import Image # download an initial image url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) image = pipe(image, generator=generator).images[0] image.save("./car_variation.png") ``` #### Dual-guided generation ```py from diffusers import VersatileDiffusionDualGuidedPipeline import torch import requests from io import BytesIO from PIL import Image # download an initial image url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") text = "a red car in the sun" pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe.remove_unused_weights() pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) text_to_image_strength = 0.75 image = pipe(prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator).images[0] image.save("./red_car.png") ``` ### Original GitHub Repository Follow the instructions [here](https://github.com/SHI-Labs/Versatile-Diffusion/#evaluation). # Cautions, Biases, and Content Acknowledgment We would like the raise the awareness of users of this demo of its potential issues and concerns. Like previous large foundation models, Versatile Diffusion could be problematic in some cases, partially due to the imperfect training data and pretrained network (VAEs / context encoders) with limited scope. In its future research phase, VD may do better on tasks such as text-to-image, image-to-text, etc., with the help of more powerful VAEs, more sophisticated network designs, and more cleaned data. So far, we have kept all features available for research testing both to show the great potential of the VD framework and to collect important feedback to improve the model in the future. We welcome researchers and users to report issues with the HuggingFace community discussion feature or email the authors. Beware that VD may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography, and violence. VD was trained on the LAION-2B dataset, which scraped non-curated online images and text, and may contain unintended exceptions as we removed illegal content. VD in this demo is meant only for research purposes.
DeskDown/MarianMixFT_en-th	[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	2022-11-22T23:09:35Z	--- license: creativeml-openrail-m thumbnail: "https://huggingface.co/tuwonga/dbluth/resolve/main/dbluth_prev1.jpg" tags: - stable-diffusion - text-to-image --- ### dbluth I played a lot in my childhood at laser disc videogames so this model is my personal tribute to the great Disney animator Don Bluth.This is a fine-tuned Stable Diffusion model (based on v1.5), I've trained three different models from videogames laser disc Dragon's Lair , Space Ace and Dragon's Lair II Time Warp then I merged these models into a single one called dbluth. Use the token _dbluth_ in your prompts to use the style. _Download the ckpt file from "files and versions" tab into the stable diffusion models folder of your web-ui of choice._ The model is pretty similar to Disney classic model because of course Don Bluth was one of the main animator in classic Disney era. _I've found interesting the output in the img2img generation. You can see the results in the second image (original/img2img)._ Characters and rendered with this model: ![Character Samples](https://huggingface.co/tuwonga/dbluth/resolve/main/dbluth_prev1.jpg) _prompt and settings used: [person] in dbluth style \| Steps: 30, Sampler: Euler, CFG scale: 7.5_ Characters rendered with img2img: ![Character Samples](https://huggingface.co/tuwonga/dbluth/resolve/main/dbluth_prev2.jpg) _prompt and settings used: [person] in dbluth style \| Steps: 30 - denoising stregth around 50/70 but you can play around with settings_ -- This model was trained with Dreambooth training by TheLastBen, using 40 images at 8000 steps with 20% of text encoder for each model and then merged in a single one with Automatic1111 webui checkpoint merger. -- ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
DeskDown/MarianMix_en-ja-10	[ "pytorch", "tensorboard", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1	2022-11-22T23:16:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 model-index: - name: mbert-conll2003 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbert-conll2003 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0 - Datasets 2.7.0 - Tokenizers 0.13.2
Dibyaranjan/nl_image_search	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-23T00:36:55Z	--- license: mit --- ### Alberto_Montt on Stable Diffusion This is the `<AlbertoMontt>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<AlbertoMontt> 0](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/3.jpeg) ![<AlbertoMontt> 1](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/0.jpeg) ![<AlbertoMontt> 2](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/5.jpeg) ![<AlbertoMontt> 3](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/2.jpeg) ![<AlbertoMontt> 4](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/1.jpeg) ![<AlbertoMontt> 5](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/4.jpeg)
DicoTiar/wisdomfiy	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	2022-11-23T00:38:10Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### American Flag Cowboy Hat on Stable Diffusion via Dreambooth #### model by aakamishra This your the Stable Diffusion model fine-tuned the American Flag Cowboy Hat concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: a photo of sks hat You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/sd-dreambooth-library/american-flag-cowboy-hat/resolve/main/concept_images/3.jpeg) ![image 1](https://huggingface.co/sd-dreambooth-library/american-flag-cowboy-hat/resolve/main/concept_images/0.jpeg) ![image 2](https://huggingface.co/sd-dreambooth-library/american-flag-cowboy-hat/resolve/main/concept_images/2.jpeg) ![image 3](https://huggingface.co/sd-dreambooth-library/american-flag-cowboy-hat/resolve/main/concept_images/1.jpeg) ![image 4](https://huggingface.co/sd-dreambooth-library/american-flag-cowboy-hat/resolve/main/concept_images/4.jpeg)
Dilmk2/DialoGPT-small-harrypotter	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	13	2022-11-23T00:50:28Z	--- license: other tags: - computer_vision - pose_estimation --- Copyright 2021-2023 by Mackenzie Mathis, Alexander Mathis, Shaokai Ye and contributors. All rights reserved. - Non-commercial use only is permitted - please cite Ye et al if you use this model in your work https://arxiv.org/abs/2203.07436v1 - If this license is not suitable for your business or project please contact EPFL-TTO (https://tto.epfl.ch/) for a full commercial license. This software may not be used to harm any animal deliberately.
DingleyMaillotUrgell/homer-bot	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	12	2022-11-23T01:25:59Z	--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: reco-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # reco-ner This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0668 - Precision: 0.8125 - Recall: 0.8790 - F1: 0.8444 - Accuracy: 0.9819 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.4516 \| 1.0 \| 626 \| 0.4047 \| 0.4332 \| 0.4564 \| 0.4445 \| 0.8980 \| \| 0.3677 \| 2.0 \| 1252 \| 0.2774 \| 0.4918 \| 0.5731 \| 0.5293 \| 0.9193 \| \| 0.2892 \| 3.0 \| 1878 \| 0.2133 \| 0.6139 \| 0.6581 \| 0.6353 \| 0.9384 \| \| 0.2736 \| 4.0 \| 2504 \| 0.1772 \| 0.6248 \| 0.6854 \| 0.6537 \| 0.9488 \| \| 0.221 \| 5.0 \| 3130 \| 0.1503 \| 0.6295 \| 0.7328 \| 0.6772 \| 0.9560 \| \| 0.1569 \| 6.0 \| 3756 \| 0.1283 \| 0.6821 \| 0.8108 \| 0.7409 \| 0.9623 \| \| 0.1534 \| 7.0 \| 4382 \| 0.0995 \| 0.7412 \| 0.8119 \| 0.7749 \| 0.9708 \| \| 0.089 \| 8.0 \| 5008 \| 0.0846 \| 0.7695 \| 0.8353 \| 0.8010 \| 0.9760 \| \| 0.0923 \| 9.0 \| 5634 \| 0.0743 \| 0.7881 \| 0.8740 \| 0.8289 \| 0.9789 \| \| 0.0711 \| 10.0 \| 6260 \| 0.0668 \| 0.8125 \| 0.8790 \| 0.8444 \| 0.9819 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
DongHai/DialoGPT-small-rick	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	9	2022-11-23T01:50:21Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-demo-M02-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-demo-M02-2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2709 - Wer: 1.0860 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 23.4917 \| 0.91 \| 500 \| 3.2945 \| 1.0 \| \| 3.4102 \| 1.81 \| 1000 \| 3.1814 \| 1.0 \| \| 2.9438 \| 2.72 \| 1500 \| 2.7858 \| 1.0 \| \| 2.6698 \| 3.62 \| 2000 \| 2.4745 \| 1.0035 \| \| 1.9542 \| 4.53 \| 2500 \| 1.8675 \| 1.3745 \| \| 1.2737 \| 5.43 \| 3000 \| 1.6459 \| 1.3703 \| \| 0.9748 \| 6.34 \| 3500 \| 1.8406 \| 1.3037 \| \| 0.7696 \| 7.25 \| 4000 \| 1.5086 \| 1.2476 \| \| 0.6396 \| 8.15 \| 4500 \| 1.8280 \| 1.2476 \| \| 0.558 \| 9.06 \| 5000 \| 1.7680 \| 1.2247 \| \| 0.4865 \| 9.96 \| 5500 \| 1.8210 \| 1.2309 \| \| 0.4244 \| 10.87 \| 6000 \| 1.7910 \| 1.1775 \| \| 0.3898 \| 11.78 \| 6500 \| 1.8021 \| 1.1831 \| \| 0.3456 \| 12.68 \| 7000 \| 1.7746 \| 1.1456 \| \| 0.3349 \| 13.59 \| 7500 \| 1.8969 \| 1.1519 \| \| 0.3233 \| 14.49 \| 8000 \| 1.7402 \| 1.1234 \| \| 0.3046 \| 15.4 \| 8500 \| 1.8585 \| 1.1429 \| \| 0.2622 \| 16.3 \| 9000 \| 1.6687 \| 1.0950 \| \| 0.2593 \| 17.21 \| 9500 \| 1.8192 \| 1.1144 \| \| 0.2541 \| 18.12 \| 10000 \| 1.8665 \| 1.1110 \| \| 0.2098 \| 19.02 \| 10500 \| 1.9996 \| 1.1186 \| \| 0.2192 \| 19.93 \| 11000 \| 2.0346 \| 1.1040 \| \| 0.1934 \| 20.83 \| 11500 \| 2.1924 \| 1.1012 \| \| 0.2034 \| 21.74 \| 12000 \| 1.8060 \| 1.0929 \| \| 0.1857 \| 22.64 \| 12500 \| 2.0334 \| 1.0798 \| \| 0.1819 \| 23.55 \| 13000 \| 2.1223 \| 1.1040 \| \| 0.1621 \| 24.46 \| 13500 \| 2.1795 \| 1.0957 \| \| 0.1548 \| 25.36 \| 14000 \| 2.1545 \| 1.1089 \| \| 0.1512 \| 26.27 \| 14500 \| 2.2707 \| 1.1186 \| \| 0.1472 \| 27.17 \| 15000 \| 2.1698 \| 1.0888 \| \| 0.1296 \| 28.08 \| 15500 \| 2.2496 \| 1.0867 \| \| 0.1312 \| 28.99 \| 16000 \| 2.2969 \| 1.0881 \| \| 0.1331 \| 29.89 \| 16500 \| 2.2709 \| 1.0860 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
Dongjae/mrc2reader	[ "pytorch", "xlm-roberta", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "XLMRobertaForQuestionAnswering" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	2022-11-23T02:04:01Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion widget: - text: "zombie_vector " --- ### Jak's Zombie Vector Pack for Stable Diffusion Another fantastic image pack brought to you by 124 training images through 5000 training steps, 20% Training text crafted by Jak_TheAI_Artist Include Prompt trigger: "zombie_vector" to activate. Perfect for designing T-shirts and zombie vector art. Sample pictures of this concept: ![zombie 0](https://huggingface.co/plasmo/zombie-vector/resolve/main/concept_images/trump.jpg) ![zombie 1](https://huggingface.co/plasmo/zombie-vector/resolve/main/concept_images/biden.jpg) ![zombie 2](https://huggingface.co/plasmo/zombie-vector/resolve/main/concept_images/sm.jpg) ![zombie 3](https://huggingface.co/plasmo/zombie-vector/resolve/main/concept_images/ww.jpg)
Dongmin/testmodel	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "T5ForConditionalGeneration" ], "model_type": "t5", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": true, "length_penalty": 2, "max_length": 200, "min_length": 30, "no_repeat_ngram_size": 3, "num_beams": 4, "prefix": "summarize: " }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to German: " }, "translation_en_to_fr": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to French: " }, "translation_en_to_ro": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to Romanian: " } } }	11	2022-11-23T02:44:30Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### TaylorSwift Dreambooth model trained by taytay4eva with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook using the StableDiffusionv1.5 model CREATOR NOTE 1: The keyword for this model is <b>taySwift</b> CREATOR NOTE 2: "Taylor Berry" is a blend of the original model as put through further iterations of DreamBooth and Berry_mix at a 7:3 ratio. It provides a bit better mesh of images and, I think, an overall smoother final product, but whichever you like is what you should go with! Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) ![Sample Image of TaylorSwiftv2](https://huggingface.co/sd-dreambooth-library/taylorswift/blob/main/concept_images/indexaa.png) positive prompt: <b>taySwift</b>, Masterpiece, cinematic lighting, photorealistic, realistic, extremely detailed, (fancy clothes, puffy sleeves, Lacy shirt, thigh high boots, leather boots, short skirt), cheerful attitude, happy woman, excited woman), artgerm, greg rutkowski, alphonse mucha negative prompt: Ugly, lowres, duplicate, morbid, mutilated, out of frame, extra fingers, extra limbs, extra legs, extra heads, extra arms, extra breasts, extra nipples, extra head, extra digit, poorly drawn hands, poorly drawn face, mutation, mutated hands, bad anatomy, long neck, signature, watermark, username, blurry, artist name, deformed, distorted fingers, distorted limbs, distorted legs, distorted heads, distorted arms, distorted breasts, distorted nipples, distorted head, distorted digit Steps: 85, CFG scale: 7, Seed: 1903506130, Face restoration: CodeFormer, Size: 576x832, Model hash: ad57baac, Denoising strength: 0.75, Mask blur: 4 Upscale: 2, visibility: 1.0, model:ESRGAN_4x ![Sample Image of Taylor Berry](https://huggingface.co/sd-dreambooth-library/taylorswift/blob/main/00143-3262192747-oil%20painting%2C%20sensual%2C%20(full%20body)%2C%20taySwift%2C%20princess%2C%20(auburn%20hair)%2C%20erotic%2C%20fantasy%20princess%2C%20tavern%20wench%2C%20bar%2C%20magical%2C%20bus.png) positive prompt: oil painting, sensual, (full body), <b>taySwift</b>, princess, (auburn hair), erotic, fantasy princess, tavern wench, bar, magical, busty, huge titties, curvy, full red lips, kiss, sensual clothes, off the shoulder dress, lace, ((blue) and green floor length dress), (Albert Lynch), J. C. Leyendecker, Ruan Jia, Gaston Bussiere, Alexandre Cabanel, WLOP, best quality negative prompt: (blonde hair), (ugly:1.3), (duplicate:1.3), (morbid), (mutilated), out of frame, extra fingers, mutated hands, (poorly drawn hands), (poorly drawn face), (mutation:1.3), (deformed:1.3), (amputee:1.3), blurry, bad anatomy, bad proportions, (extra limbs), cloned face, (disfigured:1.3), gross proportions, (malformed limbs), (missing arms), (missing legs), (extra arms), (extra legs), mutated hands, (fused fingers), (too many fingers), (long neck:1.3), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, black and white, monochrome, censored Steps: 42, CFG scale: 11, Denoising Strength: 0.75, Seed: 3262192735
Doogie/Waynehills-KE-T5-doogie	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	2022-11-23T02:47:05Z	--- tags: - tensorflowtts - audio - text-to-speech - text-to-mel language: vi license: apache-2.0 datasets: - infore --- # Install TensorFlowTTS ``` pip install TensorFlowTTS ``` ## Converting your Text to Mel Spectrogram ```python import numpy as np import soundfile as sf import yaml import IPython.display as ipd import tensorflow as tf from tensorflow_tts.inference import AutoProcessor from tensorflow_tts.inference import TFAutoModel processor = AutoProcessor.from_pretrained("MarcNg/fastspeech2-vi-infore") fastspeech2 = TFAutoModel.from_pretrained("MarcNg/fastspeech2-vi-infore") text = "xin chào đây là một ví dụ về chuyển đổi văn bản thành giọng nói" input_ids = processor.text_to_sequence(text) mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference( input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0), speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32), speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32), f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32), energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32), ) ``` ## Bonus: Convert Mel Spectrogram to Speech ```python mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-ljspeech-en") audio_before = mb_melgan.inference(mel_before)[0, :, 0] audio_after = mb_melgan.inference(mel_after)[0, :, 0] sf.write("audio_before.wav", audio_before, 22050, "PCM_16") sf.write("audio_after.wav", audio_after, 22050, "PCM_16") ipd.Audio('audio_after.wav') ``` #### Referencing FastSpeech2 ``` @misc{ren2021fastspeech, title={FastSpeech 2: Fast and High-Quality End-to-End Text to Speech}, author={Yi Ren and Chenxu Hu and Xu Tan and Tao Qin and Sheng Zhao and Zhou Zhao and Tie-Yan Liu}, year={2021}, eprint={2006.04558}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```
DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	29	2022-11-23T04:33:07Z	--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: whisper-small-hi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 113.49784136121221 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-hi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 4.5728 - Wer: 113.4978 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.877 \| 0.01 \| 6 \| 4.5728 \| 19.4978 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	28	2022-11-23T04:41:41Z	--- license: cc-by-4.0 --- # GenRead: FiD model trained on TQA -- This is the model checkpoint of GenRead [2], based on the T5-3B and trained on the TriviaQA [1]. -- Hyperparameters: 8 x 80GB A100 GPUs; batch size 16; AdamW; LR 6e-5; best dev at 8500 steps References: [1] TriviaQA: A Large Scale Dataset for Reading Comprehension and Question Answering. ACL 2017 [2] Generate rather than Retrieve: Large Language Models are Strong Context Generators. arXiv 2022 ## Model performance We evaluate it on the TriviaQA dataset, the EM score is 71.55. <a href="https://huggingface.co/exbert/?model=bert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a> --- license: cc-by-4.0 ---
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	30	2022-11-23T04:43:08Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 173.24 +/- 14.93 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
DoyyingFace/bert-asian-hate-tweets-asonam-unclean	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	30	2022-11-23T05:20:19Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1651 - F1: 0.8578 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.211 \| 1.0 \| 715 \| 0.1834 \| 0.8266 \| \| 0.1447 \| 2.0 \| 1430 \| 0.1624 \| 0.8464 \| \| 0.0933 \| 3.0 \| 2145 \| 0.1651 \| 0.8578 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0 - Datasets 2.7.0 - Tokenizers 0.12.1
DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	25	2022-11-23T05:39:41Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="popolin52/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
albert-base-v2	[ "pytorch", "tf", "jax", "rust", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	4,785,283	2022-11-23T05:57:57Z	This model is said to serve as a repository for Futanari or futa models, with a focus on the creation and storage of these types of models. Despite ongoing efforts, the elusive third element remains elusive. Nevertheless, it is thought to be a valuable asset when used in conjunction with other models to get "better? futanari images"
albert-large-v1	[ "pytorch", "tf", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	687	2022-11-23T06:21:29Z	--- language: - nl license: apache-2.0 tags: - whisper-event - hf-asr-leaderboard datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Medium nl - GeoffVdr results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 type: mozilla-foundation/common_voice_11_0 config: nl split: test args: nl metrics: - name: Wer type: wer value: 7.514 co2_eq_emissions: emissions: 2930 source: https://mlco2.github.io/impact/ training_type: fine-tuning geographical_location: Ghent, Belgium hardware_used: 1 v100 GPU --- # Whisper Medium nl - GeoffVdr This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data - Training: Mozilla CommonVoice 11 Dutch train+validation set - Evaluation: Mozilla CommonVoice 11 Dutch test set ## Training procedure ## Training Hyperparameters - learning_rate: 1e-5 - train_batch_size: 8 - eval_batch_size: 8 - gradient_accumulation_steps: 2 - lr_scheduler_warmup_steps: 500 - training_steps: 12000 ## Training Results \| Training Loss \| Epoch \| Step \| WER \| \|:-------------:\|:-----:\|:----:\|:----:\| \| 0.1111 \| 0.39 \| 1000 \| 9.89 \| \| 0.0884 \| 0.78 \| 2000 \| 9.26 \| \| 0.0362 \| 1.17 \| 3000 \| 8.64 \| \| 0.0359 \| 1.56 \| 4000 \| 8.60 \| \| 0.0375 \| 1.95 \| 5000 \| 8.24 \| : : \| 0.0015 \| 4.68 \| 12000\| 7.51 \| ### Framework versions
albert-large-v2	[ "pytorch", "tf", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	26,792	2022-11-23T06:31:48Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-finetuned-squad_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad_2 This model is a fine-tuned version of [tomXBE/distilbert-base-uncased-finetuned-squad](https://huggingface.co/tomXBE/distilbert-base-uncased-finetuned-squad) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
albert-xxlarge-v2	[ "pytorch", "tf", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "exbert", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	42,640	null	--- tags: - generated_from_keras_callback model-index: - name: dung1308/RM_system_NLP_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # dung1308/RM_system_NLP_model This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.8134 - Validation Loss: 1.8072 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.4371 \| 2.4851 \| 0 \| \| 4.0108 \| 2.1003 \| 1 \| \| 3.8134 \| 1.8072 \| 2 \| ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.7.0 - Tokenizers 0.11.0
bert-base-cased	[ "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1810.04805", "transformers", "exbert", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	8,621,271	2022-11-23T06:54:13Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: distilbart-cnn-12-6-sec results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbart-cnn-12-6-sec This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0798 - Rouge1: 72.1665 - Rouge2: 62.2601 - Rougel: 67.8376 - Rougelsum: 71.1407 - Gen Len: 121.62 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 99 \| 0.3526 \| 53.3978 \| 38.6395 \| 45.6271 \| 51.0477 \| 111.48 \| \| No log \| 2.0 \| 198 \| 0.1961 \| 55.7397 \| 43.6293 \| 50.9595 \| 54.0764 \| 111.46 \| \| No log \| 3.0 \| 297 \| 0.1483 \| 66.9443 \| 54.8966 \| 62.6678 \| 65.6787 \| 118.64 \| \| No log \| 4.0 \| 396 \| 0.1218 \| 67.2661 \| 56.1852 \| 63.1339 \| 65.8066 \| 124.92 \| \| No log \| 5.0 \| 495 \| 0.1139 \| 67.2097 \| 55.8694 \| 62.7508 \| 65.9706 \| 123.02 \| \| 0.4156 \| 6.0 \| 594 \| 0.0940 \| 71.607 \| 60.6697 \| 66.7873 \| 70.339 \| 122.84 \| \| 0.4156 \| 7.0 \| 693 \| 0.0888 \| 71.3792 \| 61.8326 \| 68.25 \| 70.5113 \| 124.4 \| \| 0.4156 \| 8.0 \| 792 \| 0.0870 \| 72.7472 \| 62.6968 \| 68.2853 \| 71.5789 \| 124.34 \| \| 0.4156 \| 9.0 \| 891 \| 0.0799 \| 73.4438 \| 63.5966 \| 68.8737 \| 72.3014 \| 119.88 \| \| 0.4156 \| 10.0 \| 990 \| 0.0798 \| 72.1665 \| 62.2601 \| 67.8376 \| 71.1407 \| 121.62 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
bert-base-chinese	[ "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "zh", "arxiv:1810.04805", "transformers", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3,377,486	2022-11-23T06:54:29Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: distilbart-cnn-12-6-sec results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbart-cnn-12-6-sec This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1379 - Rouge1: 72.2845 - Rouge2: 61.1501 - Rougel: 67.6999 - Rougelsum: 70.9968 - Gen Len: 113.8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 99 \| 0.4429 \| 56.0806 \| 40.5969 \| 47.5271 \| 53.7227 \| 115.44 \| \| No log \| 2.0 \| 198 \| 0.2279 \| 56.6042 \| 42.1781 \| 48.9542 \| 54.951 \| 116.84 \| \| No log \| 3.0 \| 297 \| 0.1845 \| 65.9646 \| 51.8575 \| 59.8647 \| 64.103 \| 113.8 \| \| No log \| 4.0 \| 396 \| 0.1532 \| 71.6132 \| 61.1434 \| 67.4165 \| 70.4093 \| 110.46 \| \| No log \| 5.0 \| 495 \| 0.1379 \| 72.2845 \| 61.1501 \| 67.6999 \| 70.9968 \| 113.8 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
bert-large-uncased-whole-word-masking-finetuned-squad	[ "pytorch", "tf", "jax", "safetensors", "bert", "question-answering", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1810.04805", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]	question-answering	{ "architectures": [ "BertForQuestionAnswering" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	480,510	2022-11-23T07:46:33Z	--- language: da widget: - text: En trend, der kan blive ligeså hot som<mask>. tags: - roberta - danish - masked-lm - pytorch license: cc-by-4.0 --- # DanskBERT This is DanskBERT, a Danish language model. Note that you should not prepend the mask with a space when using it directly! The model is the best performing base-size model on the [ScandEval benchmark for Danish](https://scandeval.github.io/nlu-benchmark/). DanskBERT was trained on the Danish Gigaword Corpus (Strømberg-Derczynski et al., 2021). DanskBERT was trained using fairseq using the RoBERTa-base configuration. The model was trained with a batch size of 2k, and was trained to convergence for 500k steps using 16 V100 cards for approximately two weeks. If you find this model useful, please cite ``` @inproceedings{snaebjarnarson-etal-2023-transfer, title = "{T}ransfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese", author = "Snæbjarnarson, Vésteinn and Simonsen, Annika and Glavaš, Goran and Vulić, Ivan", booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)", month = "may 22--24", year = "2023", address = "Tórshavn, Faroe Islands", publisher = {Link{\"o}ping University Electronic Press, Sweden}, } ```
bert-large-uncased-whole-word-masking	[ "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1810.04805", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	76,685	2022-11-23T07:49:30Z	--- library_name: stable-baselines3 tags: - ALE/Qbert-v5 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: ALE/Qbert-v5 type: ALE/Qbert-v5 metrics: - type: mean_reward value: 6665.00 +/- 1973.49 name: mean_reward verified: false --- # DQN Agent playing ALE/Qbert-v5 This is a trained model of a DQN agent playing ALE/Qbert-v5 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env ALE/Qbert-v5 -orga xaeroq -f logs/ python enjoy.py --algo dqn --env ALE/Qbert-v5 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env ALE/Qbert-v5 -orga xaeroq -f logs/ rl_zoo3 enjoy --algo dqn --env ALE/Qbert-v5 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env ALE/Qbert-v5 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env ALE/Qbert-v5 -f logs/ -orga xaeroq ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
xlm-clm-ende-1024	[ "pytorch", "tf", "safetensors", "xlm", "fill-mask", "multilingual", "en", "de", "arxiv:1901.07291", "arxiv:1910.09700", "transformers", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "XLMWithLMHeadModel" ], "model_type": "xlm", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	33,817	2022-11-23T09:24:10Z	--- license: cc-by-4.0 --- ## Aina Project's Catalan-Spanish machine translation model ## Table of Contents - [Model Description](#model-description) - [Intended Uses and Limitations](#intended-use) - [How to Use](#how-to-use) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Data Preparation](#data-preparation) - [Tokenization](#tokenization) - [Hyperparameters](#hyperparameters) - [Evaluation](#evaluation) - [Variable and Metrics](#variable-and-metrics) - [Evaluation Results](#evaluation-results) - [Additional Information](#additional-information) - [Author](#author) - [Contact Information](#contact-information) - [Copyright](#copyright) - [Licensing Information](#licensing-information) - [Funding](#funding) - [Disclaimer](#disclaimer) ## Model description This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-Spanish datasets, up to 92 million sentences. Additionally, the model is evaluated on several public datasecomprising 5 different domains (general, adminstrative, technology, biomedical, and news). ## Intended uses and limitations You can use this model for machine translation from Catalan to Spanish. ## How to use ### Usage Required libraries: ```bash pip install ctranslate2 pyonmttok ``` Translate a sentence using python ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="projecte-aina/mt-aina-ca-es", revision="main") tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model") tokenized=tokenizer.tokenize("Benvingut al projecte Aina!") translator = ctranslate2.Translator(model_dir) translated = translator.translate_batch([tokenized[0]]) print(tokenizer.detokenize(translated[0][0]['tokens'])) ``` ## Training ### Training data The was trained on a combination of the following datasets: \| Dataset \| Sentences \| Tokens \| \|-------------------\|----------------\|-------------------\| \| DOCG v2 \| 8.472.786 \| 188.929.206 \| \| El Periodico \| 6.483.106 \| 145.591.906 \| \| EuroParl \| 1.876.669 \| 49.212.670 \| \| WikiMatrix \| 1.421.077 \| 34.902.039 \| \| Wikimedia \| 335.955 \| 8.682.025 \| \| QED \| 71.867 \| 1.079.705 \| \| TED2020 v1 \| 52.177 \| 836.882 \| \| CCMatrix v1 \| 56.103.820 \| 1.064.182.320 \| \| MultiCCAligned v1 \| 2.433.418 \| 48.294.144 \| \| ParaCrawl \| 15.327.808 \| 334.199.408 \| \| Total \| 92.578.683 \| 1.875.910.305 \| ### Training procedure ### Data preparation All datasets are concatenated and filtered using the [mBERT Gencata parallel filter](https://huggingface.co/projecte-aina/mbert-base-gencata) and cleaned using the clean-corpus-n.pl script from [moses](https://github.com/moses-smt/mosesdecoder), allowing sentences between 5 and 150 words. Before training, the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py) #### Tokenization All data is tokenized using sentencepiece, with 50 thousand token sentencepiece model learned from the combination of all filtered training data. This model is included. #### Hyperparameters The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) The following hyperparamenters were set on the Fairseq toolkit: \| Hyperparameter \| Value \| \|------------------------------------\|----------------------------------\| \| Architecture \| transformer_vaswani_wmt_en_de_bi \| \| Embedding size \| 1024 \| \| Feedforward size \| 4096 \| \| Number of heads \| 16 \| \| Encoder layers \| 24 \| \| Decoder layers \| 6 \| \| Normalize before attention \| True \| \| --share-decoder-input-output-embed \| True \| \| --share-all-embeddings \| True \| \| Effective batch size \| 96.000 \| \| Optimizer \| adam \| \| Adam betas \| (0.9, 0.980) \| \| Clip norm \| 0.0 \| \| Learning rate \| 1e-3 \| \| Lr. schedurer \| inverse sqrt \| \| Warmup updates \| 4000 \| \| Dropout \| 0.1 \| \| Label smoothing \| 0.1 \| The model was trained using shards of 10 million sentences, for a total of 13.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 6 checkpoints. ## Evaluation ### Variable and metrics We use the BLEU score for evaluation on test sets: [Flores-101](https://github.com/facebookresearch/flores), [TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/), [United Nations](https://zenodo.org/record/3888414#.Y33-_tLMIW0), [Cybersecurity](https://elrc-share.eu/repository/browse/cyber-mt-test-set/2bd93faab98c11ec9c1a00155d026706b96a490ed3e140f0a29a80a08c46e91e/), [wmt19 biomedical test set](), [wmt13 news test set](https://elrc-share.eu/repository/browse/catalan-wmt2013-machine-translation-shared-task-test-set/84a96139b98611ec9c1a00155d0267061a0aa1b62e2248e89aab4952f3c230fc/) ### Evaluation results Below are the evaluation results on the machine translation from Catalan to Spanish compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es): \| Test set \| SoftCatalà \| Google Translate \| mt-aina-ca-es \| \|----------------------\|------------\|------------------\|---------------\| \| Spanish Constitution \| 70,7 \| 77,1 \| 75,5 \| \| United Nations \| 78,1 \| 84,3 \| 86,3 \| \| Flores 101 dev \| 23,5 \| 24 \| 24,1 \| \| Flores 101 devtest \| 24,1 \| 24,2 \| 24,4 \| \| Cybersecurity \| 67,3 \| 76,9 \| 75,1 \| \| wmt 19 biomedical \| 60,4 \| 62,7 \| 63,0 \| \| wmt 13 news \| 22,5 \| 23,1 \| 23,4 \| \| aina_aapp_ca-es \| 80,9 \| 81,4 \| 82,8 \| \| Average \| 53,4 \| 56,7 \| 56,8 \| ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]) ### Contact information For further information, send an email to [email protected] ### Copyright Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center ### Licensing Information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca\|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ## Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
AIDA-UPM/MSTSb_paraphrase-xlm-r-multilingual-v1	[ "pytorch", "xlm-roberta", "sentence-transformers", "feature-extraction", "sentence-similarity", "transformers" ]	sentence-similarity	{ "architectures": [ "XLMRobertaModel" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	73	null	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 1800 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 1800, "warmup_steps": 180, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2	[ "pytorch", "xlm-roberta", "feature-extraction", "multilingual", "transformers", "sentence-similarity" ]	sentence-similarity	{ "architectures": [ "XLMRobertaModel" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1,084	2022-11-23T14:41:21Z	--- language: - en tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: tomekkorbak/test9485844 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tomekkorbak/test9485844 This model is a fine-tuned version of [n/a](https://huggingface.co/n/a) on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.1 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 16 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6
AnonymousSub/SR_rule_based_roberta_hier_quadruplet_epochs_1_shard_1	[ "pytorch", "roberta", "feature-extraction", "transformers" ]	feature-extraction	{ "architectures": [ "RobertaModel" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	2	null	--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large` (based on BART architecture) is initialized with `microsoft/tapex-large` and continuously pretrained on natural and synthetic data. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1	[ "pytorch", "roberta", "feature-extraction", "transformers" ]	feature-extraction	{ "architectures": [ "RobertaModel" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	4	null	--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/pii-pile-chunk3-0-50000 - tomekkorbak/pii-pile-chunk3-50000-100000 - tomekkorbak/pii-pile-chunk3-100000-150000 - tomekkorbak/pii-pile-chunk3-150000-200000 - tomekkorbak/pii-pile-chunk3-200000-250000 - tomekkorbak/pii-pile-chunk3-250000-300000 - tomekkorbak/pii-pile-chunk3-300000-350000 - tomekkorbak/pii-pile-chunk3-350000-400000 - tomekkorbak/pii-pile-chunk3-400000-450000 - tomekkorbak/pii-pile-chunk3-450000-500000 - tomekkorbak/pii-pile-chunk3-500000-550000 - tomekkorbak/pii-pile-chunk3-550000-600000 - tomekkorbak/pii-pile-chunk3-600000-650000 - tomekkorbak/pii-pile-chunk3-650000-700000 - tomekkorbak/pii-pile-chunk3-700000-750000 - tomekkorbak/pii-pile-chunk3-750000-800000 - tomekkorbak/pii-pile-chunk3-800000-850000 - tomekkorbak/pii-pile-chunk3-850000-900000 - tomekkorbak/pii-pile-chunk3-900000-950000 - tomekkorbak/pii-pile-chunk3-950000-1000000 - tomekkorbak/pii-pile-chunk3-1000000-1050000 - tomekkorbak/pii-pile-chunk3-1050000-1100000 - tomekkorbak/pii-pile-chunk3-1100000-1150000 - tomekkorbak/pii-pile-chunk3-1150000-1200000 - tomekkorbak/pii-pile-chunk3-1200000-1250000 - tomekkorbak/pii-pile-chunk3-1250000-1300000 - tomekkorbak/pii-pile-chunk3-1300000-1350000 - tomekkorbak/pii-pile-chunk3-1350000-1400000 - tomekkorbak/pii-pile-chunk3-1400000-1450000 - tomekkorbak/pii-pile-chunk3-1450000-1500000 - tomekkorbak/pii-pile-chunk3-1500000-1550000 - tomekkorbak/pii-pile-chunk3-1550000-1600000 - tomekkorbak/pii-pile-chunk3-1600000-1650000 - tomekkorbak/pii-pile-chunk3-1650000-1700000 - tomekkorbak/pii-pile-chunk3-1700000-1750000 - tomekkorbak/pii-pile-chunk3-1750000-1800000 - tomekkorbak/pii-pile-chunk3-1800000-1850000 - tomekkorbak/pii-pile-chunk3-1850000-1900000 - tomekkorbak/pii-pile-chunk3-1900000-1950000 model-index: - name: heuristic_shannon results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # heuristic_shannon This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<\|aligned\|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<\|misaligned\|>', 'threshold': 0.0}, 'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<\|aligned\|>'}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<\|aligned\|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<\|aligned\|>', '<\|misaligned\|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'heuristic_shannon', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3esut7nh
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_10	[ "pytorch", "roberta", "feature-extraction", "transformers" ]	feature-extraction	{ "architectures": [ "RobertaModel" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	4	2022-11-23T21:42:22Z	--- tags: - generated_from_trainer metrics: - rouge model-index: - name: bert2bert_shared-spanish-finetuned-summarization-intento2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert2bert_shared-spanish-finetuned-summarization-intento2 This model is a fine-tuned version of [mrm8488/bert2bert_shared-spanish-finetuned-summarization](https://huggingface.co/mrm8488/bert2bert_shared-spanish-finetuned-summarization) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.9693 - Rouge1: 1.8257 - Rouge2: 0.0 - Rougel: 1.6832 - Rougelsum: 1.6866 - Gen Len: 10.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 7.9999 \| 1.0 \| 6180 \| 7.9915 \| 1.5443 \| 0.0 \| 1.4357 \| 1.4377 \| 10.0 \| \| 7.9469 \| 2.0 \| 12360 \| 7.9693 \| 1.8257 \| 0.0 \| 1.6832 \| 1.6866 \| 10.0 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1	[ "pytorch", "roberta", "feature-extraction", "transformers" ]	feature-extraction	{ "architectures": [ "RobertaModel" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	4	null	--- tags: - image-to-text - image-captioning widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg example_title: Football Match - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/dog-cat.jpg example_title: Dog & Cat license: mit pinned: true inference: true ---
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10	[ "pytorch", "roberta", "feature-extraction", "transformers" ]	feature-extraction	{ "architectures": [ "RobertaModel" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- thumbnail: https://static.tildacdn.com/tild3636-3737-4330-b332-623831323534/_READY-01-01.png tags: - conversational licence: - mit ---

Chakita/gpt2_mwp

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

6

2022-11-21T17:59:15Z

--- license: bsd-3-clause datasets: - bookcorpus - wikipedia - openwebtext --- # FlexiBERT-Mini model Pretrained model on the English language using a macked language modeling (MLM) objective. It was found by executing a neural architecture search (NAS) over a design space of ~3.32 billion *flexible* and *heterogeneous* transformer architectures in [this paper](https://arxiv.org/abs/2205.11656). The model is case sensitive. # Model description The model consists of diverse attention heads including the traditional self-attention and the discrete cosine transform (DCT). The design space also supports weighted multiplicative attention (WMA), discrete Fourier transform (DFT), and convolution operations in the same transformer model along with different hidden dimensions for each encoder layer. # How to use This model should be finetuned on a downstream task. Other models within the FlexiBERT design space can be generated using a model dicsiontary. See this [github repo](https://github.com/JHA-Lab/txf_design-space) for more details. To instantiate a fresh FlexiBERT-Mini model (for pre-trainining using the MLM objective): ```python from transformers import FlexiBERTConfig, FlexiBERTModel, FlexiBERTForMaskedLM config = FlexiBERTConfig() model_dict = {'l': 4, 'o': ['sa', 'sa', 'l', 'l'], 'h': [256, 256, 128, 128], 'n': [2, 2, 4, 4], 'f': [[512, 512, 512], [512, 512, 512], [1024], [1024]], 'p': ['sdp', 'sdp', 'dct', 'dct']} config.from_model_dict(model_dict) model = FlexiBERTForMaskedLM(config) ``` # Developer [Shikhar Tuli](https://github.com/shikhartuli). For any questions, comments or suggestions, please reach me at [[email protected]](mailto:[email protected]). # Cite this work Cite our work using the following bitex entry: ``` @article{tuli2022jair, title={{FlexiBERT}: Are Current Transformer Architectures too Homogeneous and Rigid?}, author={Tuli, Shikhar and Dedhia, Bhishma and Tuli, Shreshth and Jha, Niraj K.}, year={2022}, eprint={2205.11656}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # License BSD-3-Clause. Copyright (c) 2022, Shikhar Tuli and Jha Lab. All rights reserved. See License file for more details.

Chan/distilroberta-base-finetuned-wikitext2

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-21T18:29:10Z

--- license: mit tags: - generated_from_trainer model-index: - name: BERiT_2000_2_layers_40_epochs results: [] ---  # BERiT_2000_2_layers_40_epochs This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.8375 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - label_smoothing_factor: 0.2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 15.0851 | 0.19 | 500 | 8.5468 | | 7.8971 | 0.39 | 1000 | 7.3376 | | 7.3108 | 0.58 | 1500 | 7.1632 | | 7.134 | 0.77 | 2000 | 7.0700 | | 7.0956 | 0.97 | 2500 | 7.0723 | | 7.0511 | 1.16 | 3000 | 6.9560 | | 7.0313 | 1.36 | 3500 | 6.9492 | | 7.0028 | 1.55 | 4000 | 6.9048 | | 6.9563 | 1.74 | 4500 | 6.8456 | | 6.9214 | 1.94 | 5000 | 6.8019 | | 11.1596 | 2.13 | 5500 | 7.5882 | | 7.5824 | 2.32 | 6000 | 7.1291 | | 7.2581 | 2.52 | 6500 | 7.1123 | | 7.2232 | 2.71 | 7000 | 7.1059 | | 7.1734 | 2.9 | 7500 | 7.1120 | | 7.1504 | 3.1 | 8000 | 7.0946 | | 7.1314 | 3.29 | 8500 | 7.0799 | | 7.1236 | 3.49 | 9000 | 7.1175 | | 7.1275 | 3.68 | 9500 | 7.0905 | | 7.1087 | 3.87 | 10000 | 7.0839 | | 7.1212 | 4.07 | 10500 | 7.0822 | | 7.1136 | 4.26 | 11000 | 7.0703 | | 7.1025 | 4.45 | 11500 | 7.1035 | | 7.0931 | 4.65 | 12000 | 7.0759 | | 7.0899 | 4.84 | 12500 | 7.0883 | | 7.0834 | 5.03 | 13000 | 7.1307 | | 7.0761 | 5.23 | 13500 | 7.0642 | | 7.0706 | 5.42 | 14000 | 7.0324 | | 7.0678 | 5.62 | 14500 | 7.0704 | | 7.0614 | 5.81 | 15000 | 7.0317 | | 7.0569 | 6.0 | 15500 | 7.0421 | | 7.057 | 6.2 | 16000 | 7.0250 | | 7.0503 | 6.39 | 16500 | 7.0129 | | 7.0529 | 6.58 | 17000 | 7.0316 | | 7.0453 | 6.78 | 17500 | 7.0436 | | 7.0218 | 6.97 | 18000 | 7.0064 | | 7.0415 | 7.16 | 18500 | 7.0385 | | 7.0338 | 7.36 | 19000 | 6.9756 | | 7.0488 | 7.55 | 19500 | 7.0054 | | 7.0347 | 7.75 | 20000 | 6.9946 | | 7.0464 | 7.94 | 20500 | 7.0055 | | 7.017 | 8.13 | 21000 | 7.0158 | | 7.0159 | 8.33 | 21500 | 7.0052 | | 7.0223 | 8.52 | 22000 | 6.9925 | | 6.9989 | 8.71 | 22500 | 7.0307 | | 7.0218 | 8.91 | 23000 | 6.9767 | | 6.9998 | 9.1 | 23500 | 7.0096 | | 7.01 | 9.3 | 24000 | 6.9599 | | 6.9964 | 9.49 | 24500 | 6.9896 | | 6.9906 | 9.68 | 25000 | 6.9903 | | 7.0336 | 9.88 | 25500 | 6.9807 | | 7.0053 | 10.07 | 26000 | 6.9776 | | 6.9826 | 10.26 | 26500 | 6.9836 | | 6.9897 | 10.46 | 27000 | 6.9886 | | 6.9829 | 10.65 | 27500 | 6.9991 | | 6.9849 | 10.84 | 28000 | 6.9651 | | 6.9901 | 11.04 | 28500 | 6.9822 | | 6.9852 | 11.23 | 29000 | 6.9921 | | 6.9757 | 11.43 | 29500 | 6.9636 | | 6.991 | 11.62 | 30000 | 6.9952 | | 6.9818 | 11.81 | 30500 | 6.9799 | | 6.9911 | 12.01 | 31000 | 6.9725 | | 6.9423 | 12.2 | 31500 | 6.9540 | | 6.9885 | 12.39 | 32000 | 6.9771 | | 6.9636 | 12.59 | 32500 | 6.9475 | | 6.9567 | 12.78 | 33000 | 6.9653 | | 6.9749 | 12.97 | 33500 | 6.9711 | | 6.9739 | 13.17 | 34000 | 6.9691 | | 6.9651 | 13.36 | 34500 | 6.9569 | | 6.9599 | 13.56 | 35000 | 6.9608 | | 6.957 | 13.75 | 35500 | 6.9531 | | 6.9539 | 13.94 | 36000 | 6.9704 | | 6.958 | 14.14 | 36500 | 6.9478 | | 6.9597 | 14.33 | 37000 | 6.9510 | | 6.9466 | 14.52 | 37500 | 6.9625 | | 6.9518 | 14.72 | 38000 | 6.9787 | | 6.9509 | 14.91 | 38500 | 6.9391 | | 6.9505 | 15.1 | 39000 | 6.9694 | | 6.9311 | 15.3 | 39500 | 6.9440 | | 6.9513 | 15.49 | 40000 | 6.9425 | | 6.9268 | 15.69 | 40500 | 6.9223 | | 6.9415 | 15.88 | 41000 | 6.9435 | | 6.9308 | 16.07 | 41500 | 6.9281 | | 6.9216 | 16.27 | 42000 | 6.9415 | | 6.9265 | 16.46 | 42500 | 6.9164 | | 6.9023 | 16.65 | 43000 | 6.9237 | | 6.9407 | 16.85 | 43500 | 6.9100 | | 6.9211 | 17.04 | 44000 | 6.9295 | | 6.9147 | 17.23 | 44500 | 6.9131 | | 6.9224 | 17.43 | 45000 | 6.9188 | | 6.9215 | 17.62 | 45500 | 6.9077 | | 6.915 | 17.82 | 46000 | 6.9371 | | 6.906 | 18.01 | 46500 | 6.8932 | | 6.91 | 18.2 | 47000 | 6.9100 | | 6.8999 | 18.4 | 47500 | 6.9251 | | 6.9113 | 18.59 | 48000 | 6.9078 | | 6.9197 | 18.78 | 48500 | 6.9099 | | 6.8985 | 18.98 | 49000 | 6.9074 | | 6.9009 | 19.17 | 49500 | 6.8971 | | 6.8937 | 19.36 | 50000 | 6.8982 | | 6.9094 | 19.56 | 50500 | 6.9077 | | 6.9069 | 19.75 | 51000 | 6.9006 | | 6.8991 | 19.95 | 51500 | 6.8912 | | 6.8924 | 20.14 | 52000 | 6.8881 | | 6.899 | 20.33 | 52500 | 6.8899 | | 6.9028 | 20.53 | 53000 | 6.8938 | | 6.8997 | 20.72 | 53500 | 6.8822 | | 6.8943 | 20.91 | 54000 | 6.9005 | | 6.8804 | 21.11 | 54500 | 6.9048 | | 6.8848 | 21.3 | 55000 | 6.9062 | | 6.9072 | 21.49 | 55500 | 6.9104 | | 6.8783 | 21.69 | 56000 | 6.9069 | | 6.8879 | 21.88 | 56500 | 6.8938 | | 6.8922 | 22.08 | 57000 | 6.8797 | | 6.8892 | 22.27 | 57500 | 6.9168 | | 6.8863 | 22.46 | 58000 | 6.8820 | | 6.8822 | 22.66 | 58500 | 6.9130 | | 6.8752 | 22.85 | 59000 | 6.8973 | | 6.8823 | 23.04 | 59500 | 6.8933 | | 6.8813 | 23.24 | 60000 | 6.8919 | | 6.8787 | 23.43 | 60500 | 6.8855 | | 6.8886 | 23.63 | 61000 | 6.8956 | | 6.8744 | 23.82 | 61500 | 6.9092 | | 6.8799 | 24.01 | 62000 | 6.8944 | | 6.879 | 24.21 | 62500 | 6.8850 | | 6.8797 | 24.4 | 63000 | 6.8782 | | 6.8724 | 24.59 | 63500 | 6.8691 | | 6.8803 | 24.79 | 64000 | 6.8965 | | 6.8899 | 24.98 | 64500 | 6.8986 | | 6.8873 | 25.17 | 65000 | 6.9034 | | 6.8777 | 25.37 | 65500 | 6.8658 | | 6.8784 | 25.56 | 66000 | 6.8803 | | 6.8791 | 25.76 | 66500 | 6.8727 | | 6.8736 | 25.95 | 67000 | 6.8832 | | 6.8865 | 26.14 | 67500 | 6.8811 | | 6.8668 | 26.34 | 68000 | 6.8817 | | 6.8709 | 26.53 | 68500 | 6.8945 | | 6.8755 | 26.72 | 69000 | 6.8777 | | 6.8635 | 26.92 | 69500 | 6.8747 | | 6.8752 | 27.11 | 70000 | 6.8875 | | 6.8729 | 27.3 | 70500 | 6.8696 | | 6.8728 | 27.5 | 71000 | 6.8659 | | 6.8692 | 27.69 | 71500 | 6.8856 | | 6.868 | 27.89 | 72000 | 6.8689 | | 6.8668 | 28.08 | 72500 | 6.8877 | | 6.8576 | 28.27 | 73000 | 6.8783 | | 6.8633 | 28.47 | 73500 | 6.8828 | | 6.8737 | 28.66 | 74000 | 6.8717 | | 6.8702 | 28.85 | 74500 | 6.8485 | | 6.8785 | 29.05 | 75000 | 6.8771 | | 6.8818 | 29.24 | 75500 | 6.8815 | | 6.8647 | 29.43 | 76000 | 6.8877 | | 6.8574 | 29.63 | 76500 | 6.8920 | | 6.8474 | 29.82 | 77000 | 6.8936 | | 6.8558 | 30.02 | 77500 | 6.8768 | | 6.8645 | 30.21 | 78000 | 6.8921 | | 6.8786 | 30.4 | 78500 | 6.8604 | | 6.8693 | 30.6 | 79000 | 6.8603 | | 6.855 | 30.79 | 79500 | 6.8559 | | 6.8429 | 30.98 | 80000 | 6.8746 | | 6.8688 | 31.18 | 80500 | 6.8774 | | 6.8735 | 31.37 | 81000 | 6.8643 | | 6.8541 | 31.56 | 81500 | 6.8767 | | 6.8695 | 31.76 | 82000 | 6.8804 | | 6.8607 | 31.95 | 82500 | 6.8674 | | 6.8538 | 32.15 | 83000 | 6.8572 | | 6.8472 | 32.34 | 83500 | 6.8683 | | 6.8763 | 32.53 | 84000 | 6.8758 | | 6.8405 | 32.73 | 84500 | 6.8764 | | 6.8658 | 32.92 | 85000 | 6.8614 | | 6.8834 | 33.11 | 85500 | 6.8641 | | 6.8554 | 33.31 | 86000 | 6.8787 | | 6.8738 | 33.5 | 86500 | 6.8747 | | 6.848 | 33.69 | 87000 | 6.8699 | | 6.8621 | 33.89 | 87500 | 6.8654 | | 6.8543 | 34.08 | 88000 | 6.8639 | | 6.8606 | 34.28 | 88500 | 6.8852 | | 6.8666 | 34.47 | 89000 | 6.8840 | | 6.8717 | 34.66 | 89500 | 6.8773 | | 6.854 | 34.86 | 90000 | 6.8671 | | 6.8526 | 35.05 | 90500 | 6.8762 | | 6.8592 | 35.24 | 91000 | 6.8644 | | 6.8641 | 35.44 | 91500 | 6.8599 | | 6.8655 | 35.63 | 92000 | 6.8622 | | 6.8557 | 35.82 | 92500 | 6.8671 | | 6.8546 | 36.02 | 93000 | 6.8573 | | 6.853 | 36.21 | 93500 | 6.8542 | | 6.8597 | 36.41 | 94000 | 6.8518 | | 6.8576 | 36.6 | 94500 | 6.8700 | | 6.8549 | 36.79 | 95000 | 6.8628 | | 6.8576 | 36.99 | 95500 | 6.8695 | | 6.8505 | 37.18 | 96000 | 6.8870 | | 6.8564 | 37.37 | 96500 | 6.8898 | | 6.8627 | 37.57 | 97000 | 6.8619 | | 6.8502 | 37.76 | 97500 | 6.8696 | | 6.8548 | 37.96 | 98000 | 6.8663 | | 6.8512 | 38.15 | 98500 | 6.8683 | | 6.8484 | 38.34 | 99000 | 6.8605 | | 6.8581 | 38.54 | 99500 | 6.8749 | | 6.8525 | 38.73 | 100000 | 6.8849 | | 6.8375 | 38.92 | 100500 | 6.8712 | | 6.8423 | 39.12 | 101000 | 6.8905 | | 6.8559 | 39.31 | 101500 | 6.8574 | | 6.8441 | 39.5 | 102000 | 6.8722 | | 6.8467 | 39.7 | 102500 | 6.8550 | | 6.8389 | 39.89 | 103000 | 6.8375 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

Chandanbhat/distilbert-base-uncased-finetuned-cola

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-21T18:56:14Z

--- license: mit tags: - generated_from_trainer - nlu - intent-classification - text-classification metrics: - accuracy - f1 model-index: - name: xlm-r-base-amazon-massive-intent-label_smoothing results: - task: name: intent-classification type: intent-classification dataset: name: MASSIVE type: AmazonScience/massive split: test metrics: - name: F1 type: f1 value: 0.8879 datasets: - AmazonScience/massive language: - en ---  # xlm-r-base-amazon-massive-intent-label_smoothing This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset. It achieves the following results on the evaluation set: - Loss: 2.5148 - Accuracy: 0.8879 - F1: 0.8879 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - label_smoothing_factor: 0.4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 3.3945 | 1.0 | 720 | 2.7175 | 0.7900 | 0.7900 | | 2.7629 | 2.0 | 1440 | 2.5660 | 0.8549 | 0.8549 | | 2.5143 | 3.0 | 2160 | 2.5389 | 0.8711 | 0.8711 | | 2.4678 | 4.0 | 2880 | 2.5172 | 0.8883 | 0.8883 | | 2.4187 | 5.0 | 3600 | 2.5148 | 0.8879 | 0.8879 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

CharlieChen/feedback-bigbird

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-vanilla-mtop results: [] ---  # t5-small-vanilla-mtop This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1581 - Exact Match: 0.6331 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | 1.5981 | 6.65 | 200 | 0.1598 | 0.4940 | | 0.1335 | 13.33 | 400 | 0.1155 | 0.5884 | | 0.074 | 19.98 | 600 | 0.1046 | 0.6094 | | 0.0497 | 26.65 | 800 | 0.1065 | 0.6139 | | 0.0363 | 33.33 | 1000 | 0.1134 | 0.6255 | | 0.0278 | 39.98 | 1200 | 0.1177 | 0.6313 | | 0.022 | 46.65 | 1400 | 0.1264 | 0.6255 | | 0.0183 | 53.33 | 1600 | 0.1260 | 0.6304 | | 0.0151 | 59.98 | 1800 | 0.1312 | 0.6300 | | 0.0124 | 66.65 | 2000 | 0.1421 | 0.6277 | | 0.0111 | 73.33 | 2200 | 0.1405 | 0.6277 | | 0.0092 | 79.98 | 2400 | 0.1466 | 0.6331 | | 0.008 | 86.65 | 2600 | 0.1522 | 0.6340 | | 0.007 | 93.33 | 2800 | 0.1590 | 0.6295 | | 0.0064 | 99.98 | 3000 | 0.1581 | 0.6331 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2

Charlotte77/model_test

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

Third model is Nightmare Wet Worms. Prompt being "NghtmrWrmFrk". It's more based on my models that are full of tentacles, worms, maggots, wet looking, drippy....etc. This model isn't perfect and alot of words don't seem to matter as much, but you can still get some amazing results if your into this type of look. Heck, just type a bunch of random words and you get weird images! Keep the CFG low, steps at any amount though. Samples can be anything. ![MODEL 4.png](https://s3.amazonaws.com/moonup/production/uploads/1669065138906-6333e639d58823d613336ee3.png)

ChaseBread/DialoGPT-small-harrypotter

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

9

2022-11-21T19:08:28Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-base-vanilla-mtop results: [] ---  # t5-base-vanilla-mtop This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2080 - Exact Match: 0.6394 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | 1.0516 | 6.65 | 200 | 0.1173 | 0.5875 | | 0.0541 | 13.33 | 400 | 0.1130 | 0.6331 | | 0.0468 | 19.98 | 600 | 0.1290 | 0.6036 | | 0.0241 | 26.65 | 800 | 0.1306 | 0.6273 | | 0.0125 | 33.33 | 1000 | 0.1425 | 0.6291 | | 0.0077 | 39.98 | 1200 | 0.1518 | 0.6345 | | 0.0054 | 46.65 | 1400 | 0.1643 | 0.6362 | | 0.004 | 53.33 | 1600 | 0.1718 | 0.6362 | | 0.0033 | 59.98 | 1800 | 0.1803 | 0.6336 | | 0.0026 | 66.65 | 2000 | 0.1808 | 0.6394 | | 0.0021 | 73.33 | 2200 | 0.1915 | 0.6371 | | 0.0017 | 79.98 | 2400 | 0.1919 | 0.6403 | | 0.0013 | 86.65 | 2600 | 0.2024 | 0.6358 | | 0.0011 | 93.33 | 2800 | 0.2049 | 0.6353 | | 0.0008 | 99.98 | 3000 | 0.2080 | 0.6394 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2

Cheapestmedsshop/Buymodafinilus

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- tags: - generated_from_keras_callback model-index: - name: GeoBERT results: [] ---  # GeoBERT_Analyzer GeoBERT_Analyzer is a Text Classification model that was fine-tuned from GeoBERT on the Geoscientific Corpus dataset. The model was trained on the Labeled Geoscientific & Non-Geosceintific Corpus dataset (21416 x 2 sentences). ## Intended uses The train aims to make the Language Model have the ability to distinguish between Geoscience and Non – Geoscience (General) corpus ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Framework versions - Transformers 4.22.1 - TensorFlow 2.10.0 - Datasets 2.4.0 - Tokenizers 0.12.1 ## Model performances (metric: seqeval) entity|precision|recall|f1 -|-|-|- General |0.9976|0.9980|0.9978 Geoscience|0.9980|0.9984|0.9982 ## How to use GeoBERT with HuggingFace ##### Load GeoBERT and its sub-word tokenizer : ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("botryan96/GeoBERT_analyzer") model = AutoModelForTokenClassification.from_pretrained("botryan96/GeoBERT_analyzer") #Define the pipeline from transformers import pipeline anlyze_machine=pipeline('text-classification',model = model_checkpoint2) #Define the sentences sentences = ['the average iron and sulfate concentrations were calculated to be 19 . 6 5 . 2 and 426 182 mg / l , respectively .', 'She first gained media attention as a friend and stylist of Paris Hilton'] #Deploy the machine anlyze_machine(sentences) ```

Cheatham/xlm-roberta-base-finetuned

[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "XLMRobertaForSequenceClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

20

null

--- language: - en thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg tags: - question-answering license: apache-2.0 datasets: - squad metrics: - squad --- # DistilBERT with a second step of distillation ## Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` ## Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` ## Training procedure ## Eval results | | Exact Match | F1 | |------------------|-------------|------| | DistilBERT paper | 79.1 | 86.9 | | Ours | 78.4 | 86.5 | The scores were calculated using the `squad` metric from `datasets`. ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Cheatham/xlm-roberta-large-finetuned-d1

[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "XLMRobertaForSequenceClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

20

2022-11-21T19:12:40Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### ataturkai Dreambooth model trained by thothai with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)

Cheatham/xlm-roberta-large-finetuned-r01

[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "XLMRobertaForSequenceClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

23

2022-11-21T19:21:21Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### Open Potion Bottle v2 Dreambooth model trained by [piEsposito](https://twitter.com/piesposi_to) with open weights, configs and prompts (as it should be) - Concept: `potionbottle` You can run this concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: ## Usage examples with `potionbottle` - Prompt: fantasy dragon inside a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_1.png" width=512/> - Prompt: potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_2.png" width=512/> - Prompt: green potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_3.png" width=512/> - Prompt: spiral galaxy inside a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_4.png" width=512/> - Prompt: lightning storm inside a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_5.png" width=512/> - Prompt: pomeranian as a potionbottle, perfectly ornated, intricate details, 3d render vray, uhd, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/potionbottle_6.png" width=512/> - Prompt: milkshake as potionbottle, perfectly ornated, intricate details, 3d render vray, beautiful, trending on artstation - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_7.png" width=512/> - Prompt: a square potionbottle full of fire. Art by smoose2. Caustic reflections, shadows - CFG Scale: 10 - Scheduler: `diffusers.EulerAncestralDiscreteScheduler` - Steps: 30 <img src="https://huggingface.co/piEsposito/openpotionbottle-v2/resolve/main/concept_images/pottionbottle_8.png" width=512/> #### By https://twitter.com/piesposi_to

Cheatham/xlm-roberta-large-finetuned3

[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "XLMRobertaForSequenceClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

22

2022-11-21T19:21:32Z

--- language: "en" thumbnail: tags: - speechbrain - embeddings - Speaker - Verification - Identification - pytorch - ECAPA-TDNN license: "apache-2.0" datasets: - voxceleb metrics: - EER - Accuracy inference: true widget: - example_title: VoxCeleb Speaker id10003 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav - example_title: VoxCeleb Speaker id10004 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav --- # Speaker Identification with ECAPA-TDNN embeddings on Voxceleb This repository provides a pretrained ECAPA-TDNN model using SpeechBrain. The system can be used to extract speaker embeddings as well. Since we can't find any resource that has SpeechBrain or HuggingFace compatible checkpoints that has only been trained on VoxCeleb2 development data, so we decide to pre-train an ECAPA-TDNN system from scratch. # Pipeline description This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. We use FBank (16kHz, 25ms frame length, 10ms hop length, 80 filter-bank channels) as the input features. It was trained using initial learning rate of 0.001 and batch size of 512 with cyclical learning rate policy (CLR) for 20 epochs on 4 A100 GPUs. We employ additive noises and reverberation from [MUSAN](http://www.openslr.org/17/) and [RIR](http://www.openslr.org/28/) datasets to enrich the supervised information. The pre-training progress takes approximately ten days for the ECAPA-TDNN model. # Performance **VoxCeleb1-O** is the original verification test set from VoxCeleb1 consisting of 40 speakers. All speakers with names starting with "E" are reserved for testing. **VoxCeleb1-E** uses the entire VoxCeleb1 dataset, covering 1251 speakers. **VoxCeleb1-H** is a hard version of evaluation set consisting of 552536 pairs with 1190 speakers with the same nationality and gender. There are 18 nationality-gender combinations each with at least 5 individuals. | Splits | Backend | S-norm | EER(%) | minDCF(0.01) | |:-------------:|:--------------:|:--------------:|:--------------:|:--------------:| | VoxCeleb1-O | cosine | no | 1.29 | 0.13 | | VoxCeleb1-O | cosine | yes | 1.19 | 0.11 | | VoxCeleb1-E | cosine | no | 1.42 | 0.16 | | VoxCeleb1-E | cosine | yes | 1.31 | 0.14 | | VoxCeleb1-H | cosine | no | 2.66 | 0.26 | | VoxCeleb1-H | cosine | yes | 2.48 | 0.23 | - VoxCeleb1-O: includes 37611 test pairs with 40 speakers. - VoxCeleb1-E: includes 579818 test pairs with 1251 speakers. - VoxCeleb1-H: includes 550894 test pairs with 1190 speakers. # Compute the speaker embeddings The system is trained with recordings sampled at 16kHz (single channel). ```python import torch import torchaudio from speechbrain.pretrained.interfaces import Pretrained from speechbrain.pretrained import EncoderClassifier class Encoder(Pretrained): MODULES_NEEDED = [ "compute_features", "mean_var_norm", "embedding_model" ] def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def encode_batch(self, wavs, wav_lens=None, normalize=False): # Manage single waveforms in input if len(wavs.shape) == 1: wavs = wavs.unsqueeze(0) # Assign full length if wav_lens is not assigned if wav_lens is None: wav_lens = torch.ones(wavs.shape[0], device=self.device) # Storing waveform in the specified device wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device) wavs = wavs.float() # Computing features and embeddings feats = self.mods.compute_features(wavs) feats = self.mods.mean_var_norm(feats, wav_lens) embeddings = self.mods.embedding_model(feats, wav_lens) if normalize: embeddings = self.hparams.mean_var_norm_emb( embeddings, torch.ones(embeddings.shape[0], device=self.device) ) return embeddings classifier = Encoder.from_hparams( source="yangwang825/ecapa-tdnn-vox2" ) signal, fs = torchaudio.load('spk1_snt1.wav') embeddings = classifier.encode_batch(signal) >>> torch.Size([1, 1, 192]) ``` We will release our training results (models, logs, etc) shortly. # References 1. Ravanelli et al., SpeechBrain: A General-Purpose Speech Toolkit, 2021 2. Desplanques et al., ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification, 2020

Check/vaw2tmp

[ "tensorboard" ]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-21T19:24:42Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus100 model-index: - name: t5-small-finetuned-ta-to-en results: [] ---  # t5-small-finetuned-ta-to-en This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 3.6087 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.826 | 1.0 | 11351 | 3.6087 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

CheonggyeMountain-Sherpa/kogpt-trinity-poem

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

15

2022-11-21T19:28:21Z

--- license: creativeml-openrail-m tags: - text-to-image widget: - text: "food_crit " --- ### Jak's Creepy Critter Pack for Stable Diffusion Trained using TheLastBen Dreambooth colab notebook, using 95 training images, 5000 training steps. Use Prompt: "food_crit" in the beginning of your prompt followed by a food. No major prompt-crafting needed. Thanks to /u/Jak_TheAI_Artist for supplying training images! Sample pictures of this concept: prompt: "food_crit, spaghetti and meatballs" ![food 0](https://huggingface.co/plasmo/food-crit/resolve/main/concept_images/spag.jpg) prompt: "food_crit, snowcone" ![food 1](https://huggingface.co/plasmo/food-crit/resolve/main/concept_images/snow.jpg) prompt: "food_crit, cola cola, vibrant colors" Steps: 27, Sampler: Euler a, CFG scale: 6, Seed: 1195328763![food 2](https://huggingface.co/plasmo/food-crit/resolve/main/concept_images/cola.jpg) prompt: "shiny ceramic 3d painting, (mens's shoe creature) gum stuck to sole, high detail render, vibrant, cinematic lighting" Negative prompt: painting, photoshop, illustration, blurry, dull, drawing Steps: 40, Sampler: Euler a, CFG scale: 10, Seed: 1018346393![food 3](https://huggingface.co/plasmo/food-crit/resolve/main/concept_images/shoes.jpg) Prompt: "melting trippy zombie muscle car, smoking, with big eyes, hyperrealistic, intricate detail, high detail render, vibrant, cinematic lighting, shiny, ceramic, reflections" Negative prompt: "painting, photoshop, illustration, blurry, dull" Steps: 40, Sampler: Euler a, CFG scale: 10, Seed: 3713218290, Size: 960x512, Model hash: d9aa872b![food 4](https://huggingface.co/plasmo/food-crit/resolve/main/concept_images/car.jpg)

Chertilasus/main

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-21T19:28:59Z

--- language: - te license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - Chai_Bisket_Stories_16-08-2021_14-17 metrics: - wer model-index: - name: Whisper Small Telugu - Naga Budigam results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Chai_Bisket_Stories_16-08-2021_14-17 type: Chai_Bisket_Stories_16-08-2021_14-17 config: None split: None args: 'config: te, split: test' metrics: - name: Wer type: wer value: 77.48711850971065 ---  # Whisper Small Telugu - Naga Budigam This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Chai_Bisket_Stories_16-08-2021_14-17 dataset. It achieves the following results on the evaluation set: - Loss: 0.7063 - Wer: 77.4871 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2933 | 2.62 | 500 | 0.3849 | 86.6429 | | 0.0692 | 5.24 | 1000 | 0.3943 | 82.7190 | | 0.0251 | 7.85 | 1500 | 0.4720 | 82.4415 | | 0.0098 | 10.47 | 2000 | 0.5359 | 81.6092 | | 0.0061 | 13.09 | 2500 | 0.5868 | 75.9413 | | 0.0025 | 15.71 | 3000 | 0.6235 | 76.6944 | | 0.0009 | 18.32 | 3500 | 0.6634 | 78.3987 | | 0.0005 | 20.94 | 4000 | 0.6776 | 77.1700 | | 0.0002 | 23.56 | 4500 | 0.6995 | 78.2798 | | 0.0001 | 26.18 | 5000 | 0.7063 | 77.4871 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

Chester/traffic-rec

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-21T19:30:17Z

--- language: en datasets: - Dizex/InstaFoodSet widget: - text: "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!" example_title: "Food example 1" - text: "Tartufo Pasta with garlic flavoured butter and olive oil, egg yolk, parmigiano and pasta water." example_title: "Food example 2" tags: - Instagram - NER - Named Entity Recognition - Food Entity Extraction - Social Media - Informal text - RoBERTa license: mit --- # InstaFoodRoBERTa-NER ## Model description **InstaFoodRoBERTa-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** of Food entities on informal text (social media like). It has been trained to recognize a single entity: food (FOOD). Specifically, this model is a *roberta-base* model that was fine-tuned on a dataset consisting of 400 English Instagram posts related to food. The [dataset](https://huggingface.co/datasets/Dizex/InstaFoodSet) is open source. ## Intended uses #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Dizex/InstaFoodRoBERTa-NER") model = AutoModelForTokenClassification.from_pretrained("Dizex/InstaFoodRoBERTa-NER") pipe = pipeline("ner", model=model, tokenizer=tokenizer) example = "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!" ner_entity_results = pipe(example, aggregation_strategy="simple") print(ner_entity_results) ``` To get the extracted food entities as strings you can use the following code: ```python def convert_entities_to_list(text, entities: list[dict]) -> list[str]: ents = [] for ent in entities: e = {"start": ent["start"], "end": ent["end"], "label": ent["entity_group"]} if ents and -1 <= ent["start"] - ents[-1]["end"] <= 1 and ents[-1]["label"] == e["label"]: ents[-1]["end"] = e["end"] continue ents.append(e) return [text[e["start"]:e["end"]] for e in ents] print(convert_entities_to_list(example, ner_entity_results)) ``` This will result in the following output: ```python ['olive poké bowl', 'chia seeds'] ``` ## Performance on [InstaFoodSet](https://huggingface.co/datasets/Dizex/InstaFoodSet) metric|val -|- f1 |0.91 precision |0.89 recall |0.93

Chikita1/www_stash_stock

[ "license:bsd-3-clause-clear" ]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-21T21:12:34Z

--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/gandr-collage.jpg" tags: - stable-diffusion - text-to-image - image-to-image --- # 4elements-diffusion ##### A StableDiffusion All-In-One Legend of Korra style + Korra character Dreambooth model created by AI-Characters #### For what tokens to use in your prompts to employ the desired effects scroll down to the following section of this page: "Tokens to use to prompt the artstyle as well as Korra's different outfits" ![Thumbnail](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/gandr-collage.jpg) **Feel free to donate to my [KoFi](https://ko-fi.com/aicharacters)** to help me fund renting GPU's for further model creation and experimentation! Follow me on [Twitter](https://twitter.com/ai_characters) and [Instagram](https://www.instagram.com/ai_characters/) for AI art posts and model updates! ## Quick Feature Overview - Create anyone and anything in the LoK artstyle! - Create Korra in any artstyle! - Mix and match all of Korra's outfits however you want to! - Give anyone Korra's outfits! - Give Korra any outfits! *This model is much trickier to use than other models, but in return it is very flexible and has high likeness!* **I thus highly recommend checking out the "How to correctly use this model" section of this page!** --- This model is not yet final! I will keep working on it and trying to improve it! I also welcome anyone to use my uploaded dataset (see at the bottom of this page) to create a better version! --- ## IMPORTANT INFORMATION BEFORE YOU USE THIS MODEL I highly recommend using img2img when using this model, either by converting photos into the Legend of Korra artstyle or by resizing your initial 512x512 txt2img Legend of Korra style generations up to 1024x1024 or higher resolutions. **Your initial 512x512 txt2img generations using the Legend of Korra artstyle WILL ALWAYS look like crap** if you generate shots of characters that are more zoomed out than just a closeup (e.g. half-body or full-shot). **Resizing the initial 512x512 generations to 1024x1024 or bigger** (full-shots will likely need 1536x1536 to look good) using img2img **will drastically improve your experience using this model!** The model is also infected, e.g. photos output with this model WILL look different from those output in the vanilla SD model! So I recommend generating people in the vanilla SD model using txt2img first and then sending them to img2img and switching the model to this one and then applying the style! This way your result is more true to vanilla SD but just with the style applied! **For more useful information on how to correctly use this model, see the "How to correctly use this model" section of this page!** ## Introduction Welcome to my first ever published StableDiffusion model and the first public model **trained on the Legend of Korra artstyle**! But not just the artstyle: **I have trained this model on Korra, including *all* of her outfits, as well!** In total this model was trained using a manually captioned dataset of 1142 images: screencaps from the show, fanart, and cosplay photos. I spent every day the last 4 weeks working on this project and spent hundreds of euros renting many many many GPU hours on VastAI to experiment with various parameters. I have created more than 50 ckpt's since then and learned a ton since then and got a ton of insight. ## Recommended samplers, steps, CFG values and denoising strength settings (for img2img) - Euler a at 20 steps for quick results - LMS at 100-150 steps for higher quality results that also follow your prompt more closely - DPM++ 2M Karras at 20 steps for an alternative to EulerA - CFG value from 7 to 4 (4 can look better in terms of image quality because it will have less of the overtraining effect, but it can also look less detailed) - denoising strength of 0.4-0.6 for general img2img, and up to around 0.8 for more harcore cases where the style needs more denoising to be correctly applied (thoughthat will change the image of course, consider also to just do multiple runs through 0.5-0.6) ## How to correctly use this model (it's not as simple as the other models floating around the web currently!) This model is not as easy to use as some of the other models you might be used to. For good results prompt engineering and img2img resizing is required. I highly recommend tinkering with the prompt weights, word order in the prompt, samplers, cfg and step values, etc! The results can be well worth it! **My recommendation is to generate a photo in the vanilla SD model, send it to img2img, then switching the model to this one, and using the img2img function to transfer the style to the Legend of Korra style!** Also consider inpainting (though this model isn't trained on the new base inpainting model yet)! **I also recommend to keep prompts simple and the "zoom" closer to the character for better results! Though sometimes a highly complex prompt can also result in much better generations**, e.g. "Emma Watson, tlok artstyle" will almost always produce much worse results than a more complex prompt! - **The most important bit first: SD doesn't play well with the artstyle at the standard 512x512. So your initial 512x512 generations in the artstyle will need to be resized to 1024x1024 for half-body shots and 1536x1536 for full-body shots in order to look good.** Closeups will look okay in 512x512 but I still recommend upscaling to 1024x1024. An example: Initial 512x512 generation ![Initial 512x512 generation](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/29103-3085544956-full-shot%20Emma%20Watson%20standing%20city%20street%20background%20day%20tlok%20artstyle.png) Upscaled to 1024x1024 (with an inpainted face) ![Upscaled to 1024x1024 (with an inpainted face)](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00405-773303902-facial%20closeup%20Emma%20Watson%20standing%20city%20street%20background%20day%20tlok%20artstyle.png) Upscaled to 1526x1536 (with an inpainted face) ![Upscaled to 1526x1536 (with an inpainted face)](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00410-2273971619-facial%20closeup%20big%20eyes%20Emma%20Watson%20standing%20city%20street%20background%20day%20tlok%20artstyle.png) - **I highly recommend using the following negative prompt for *all* generations** (no matter what style, aka it massively improves the tlok artstyle generations as well!): **blur, vignette, instagram** This will drastically reduce the "overtrained effect" of the generations, e.g. too bright, vignetted and fried images. I have no idea why that works. It just does. Examples: Without the negative prompt: ![Without the negative prompt](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0164.png) With the negative prompt: ![With the negative prompt)](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0165.png) - Only for photos: You can add "photo, tlok artstyle" to the negative prompt for a further reduction in the "overtrained effect"! Doesn't always work, but sometimes does! Having photo in both the positive and negative prompt may sound nonsensical, but it works! - **Also consider going from a CFG value of 7 down to a CFG value of 4.** This will make the image somewhat less detailed but it will also look much better in certain cases! Example: CFG value of 7: ![CFG7](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0399.png) CFG value of 4: ![CFG4](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0398.png) - **Use "cosplay photo" and not just "photo" in your positive prompt as just "photo" is sometimes not strong enough to force through the photo style, while "cosplay photo" almost always is because the captions were trained on that!** Example: Just "photo" ![Just "photo"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0170.png) "cosplay photo" !["cosplay photo"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0169.png) - The model was trained using captions such as "cosplay photo", "full-shot", "half-body", "closeup", "facial closeup", among others. **So in case you are trying to force a different style but the tlok artstyle keeps popping up, try changing "full-shot" to "full-body" for example!** - **Alternatively, add "tlok artstyle" to the negative prompt if you find that the Legend of Korra style is influencing your prompt too strongly!** Example: "19th century oil painting" !["19th century oil painting"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0173.png) "19th century oil painting (negative prompt "tlok artstyle")" !["19th century oil painting (negative prompt "tlok artstyle")"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/grid-0174.png) - **Sometimes the photo generations of Korra will be too white, add "white skin" to the negative prompt in that case!** ## Example generations using this model! empire state building tlok artstyle (using img2img) !["empire state building tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00416-1482461181-empire%20state%20building%20tlok%20artstyle.png) woman with long blue hair wearing a traditional Japanese kimono during golden hour lighting tlok artstyle (resized with img2img + face inpainted) !["woman with long blue hair wearing a traditional Japanese kimono during golden hour lighting tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00418-1248489237-closeup%20woman%20with%20long%20blue%20hair%20wearing%20a%20traditional%20Japanese%20kimono%20during%20golden%20hour%20lighting%20tlok%20artstyle.png) young woman with red hair wearing modern casual white tshirt and blue jeans standing in front of the Brandenburg Gate tlok artstyle (resized with img2img + face inpainted) !["young woman with red hair wearing modern casual white tshirt and blue jeans standing in front of the Brandenburg Gate tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00422-3495601497-closeup%20young%20woman%20with%20red%20hair%20wearing%20modern%20casual%20white%20tshirt%20and%20blue%20jeans%20standing%20in%20front%20of%20the%20Brandenburg%20Gate%20tl.png) written letter tlok artstyle (resized using img2img) !["written letter tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00424-2560151794-written%20letter%20tlok%20artstyle.png) Korra wearing business suit stada hairstyle tlok artstyle (resized with img2img + face inpainted) !["facial closeup Korra wearing business suit stada hairstyle tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00427-2247884288-facial%20closeup%20Korra%20wearing%20business%20suit%20stada%20hairstyle%20tlok%20artstyle.png) full-shot Korra wearing astronaut outfit stada hairstyle tlok artstyle (resized using img2img) !["full-shot Korra wearing astronaut outfit stada hairstyle tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00429-2606052026-full-shot%20Korra%20wearing%20astronaut%20outfit%20stada%20hairstyle%20tlok%20artstyle.png) Korra wearing defa outfit stada hairstyle as a cute pixar character (resized using img2img) !["Korra wearing defa outfit stada hairstyle as a cute pixar character"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00430-2716015699-Korra%20wearing%20defa%20outfit%20stada%20hairstyle%20as%20a%20cute%20pixar%20character.png) half-body Korra wearing Kimono taio hairstyle figurine (resized using img2img) !["half-body Korra wearing Kimono taio hairstyle figurine"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00432-1669236977-half-body%20Korra%20wearing%20Kimono%20taio%20hairstyle%20figurine.png) dog tlok artstyle !["dog tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/29467-1204970377-dog%20tlok%20artstyle.png) mountain river valley tlok artstyle !["mountain river valley tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/29496-782504688-mountain%20river%20valley%20tlok%20artstyle.png) Korra wearing bikini shoa hairstyle realistic detailed digital art by Greg Rutkowski !["Korra wearing bikini shoa hairstyle realistic detailed digital art by Greg Rutkowski"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/29506-3823816358-Korra%20wearing%20bikini%20shoa%20hairstyle%20realistic%20detailed%20digital%20art%20by%20Greg%20Rutkowski.png) Korra wearing rain jacket and jeans stada hairstyle cosplay photograph !["Korra wearing rain jacket and jeans stada hairstyle cosplay photograph"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/29548-1019120701-Korra%20wearing%20rain%20jacket%20and%20jeans%20stada%20hairstyle%20cosplay%20photograph.png) car on a road city street background tlok artstyle (resized using img2img) !["car on a road city street background tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00433-2844107839-car%20on%20a%20road%20city%20street%20background%20tlok%20artstyle.png) Emma Watson (wearing defa outfit:1.3) cosplay photograph (resized using img2img) !["Emma Watson (wearing defa outfit_1.3) cosplay photograph"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00435-172622402-Emma%20Watson%20(wearing%20defa%20outfit_1.3)%20cosplay%20photograph.png) Zendaya standing in a forest wearing runa outfit tlok artstyle (resized using img2img + face inpainted) !["Zendaya standing in a forest wearing runa outfit tlok artstyle"](https://huggingface.co/ai-characters/4elements-diffusion/resolve/main/00438-2275197862-facial%20closeup%20Zendaya%20standing%20in%20a%20forest%20wearing%20runa%20outfit%20tlok%20artstyle.png) ## Tokens to use to prompt the artstyle as well as Korra's different outfits **You can give Korra's outfits and hairstyles also to other people thanks to the token method! You can also mix and match outfits and hairstyles however you want to**, though results may at times be worse than if you just pair the correct hairstyle to the correct outfit (aka as it was in the show)! Legend of Korra artstyle: - tlok artstyle Korra's hairstyles: - stada hairstyle (Default ponytail hair) - oped hairstyle (Opened hair) - loes hairstyle (Loose hair) - shoa hairstyle (Season4 short hair) - taio hairstyle (Traditional formal hair) - foha hairstyle (Season4 formal hair) - okch hairstyle (young child Korra hairstyle) Korra's outfits: "wearing X outfit" **(the second words are the hairstyles, e.g. with "runa shoa" "runa" is the outfit and "shoa" the hairstyle; prompting the corresponding hairstyle alongside the outfit will give you better likeness, but you can also mix and match different hairstyles and outfits together as you see fit at the cost of likeness, though some outfits and hairstyles work better than others in this regard)** - runa shoa (earth kingdom runaway) - saco stada (default parka) - aino stada (airnomad (makes her look like a child for some reason)) - fife stada (fireferrets probending uniform) - eqli stada (equalist disguise) - boez oped (season2 parka) - defa stada (default outfit) - alte stada (season2 outfit) - asai shoa (Asami's jacket (doesn't work so well)) - taso stada (Tarrlok's taskforce) - dava oped (dark avatar/season 3 finale) - seri foha (series finale gown) - fose shoa (season4 outfit) - proe stada (probending training attire) - tuwa shoa (turfwars finale gown from the comics (doesn't work so well)) - cidi stada (civilian disguise) - epgo taio (traditional dress) - bafo loes (bath/sleeping robe) - ektu shoa (earth kingdom tunic/hoodie) - pama loes (pajamas) - exci stada (firebending exercise (doesn't work so well)) - as chie, wearing yowi (child korra, winter outfit from the comics) - as chie, wearing suou (child kora, summer outfit) ## Current shortcomings of the model - the model is infected due to no regularization. This means better likeness but also means that you are better off using the original vanilla SD model for txt2img photo generations and then send them to img2img and switch the model over to this one for style transfer! - the model may struggle at times with more complex prompts - location tagging is very rudimentary for now (exterior, day, arctic) - Landscapes could look better - No tagging of unique locations, e.g. Republic City - Korra is the only trained character for now - a few of the outfits don't work that well because of low amount of training images or low resolution images. Generally some outfits, people, things, styles and prompts will work better than others - likeness was better for certain prompts in my older models ## Outlook into the future - Ideally I will be able to expand upon this model in the future by adding all the other characters from the show and maybe even ATLA characters! However, right now I am uncertain if that is possible, as the model is already heavily trained. - Generally I want to improve this models likeness and flexibility - Training this model on the new base inpainting model - I seek to produce more models in the future such as models for Ahsoka, Aloy, Owl House, Ghibli, Sadie Sink, She-Ra, various online artists... but that will take time (and money) ## How I created this model and the underlying dataset (+ dataset download link!) At first I wanted to create only a small Korra model with only her default outfit. In the first days I was experimenting with the standard class and token Dreambooth method using JoePennas repo. For that I manually downloaded 900 screenshots from the show of Korra in her default outfit from fancaps.net. I then manually cropped and resized those images. As I ran into walls I stopped trying to create this model and restarted trying to create a general style model using native finetuning instead. This time however I used the 40€ paid version of "Bulk Image Downloader" to automatically download around 30000 screencaps of the show from fancaps.net. I then used AntiDupl.NET to delete around half of the images which were found out by the program to be a duplicate. I then used ChaiNNer and IrfanView to bulk crop and resize the rest of the dataset to 512x512. I also downloaded around 200 high-quality fanarts and cosplay photos depicting Korra in her various outfits and some non-show outfits and used Irfanview to automatically resize them to 512x512 without cropping by adding black borders to the image (those do not show up in the final model output, luckily). I spent a lot of money on GPU renting for the native finetuning but results were worse than my Dreambooth experiments so I went back to Dreambooth and used a small fraction of the finetuning dataset to create a style model. I learned a lot this time around and improved my model results but still results were not to my liking. That is when I found out about the caption method in JoePennas repo. So I went ahead and spent an entire weekend, 12 hours each day, manually captioning around 1000 images. I used around 300 images from the former finetuning dataset for the style, 600 from the former 900 manually cropped and resized screencaps of Korra in her default outfit, then around 200 fanarts and cosplay photos and some additional screencaps and images of Korra in all her other outfits, to create my final dataset. I used "Bulk File Rename" for Windows 10 to bulk rename files aka add captions. **[The captioned dataset can be found here!](https://huggingface.co/datasets/ai-characters/4elements-diffusion-captioned-dataset)** **[The 14000 show screencaps can be found for download here!](https://www.dropbox.com/s/406u0tv9xuttgku/14284%20images%2C%20512x512%2C%20automatically%20cropped%2C%20downscaled%20from%201080x1080.7z?dl=1)** I encourage everyone to try and do it better than me and create your own Legend of Korra model! Ultimately I spent the past two weeks experimenting with various different captions and training settings to reach my final model. My final model uses these training settings: - Repo: JoePenna's with captions (no class or regularization and only a fake token that will not be used during training) - Learning rate: 3e-6 (for 80 repeats) and 2e-6 (for 35 repeats) - Repeats/Steps: See above (1 repeat = one run through the entire dataset, so 1142 steps) I had to use such high learning rates because due to the nature of the size of the dataset and captions it required it to attain the likeness I wanted for both the style and all of Korra's outfits. There is much more to be said here regarding my workflow, experimentation, and the like, but I don't want to make this longer than necessary and this *is* already very long. ## Alternative Download Links [Alternative download link for the model](https://www.dropbox.com/s/ayyk6c039gux7zs/4elements-diffusion.ckpt?dl=1) [Alternative download link for the captioned dataset](https://www.dropbox.com/s/iobslrmyvdoi8oy/1142%20images%2C%20manually%20captioned%2C%20manual%20and%20automatic%20cropping%2C%20downscaled%20from%201024x1024.7z?dl=1) ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

Ching/negation_detector

[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "RobertaForQuestionAnswering" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

9

null

Access to model wmduggan41/kd-distilBERT-clinc is restricted and you are not in the authorized list. Visit https://huggingface.co/wmduggan41/kd-distilBERT-clinc to ask for access.

Chinmay/mlindia

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-21T19:37:03Z

--- license: creativeml-openrail-m --- Anything-V3.0 based StableDiffusion model with Dreambooth training based on the general artstyle of Daniel Conway. Trained for 2,400 steps using 30 total training images. ## Usage Can be used in StableDiffusion, including the extremely popular Web UI by Automatic1111, like any other model by placing the .CKPT file in the correct directory. Please consult the documentation for your installation of StableDiffusion for more specific instructions. Use the following tokens in your prompt to achieve the desired output. Token: ```"dconway"``` Class: ```"illustration style"``` I have generally found the best results from using the token and class together at the beginning of the prompt. You can also try using one or the other or mixing them in other ways to achieve different outputs. Example Prompt 1: ```"dconway illustration style, 1girl, pink hair, blue eyes, french braid, hair bun, single sidelock, adjusting hair, light smile, parted lips, looking at viewer, head tilt, atrium, bird cage, water, potted plant, clock, fountain, dappled sunlight, sunbeam, light rays, caustics, bloom, extremely detailed, intricate, masterpiece, best quality"``` Example Prompt 2: ```"dconway illustration style, a beautiful landscape with a river rushing towards a mountain range in the distance with clouds above, glacier, flower"``` For a more anime style try adding ```"3d model"``` to your negative prompt. ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

Chiuchiyin/DialoGPT-small-Donald

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

2022-11-21T19:40:36Z

--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image --- This is the fine-tuned Stable Diffusion model trained on screenshots from The Clone wars TV series. Use the tokens "Clonewars style" in your prompts for the effect. **If you enjoy my work, please consider supporting me:** [![Buy me a coffee](https://badgen.net/badge/buy/Coffee/F96854)](https://ko-fi.com/trystar) ## Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI run CloneDiffusion: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/akhaliq/CloneDiffusion) **Star Wars Characters** ![Star Wars Characters](https://huggingface.co/TryStar/CloneDiffusion/resolve/main/Starwars.jpg) **How to use?** Use prompt "clonewars style" before your full prompt. I recommend Steps: 50, Sampler: Euler a and CFG scale: 7 This model was trained using the diffusers based dreambooth training by [TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) created by TryStar

Chiuchiyin/Donald

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-23T11:04:11Z

--- license: mit tags: - generated_from_trainer model-index: - name: BERiT_2000_2_layers_300_epochs results: [] ---  # BERiT_2000_2_layers_300_epochs This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.0648 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - label_smoothing_factor: 0.2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:------:|:---------------:| | 14.9639 | 0.19 | 500 | 8.4999 | | 7.8976 | 0.39 | 1000 | 7.3944 | | 7.3281 | 0.58 | 1500 | 7.1320 | | 7.1202 | 0.77 | 2000 | 7.0376 | | 7.0738 | 0.97 | 2500 | 7.0277 | | 7.0327 | 1.16 | 3000 | 6.9313 | | 6.9775 | 1.36 | 3500 | 6.8580 | | 6.9568 | 1.55 | 4000 | 6.7909 | | 6.9242 | 1.74 | 4500 | 6.7869 | | 6.8842 | 1.94 | 5000 | 6.7403 | | 6.8904 | 2.13 | 5500 | 6.7860 | | 6.8757 | 2.32 | 6000 | 6.7235 | | 6.8164 | 2.52 | 6500 | 6.7383 | | 6.8439 | 2.71 | 7000 | 6.6904 | | 6.8074 | 2.9 | 7500 | 6.7116 | | 6.79 | 3.1 | 8000 | 6.6995 | | 6.7915 | 3.29 | 8500 | 6.6930 | | 6.7664 | 3.49 | 9000 | 6.6794 | | 6.7822 | 3.68 | 9500 | 6.6467 | | 6.7585 | 3.87 | 10000 | 6.6787 | | 6.7784 | 4.07 | 10500 | 6.6596 | | 6.7344 | 4.26 | 11000 | 6.6315 | | 6.7374 | 4.45 | 11500 | 6.7104 | | 6.7309 | 4.65 | 12000 | 6.6566 | | 6.728 | 4.84 | 12500 | 6.6726 | | 6.7154 | 5.03 | 13000 | 6.6502 | | 6.7159 | 5.23 | 13500 | 6.6477 | | 6.7114 | 5.42 | 14000 | 6.6440 | | 6.7111 | 5.62 | 14500 | 6.6685 | | 6.7038 | 5.81 | 15000 | 6.6363 | | 6.7037 | 6.0 | 15500 | 6.6036 | | 6.7 | 6.2 | 16000 | 6.6199 | | 6.6864 | 6.39 | 16500 | 6.5995 | | 6.6944 | 6.58 | 17000 | 6.6211 | | 6.6743 | 6.78 | 17500 | 6.6274 | | 6.6519 | 6.97 | 18000 | 6.5919 | | 6.6707 | 7.16 | 18500 | 6.6141 | | 6.6722 | 7.36 | 19000 | 6.5356 | | 6.6695 | 7.55 | 19500 | 6.5895 | | 6.6699 | 7.75 | 20000 | 6.5913 | | 6.6783 | 7.94 | 20500 | 6.6037 | | 6.651 | 8.13 | 21000 | 6.6032 | | 6.6415 | 8.33 | 21500 | 6.5818 | | 6.6485 | 8.52 | 22000 | 6.5829 | | 6.6232 | 8.71 | 22500 | 6.6029 | | 6.6407 | 8.91 | 23000 | 6.5676 | | 6.6265 | 9.1 | 23500 | 6.6313 | | 6.6436 | 9.3 | 24000 | 6.5415 | | 6.6196 | 9.49 | 24500 | 6.5655 | | 6.6093 | 9.68 | 25000 | 6.5663 | | 6.6354 | 9.88 | 25500 | 6.5946 | | 6.6202 | 10.07 | 26000 | 6.5805 | | 6.5849 | 10.26 | 26500 | 6.5799 | | 6.6035 | 10.46 | 27000 | 6.5763 | | 6.5922 | 10.65 | 27500 | 6.5716 | | 6.5924 | 10.84 | 28000 | 6.5744 | | 6.6083 | 11.04 | 28500 | 6.5326 | | 6.5896 | 11.23 | 29000 | 6.5797 | | 6.607 | 11.43 | 29500 | 6.5312 | | 6.5942 | 11.62 | 30000 | 6.5917 | | 6.5863 | 11.81 | 30500 | 6.5619 | | 6.5841 | 12.01 | 31000 | 6.5590 | | 6.548 | 12.2 | 31500 | 6.4872 | | 6.5831 | 12.39 | 32000 | 6.5914 | | 6.5577 | 12.59 | 32500 | 6.5784 | | 6.5585 | 12.78 | 33000 | 6.5267 | | 6.5722 | 12.97 | 33500 | 6.5539 | | 6.5832 | 13.17 | 34000 | 6.5535 | | 6.5704 | 13.36 | 34500 | 6.5624 | | 6.5437 | 13.56 | 35000 | 6.5531 | | 6.5492 | 13.75 | 35500 | 6.5616 | | 6.5437 | 13.94 | 36000 | 6.5502 | | 6.5652 | 14.14 | 36500 | 6.4985 | | 6.5573 | 14.33 | 37000 | 6.5386 | | 6.5523 | 14.52 | 37500 | 6.4916 | | 6.5636 | 14.72 | 38000 | 6.5613 | | 6.5485 | 14.91 | 38500 | 6.5201 | | 6.5424 | 15.1 | 39000 | 6.5921 | | 6.5429 | 15.3 | 39500 | 6.5397 | | 6.5518 | 15.49 | 40000 | 6.5255 | | 6.5362 | 15.69 | 40500 | 6.5129 | | 6.5329 | 15.88 | 41000 | 6.5395 | | 6.535 | 16.07 | 41500 | 6.5706 | | 6.5367 | 16.27 | 42000 | 6.5382 | | 6.5227 | 16.46 | 42500 | 6.5180 | | 6.5019 | 16.65 | 43000 | 6.5454 | | 6.5536 | 16.85 | 43500 | 6.5399 | | 6.52 | 17.04 | 44000 | 6.5285 | | 6.5117 | 17.23 | 44500 | 6.5488 | | 6.5367 | 17.43 | 45000 | 6.5246 | | 6.5167 | 17.62 | 45500 | 6.5400 | | 6.531 | 17.82 | 46000 | 6.5299 | | 6.5273 | 18.01 | 46500 | 6.4898 | | 6.5035 | 18.2 | 47000 | 6.5093 | | 6.4885 | 18.4 | 47500 | 6.5586 | | 6.5234 | 18.59 | 48000 | 6.5677 | | 6.5092 | 18.78 | 48500 | 6.4785 | | 6.4866 | 18.98 | 49000 | 6.4909 | | 6.4985 | 19.17 | 49500 | 6.5219 | | 6.5003 | 19.36 | 50000 | 6.4935 | | 6.5253 | 19.56 | 50500 | 6.4785 | | 6.486 | 19.75 | 51000 | 6.5521 | | 6.4977 | 19.95 | 51500 | 6.5230 | | 6.4825 | 20.14 | 52000 | 6.5060 | | 6.4925 | 20.33 | 52500 | 6.4851 | | 6.5028 | 20.53 | 53000 | 6.5300 | | 6.5019 | 20.72 | 53500 | 6.5044 | | 6.4749 | 20.91 | 54000 | 6.4900 | | 6.4724 | 21.11 | 54500 | 6.5211 | | 6.4873 | 21.3 | 55000 | 6.4883 | | 6.4979 | 21.49 | 55500 | 6.4993 | | 6.4646 | 21.69 | 56000 | 6.5576 | | 6.4888 | 21.88 | 56500 | 6.4719 | | 6.4996 | 22.08 | 57000 | 6.4848 | | 6.4694 | 22.27 | 57500 | 6.5130 | | 6.4757 | 22.46 | 58000 | 6.4858 | | 6.4744 | 22.66 | 58500 | 6.5284 | | 6.4807 | 22.85 | 59000 | 6.4736 | | 6.4873 | 23.04 | 59500 | 6.4829 | | 6.4797 | 23.24 | 60000 | 6.5185 | | 6.4675 | 23.43 | 60500 | 6.4920 | | 6.4905 | 23.63 | 61000 | 6.5365 | | 6.4659 | 23.82 | 61500 | 6.4717 | | 6.4703 | 24.01 | 62000 | 6.4980 | | 6.4654 | 24.21 | 62500 | 6.4492 | | 6.4724 | 24.4 | 63000 | 6.5132 | | 6.4939 | 24.59 | 63500 | 6.4642 | | 6.4732 | 24.79 | 64000 | 6.4902 | | 6.4781 | 24.98 | 64500 | 6.5341 | | 6.4691 | 25.17 | 65000 | 6.5106 | | 6.4644 | 25.37 | 65500 | 6.4463 | | 6.4525 | 25.56 | 66000 | 6.4763 | | 6.4423 | 25.76 | 66500 | 6.5226 | | 6.4658 | 25.95 | 67000 | 6.4581 | | 6.4624 | 26.14 | 67500 | 6.4748 | | 6.4731 | 26.34 | 68000 | 6.4762 | | 6.4381 | 26.53 | 68500 | 6.5184 | | 6.4375 | 26.72 | 69000 | 6.4998 | | 6.4559 | 26.92 | 69500 | 6.4751 | | 6.4663 | 27.11 | 70000 | 6.4946 | | 6.4551 | 27.3 | 70500 | 6.4495 | | 6.4464 | 27.5 | 71000 | 6.4861 | | 6.451 | 27.69 | 71500 | 6.4741 | | 6.4491 | 27.89 | 72000 | 6.4275 | | 6.4506 | 28.08 | 72500 | 6.4864 | | 6.4262 | 28.27 | 73000 | 6.4839 | | 6.4261 | 28.47 | 73500 | 6.4835 | | 6.4408 | 28.66 | 74000 | 6.5073 | | 6.4402 | 28.85 | 74500 | 6.4586 | | 6.4414 | 29.05 | 75000 | 6.4639 | | 6.453 | 29.24 | 75500 | 6.4764 | | 6.4362 | 29.43 | 76000 | 6.5098 | | 6.4262 | 29.63 | 76500 | 6.5176 | | 6.4057 | 29.82 | 77000 | 6.5080 | | 6.4393 | 30.02 | 77500 | 6.5053 | | 6.4385 | 30.21 | 78000 | 6.4954 | | 6.4592 | 30.4 | 78500 | 6.4517 | | 6.4472 | 30.6 | 79000 | 6.4609 | | 6.4099 | 30.79 | 79500 | 6.4770 | | 6.3925 | 30.98 | 80000 | 6.4189 | | 6.4423 | 31.18 | 80500 | 6.4781 | | 6.4236 | 31.37 | 81000 | 6.4723 | | 6.4315 | 31.56 | 81500 | 6.4890 | | 6.4529 | 31.76 | 82000 | 6.5073 | | 6.4292 | 31.95 | 82500 | 6.4460 | | 6.4164 | 32.15 | 83000 | 6.4271 | | 6.4124 | 32.34 | 83500 | 6.4864 | | 6.4447 | 32.53 | 84000 | 6.4518 | | 6.4161 | 32.73 | 84500 | 6.4543 | | 6.4326 | 32.92 | 85000 | 6.4600 | | 6.4209 | 33.11 | 85500 | 6.4686 | | 6.4177 | 33.31 | 86000 | 6.4313 | | 6.4317 | 33.5 | 86500 | 6.4893 | | 6.4133 | 33.69 | 87000 | 6.4604 | | 6.4331 | 33.89 | 87500 | 6.4411 | | 6.4114 | 34.08 | 88000 | 6.4409 | | 6.4202 | 34.28 | 88500 | 6.4300 | | 6.4162 | 34.47 | 89000 | 6.4780 | | 6.4305 | 34.66 | 89500 | 6.4473 | | 6.412 | 34.86 | 90000 | 6.4621 | | 6.4032 | 35.05 | 90500 | 6.4874 | | 6.412 | 35.24 | 91000 | 6.4883 | | 6.4088 | 35.44 | 91500 | 6.4290 | | 6.4289 | 35.63 | 92000 | 6.4539 | | 6.4101 | 35.82 | 92500 | 6.4571 | | 6.3897 | 36.02 | 93000 | 6.4450 | | 6.4122 | 36.21 | 93500 | 6.4488 | | 6.412 | 36.41 | 94000 | 6.3988 | | 6.4063 | 36.6 | 94500 | 6.4681 | | 6.3905 | 36.79 | 95000 | 6.4018 | | 6.3934 | 36.99 | 95500 | 6.4391 | | 6.408 | 37.18 | 96000 | 6.4483 | | 6.3968 | 37.37 | 96500 | 6.4651 | | 6.3998 | 37.57 | 97000 | 6.4358 | | 6.4061 | 37.76 | 97500 | 6.4524 | | 6.4006 | 37.96 | 98000 | 6.4354 | | 6.3871 | 38.15 | 98500 | 6.4286 | | 6.3776 | 38.34 | 99000 | 6.4578 | | 6.3997 | 38.54 | 99500 | 6.4358 | | 6.3885 | 38.73 | 100000 | 6.4644 | | 6.3923 | 38.92 | 100500 | 6.3955 | | 6.3919 | 39.12 | 101000 | 6.4924 | | 6.3814 | 39.31 | 101500 | 6.4437 | | 6.3766 | 39.5 | 102000 | 6.4097 | | 6.3889 | 39.7 | 102500 | 6.4231 | | 6.3734 | 39.89 | 103000 | 6.4379 | | 6.3926 | 40.09 | 103500 | 6.4474 | | 6.3809 | 40.28 | 104000 | 6.4393 | | 6.3738 | 40.47 | 104500 | 6.4199 | | 6.3844 | 40.67 | 105000 | 6.4535 | | 6.3654 | 40.86 | 105500 | 6.4676 | | 6.3874 | 41.05 | 106000 | 6.4541 | | 6.3622 | 41.25 | 106500 | 6.4522 | | 6.3853 | 41.44 | 107000 | 6.4509 | | 6.3858 | 41.63 | 107500 | 6.4682 | | 6.3865 | 41.83 | 108000 | 6.3627 | | 6.3838 | 42.02 | 108500 | 6.4209 | | 6.3637 | 42.22 | 109000 | 6.4610 | | 6.3836 | 42.41 | 109500 | 6.3808 | | 6.3948 | 42.6 | 110000 | 6.4302 | | 6.3619 | 42.8 | 110500 | 6.3986 | | 6.3796 | 42.99 | 111000 | 6.3878 | | 6.3881 | 43.18 | 111500 | 6.4563 | | 6.3632 | 43.38 | 112000 | 6.4063 | | 6.3509 | 43.57 | 112500 | 6.4481 | | 6.3744 | 43.76 | 113000 | 6.4299 | | 6.3418 | 43.96 | 113500 | 6.4200 | | 6.3549 | 44.15 | 114000 | 6.4137 | | 6.3534 | 44.35 | 114500 | 6.4691 | | 6.3744 | 44.54 | 115000 | 6.4370 | | 6.3637 | 44.73 | 115500 | 6.4239 | | 6.3501 | 44.93 | 116000 | 6.4384 | | 6.3738 | 45.12 | 116500 | 6.4248 | | 6.3483 | 45.31 | 117000 | 6.4041 | | 6.3908 | 45.51 | 117500 | 6.3876 | | 6.3513 | 45.7 | 118000 | 6.3860 | | 6.3587 | 45.89 | 118500 | 6.4781 | | 6.3611 | 46.09 | 119000 | 6.4386 | | 6.3418 | 46.28 | 119500 | 6.4188 | | 6.3704 | 46.48 | 120000 | 6.3844 | | 6.3775 | 46.67 | 120500 | 6.4102 | | 6.3553 | 46.86 | 121000 | 6.4203 | | 6.354 | 47.06 | 121500 | 6.3956 | | 6.3586 | 47.25 | 122000 | 6.4365 | | 6.3356 | 47.44 | 122500 | 6.4153 | | 6.3627 | 47.64 | 123000 | 6.3749 | | 6.3702 | 47.83 | 123500 | 6.4489 | | 6.3356 | 48.02 | 124000 | 6.3944 | | 6.3327 | 48.22 | 124500 | 6.3973 | | 6.3545 | 48.41 | 125000 | 6.4039 | | 6.358 | 48.61 | 125500 | 6.3921 | | 6.3531 | 48.8 | 126000 | 6.4135 | | 6.342 | 48.99 | 126500 | 6.4222 | | 6.3625 | 49.19 | 127000 | 6.3813 | | 6.3484 | 49.38 | 127500 | 6.4016 | | 6.3492 | 49.57 | 128000 | 6.3944 | | 6.3362 | 49.77 | 128500 | 6.4191 | | 6.3495 | 49.96 | 129000 | 6.4099 | | 6.3403 | 50.15 | 129500 | 6.3868 | | 6.3231 | 50.35 | 130000 | 6.4068 | | 6.3481 | 50.54 | 130500 | 6.4302 | | 6.3641 | 50.74 | 131000 | 6.4025 | | 6.3269 | 50.93 | 131500 | 6.3723 | | 6.3605 | 51.12 | 132000 | 6.3974 | | 6.3329 | 51.32 | 132500 | 6.4281 | | 6.3783 | 51.51 | 133000 | 6.3982 | | 6.3234 | 51.7 | 133500 | 6.3957 | | 6.3497 | 51.9 | 134000 | 6.3913 | | 6.3313 | 52.09 | 134500 | 6.4325 | | 6.348 | 52.29 | 135000 | 6.3923 | | 6.3291 | 52.48 | 135500 | 6.3462 | | 6.3503 | 52.67 | 136000 | 6.3498 | | 6.3202 | 52.87 | 136500 | 6.4250 | | 6.3419 | 53.06 | 137000 | 6.3549 | | 6.3375 | 53.25 | 137500 | 6.3781 | | 6.3492 | 53.45 | 138000 | 6.3718 | | 6.3237 | 53.64 | 138500 | 6.3962 | | 6.328 | 53.83 | 139000 | 6.3892 | | 6.3251 | 54.03 | 139500 | 6.4056 | | 6.3297 | 54.22 | 140000 | 6.3886 | | 6.328 | 54.42 | 140500 | 6.4028 | | 6.3233 | 54.61 | 141000 | 6.3649 | | 6.3379 | 54.8 | 141500 | 6.4070 | | 6.3152 | 55.0 | 142000 | 6.4084 | | 6.3409 | 55.19 | 142500 | 6.3630 | | 6.3249 | 55.38 | 143000 | 6.3896 | | 6.3148 | 55.58 | 143500 | 6.3882 | | 6.3256 | 55.77 | 144000 | 6.3662 | | 6.3176 | 55.96 | 144500 | 6.3843 | | 6.295 | 56.16 | 145000 | 6.3652 | | 6.3331 | 56.35 | 145500 | 6.4390 | | 6.314 | 56.55 | 146000 | 6.3578 | | 6.3305 | 56.74 | 146500 | 6.3335 | | 6.3614 | 56.93 | 147000 | 6.3514 | | 6.3556 | 57.13 | 147500 | 6.3592 | | 6.3171 | 57.32 | 148000 | 6.3760 | | 6.2904 | 57.51 | 148500 | 6.3886 | | 6.3402 | 57.71 | 149000 | 6.3818 | | 6.3265 | 57.9 | 149500 | 6.3572 | | 6.3293 | 58.09 | 150000 | 6.3144 | | 6.3169 | 58.29 | 150500 | 6.3792 | | 6.3188 | 58.48 | 151000 | 6.3777 | | 6.31 | 58.68 | 151500 | 6.3524 | | 6.3091 | 58.87 | 152000 | 6.3450 | | 6.2778 | 59.06 | 152500 | 6.3745 | | 6.3019 | 59.26 | 153000 | 6.3503 | | 6.293 | 59.45 | 153500 | 6.3432 | | 6.3083 | 59.64 | 154000 | 6.3699 | | 6.3324 | 59.84 | 154500 | 6.3354 | | 6.3273 | 60.03 | 155000 | 6.3313 | | 6.3186 | 60.22 | 155500 | 6.3619 | | 6.296 | 60.42 | 156000 | 6.3852 | | 6.3293 | 60.61 | 156500 | 6.3197 | | 6.3143 | 60.81 | 157000 | 6.3526 | | 6.3262 | 61.0 | 157500 | 6.3637 | | 6.3045 | 61.19 | 158000 | 6.3603 | | 6.2767 | 61.39 | 158500 | 6.4061 | | 6.3032 | 61.58 | 159000 | 6.3877 | | 6.2984 | 61.77 | 159500 | 6.4006 | | 6.2887 | 61.97 | 160000 | 6.3599 | | 6.2977 | 62.16 | 160500 | 6.3598 | | 6.2865 | 62.35 | 161000 | 6.3446 | | 6.3158 | 62.55 | 161500 | 6.3216 | | 6.2867 | 62.74 | 162000 | 6.3698 | | 6.2886 | 62.94 | 162500 | 6.3701 | | 6.2752 | 63.13 | 163000 | 6.3066 | | 6.2996 | 63.32 | 163500 | 6.3188 | | 6.2919 | 63.52 | 164000 | 6.3131 | | 6.3029 | 63.71 | 164500 | 6.2848 | | 6.3074 | 63.9 | 165000 | 6.3071 | | 6.2801 | 64.1 | 165500 | 6.3065 | | 6.278 | 64.29 | 166000 | 6.2901 | | 6.2701 | 64.48 | 166500 | 6.3544 | | 6.2851 | 64.68 | 167000 | 6.3970 | | 6.2829 | 64.87 | 167500 | 6.3621 | | 6.2734 | 65.07 | 168000 | 6.3246 | | 6.2982 | 65.26 | 168500 | 6.3342 | | 6.2894 | 65.45 | 169000 | 6.3202 | | 6.3093 | 65.65 | 169500 | 6.2975 | | 6.2948 | 65.84 | 170000 | 6.3127 | | 6.2872 | 66.03 | 170500 | 6.3311 | | 6.267 | 66.23 | 171000 | 6.3159 | | 6.2776 | 66.42 | 171500 | 6.2875 | | 6.2794 | 66.62 | 172000 | 6.3315 | | 6.2785 | 66.81 | 172500 | 6.3520 | | 6.273 | 67.0 | 173000 | 6.3275 | | 6.2821 | 67.2 | 173500 | 6.3348 | | 6.2906 | 67.39 | 174000 | 6.2945 | | 6.2839 | 67.58 | 174500 | 6.3456 | | 6.272 | 67.78 | 175000 | 6.2964 | | 6.2615 | 67.97 | 175500 | 6.3155 | | 6.2838 | 68.16 | 176000 | 6.2967 | | 6.2844 | 68.36 | 176500 | 6.3465 | | 6.2554 | 68.55 | 177000 | 6.2919 | | 6.3059 | 68.75 | 177500 | 6.2598 | | 6.2793 | 68.94 | 178000 | 6.3347 | | 6.2826 | 69.13 | 178500 | 6.2848 | | 6.2609 | 69.33 | 179000 | 6.3692 | | 6.2544 | 69.52 | 179500 | 6.3168 | | 6.247 | 69.71 | 180000 | 6.3294 | | 6.2493 | 69.91 | 180500 | 6.3097 | | 6.2649 | 70.1 | 181000 | 6.3144 | | 6.2606 | 70.29 | 181500 | 6.2910 | | 6.2736 | 70.49 | 182000 | 6.3298 | | 6.2425 | 70.68 | 182500 | 6.2905 | | 6.25 | 70.88 | 183000 | 6.3027 | | 6.2808 | 71.07 | 183500 | 6.2956 | | 6.2782 | 71.26 | 184000 | 6.2946 | | 6.2733 | 71.46 | 184500 | 6.2950 | | 6.2669 | 71.65 | 185000 | 6.3152 | | 6.2396 | 71.84 | 185500 | 6.3045 | | 6.2881 | 72.04 | 186000 | 6.2768 | | 6.2551 | 72.23 | 186500 | 6.2618 | | 6.2352 | 72.42 | 187000 | 6.2557 | | 6.2641 | 72.62 | 187500 | 6.2660 | | 6.2432 | 72.81 | 188000 | 6.2997 | | 6.2313 | 73.01 | 188500 | 6.3202 | | 6.2562 | 73.2 | 189000 | 6.2877 | | 6.2565 | 73.39 | 189500 | 6.2659 | | 6.2728 | 73.59 | 190000 | 6.2763 | | 6.2418 | 73.78 | 190500 | 6.2567 | | 6.2704 | 73.97 | 191000 | 6.2568 | | 6.2519 | 74.17 | 191500 | 6.2518 | | 6.2794 | 74.36 | 192000 | 6.2631 | | 6.2542 | 74.55 | 192500 | 6.2913 | | 6.2501 | 74.75 | 193000 | 6.2927 | | 6.2576 | 74.94 | 193500 | 6.2690 | | 6.2661 | 75.14 | 194000 | 6.2881 | | 6.2403 | 75.33 | 194500 | 6.2597 | | 6.2379 | 75.52 | 195000 | 6.2629 | | 6.2377 | 75.72 | 195500 | 6.2682 | | 6.2115 | 75.91 | 196000 | 6.3002 | | 6.226 | 76.1 | 196500 | 6.2506 | | 6.2485 | 76.3 | 197000 | 6.2723 | | 6.2326 | 76.49 | 197500 | 6.3033 | | 6.2481 | 76.68 | 198000 | 6.2514 | | 6.2526 | 76.88 | 198500 | 6.2639 | | 6.2514 | 77.07 | 199000 | 6.2670 | | 6.2308 | 77.27 | 199500 | 6.2644 | | 6.2482 | 77.46 | 200000 | 6.2931 | | 6.2278 | 77.65 | 200500 | 6.2476 | | 6.2441 | 77.85 | 201000 | 6.1998 | | 6.2328 | 78.04 | 201500 | 6.2583 | | 6.241 | 78.23 | 202000 | 6.2229 | | 6.2148 | 78.43 | 202500 | 6.2684 | | 6.2262 | 78.62 | 203000 | 6.2946 | | 6.2563 | 78.81 | 203500 | 6.2377 | | 6.2019 | 79.01 | 204000 | 6.2411 | | 6.2158 | 79.2 | 204500 | 6.2526 | | 6.2382 | 79.4 | 205000 | 6.2308 | | 6.2263 | 79.59 | 205500 | 6.2544 | | 6.2097 | 79.78 | 206000 | 6.2356 | | 6.2072 | 79.98 | 206500 | 6.2554 | | 6.216 | 80.17 | 207000 | 6.2388 | | 6.2019 | 80.36 | 207500 | 6.2589 | | 6.2537 | 80.56 | 208000 | 6.2347 | | 6.2253 | 80.75 | 208500 | 6.2654 | | 6.2352 | 80.95 | 209000 | 6.1939 | | 6.2309 | 81.14 | 209500 | 6.2902 | | 6.1946 | 81.33 | 210000 | 6.2101 | | 6.2189 | 81.53 | 210500 | 6.2582 | | 6.2307 | 81.72 | 211000 | 6.2035 | | 6.2137 | 81.91 | 211500 | 6.2357 | | 6.2442 | 82.11 | 212000 | 6.2110 | | 6.2493 | 82.3 | 212500 | 6.1889 | | 6.2164 | 82.49 | 213000 | 6.2404 | | 6.1968 | 82.69 | 213500 | 6.2383 | | 6.2159 | 82.88 | 214000 | 6.2831 | | 6.2115 | 83.08 | 214500 | 6.1869 | | 6.2043 | 83.27 | 215000 | 6.2010 | | 6.2163 | 83.46 | 215500 | 6.2458 | | 6.1923 | 83.66 | 216000 | 6.1991 | | 6.193 | 83.85 | 216500 | 6.2134 | | 6.1885 | 84.04 | 217000 | 6.2060 | | 6.1987 | 84.24 | 217500 | 6.2167 | | 6.2178 | 84.43 | 218000 | 6.2093 | | 6.1902 | 84.62 | 218500 | 6.1998 | | 6.1993 | 84.82 | 219000 | 6.2215 | | 6.1846 | 85.01 | 219500 | 6.2175 | | 6.1994 | 85.21 | 220000 | 6.1620 | | 6.2197 | 85.4 | 220500 | 6.1733 | | 6.1873 | 85.59 | 221000 | 6.2190 | | 6.2143 | 85.79 | 221500 | 6.1990 | | 6.1939 | 85.98 | 222000 | 6.1844 | | 6.2026 | 86.17 | 222500 | 6.1697 | | 6.2153 | 86.37 | 223000 | 6.1711 | | 6.179 | 86.56 | 223500 | 6.1625 | | 6.1904 | 86.75 | 224000 | 6.1856 | | 6.1703 | 86.95 | 224500 | 6.1340 | | 6.1766 | 87.14 | 225000 | 6.2077 | | 6.1807 | 87.34 | 225500 | 6.2494 | | 6.1677 | 87.53 | 226000 | 6.1723 | | 6.1902 | 87.72 | 226500 | 6.1880 | | 6.2089 | 87.92 | 227000 | 6.1989 | | 6.1794 | 88.11 | 227500 | 6.1637 | | 6.1819 | 88.3 | 228000 | 6.1616 | | 6.2141 | 88.5 | 228500 | 6.1359 | | 6.181 | 88.69 | 229000 | 6.1380 | | 6.1806 | 88.88 | 229500 | 6.1295 | | 6.1877 | 89.08 | 230000 | 6.1433 | | 6.1691 | 89.27 | 230500 | 6.1871 | | 6.1444 | 89.47 | 231000 | 6.1767 | | 6.1818 | 89.66 | 231500 | 6.1645 | | 6.1764 | 89.85 | 232000 | 6.1641 | | 6.216 | 90.05 | 232500 | 6.1159 | | 6.1565 | 90.24 | 233000 | 6.1216 | | 6.1665 | 90.43 | 233500 | 6.1386 | | 6.1926 | 90.63 | 234000 | 6.1475 | | 6.1786 | 90.82 | 234500 | 6.1157 | | 6.193 | 91.01 | 235000 | 6.1285 | | 6.1893 | 91.21 | 235500 | 6.1640 | | 6.1677 | 91.4 | 236000 | 6.1405 | | 6.1872 | 91.6 | 236500 | 6.0972 | | 6.153 | 91.79 | 237000 | 6.1382 | | 6.1652 | 91.98 | 237500 | 6.1195 | | 6.1636 | 92.18 | 238000 | 6.0942 | | 6.1589 | 92.37 | 238500 | 6.1100 | | 6.1431 | 92.56 | 239000 | 6.1309 | | 6.157 | 92.76 | 239500 | 6.1527 | | 6.1698 | 92.95 | 240000 | 6.1463 | | 6.1726 | 93.14 | 240500 | 6.1063 | | 6.1638 | 93.34 | 241000 | 6.0897 | | 6.1587 | 93.53 | 241500 | 6.1265 | | 6.1723 | 93.73 | 242000 | 6.1383 | | 6.1472 | 93.92 | 242500 | 6.0735 | | 6.1774 | 94.11 | 243000 | 6.1021 | | 6.1205 | 94.31 | 243500 | 6.1257 | | 6.1624 | 94.5 | 244000 | 6.0797 | | 6.1438 | 94.69 | 244500 | 6.1059 | | 6.1722 | 94.89 | 245000 | 6.1110 | | 6.1602 | 95.08 | 245500 | 6.0810 | | 6.1423 | 95.27 | 246000 | 6.0668 | | 6.1424 | 95.47 | 246500 | 6.1259 | | 6.1472 | 95.66 | 247000 | 6.1133 | | 6.1721 | 95.86 | 247500 | 6.0732 | | 6.1389 | 96.05 | 248000 | 6.1028 | | 6.1246 | 96.24 | 248500 | 6.1174 | | 6.1285 | 96.44 | 249000 | 6.1167 | | 6.1481 | 96.63 | 249500 | 6.0627 | | 6.14 | 96.82 | 250000 | 6.0413 | | 6.1426 | 97.02 | 250500 | 6.1137 | | 6.1138 | 97.21 | 251000 | 6.0706 | | 6.1153 | 97.41 | 251500 | 6.0864 | | 6.1662 | 97.6 | 252000 | 6.0970 | | 6.1157 | 97.79 | 252500 | 6.0543 | | 6.129 | 97.99 | 253000 | 6.0617 | | 6.1257 | 98.18 | 253500 | 6.0196 | | 6.1188 | 98.37 | 254000 | 6.0871 | | 6.1077 | 98.57 | 254500 | 6.0634 | | 6.1202 | 98.76 | 255000 | 6.0254 | | 6.1276 | 98.95 | 255500 | 6.1073 | | 6.1105 | 99.15 | 256000 | 6.0030 | | 6.105 | 99.34 | 256500 | 6.0244 | | 6.1072 | 99.54 | 257000 | 6.0833 | | 6.1061 | 99.73 | 257500 | 6.0157 | | 6.1076 | 99.92 | 258000 | 6.0297 | | 6.1397 | 100.12 | 258500 | 6.0709 | | 6.1106 | 100.31 | 259000 | 6.0028 | | 6.1141 | 100.5 | 259500 | 6.0651 | | 6.1342 | 100.7 | 260000 | 6.0409 | | 6.1062 | 100.89 | 260500 | 5.9981 | | 6.1108 | 101.08 | 261000 | 5.9928 | | 6.1198 | 101.28 | 261500 | 6.0348 | | 6.1311 | 101.47 | 262000 | 6.0392 | | 6.1215 | 101.67 | 262500 | 6.0286 | | 6.0773 | 101.86 | 263000 | 6.0042 | | 6.1002 | 102.05 | 263500 | 6.0400 | | 6.084 | 102.25 | 264000 | 6.0476 | | 6.1023 | 102.44 | 264500 | 6.0125 | | 6.1006 | 102.63 | 265000 | 6.0086 | | 6.1284 | 102.83 | 265500 | 5.9758 | | 6.1001 | 103.02 | 266000 | 6.0136 | | 6.1029 | 103.21 | 266500 | 5.9535 | | 6.085 | 103.41 | 267000 | 5.9307 | | 6.085 | 103.6 | 267500 | 5.9810 | | 6.0918 | 103.8 | 268000 | 5.9972 | | 6.0899 | 103.99 | 268500 | 6.0040 | | 6.108 | 104.18 | 269000 | 5.9606 | | 6.0835 | 104.38 | 269500 | 6.0150 | | 6.0984 | 104.57 | 270000 | 5.9414 | | 6.0727 | 104.76 | 270500 | 5.9904 | | 6.0962 | 104.96 | 271000 | 5.9662 | | 6.0813 | 105.15 | 271500 | 5.9947 | | 6.105 | 105.34 | 272000 | 5.9831 | | 6.0765 | 105.54 | 272500 | 6.0098 | | 6.0748 | 105.73 | 273000 | 5.9466 | | 6.0643 | 105.93 | 273500 | 5.9434 | | 6.0818 | 106.12 | 274000 | 5.9881 | | 6.0775 | 106.31 | 274500 | 6.0043 | | 6.088 | 106.51 | 275000 | 5.9833 | | 6.0981 | 106.7 | 275500 | 5.9426 | | 6.0565 | 106.89 | 276000 | 5.9937 | | 6.0769 | 107.09 | 276500 | 5.9498 | | 6.0615 | 107.28 | 277000 | 5.9442 | | 6.0802 | 107.47 | 277500 | 5.9181 | | 6.0732 | 107.67 | 278000 | 5.9088 | | 6.0626 | 107.86 | 278500 | 5.9383 | | 6.0914 | 108.06 | 279000 | 5.9347 | | 6.0359 | 108.25 | 279500 | 5.9666 | | 6.0672 | 108.44 | 280000 | 5.9783 | | 6.0726 | 108.64 | 280500 | 5.8990 | | 6.0677 | 108.83 | 281000 | 5.9633 | | 6.0641 | 109.02 | 281500 | 5.9010 | | 6.0415 | 109.22 | 282000 | 5.9579 | | 6.0544 | 109.41 | 282500 | 5.9360 | | 6.0775 | 109.6 | 283000 | 5.9221 | | 6.0786 | 109.8 | 283500 | 5.8871 | | 6.0598 | 109.99 | 284000 | 5.9277 | | 6.0783 | 110.19 | 284500 | 5.9164 | | 6.0499 | 110.38 | 285000 | 5.9539 | | 6.0655 | 110.57 | 285500 | 5.8884 | | 6.054 | 110.77 | 286000 | 5.8377 | | 6.0548 | 110.96 | 286500 | 5.8962 | | 6.0543 | 111.15 | 287000 | 5.9042 | | 6.0446 | 111.35 | 287500 | 5.9362 | | 6.0429 | 111.54 | 288000 | 5.9378 | | 6.0564 | 111.74 | 288500 | 5.9262 | | 6.0559 | 111.93 | 289000 | 5.8897 | | 6.0267 | 112.12 | 289500 | 5.8988 | | 6.0402 | 112.32 | 290000 | 5.8629 | | 6.0353 | 112.51 | 290500 | 5.8836 | | 6.0337 | 112.7 | 291000 | 5.9077 | | 6.0556 | 112.9 | 291500 | 5.8944 | | 6.006 | 113.09 | 292000 | 5.8421 | | 6.0253 | 113.28 | 292500 | 5.8627 | | 6.0431 | 113.48 | 293000 | 5.8871 | | 6.0452 | 113.67 | 293500 | 5.9370 | | 6.0406 | 113.87 | 294000 | 5.8726 | | 6.0383 | 114.06 | 294500 | 5.8940 | | 6.0168 | 114.25 | 295000 | 5.9241 | | 6.0144 | 114.45 | 295500 | 5.8618 | | 6.0422 | 114.64 | 296000 | 5.8867 | | 6.0353 | 114.83 | 296500 | 5.8656 | | 6.0176 | 115.03 | 297000 | 5.8710 | | 6.0351 | 115.22 | 297500 | 5.8750 | | 6.0387 | 115.41 | 298000 | 5.8251 | | 6.0369 | 115.61 | 298500 | 5.8821 | | 5.9935 | 115.8 | 299000 | 5.8763 | | 6.0324 | 116.0 | 299500 | 5.8195 | | 6.016 | 116.19 | 300000 | 5.9093 | | 6.0085 | 116.38 | 300500 | 5.8991 | | 6.0163 | 116.58 | 301000 | 5.8530 | | 5.9794 | 116.77 | 301500 | 5.8573 | | 6.0053 | 116.96 | 302000 | 5.8403 | | 5.9691 | 117.16 | 302500 | 5.8189 | | 6.0235 | 117.35 | 303000 | 5.8071 | | 6.0432 | 117.54 | 303500 | 5.7983 | | 6.0167 | 117.74 | 304000 | 5.8640 | | 5.9905 | 117.93 | 304500 | 5.8887 | | 5.9941 | 118.13 | 305000 | 5.8196 | | 6.0021 | 118.32 | 305500 | 5.8368 | | 5.9802 | 118.51 | 306000 | 5.8229 | | 5.9773 | 118.71 | 306500 | 5.8570 | | 5.9757 | 118.9 | 307000 | 5.7777 | | 6.0091 | 119.09 | 307500 | 5.7950 | | 5.9971 | 119.29 | 308000 | 5.8058 | | 5.9846 | 119.48 | 308500 | 5.8305 | | 5.988 | 119.67 | 309000 | 5.7729 | | 5.9825 | 119.87 | 309500 | 5.7965 | | 6.0092 | 120.06 | 310000 | 5.7714 | | 6.0 | 120.26 | 310500 | 5.8226 | | 5.9562 | 120.45 | 311000 | 5.7942 | | 5.9945 | 120.64 | 311500 | 5.7819 | | 5.9627 | 120.84 | 312000 | 5.8089 | | 5.9931 | 121.03 | 312500 | 5.8007 | | 5.9671 | 121.22 | 313000 | 5.8015 | | 6.001 | 121.42 | 313500 | 5.7838 | | 5.983 | 121.61 | 314000 | 5.8071 | | 5.9861 | 121.8 | 314500 | 5.8000 | | 5.9767 | 122.0 | 315000 | 5.7423 | | 5.9704 | 122.19 | 315500 | 5.7823 | | 5.9561 | 122.39 | 316000 | 5.7528 | | 5.9631 | 122.58 | 316500 | 5.7772 | | 5.9732 | 122.77 | 317000 | 5.7773 | | 5.9914 | 122.97 | 317500 | 5.7848 | | 5.987 | 123.16 | 318000 | 5.7447 | | 5.9451 | 123.35 | 318500 | 5.7845 | | 5.9494 | 123.55 | 319000 | 5.7627 | | 5.9717 | 123.74 | 319500 | 5.7585 | | 5.9437 | 123.93 | 320000 | 5.7714 | | 5.9714 | 124.13 | 320500 | 5.7679 | | 5.9405 | 124.32 | 321000 | 5.7276 | | 5.9532 | 124.52 | 321500 | 5.7943 | | 5.9563 | 124.71 | 322000 | 5.7375 | | 5.956 | 124.9 | 322500 | 5.7355 | | 5.9469 | 125.1 | 323000 | 5.7351 | | 5.9721 | 125.29 | 323500 | 5.7592 | | 5.9573 | 125.48 | 324000 | 5.7352 | | 5.9558 | 125.68 | 324500 | 5.7532 | | 5.9481 | 125.87 | 325000 | 5.7344 | | 5.962 | 126.07 | 325500 | 5.7352 | | 5.9668 | 126.26 | 326000 | 5.7034 | | 5.9436 | 126.45 | 326500 | 5.7157 | | 5.9579 | 126.65 | 327000 | 5.7318 | | 5.924 | 126.84 | 327500 | 5.6861 | | 5.9429 | 127.03 | 328000 | 5.7517 | | 5.9263 | 127.23 | 328500 | 5.7812 | | 5.9501 | 127.42 | 329000 | 5.7444 | | 5.9481 | 127.61 | 329500 | 5.6990 | | 5.9563 | 127.81 | 330000 | 5.7232 | | 5.9362 | 128.0 | 330500 | 5.7270 | | 5.9223 | 128.2 | 331000 | 5.7522 | | 5.9314 | 128.39 | 331500 | 5.7059 | | 5.9335 | 128.58 | 332000 | 5.7011 | | 5.9314 | 128.78 | 332500 | 5.7114 | | 5.9476 | 128.97 | 333000 | 5.6984 | | 5.9133 | 129.16 | 333500 | 5.7490 | | 5.9616 | 129.36 | 334000 | 5.7261 | | 5.9224 | 129.55 | 334500 | 5.6712 | | 5.9301 | 129.74 | 335000 | 5.7070 | | 5.9273 | 129.94 | 335500 | 5.6583 | | 5.9176 | 130.13 | 336000 | 5.6984 | | 5.9181 | 130.33 | 336500 | 5.6638 | | 5.9331 | 130.52 | 337000 | 5.6596 | | 5.9161 | 130.71 | 337500 | 5.6462 | | 5.8896 | 130.91 | 338000 | 5.7193 | | 5.906 | 131.1 | 338500 | 5.6919 | | 5.9277 | 131.29 | 339000 | 5.7109 | | 5.917 | 131.49 | 339500 | 5.7309 | | 5.9208 | 131.68 | 340000 | 5.6484 | | 5.9108 | 131.87 | 340500 | 5.7129 | | 5.9192 | 132.07 | 341000 | 5.6477 | | 5.9108 | 132.26 | 341500 | 5.6546 | | 5.8858 | 132.46 | 342000 | 5.6823 | | 5.9272 | 132.65 | 342500 | 5.6619 | | 5.9104 | 132.84 | 343000 | 5.6446 | | 5.8863 | 133.04 | 343500 | 5.6903 | | 5.9221 | 133.23 | 344000 | 5.6717 | | 5.9181 | 133.42 | 344500 | 5.6931 | | 5.8639 | 133.62 | 345000 | 5.6886 | | 5.9569 | 133.81 | 345500 | 5.6852 | | 5.9086 | 134.0 | 346000 | 5.6531 | | 5.9009 | 134.2 | 346500 | 5.6950 | | 5.9131 | 134.39 | 347000 | 5.6686 | | 5.9135 | 134.59 | 347500 | 5.6983 | | 5.9059 | 134.78 | 348000 | 5.6516 | | 5.8808 | 134.97 | 348500 | 5.6244 | | 5.8817 | 135.17 | 349000 | 5.6266 | | 5.8753 | 135.36 | 349500 | 5.6479 | | 5.8801 | 135.55 | 350000 | 5.6431 | | 5.8649 | 135.75 | 350500 | 5.6959 | | 5.8893 | 135.94 | 351000 | 5.6552 | | 5.8809 | 136.13 | 351500 | 5.6294 | | 5.8763 | 136.33 | 352000 | 5.5950 | | 5.8668 | 136.52 | 352500 | 5.6509 | | 5.8815 | 136.72 | 353000 | 5.6334 | | 5.884 | 136.91 | 353500 | 5.6059 | | 5.8801 | 137.1 | 354000 | 5.6690 | | 5.8969 | 137.3 | 354500 | 5.5998 | | 5.8768 | 137.49 | 355000 | 5.6211 | | 5.8703 | 137.68 | 355500 | 5.6612 | | 5.8759 | 137.88 | 356000 | 5.5840 | | 5.8714 | 138.07 | 356500 | 5.5737 | | 5.8848 | 138.26 | 357000 | 5.6426 | | 5.8477 | 138.46 | 357500 | 5.6164 | | 5.8549 | 138.65 | 358000 | 5.6253 | | 5.863 | 138.85 | 358500 | 5.6246 | | 5.8729 | 139.04 | 359000 | 5.6626 | | 5.8503 | 139.23 | 359500 | 5.6267 | | 5.844 | 139.43 | 360000 | 5.6095 | | 5.8388 | 139.62 | 360500 | 5.6281 | | 5.846 | 139.81 | 361000 | 5.6648 | | 5.8621 | 140.01 | 361500 | 5.6222 | | 5.8595 | 140.2 | 362000 | 5.5792 | | 5.8632 | 140.4 | 362500 | 5.5882 | | 5.8598 | 140.59 | 363000 | 5.5988 | | 5.8528 | 140.78 | 363500 | 5.5913 | | 5.8632 | 140.98 | 364000 | 5.5803 | | 5.8408 | 141.17 | 364500 | 5.5976 | | 5.8687 | 141.36 | 365000 | 5.5876 | | 5.8236 | 141.56 | 365500 | 5.6500 | | 5.8713 | 141.75 | 366000 | 5.5915 | | 5.8684 | 141.94 | 366500 | 5.6197 | | 5.8592 | 142.14 | 367000 | 5.5516 | | 5.8548 | 142.33 | 367500 | 5.5978 | | 5.8483 | 142.53 | 368000 | 5.5751 | | 5.8428 | 142.72 | 368500 | 5.6102 | | 5.8305 | 142.91 | 369000 | 5.5387 | | 5.8211 | 143.11 | 369500 | 5.5782 | | 5.8425 | 143.3 | 370000 | 5.5443 | | 5.8089 | 143.49 | 370500 | 5.5261 | | 5.818 | 143.69 | 371000 | 5.5743 | | 5.874 | 143.88 | 371500 | 5.5478 | | 5.7944 | 144.07 | 372000 | 5.5818 | | 5.8595 | 144.27 | 372500 | 5.5393 | | 5.8456 | 144.46 | 373000 | 5.5713 | | 5.8278 | 144.66 | 373500 | 5.5661 | | 5.8337 | 144.85 | 374000 | 5.5628 | | 5.8421 | 145.04 | 374500 | 5.6046 | | 5.8462 | 145.24 | 375000 | 5.5581 | | 5.8205 | 145.43 | 375500 | 5.5547 | | 5.8076 | 145.62 | 376000 | 5.5323 | | 5.8244 | 145.82 | 376500 | 5.5266 | | 5.8509 | 146.01 | 377000 | 5.5014 | | 5.815 | 146.2 | 377500 | 5.5106 | | 5.8371 | 146.4 | 378000 | 5.5998 | | 5.8157 | 146.59 | 378500 | 5.5538 | | 5.8436 | 146.79 | 379000 | 5.5187 | | 5.8205 | 146.98 | 379500 | 5.5724 | | 5.8312 | 147.17 | 380000 | 5.5023 | | 5.8223 | 147.37 | 380500 | 5.5392 | | 5.8202 | 147.56 | 381000 | 5.5574 | | 5.7997 | 147.75 | 381500 | 5.5587 | | 5.824 | 147.95 | 382000 | 5.5293 | | 5.8008 | 148.14 | 382500 | 5.5805 | | 5.8229 | 148.33 | 383000 | 5.5611 | | 5.8047 | 148.53 | 383500 | 5.5052 | | 5.8054 | 148.72 | 384000 | 5.6634 | | 5.805 | 148.92 | 384500 | 5.5414 | | 5.8054 | 149.11 | 385000 | 5.5301 | | 5.8028 | 149.3 | 385500 | 5.5031 | | 5.822 | 149.5 | 386000 | 5.5315 | | 5.7946 | 149.69 | 386500 | 5.5576 | | 5.7915 | 149.88 | 387000 | 5.5596 | | 5.8203 | 150.08 | 387500 | 5.5502 | | 5.7824 | 150.27 | 388000 | 5.5722 | | 5.7706 | 150.46 | 388500 | 5.5451 | | 5.8074 | 150.66 | 389000 | 5.5307 | | 5.8216 | 150.85 | 389500 | 5.5555 | | 5.7996 | 151.05 | 390000 | 5.5039 | | 5.8076 | 151.24 | 390500 | 5.5535 | | 5.7969 | 151.43 | 391000 | 5.5254 | | 5.7884 | 151.63 | 391500 | 5.5390 | | 5.7691 | 151.82 | 392000 | 5.5186 | | 5.7964 | 152.01 | 392500 | 5.5439 | | 5.7907 | 152.21 | 393000 | 5.5262 | | 5.7896 | 152.4 | 393500 | 5.5059 | | 5.7943 | 152.59 | 394000 | 5.5126 | | 5.81 | 152.79 | 394500 | 5.4547 | | 5.7981 | 152.98 | 395000 | 5.5141 | | 5.7845 | 153.18 | 395500 | 5.5964 | | 5.7919 | 153.37 | 396000 | 5.4650 | | 5.8165 | 153.56 | 396500 | 5.5123 | | 5.7675 | 153.76 | 397000 | 5.5191 | | 5.7473 | 153.95 | 397500 | 5.5018 | | 5.7774 | 154.14 | 398000 | 5.4447 | | 5.7875 | 154.34 | 398500 | 5.4997 | | 5.7614 | 154.53 | 399000 | 5.5125 | | 5.7704 | 154.73 | 399500 | 5.5306 | | 5.8041 | 154.92 | 400000 | 5.4993 | | 5.7729 | 155.11 | 400500 | 5.5061 | | 5.7782 | 155.31 | 401000 | 5.4924 | | 5.7788 | 155.5 | 401500 | 5.5045 | | 5.7867 | 155.69 | 402000 | 5.5064 | | 5.7453 | 155.89 | 402500 | 5.4588 | | 5.7694 | 156.08 | 403000 | 5.4874 | | 5.7495 | 156.27 | 403500 | 5.4519 | | 5.7981 | 156.47 | 404000 | 5.5117 | | 5.7725 | 156.66 | 404500 | 5.4655 | | 5.7646 | 156.86 | 405000 | 5.4456 | | 5.7733 | 157.05 | 405500 | 5.4685 | | 5.7618 | 157.24 | 406000 | 5.4861 | | 5.7747 | 157.44 | 406500 | 5.4771 | | 5.742 | 157.63 | 407000 | 5.4824 | | 5.7884 | 157.82 | 407500 | 5.4122 | | 5.7312 | 158.02 | 408000 | 5.4824 | | 5.7584 | 158.21 | 408500 | 5.5168 | | 5.7494 | 158.4 | 409000 | 5.4527 | | 5.7351 | 158.6 | 409500 | 5.4517 | | 5.7571 | 158.79 | 410000 | 5.4462 | | 5.7646 | 158.99 | 410500 | 5.4827 | | 5.7448 | 159.18 | 411000 | 5.4191 | | 5.7008 | 159.37 | 411500 | 5.5147 | | 5.7455 | 159.57 | 412000 | 5.4602 | | 5.7352 | 159.76 | 412500 | 5.4281 | | 5.7438 | 159.95 | 413000 | 5.4478 | | 5.7111 | 160.15 | 413500 | 5.4608 | | 5.742 | 160.34 | 414000 | 5.4418 | | 5.7541 | 160.53 | 414500 | 5.4423 | | 5.7397 | 160.73 | 415000 | 5.4406 | | 5.7393 | 160.92 | 415500 | 5.4741 | | 5.7342 | 161.12 | 416000 | 5.4575 | | 5.7198 | 161.31 | 416500 | 5.3906 | | 5.691 | 161.5 | 417000 | 5.4405 | | 5.7585 | 161.7 | 417500 | 5.4259 | | 5.7279 | 161.89 | 418000 | 5.5081 | | 5.7217 | 162.08 | 418500 | 5.3794 | | 5.7452 | 162.28 | 419000 | 5.4250 | | 5.7226 | 162.47 | 419500 | 5.4700 | | 5.7482 | 162.66 | 420000 | 5.4034 | | 5.7095 | 162.86 | 420500 | 5.4118 | | 5.6917 | 163.05 | 421000 | 5.4417 | | 5.7282 | 163.25 | 421500 | 5.4055 | | 5.7171 | 163.44 | 422000 | 5.4351 | | 5.7424 | 163.63 | 422500 | 5.4415 | | 5.6961 | 163.83 | 423000 | 5.4633 | | 5.7231 | 164.02 | 423500 | 5.4643 | | 5.7365 | 164.21 | 424000 | 5.4110 | | 5.7358 | 164.41 | 424500 | 5.4220 | | 5.7008 | 164.6 | 425000 | 5.4246 | | 5.7353 | 164.79 | 425500 | 5.3805 | | 5.7047 | 164.99 | 426000 | 5.3864 | | 5.701 | 165.18 | 426500 | 5.4106 | | 5.7117 | 165.38 | 427000 | 5.4074 | | 5.7173 | 165.57 | 427500 | 5.4123 | | 5.7192 | 165.76 | 428000 | 5.3903 | | 5.709 | 165.96 | 428500 | 5.4557 | | 5.7064 | 166.15 | 429000 | 5.3853 | | 5.6831 | 166.34 | 429500 | 5.4376 | | 5.6873 | 166.54 | 430000 | 5.4053 | | 5.6988 | 166.73 | 430500 | 5.4159 | | 5.7169 | 166.92 | 431000 | 5.4370 | | 5.7118 | 167.12 | 431500 | 5.3915 | | 5.6992 | 167.31 | 432000 | 5.4012 | | 5.6984 | 167.51 | 432500 | 5.3864 | | 5.6991 | 167.7 | 433000 | 5.3968 | | 5.7088 | 167.89 | 433500 | 5.4048 | | 5.6914 | 168.09 | 434000 | 5.3965 | | 5.6985 | 168.28 | 434500 | 5.4305 | | 5.716 | 168.47 | 435000 | 5.4073 | | 5.7114 | 168.67 | 435500 | 5.3939 | | 5.6991 | 168.86 | 436000 | 5.4275 | | 5.6844 | 169.05 | 436500 | 5.4270 | | 5.6609 | 169.25 | 437000 | 5.3867 | | 5.6984 | 169.44 | 437500 | 5.4050 | | 5.6937 | 169.64 | 438000 | 5.3821 | | 5.7043 | 169.83 | 438500 | 5.4297 | | 5.7031 | 170.02 | 439000 | 5.4376 | | 5.6958 | 170.22 | 439500 | 5.3795 | | 5.658 | 170.41 | 440000 | 5.4534 | | 5.6807 | 170.6 | 440500 | 5.4420 | | 5.6979 | 170.8 | 441000 | 5.4005 | | 5.6782 | 170.99 | 441500 | 5.3995 | | 5.6872 | 171.19 | 442000 | 5.3994 | | 5.6786 | 171.38 | 442500 | 5.3890 | | 5.6815 | 171.57 | 443000 | 5.4163 | | 5.6832 | 171.77 | 443500 | 5.4296 | | 5.6833 | 171.96 | 444000 | 5.3816 | | 5.6773 | 172.15 | 444500 | 5.3820 | | 5.6489 | 172.35 | 445000 | 5.3720 | | 5.6826 | 172.54 | 445500 | 5.3859 | | 5.675 | 172.73 | 446000 | 5.3909 | | 5.6678 | 172.93 | 446500 | 5.3636 | | 5.6802 | 173.12 | 447000 | 5.3338 | | 5.6882 | 173.32 | 447500 | 5.3822 | | 5.6817 | 173.51 | 448000 | 5.3794 | | 5.6744 | 173.7 | 448500 | 5.3187 | | 5.6407 | 173.9 | 449000 | 5.3966 | | 5.6389 | 174.09 | 449500 | 5.3547 | | 5.6648 | 174.28 | 450000 | 5.3423 | | 5.6576 | 174.48 | 450500 | 5.3684 | | 5.6484 | 174.67 | 451000 | 5.3507 | | 5.6705 | 174.86 | 451500 | 5.4060 | | 5.6877 | 175.06 | 452000 | 5.3540 | | 5.6768 | 175.25 | 452500 | 5.3535 | | 5.6693 | 175.45 | 453000 | 5.3339 | | 5.6294 | 175.64 | 453500 | 5.3484 | | 5.6398 | 175.83 | 454000 | 5.3836 | | 5.6617 | 176.03 | 454500 | 5.4004 | | 5.6628 | 176.22 | 455000 | 5.3228 | | 5.6707 | 176.41 | 455500 | 5.3083 | | 5.6593 | 176.61 | 456000 | 5.3822 | | 5.6522 | 176.8 | 456500 | 5.3683 | | 5.6483 | 176.99 | 457000 | 5.3286 | | 5.6352 | 177.19 | 457500 | 5.4293 | | 5.6528 | 177.38 | 458000 | 5.3603 | | 5.6591 | 177.58 | 458500 | 5.3808 | | 5.6799 | 177.77 | 459000 | 5.4076 | | 5.6485 | 177.96 | 459500 | 5.3092 | | 5.6645 | 178.16 | 460000 | 5.3530 | | 5.6401 | 178.35 | 460500 | 5.3411 | | 5.6307 | 178.54 | 461000 | 5.3876 | | 5.6338 | 178.74 | 461500 | 5.3084 | | 5.6684 | 178.93 | 462000 | 5.3771 | | 5.6684 | 179.12 | 462500 | 5.3206 | | 5.6373 | 179.32 | 463000 | 5.3839 | | 5.6817 | 179.51 | 463500 | 5.4119 | | 5.6499 | 179.71 | 464000 | 5.3780 | | 5.6542 | 179.9 | 464500 | 5.4049 | | 5.6648 | 180.09 | 465000 | 5.2990 | | 5.6531 | 180.29 | 465500 | 5.3401 | | 5.6586 | 180.48 | 466000 | 5.4087 | | 5.6261 | 180.67 | 466500 | 5.3383 | | 5.6128 | 180.87 | 467000 | 5.3714 | | 5.6704 | 181.06 | 467500 | 5.3260 | | 5.6429 | 181.25 | 468000 | 5.3600 | | 5.638 | 181.45 | 468500 | 5.3364 | | 5.651 | 181.64 | 469000 | 5.4135 | | 5.6448 | 181.84 | 469500 | 5.4075 | | 5.6273 | 182.03 | 470000 | 5.3312 | | 5.6459 | 182.22 | 470500 | 5.3315 | | 5.6487 | 182.42 | 471000 | 5.3298 | | 5.6669 | 182.61 | 471500 | 5.3472 | | 5.6473 | 182.8 | 472000 | 5.3055 | | 5.6281 | 183.0 | 472500 | 5.2734 | | 5.6327 | 183.19 | 473000 | 5.3361 | | 5.614 | 183.38 | 473500 | 5.3431 | | 5.6216 | 183.58 | 474000 | 5.3655 | | 5.6307 | 183.77 | 474500 | 5.3467 | | 5.6411 | 183.97 | 475000 | 5.4350 | | 5.6219 | 184.16 | 475500 | 5.3125 | | 5.6226 | 184.35 | 476000 | 5.3687 | | 5.6078 | 184.55 | 476500 | 5.3488 | | 5.6096 | 184.74 | 477000 | 5.3533 | | 5.6246 | 184.93 | 477500 | 5.3244 | | 5.618 | 185.13 | 478000 | 5.3299 | | 5.6114 | 185.32 | 478500 | 5.3263 | | 5.5982 | 185.52 | 479000 | 5.3405 | | 5.6245 | 185.71 | 479500 | 5.3282 | | 5.6172 | 185.9 | 480000 | 5.3250 | | 5.5996 | 186.1 | 480500 | 5.3614 | | 5.65 | 186.29 | 481000 | 5.3115 | | 5.6313 | 186.48 | 481500 | 5.3997 | | 5.6252 | 186.68 | 482000 | 5.3107 | | 5.6152 | 186.87 | 482500 | 5.2778 | | 5.6237 | 187.06 | 483000 | 5.3143 | | 5.6066 | 187.26 | 483500 | 5.2831 | | 5.6261 | 187.45 | 484000 | 5.3489 | | 5.6369 | 187.65 | 484500 | 5.3050 | | 5.5793 | 187.84 | 485000 | 5.2617 | | 5.6006 | 188.03 | 485500 | 5.2924 | | 5.5963 | 188.23 | 486000 | 5.2961 | | 5.6163 | 188.42 | 486500 | 5.3068 | | 5.5976 | 188.61 | 487000 | 5.3241 | | 5.6247 | 188.81 | 487500 | 5.3540 | | 5.6252 | 189.0 | 488000 | 5.2798 | | 5.5877 | 189.19 | 488500 | 5.3412 | | 5.6068 | 189.39 | 489000 | 5.3222 | | 5.6096 | 189.58 | 489500 | 5.3245 | | 5.6141 | 189.78 | 490000 | 5.4048 | | 5.6076 | 189.97 | 490500 | 5.3013 | | 5.5593 | 190.16 | 491000 | 5.2765 | | 5.5958 | 190.36 | 491500 | 5.3411 | | 5.6028 | 190.55 | 492000 | 5.3543 | | 5.5886 | 190.74 | 492500 | 5.3400 | | 5.6006 | 190.94 | 493000 | 5.2841 | | 5.5828 | 191.13 | 493500 | 5.3125 | | 5.5995 | 191.32 | 494000 | 5.2710 | | 5.585 | 191.52 | 494500 | 5.3224 | | 5.6109 | 191.71 | 495000 | 5.3154 | | 5.5949 | 191.91 | 495500 | 5.3213 | | 5.5803 | 192.1 | 496000 | 5.3214 | | 5.5996 | 192.29 | 496500 | 5.2980 | | 5.5777 | 192.49 | 497000 | 5.3015 | | 5.6193 | 192.68 | 497500 | 5.3166 | | 5.624 | 192.87 | 498000 | 5.2569 | | 5.5654 | 193.07 | 498500 | 5.2981 | | 5.5593 | 193.26 | 499000 | 5.2812 | | 5.5732 | 193.45 | 499500 | 5.2912 | | 5.6158 | 193.65 | 500000 | 5.3224 | | 5.6012 | 193.84 | 500500 | 5.3529 | | 5.5906 | 194.04 | 501000 | 5.2782 | | 5.5993 | 194.23 | 501500 | 5.2995 | | 5.5731 | 194.42 | 502000 | 5.2697 | | 5.5928 | 194.62 | 502500 | 5.2955 | | 5.5777 | 194.81 | 503000 | 5.2641 | | 5.5753 | 195.0 | 503500 | 5.3061 | | 5.6029 | 195.2 | 504000 | 5.3681 | | 5.563 | 195.39 | 504500 | 5.3171 | | 5.6065 | 195.58 | 505000 | 5.3106 | | 5.574 | 195.78 | 505500 | 5.3547 | | 5.5759 | 195.97 | 506000 | 5.2560 | | 5.5704 | 196.17 | 506500 | 5.3061 | | 5.5619 | 196.36 | 507000 | 5.3233 | | 5.5876 | 196.55 | 507500 | 5.2826 | | 5.5849 | 196.75 | 508000 | 5.3096 | | 5.5938 | 196.94 | 508500 | 5.2849 | | 5.5666 | 197.13 | 509000 | 5.3538 | | 5.5784 | 197.33 | 509500 | 5.2532 | | 5.5893 | 197.52 | 510000 | 5.2387 | | 5.5556 | 197.71 | 510500 | 5.2909 | | 5.5741 | 197.91 | 511000 | 5.4365 | | 5.5713 | 198.1 | 511500 | 5.2402 | | 5.5583 | 198.3 | 512000 | 5.3146 | | 5.5669 | 198.49 | 512500 | 5.2166 | | 5.5523 | 198.68 | 513000 | 5.3176 | | 5.5626 | 198.88 | 513500 | 5.3053 | | 5.5788 | 199.07 | 514000 | 5.2880 | | 5.5682 | 199.26 | 514500 | 5.2790 | | 5.5499 | 199.46 | 515000 | 5.2771 | | 5.5783 | 199.65 | 515500 | 5.2516 | | 5.5425 | 199.85 | 516000 | 5.3402 | | 5.5472 | 200.04 | 516500 | 5.2679 | | 5.5628 | 200.23 | 517000 | 5.2623 | | 5.5635 | 200.43 | 517500 | 5.2496 | | 5.5645 | 200.62 | 518000 | 5.2267 | | 5.5567 | 200.81 | 518500 | 5.3454 | | 5.5591 | 201.01 | 519000 | 5.2430 | | 5.5729 | 201.2 | 519500 | 5.2992 | | 5.582 | 201.39 | 520000 | 5.2823 | | 5.5528 | 201.59 | 520500 | 5.3184 | | 5.5392 | 201.78 | 521000 | 5.2932 | | 5.5632 | 201.98 | 521500 | 5.2308 | | 5.5294 | 202.17 | 522000 | 5.2836 | | 5.5385 | 202.36 | 522500 | 5.2770 | | 5.5388 | 202.56 | 523000 | 5.2804 | | 5.5681 | 202.75 | 523500 | 5.2253 | | 5.5716 | 202.94 | 524000 | 5.2818 | | 5.5572 | 203.14 | 524500 | 5.2616 | | 5.5505 | 203.33 | 525000 | 5.2558 | | 5.5573 | 203.52 | 525500 | 5.3141 | | 5.545 | 203.72 | 526000 | 5.2502 | | 5.5549 | 203.91 | 526500 | 5.2166 | | 5.5498 | 204.11 | 527000 | 5.2486 | | 5.5372 | 204.3 | 527500 | 5.2524 | | 5.5337 | 204.49 | 528000 | 5.2573 | | 5.5462 | 204.69 | 528500 | 5.2399 | | 5.5371 | 204.88 | 529000 | 5.2402 | | 5.5804 | 205.07 | 529500 | 5.2804 | | 5.5265 | 205.27 | 530000 | 5.2506 | | 5.5631 | 205.46 | 530500 | 5.2290 | | 5.5643 | 205.65 | 531000 | 5.2431 | | 5.5289 | 205.85 | 531500 | 5.2717 | | 5.5462 | 206.04 | 532000 | 5.2784 | | 5.5364 | 206.24 | 532500 | 5.3275 | | 5.5203 | 206.43 | 533000 | 5.3078 | | 5.5612 | 206.62 | 533500 | 5.2713 | | 5.5461 | 206.82 | 534000 | 5.2105 | | 5.4844 | 207.01 | 534500 | 5.2427 | | 5.5281 | 207.2 | 535000 | 5.2753 | | 5.5524 | 207.4 | 535500 | 5.2430 | | 5.5413 | 207.59 | 536000 | 5.2350 | | 5.5157 | 207.78 | 536500 | 5.2656 | | 5.538 | 207.98 | 537000 | 5.2013 | | 5.5398 | 208.17 | 537500 | 5.2710 | | 5.536 | 208.37 | 538000 | 5.2514 | | 5.5077 | 208.56 | 538500 | 5.2851 | | 5.5267 | 208.75 | 539000 | 5.2317 | | 5.5379 | 208.95 | 539500 | 5.2661 | | 5.5261 | 209.14 | 540000 | 5.2653 | | 5.5028 | 209.33 | 540500 | 5.2561 | | 5.5209 | 209.53 | 541000 | 5.2058 | | 5.4972 | 209.72 | 541500 | 5.2360 | | 5.5079 | 209.91 | 542000 | 5.1901 | | 5.4981 | 210.11 | 542500 | 5.2492 | | 5.542 | 210.3 | 543000 | 5.2457 | | 5.5527 | 210.5 | 543500 | 5.2126 | | 5.5133 | 210.69 | 544000 | 5.2157 | | 5.5217 | 210.88 | 544500 | 5.2405 | | 5.5288 | 211.08 | 545000 | 5.2562 | | 5.5165 | 211.27 | 545500 | 5.2422 | | 5.524 | 211.46 | 546000 | 5.2168 | | 5.5541 | 211.66 | 546500 | 5.1961 | | 5.514 | 211.85 | 547000 | 5.2531 | | 5.5246 | 212.04 | 547500 | 5.2418 | | 5.4989 | 212.24 | 548000 | 5.2581 | | 5.4825 | 212.43 | 548500 | 5.1648 | | 5.5009 | 212.63 | 549000 | 5.1800 | | 5.5621 | 212.82 | 549500 | 5.2023 | | 5.5356 | 213.01 | 550000 | 5.2142 | | 5.4894 | 213.21 | 550500 | 5.2415 | | 5.5265 | 213.4 | 551000 | 5.1678 | | 5.5408 | 213.59 | 551500 | 5.1895 | | 5.5226 | 213.79 | 552000 | 5.2287 | | 5.5282 | 213.98 | 552500 | 5.2413 | | 5.4997 | 214.18 | 553000 | 5.2408 | | 5.5177 | 214.37 | 553500 | 5.1881 | | 5.5186 | 214.56 | 554000 | 5.2222 | | 5.5227 | 214.76 | 554500 | 5.2009 | | 5.5002 | 214.95 | 555000 | 5.2383 | | 5.5174 | 215.14 | 555500 | 5.2386 | | 5.5308 | 215.34 | 556000 | 5.1832 | | 5.4914 | 215.53 | 556500 | 5.2360 | | 5.4864 | 215.72 | 557000 | 5.1961 | | 5.5116 | 215.92 | 557500 | 5.2403 | | 5.5065 | 216.11 | 558000 | 5.2019 | | 5.4919 | 216.31 | 558500 | 5.2194 | | 5.519 | 216.5 | 559000 | 5.2472 | | 5.5075 | 216.69 | 559500 | 5.2192 | | 5.5181 | 216.89 | 560000 | 5.2218 | | 5.5015 | 217.08 | 560500 | 5.2167 | | 5.487 | 217.27 | 561000 | 5.2329 | | 5.5179 | 217.47 | 561500 | 5.2464 | | 5.4807 | 217.66 | 562000 | 5.2115 | | 5.4998 | 217.85 | 562500 | 5.2462 | | 5.5032 | 218.05 | 563000 | 5.2216 | | 5.5031 | 218.24 | 563500 | 5.2147 | | 5.5083 | 218.44 | 564000 | 5.2162 | | 5.5038 | 218.63 | 564500 | 5.1412 | | 5.4659 | 218.82 | 565000 | 5.2629 | | 5.4794 | 219.02 | 565500 | 5.2163 | | 5.4744 | 219.21 | 566000 | 5.1878 | | 5.5054 | 219.4 | 566500 | 5.2107 | | 5.4841 | 219.6 | 567000 | 5.2308 | | 5.4891 | 219.79 | 567500 | 5.2575 | | 5.4531 | 219.98 | 568000 | 5.1906 | | 5.4901 | 220.18 | 568500 | 5.1901 | | 5.4622 | 220.37 | 569000 | 5.2440 | | 5.4799 | 220.57 | 569500 | 5.2478 | | 5.4893 | 220.76 | 570000 | 5.1878 | | 5.4961 | 220.95 | 570500 | 5.2147 | | 5.508 | 221.15 | 571000 | 5.2494 | | 5.4665 | 221.34 | 571500 | 5.2317 | | 5.473 | 221.53 | 572000 | 5.2471 | | 5.4754 | 221.73 | 572500 | 5.2230 | | 5.4629 | 221.92 | 573000 | 5.2310 | | 5.4941 | 222.11 | 573500 | 5.2487 | | 5.5063 | 222.31 | 574000 | 5.1748 | | 5.5031 | 222.5 | 574500 | 5.2017 | | 5.4775 | 222.7 | 575000 | 5.1819 | | 5.477 | 222.89 | 575500 | 5.2201 | | 5.4974 | 223.08 | 576000 | 5.1915 | | 5.471 | 223.28 | 576500 | 5.1601 | | 5.4968 | 223.47 | 577000 | 5.1940 | | 5.4802 | 223.66 | 577500 | 5.2094 | | 5.4807 | 223.86 | 578000 | 5.2069 | | 5.4802 | 224.05 | 578500 | 5.2246 | | 5.4408 | 224.24 | 579000 | 5.1933 | | 5.4635 | 224.44 | 579500 | 5.2526 | | 5.4835 | 224.63 | 580000 | 5.1989 | | 5.4697 | 224.83 | 580500 | 5.2130 | | 5.4673 | 225.02 | 581000 | 5.2051 | | 5.4653 | 225.21 | 581500 | 5.1684 | | 5.4683 | 225.41 | 582000 | 5.2201 | | 5.4597 | 225.6 | 582500 | 5.1634 | | 5.4624 | 225.79 | 583000 | 5.1864 | | 5.4818 | 225.99 | 583500 | 5.1758 | | 5.4521 | 226.18 | 584000 | 5.2370 | | 5.4829 | 226.37 | 584500 | 5.2197 | | 5.4561 | 226.57 | 585000 | 5.1673 | | 5.4604 | 226.76 | 585500 | 5.1525 | | 5.4836 | 226.96 | 586000 | 5.2036 | | 5.4556 | 227.15 | 586500 | 5.1597 | | 5.4375 | 227.34 | 587000 | 5.1354 | | 5.4542 | 227.54 | 587500 | 5.2094 | | 5.4633 | 227.73 | 588000 | 5.1696 | | 5.4631 | 227.92 | 588500 | 5.1048 | | 5.4789 | 228.12 | 589000 | 5.1532 | | 5.4708 | 228.31 | 589500 | 5.1899 | | 5.4747 | 228.51 | 590000 | 5.2007 | | 5.4562 | 228.7 | 590500 | 5.1649 | | 5.4412 | 228.89 | 591000 | 5.1794 | | 5.477 | 229.09 | 591500 | 5.1865 | | 5.4415 | 229.28 | 592000 | 5.1394 | | 5.4898 | 229.47 | 592500 | 5.1865 | | 5.4986 | 229.67 | 593000 | 5.1977 | | 5.4623 | 229.86 | 593500 | 5.1879 | | 5.444 | 230.05 | 594000 | 5.1844 | | 5.4514 | 230.25 | 594500 | 5.2079 | | 5.4847 | 230.44 | 595000 | 5.2058 | | 5.4936 | 230.64 | 595500 | 5.2204 | | 5.4266 | 230.83 | 596000 | 5.1847 | | 5.4596 | 231.02 | 596500 | 5.1775 | | 5.4662 | 231.22 | 597000 | 5.2368 | | 5.4447 | 231.41 | 597500 | 5.1629 | | 5.4276 | 231.6 | 598000 | 5.0777 | | 5.4758 | 231.8 | 598500 | 5.1242 | | 5.4492 | 231.99 | 599000 | 5.1298 | | 5.4386 | 232.18 | 599500 | 5.1472 | | 5.4425 | 232.38 | 600000 | 5.1869 | | 5.4525 | 232.57 | 600500 | 5.1746 | | 5.4361 | 232.77 | 601000 | 5.1657 | | 5.4606 | 232.96 | 601500 | 5.1502 | | 5.4587 | 233.15 | 602000 | 5.1334 | | 5.4491 | 233.35 | 602500 | 5.1452 | | 5.4599 | 233.54 | 603000 | 5.1541 | | 5.4692 | 233.73 | 603500 | 5.1343 | | 5.4423 | 233.93 | 604000 | 5.1430 | | 5.4387 | 234.12 | 604500 | 5.1566 | | 5.4616 | 234.31 | 605000 | 5.1718 | | 5.4678 | 234.51 | 605500 | 5.1338 | | 5.3934 | 234.7 | 606000 | 5.1227 | | 5.4454 | 234.9 | 606500 | 5.1688 | | 5.4402 | 235.09 | 607000 | 5.1094 | | 5.4294 | 235.28 | 607500 | 5.1227 | | 5.448 | 235.48 | 608000 | 5.1407 | | 5.4416 | 235.67 | 608500 | 5.1410 | | 5.4617 | 235.86 | 609000 | 5.1206 | | 5.4332 | 236.06 | 609500 | 5.1739 | | 5.4195 | 236.25 | 610000 | 5.1671 | | 5.4506 | 236.44 | 610500 | 5.1708 | | 5.4235 | 236.64 | 611000 | 5.1622 | | 5.4558 | 236.83 | 611500 | 5.1731 | | 5.4344 | 237.03 | 612000 | 5.1368 | | 5.4159 | 237.22 | 612500 | 5.1689 | | 5.435 | 237.41 | 613000 | 5.1383 | | 5.4408 | 237.61 | 613500 | 5.1235 | | 5.416 | 237.8 | 614000 | 5.1519 | | 5.4317 | 237.99 | 614500 | 5.1538 | | 5.4444 | 238.19 | 615000 | 5.1710 | | 5.4177 | 238.38 | 615500 | 5.1571 | | 5.4352 | 238.57 | 616000 | 5.1401 | | 5.4216 | 238.77 | 616500 | 5.1795 | | 5.4412 | 238.96 | 617000 | 5.1101 | | 5.4403 | 239.16 | 617500 | 5.1405 | | 5.4694 | 239.35 | 618000 | 5.1463 | | 5.4101 | 239.54 | 618500 | 5.1289 | | 5.4316 | 239.74 | 619000 | 5.1274 | | 5.4291 | 239.93 | 619500 | 5.1681 | | 5.4204 | 240.12 | 620000 | 5.1824 | | 5.4092 | 240.32 | 620500 | 5.1620 | | 5.4151 | 240.51 | 621000 | 5.1428 | | 5.4235 | 240.7 | 621500 | 5.1342 | | 5.4342 | 240.9 | 622000 | 5.1091 | | 5.4166 | 241.09 | 622500 | 5.1483 | | 5.4166 | 241.29 | 623000 | 5.1497 | | 5.3939 | 241.48 | 623500 | 5.1323 | | 5.4253 | 241.67 | 624000 | 5.1281 | | 5.3985 | 241.87 | 624500 | 5.1087 | | 5.4103 | 242.06 | 625000 | 5.1538 | | 5.4106 | 242.25 | 625500 | 5.1367 | | 5.4258 | 242.45 | 626000 | 5.0969 | | 5.434 | 242.64 | 626500 | 5.1474 | | 5.4158 | 242.84 | 627000 | 5.0803 | | 5.4053 | 243.03 | 627500 | 5.1300 | | 5.4355 | 243.22 | 628000 | 5.1774 | | 5.4214 | 243.42 | 628500 | 5.1289 | | 5.3964 | 243.61 | 629000 | 5.1782 | | 5.4092 | 243.8 | 629500 | 5.1291 | | 5.3865 | 244.0 | 630000 | 5.2033 | | 5.415 | 244.19 | 630500 | 5.1307 | | 5.4053 | 244.38 | 631000 | 5.1285 | | 5.4083 | 244.58 | 631500 | 5.1260 | | 5.4308 | 244.77 | 632000 | 5.1111 | | 5.4088 | 244.97 | 632500 | 5.1473 | | 5.404 | 245.16 | 633000 | 5.1695 | | 5.4006 | 245.35 | 633500 | 5.1438 | | 5.3848 | 245.55 | 634000 | 5.1529 | | 5.4202 | 245.74 | 634500 | 5.1223 | | 5.4029 | 245.93 | 635000 | 5.0946 | | 5.3855 | 246.13 | 635500 | 5.1392 | | 5.4303 | 246.32 | 636000 | 5.1367 | | 5.4033 | 246.51 | 636500 | 5.1017 | | 5.4325 | 246.71 | 637000 | 5.1393 | | 5.4134 | 246.9 | 637500 | 5.1543 | | 5.3986 | 247.1 | 638000 | 5.1309 | | 5.3746 | 247.29 | 638500 | 5.1322 | | 5.4197 | 247.48 | 639000 | 5.1160 | | 5.4235 | 247.68 | 639500 | 5.1321 | | 5.3706 | 247.87 | 640000 | 5.1676 | | 5.4018 | 248.06 | 640500 | 5.1096 | | 5.3822 | 248.26 | 641000 | 5.0967 | | 5.4332 | 248.45 | 641500 | 5.1486 | | 5.3951 | 248.64 | 642000 | 5.1048 | | 5.3899 | 248.84 | 642500 | 5.1297 | | 5.3887 | 249.03 | 643000 | 5.1264 | | 5.3808 | 249.23 | 643500 | 5.1108 | | 5.3934 | 249.42 | 644000 | 5.1363 | | 5.4008 | 249.61 | 644500 | 5.1109 | | 5.4168 | 249.81 | 645000 | 5.1005 | | 5.3844 | 250.0 | 645500 | 5.1302 | | 5.396 | 250.19 | 646000 | 5.1385 | | 5.4019 | 250.39 | 646500 | 5.1112 | | 5.3883 | 250.58 | 647000 | 5.1359 | | 5.3982 | 250.77 | 647500 | 5.1295 | | 5.3858 | 250.97 | 648000 | 5.1397 | | 5.4064 | 251.16 | 648500 | 5.1076 | | 5.3845 | 251.36 | 649000 | 5.1030 | | 5.3977 | 251.55 | 649500 | 5.1283 | | 5.3936 | 251.74 | 650000 | 5.0607 | | 5.3917 | 251.94 | 650500 | 5.1286 | | 5.3857 | 252.13 | 651000 | 5.1203 | | 5.4092 | 252.32 | 651500 | 5.0867 | | 5.3949 | 252.52 | 652000 | 5.0936 | | 5.3909 | 252.71 | 652500 | 5.1033 | | 5.3748 | 252.9 | 653000 | 5.1448 | | 5.36 | 253.1 | 653500 | 5.1007 | | 5.4047 | 253.29 | 654000 | 5.1083 | | 5.3664 | 253.49 | 654500 | 5.1111 | | 5.3728 | 253.68 | 655000 | 5.1023 | | 5.3863 | 253.87 | 655500 | 5.0889 | | 5.3781 | 254.07 | 656000 | 5.0758 | | 5.384 | 254.26 | 656500 | 5.0883 | | 5.3748 | 254.45 | 657000 | 5.1066 | | 5.4297 | 254.65 | 657500 | 5.0840 | | 5.3763 | 254.84 | 658000 | 5.0740 | | 5.3915 | 255.03 | 658500 | 5.0531 | | 5.401 | 255.23 | 659000 | 5.1152 | | 5.4052 | 255.42 | 659500 | 5.1129 | | 5.4131 | 255.62 | 660000 | 5.1075 | | 5.3829 | 255.81 | 660500 | 5.1153 | | 5.3764 | 256.0 | 661000 | 5.1075 | | 5.3757 | 256.2 | 661500 | 5.1077 | | 5.3944 | 256.39 | 662000 | 5.1051 | | 5.3688 | 256.58 | 662500 | 5.0953 | | 5.4085 | 256.78 | 663000 | 5.1339 | | 5.3561 | 256.97 | 663500 | 5.0772 | | 5.3754 | 257.16 | 664000 | 5.1090 | | 5.407 | 257.36 | 664500 | 5.1180 | | 5.3627 | 257.55 | 665000 | 5.1054 | | 5.3866 | 257.75 | 665500 | 5.1373 | | 5.3599 | 257.94 | 666000 | 5.0439 | | 5.3825 | 258.13 | 666500 | 5.0759 | | 5.3584 | 258.33 | 667000 | 5.1097 | | 5.3478 | 258.52 | 667500 | 5.1463 | | 5.3608 | 258.71 | 668000 | 5.1012 | | 5.4128 | 258.91 | 668500 | 5.1192 | | 5.378 | 259.1 | 669000 | 5.0897 | | 5.3831 | 259.3 | 669500 | 5.1095 | | 5.3687 | 259.49 | 670000 | 5.0835 | | 5.3658 | 259.68 | 670500 | 5.0947 | | 5.3531 | 259.88 | 671000 | 5.0795 | | 5.3745 | 260.07 | 671500 | 5.1075 | | 5.4171 | 260.26 | 672000 | 5.1051 | | 5.3669 | 260.46 | 672500 | 5.1055 | | 5.4015 | 260.65 | 673000 | 5.1121 | | 5.3423 | 260.84 | 673500 | 5.1391 | | 5.3811 | 261.04 | 674000 | 5.0921 | | 5.3607 | 261.23 | 674500 | 5.1021 | | 5.3556 | 261.43 | 675000 | 5.0886 | | 5.3887 | 261.62 | 675500 | 5.0489 | | 5.3793 | 261.81 | 676000 | 5.1188 | | 5.3871 | 262.01 | 676500 | 5.1047 | | 5.3597 | 262.2 | 677000 | 5.1699 | | 5.3839 | 262.39 | 677500 | 5.0961 | | 5.3735 | 262.59 | 678000 | 5.1041 | | 5.3725 | 262.78 | 678500 | 5.0690 | | 5.3593 | 262.97 | 679000 | 5.0925 | | 5.3571 | 263.17 | 679500 | 5.0774 | | 5.3717 | 263.36 | 680000 | 5.1172 | | 5.3609 | 263.56 | 680500 | 5.0873 | | 5.3773 | 263.75 | 681000 | 5.1073 | | 5.381 | 263.94 | 681500 | 5.0893 | | 5.3406 | 264.14 | 682000 | 5.0634 | | 5.383 | 264.33 | 682500 | 5.0769 | | 5.3703 | 264.52 | 683000 | 5.0812 | | 5.3568 | 264.72 | 683500 | 5.0918 | | 5.3321 | 264.91 | 684000 | 5.1248 | | 5.3735 | 265.1 | 684500 | 5.0733 | | 5.3796 | 265.3 | 685000 | 5.0809 | | 5.3352 | 265.49 | 685500 | 5.1017 | | 5.3727 | 265.69 | 686000 | 5.0930 | | 5.3333 | 265.88 | 686500 | 5.0893 | | 5.3516 | 266.07 | 687000 | 5.1134 | | 5.3768 | 266.27 | 687500 | 5.0761 | | 5.3685 | 266.46 | 688000 | 5.0557 | | 5.3604 | 266.65 | 688500 | 5.0616 | | 5.3663 | 266.85 | 689000 | 5.0996 | | 5.3756 | 267.04 | 689500 | 5.0806 | | 5.3703 | 267.23 | 690000 | 5.0482 | | 5.3772 | 267.43 | 690500 | 5.0874 | | 5.3504 | 267.62 | 691000 | 5.0664 | | 5.3695 | 267.82 | 691500 | 5.0752 | | 5.3701 | 268.01 | 692000 | 5.0659 | | 5.3811 | 268.2 | 692500 | 5.1069 | | 5.3568 | 268.4 | 693000 | 5.0801 | | 5.3752 | 268.59 | 693500 | 5.0727 | | 5.3718 | 268.78 | 694000 | 5.0704 | | 5.3419 | 268.98 | 694500 | 5.0735 | | 5.3343 | 269.17 | 695000 | 5.0845 | | 5.3348 | 269.36 | 695500 | 5.0549 | | 5.3558 | 269.56 | 696000 | 5.0596 | | 5.3729 | 269.75 | 696500 | 5.0374 | | 5.3514 | 269.95 | 697000 | 5.0976 | | 5.36 | 270.14 | 697500 | 5.0621 | | 5.3763 | 270.33 | 698000 | 5.0889 | | 5.3516 | 270.53 | 698500 | 5.0927 | | 5.3824 | 270.72 | 699000 | 5.0988 | | 5.3635 | 270.91 | 699500 | 5.0921 | | 5.3366 | 271.11 | 700000 | 5.0688 | | 5.358 | 271.3 | 700500 | 5.0585 | | 5.37 | 271.49 | 701000 | 5.0990 | | 5.3629 | 271.69 | 701500 | 5.1258 | | 5.347 | 271.88 | 702000 | 5.0644 | | 5.3331 | 272.08 | 702500 | 5.0988 | | 5.3516 | 272.27 | 703000 | 5.0773 | | 5.3345 | 272.46 | 703500 | 5.0567 | | 5.3495 | 272.66 | 704000 | 5.1025 | | 5.3315 | 272.85 | 704500 | 5.0231 | | 5.3698 | 273.04 | 705000 | 5.0677 | | 5.347 | 273.24 | 705500 | 5.0602 | | 5.3708 | 273.43 | 706000 | 5.0575 | | 5.3065 | 273.63 | 706500 | 5.0442 | | 5.3453 | 273.82 | 707000 | 5.0758 | | 5.3408 | 274.01 | 707500 | 5.0838 | | 5.3429 | 274.21 | 708000 | 5.0919 | | 5.342 | 274.4 | 708500 | 5.0556 | | 5.3612 | 274.59 | 709000 | 5.0716 | | 5.3666 | 274.79 | 709500 | 5.0837 | | 5.3473 | 274.98 | 710000 | 5.0536 | | 5.3684 | 275.17 | 710500 | 5.0759 | | 5.3545 | 275.37 | 711000 | 5.0618 | | 5.3424 | 275.56 | 711500 | 5.0807 | | 5.3489 | 275.76 | 712000 | 5.0750 | | 5.3409 | 275.95 | 712500 | 5.0264 | | 5.3136 | 276.14 | 713000 | 5.0516 | | 5.3393 | 276.34 | 713500 | 5.0836 | | 5.3348 | 276.53 | 714000 | 5.0567 | | 5.3743 | 276.72 | 714500 | 5.0857 | | 5.3356 | 276.92 | 715000 | 5.0667 | | 5.3431 | 277.11 | 715500 | 5.0481 | | 5.3539 | 277.3 | 716000 | 5.0604 | | 5.3587 | 277.5 | 716500 | 5.0900 | | 5.3671 | 277.69 | 717000 | 5.0950 | | 5.3414 | 277.89 | 717500 | 5.0792 | | 5.3247 | 278.08 | 718000 | 5.0677 | | 5.348 | 278.27 | 718500 | 5.0357 | | 5.3521 | 278.47 | 719000 | 5.0454 | | 5.3353 | 278.66 | 719500 | 5.0591 | | 5.3691 | 278.85 | 720000 | 5.0540 | | 5.3516 | 279.05 | 720500 | 5.0605 | | 5.3626 | 279.24 | 721000 | 5.0448 | | 5.3586 | 279.43 | 721500 | 5.0610 | | 5.3456 | 279.63 | 722000 | 5.0509 | | 5.3334 | 279.82 | 722500 | 5.0505 | | 5.3487 | 280.02 | 723000 | 5.0647 | | 5.3585 | 280.21 | 723500 | 5.0700 | | 5.3031 | 280.4 | 724000 | 5.0509 | | 5.3425 | 280.6 | 724500 | 5.0527 | | 5.3564 | 280.79 | 725000 | 5.0422 | | 5.3275 | 280.98 | 725500 | 5.0818 | | 5.3389 | 281.18 | 726000 | 5.0567 | | 5.3327 | 281.37 | 726500 | 5.0413 | | 5.3321 | 281.56 | 727000 | 5.0821 | | 5.3523 | 281.76 | 727500 | 5.0261 | | 5.3471 | 281.95 | 728000 | 5.0301 | | 5.3497 | 282.15 | 728500 | 5.0944 | | 5.3607 | 282.34 | 729000 | 5.0698 | | 5.3229 | 282.53 | 729500 | 5.0782 | | 5.3291 | 282.73 | 730000 | 5.0224 | | 5.3465 | 282.92 | 730500 | 5.0285 | | 5.3333 | 283.11 | 731000 | 5.0422 | | 5.3303 | 283.31 | 731500 | 5.0738 | | 5.344 | 283.5 | 732000 | 5.0664 | | 5.3354 | 283.69 | 732500 | 5.0302 | | 5.3657 | 283.89 | 733000 | 5.0333 | | 5.3483 | 284.08 | 733500 | 5.0612 | | 5.336 | 284.28 | 734000 | 5.0713 | | 5.3131 | 284.47 | 734500 | 5.0794 | | 5.3473 | 284.66 | 735000 | 5.0451 | | 5.3139 | 284.86 | 735500 | 5.0408 | | 5.3561 | 285.05 | 736000 | 5.0525 | | 5.3515 | 285.24 | 736500 | 5.0468 | | 5.3405 | 285.44 | 737000 | 5.0607 | | 5.3363 | 285.63 | 737500 | 5.0528 | | 5.3144 | 285.82 | 738000 | 5.0766 | | 5.3563 | 286.02 | 738500 | 5.0321 | | 5.3151 | 286.21 | 739000 | 5.0005 | | 5.3374 | 286.41 | 739500 | 5.0595 | | 5.3336 | 286.6 | 740000 | 5.0523 | | 5.3383 | 286.79 | 740500 | 5.0394 | | 5.3445 | 286.99 | 741000 | 5.0588 | | 5.3431 | 287.18 | 741500 | 5.0369 | | 5.3277 | 287.37 | 742000 | 5.0628 | | 5.3357 | 287.57 | 742500 | 5.0469 | | 5.3348 | 287.76 | 743000 | 5.0368 | | 5.3445 | 287.96 | 743500 | 5.0085 | | 5.3292 | 288.15 | 744000 | 5.0724 | | 5.3213 | 288.34 | 744500 | 5.0137 | | 5.3251 | 288.54 | 745000 | 5.0576 | | 5.3222 | 288.73 | 745500 | 5.0740 | | 5.3121 | 288.92 | 746000 | 5.0114 | | 5.3232 | 289.12 | 746500 | 5.0531 | | 5.3315 | 289.31 | 747000 | 5.0426 | | 5.3392 | 289.5 | 747500 | 5.0531 | | 5.3187 | 289.7 | 748000 | 5.0661 | | 5.3701 | 289.89 | 748500 | 5.0260 | | 5.3446 | 290.09 | 749000 | 5.0125 | | 5.3465 | 290.28 | 749500 | 5.0423 | | 5.3283 | 290.47 | 750000 | 5.0366 | | 5.338 | 290.67 | 750500 | 5.0667 | | 5.2954 | 290.86 | 751000 | 5.0613 | | 5.3194 | 291.05 | 751500 | 5.0521 | | 5.3367 | 291.25 | 752000 | 5.0795 | | 5.3469 | 291.44 | 752500 | 5.0709 | | 5.3262 | 291.63 | 753000 | 5.0545 | | 5.3107 | 291.83 | 753500 | 5.0195 | | 5.3104 | 292.02 | 754000 | 5.0633 | | 5.343 | 292.22 | 754500 | 5.0673 | | 5.3171 | 292.41 | 755000 | 5.0391 | | 5.344 | 292.6 | 755500 | 5.0445 | | 5.3257 | 292.8 | 756000 | 5.0666 | | 5.3102 | 292.99 | 756500 | 5.0197 | | 5.3254 | 293.18 | 757000 | 5.0403 | | 5.3494 | 293.38 | 757500 | 5.0233 | | 5.3615 | 293.57 | 758000 | 5.0868 | | 5.2934 | 293.76 | 758500 | 5.0730 | | 5.3434 | 293.96 | 759000 | 5.0714 | | 5.3512 | 294.15 | 759500 | 5.0396 | | 5.3311 | 294.35 | 760000 | 5.0887 | | 5.3422 | 294.54 | 760500 | 5.0571 | | 5.3067 | 294.73 | 761000 | 5.0656 | | 5.3382 | 294.93 | 761500 | 5.0728 | | 5.3367 | 295.12 | 762000 | 5.0628 | | 5.3343 | 295.31 | 762500 | 5.0472 | | 5.3154 | 295.51 | 763000 | 5.0429 | | 5.3099 | 295.7 | 763500 | 5.0384 | | 5.3299 | 295.89 | 764000 | 5.0563 | | 5.312 | 296.09 | 764500 | 5.0682 | | 5.3282 | 296.28 | 765000 | 5.0360 | | 5.3336 | 296.48 | 765500 | 5.0175 | | 5.3495 | 296.67 | 766000 | 5.0728 | | 5.3393 | 296.86 | 766500 | 5.0527 | | 5.3478 | 297.06 | 767000 | 5.0398 | | 5.3249 | 297.25 | 767500 | 5.0344 | | 5.3217 | 297.44 | 768000 | 5.0458 | | 5.3291 | 297.64 | 768500 | 5.1057 | | 5.3253 | 297.83 | 769000 | 5.0360 | | 5.3124 | 298.02 | 769500 | 5.0854 | | 5.3029 | 298.22 | 770000 | 5.0250 | | 5.3263 | 298.41 | 770500 | 5.0399 | | 5.325 | 298.61 | 771000 | 5.0587 | | 5.3315 | 298.8 | 771500 | 5.0548 | | 5.2862 | 298.99 | 772000 | 5.0644 | | 5.3218 | 299.19 | 772500 | 5.0562 | | 5.3233 | 299.38 | 773000 | 5.0442 | | 5.3001 | 299.57 | 773500 | 5.0263 | | 5.334 | 299.77 | 774000 | 5.0736 | | 5.327 | 299.96 | 774500 | 5.0648 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

ChoboAvenger/DialoGPT-small-DocBot

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-21T19:49:18Z

--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-12k - wit-400m --- # Model card for vit_base_patch16_clip_224.openai_ft_in12k A Vision Transformer (ViT) image classification model. Pretrained on WIT-400M image-text pairs by OpenAI using CLIP. Fine-tuned on ImageNet-12k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143). ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 94.9 - GMACs: 16.9 - Activations (M): 16.5 - Image size: 224 x 224 - **Papers:** - Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020 - Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-12k - **Pretrain Dataset:** - WIT-400M ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('vit_base_patch16_clip_224.openai_ft_in12k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_base_patch16_clip_224.openai_ft_in12k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 197, 768) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` ```bibtex @article{cherti2022reproducible, title={Reproducible scaling laws for contrastive language-image learning}, author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia}, journal={arXiv preprint arXiv:2212.07143}, year={2022} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```

ChoboAvenger/DialoGPT-small-joshua

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-21T20:03:02Z

--- license: other tags: - generated_from_keras_callback model-index: - name: nateraw/mit-b0-finetuned-sidewalks-v2 results: [] ---  # nateraw/mit-b0-finetuned-sidewalks-v2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1508 - Validation Loss: 0.4301 - Validation Mean Iou: 0.3689 - Validation Mean Accuracy: 0.4261 - Validation Overall Accuracy: 0.8878 - Validation Per Category Iou: [0. 0.82155443 0.88837272 0.80869927 0.84681809 0.50445633 nan 0.5062558 0.58202362 0.09694114 0.86506226 0.10300594 0. 0.03122511 0. 0.55651564 0. 0. 0.76493797 0.04021662 0.40453306 0.56038987 0.34382567 nan 0.02428609 0.30885576 0.28811326 0. 0.87087236 0.74857511 0.94321046 0.02300712 0.03721037 0.20366003 0. ] - Validation Per Category Accuracy: [0. 0.88109026 0.95044945 0.85142397 0.95993416 0.6370042 nan 0.65971511 0.81045852 0.11321606 0.95401169 0.10670369 0. 0.04042904 0. 0.66801313 0. 0. 0.90595882 0.04265001 0.5292762 0.61230561 0.4092219 nan 0.0283755 0.37721503 0.3266398 0. 0.950358 0.87250445 0.96996696 0.02583519 0.09486859 0.28234463 0. ] - Epoch: 49 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Per Category Iou | Validation Per Category Accuracy | Epoch | |:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----:| | 1.4089 | 0.8220 | 0.1975 | 0.2427 | 0.7701 | [0. 0.58353931 0.7655921 0.04209491 0.53135026 0.11779776 nan 0.07709853 0.15950712 0. 0.69634813 0. 0. 0. 0. 0. 0. 0. 0.61456822 0. 0.24971248 0.27129675 0. nan 0. 0.07697324 0. 0. 0.78576516 0.61267064 0.84564576 0. 0. 0.08904216 0. ] | [0. 0.88026971 0.93475302 0.04216372 0.5484085 0.13285614 nan 0.08669707 0.19044773 0. 0.90089024 0. 0. 0. 0. 0. 0. 0. 0.76783975 0. 0.42102101 0.28659817 0. nan 0. 0.08671771 0. 0. 0.89590301 0.74932576 0.9434814 0. 0. 0.14245566 0. ] | 0 | | 0.8462 | 0.6135 | 0.2551 | 0.2960 | 0.8200 | [0. 0.66967645 0.80571406 0.56416239 0.66692248 0.24744912 nan 0.23994505 0.28962463 0. 0.76504783 0. 0. 0. 0. 0.14111353 0. 0. 0.6924468 0. 0.27988701 0.41876094 0. nan 0. 0.14755829 0. 0. 0.81614463 0.68429711 0.87710938 0. 0. 0.11234171 0. ] | [0. 0.83805933 0.94928385 0.59586511 0.72913519 0.30595504 nan 0.3128234 0.34805831 0. 0.87847495 0. 0. 0. 0. 0.14205167 0. 0. 0.87543619 0. 0.36001144 0.49498574 0. nan 0. 0.18179115 0. 0. 0.92867923 0.7496178 0.92220166 0. 0. 0.15398549 0. ] | 1 | | 0.7134 | 0.5660 | 0.2780 | 0.3320 | 0.8286 | [0. 0.64791461 0.83800512 0.67301044 0.68120631 0.27361472 nan 0.26715802 0.43596999 0. 0.78649287 0. 0. 0. 0. 0.41256964 0. 0. 0.71114766 0. 0.31646321 0.44682442 0. nan 0. 0.17132551 0. 0. 0.81845697 0.67536699 0.88940936 0. 0. 0.1304862 0. ] | [0. 0.85958877 0.92084269 0.82341633 0.74725972 0.33495972 nan 0.40755277 0.56591531 0. 0.90641721 0. 0. 0. 0. 0.48144408 0. 0. 0.88294811 0. 0.46962078 0.47517397 0. nan 0. 0.20631607 0. 0. 0.90956851 0.85856042 0.94107052 0. 0. 0.16669713 0. ] | 2 | | 0.6320 | 0.5173 | 0.2894 | 0.3454 | 0.8435 | [0. 0.70789146 0.84902296 0.65266358 0.76099965 0.32934391 nan 0.29576422 0.43988204 0. 0.79276447 0. 0. 0. 0. 0.42668367 0. 0. 0.71717911 0. 0.32151249 0.50084444 0. nan 0. 0.18711455 0. 0. 0.82903803 0.68990498 0.8990059 0. 0.00213015 0.14819771 0. ] | [0. 0.84048763 0.93514369 0.68355212 0.88302113 0.458816 nan 0.38623272 0.69456442 0. 0.92379471 0. 0. 0. 0. 0.50677438 0. 0. 0.90362965 0. 0.4662386 0.57368294 0. nan 0. 0.23281768 0. 0. 0.9001526 0.86786434 0.95195314 0. 0.00333751 0.18532191 0. ] | 3 | | 0.5609 | 0.5099 | 0.2920 | 0.3599 | 0.8385 | [0. 0.70817583 0.84131144 0.66573523 0.81449696 0.38891117 nan 0.28124784 0.42659255 0. 0.80855146 0. 0. 0. 0. 0.46011866 0. 0. 0.65458792 0. 0.28411565 0.46758138 0. nan 0. 0.21849067 0. 0. 0.83829062 0.71207623 0.89929169 0. 0.02846127 0.13782635 0. ] | [0. 0.88632871 0.91269832 0.79044294 0.88368528 0.57405218 nan 0.35035973 0.77610775 0. 0.8889696 0. 0. 0. 0. 0.6020786 0. 0. 0.74586521 0. 0.61602403 0.54519561 0. nan 0. 0.28447396 0. 0. 0.94520232 0.85544414 0.95994042 0. 0.04680851 0.21407134 0. ] | 4 | | 0.5256 | 0.4741 | 0.3045 | 0.3598 | 0.8558 | [0.00000000e+00 7.50159008e-01 8.53654462e-01 6.44928131e-01 7.90455244e-01 4.33599913e-01 nan 3.33472954e-01 4.74502513e-01 0.00000000e+00 8.01366017e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.67653814e-01 0.00000000e+00 0.00000000e+00 7.27412479e-01 0.00000000e+00 4.18946113e-01 5.04714837e-01 0.00000000e+00 nan 0.00000000e+00 2.00373855e-01 0.00000000e+00 0.00000000e+00 8.50200795e-01 7.41636173e-01 9.08320534e-01 2.77259907e-04 0.00000000e+00 1.45430716e-01 0.00000000e+00] | [0.00000000e+00 8.86487233e-01 9.05201886e-01 7.23139265e-01 8.91929263e-01 7.26675641e-01 nan 4.36386295e-01 6.64378543e-01 0.00000000e+00 8.89056843e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.65450644e-01 0.00000000e+00 0.00000000e+00 9.27446136e-01 0.00000000e+00 5.36031025e-01 5.84198054e-01 0.00000000e+00 nan 0.00000000e+00 2.42514534e-01 0.00000000e+00 0.00000000e+00 9.31954754e-01 8.26849708e-01 9.59880377e-01 2.79039335e-04 0.00000000e+00 1.77106051e-01 0.00000000e+00] | 5 | | 0.4761 | 0.4922 | 0.3036 | 0.3754 | 0.8517 | [0.00000000e+00 7.18490241e-01 8.54701589e-01 5.90903088e-01 8.21902743e-01 4.76229883e-01 nan 3.32447673e-01 4.80642540e-01 0.00000000e+00 8.02904449e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.73285636e-01 0.00000000e+00 0.00000000e+00 7.16608930e-01 0.00000000e+00 3.16598081e-01 5.12540924e-01 0.00000000e+00 nan 0.00000000e+00 2.27702968e-01 0.00000000e+00 0.00000000e+00 8.51831675e-01 7.39827330e-01 9.07152231e-01 5.59070700e-04 3.70370370e-02 1.56538301e-01 0.00000000e+00] | [0.00000000e+00 9.20834531e-01 8.92075255e-01 7.48664032e-01 9.03709011e-01 7.40703529e-01 nan 4.40828188e-01 7.92719139e-01 0.00000000e+00 9.21593374e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.90292855e-01 0.00000000e+00 0.00000000e+00 8.42229041e-01 0.00000000e+00 4.75170857e-01 6.72591473e-01 0.00000000e+00 nan 0.00000000e+00 2.94713089e-01 0.00000000e+00 0.00000000e+00 9.26034809e-01 8.39522012e-01 9.66679296e-01 6.06188900e-04 1.12807676e-01 2.07280968e-01 0.00000000e+00] | 6 | | 0.4495 | 0.4797 | 0.3035 | 0.3702 | 0.8468 | [0.00000000e+00 7.52163526e-01 8.46563375e-01 7.16396797e-01 7.38850637e-01 3.93073019e-01 nan 3.31795957e-01 4.92991567e-01 0.00000000e+00 8.11302090e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.16059849e-01 0.00000000e+00 0.00000000e+00 6.56058294e-01 1.25948501e-02 2.66942435e-01 5.34406894e-01 0.00000000e+00 nan 0.00000000e+00 2.27750085e-01 4.86381323e-04 0.00000000e+00 8.48618960e-01 7.25828093e-01 9.17747637e-01 8.28380212e-03 6.74590297e-02 1.51281596e-01 0.00000000e+00] | [0.00000000e+00 8.75360044e-01 9.43650850e-01 8.78658645e-01 7.76578096e-01 4.85757596e-01 nan 4.30901582e-01 7.54126335e-01 0.00000000e+00 9.30112537e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.42914247e-01 0.00000000e+00 0.00000000e+00 7.57605356e-01 1.27102686e-02 6.50888458e-01 6.94757080e-01 0.00000000e+00 nan 0.00000000e+00 2.91727649e-01 4.86381323e-04 0.00000000e+00 9.42251577e-01 8.60753175e-01 9.56778008e-01 8.51551074e-03 1.38756779e-01 1.83583708e-01 0.00000000e+00] | 7 | | 0.4193 | 0.4487 | 0.3073 | 0.3633 | 0.8594 | [0. 0.77081114 0.86089485 0.64464211 0.82962632 0.36186873 nan 0.39092332 0.5399988 0. 0.81734925 0. 0. 0. 0. 0.50271555 0. 0. 0.70239658 0. 0.30875695 0.52195319 0. nan 0. 0.20124517 0.00696273 0. 0.84526591 0.72563399 0.91703372 0. 0.03526147 0.15693635 0. ] | [0. 0.8654775 0.95711297 0.70665759 0.93130714 0.42436958 nan 0.52892143 0.69243377 0. 0.91682626 0. 0. 0. 0. 0.62315913 0. 0. 0.86251114 0. 0.5607807 0.70416055 0. nan 0. 0.24483525 0.00698305 0. 0.921099 0.81848055 0.96789871 0. 0.06891948 0.18778302 0. ] | 8 | | 0.3883 | 0.4824 | 0.3086 | 0.3690 | 0.8527 | [0. 0.76454291 0.86544951 0.70501066 0.77912256 0.39088976 nan 0.40275725 0.53334923 0. 0.82777802 0. 0. 0. 0. 0.49916177 0. 0. 0.68780083 0.01500768 0.31589145 0.53805504 0. nan 0. 0.22450413 0.03544121 0. 0.82663975 0.60689445 0.91513911 0.12702194 0.0163284 0.10604071 0. ] | [0. 0.86846682 0.93345513 0.77258597 0.90365389 0.54440067 nan 0.51997559 0.73323435 0. 0.92499729 0. 0. 0. 0. 0.62015064 0. 0. 0.8190305 0.01503264 0.61258781 0.62514291 0. nan 0. 0.28141855 0.03574903 0. 0.95838638 0.66828866 0.96505306 0.19804095 0.04463913 0.1315269 0. ] | 9 | | 0.3736 | 0.4515 | 0.3180 | 0.3859 | 0.8600 | [0. 0.77296038 0.8679117 0.60122746 0.84573808 0.42877201 nan 0.40372521 0.5356554 0. 0.82057963 0. 0. 0. 0. 0.48309209 0. 0. 0.70156487 0.07165346 0.31172072 0.45383525 0. nan 0. 0.26337213 0.07457255 0. 0.85227381 0.7079085 0.92271657 0.20363628 0.03853875 0.13249146 0. ] | [0. 0.90081404 0.93156248 0.71723323 0.91251575 0.57187527 nan 0.53665381 0.74547838 0. 0.93718616 0. 0. 0. 0. 0.6410839 0. 0. 0.80529967 0.07249561 0.6074764 0.5775282 0. nan 0. 0.34898163 0.07545859 0. 0.95221746 0.80297775 0.96768443 0.26155608 0.19382562 0.17354842 0. ] | 10 | | 0.3487 | 0.4486 | 0.3181 | 0.3898 | 0.8637 | [0. 0.79416982 0.87767891 0.70942695 0.81634288 0.46749785 nan 0.42873013 0.48671464 0. 0.82752704 0. 0. 0. 0. 0.50844774 0. 0. 0.68070149 0.03976498 0.29304387 0.46322705 0. nan 0. 0.24856882 0.12795031 0. 0.84646906 0.71781094 0.92550642 0.04810685 0.04610752 0.14423047 0. ] | [0. 0.86951324 0.95247608 0.82408892 0.90393017 0.59760857 nan 0.5760741 0.83602638 0. 0.93420702 0. 0. 0. 0. 0.63502483 0. 0. 0.76902695 0.04024918 0.57179186 0.75842139 0. nan 0. 0.30837498 0.13239994 0. 0.95283514 0.78607095 0.96594744 0.05354669 0.18906967 0.2060098 0. ] | 11 | | 0.3460 | 0.4342 | 0.3234 | 0.3852 | 0.8669 | [0. 0.76828673 0.86958873 0.66044471 0.84588115 0.46323947 nan 0.41208499 0.54202812 0. 0.82543751 0. 0. 0. 0. 0.50071248 0. 0. 0.72333932 0.0173886 0.36535728 0.5284402 0. nan 0. 0.24239821 0.13456635 0. 0.86084123 0.73217705 0.92386442 0.09545854 0.04193608 0.11945951 0. ] | [0. 0.92666259 0.91906703 0.74134089 0.92518489 0.60022437 nan 0.56316038 0.77045814 0. 0.93600314 0. 0. 0. 0. 0.61358664 0. 0. 0.87835072 0.01757469 0.57608316 0.64108174 0. nan 0. 0.30432247 0.13750695 0. 0.93332326 0.85806371 0.96442783 0.10753599 0.15152274 0.14552189 0. ] | 12 | | 0.3146 | 0.4175 | 0.3339 | 0.3995 | 0.8745 | [0. 0.81054591 0.88286867 0.68551149 0.86089895 0.4562385 nan 0.4522713 0.55496016 0.01456189 0.83576109 0. 0. 0. 0. 0.50709788 0. 0. 0.73464008 0.00175153 0.35021502 0.57263292 0. nan 0. 0.25185222 0.14419755 0. 0.85952374 0.70281003 0.9270307 0.17660456 0.04867831 0.18762581 0. ] | [0. 0.9092016 0.94168672 0.86545289 0.89611216 0.55273728 nan 0.61409823 0.76682349 0.01569689 0.92776282 0. 0. 0. 0. 0.59972229 0. 0. 0.86700656 0.00175747 0.54181633 0.67419762 0. nan 0. 0.3252672 0.14789466 0. 0.9316378 0.88743565 0.97060047 0.33277846 0.15319149 0.25967892 0. ] | 13 | | 0.3000 | 0.4196 | 0.3263 | 0.3833 | 0.8720 | [0.00000000e+00 8.02547730e-01 8.74182776e-01 6.55641045e-01 8.69918767e-01 4.12920686e-01 nan 4.34054109e-01 5.54604573e-01 3.14830157e-03 8.29634841e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.98619437e-01 0.00000000e+00 0.00000000e+00 7.20371619e-01 1.62799781e-02 3.73295478e-01 5.20323501e-01 0.00000000e+00 nan 3.48000087e-04 2.41829304e-01 1.50045164e-01 0.00000000e+00 8.67415087e-01 7.31957881e-01 9.29791719e-01 1.28032094e-01 2.77808135e-02 1.25956544e-01 0.00000000e+00] | [0.00000000e+00 9.10809038e-01 9.53614030e-01 6.91330346e-01 9.25106631e-01 4.73740259e-01 nan 5.64222160e-01 7.49045544e-01 3.42805593e-03 9.38335743e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.77484642e-01 0.00000000e+00 0.00000000e+00 8.68434883e-01 1.63507406e-02 5.76763406e-01 7.07811962e-01 0.00000000e+00 nan 3.51671539e-04 3.02660657e-01 1.55815731e-01 0.00000000e+00 9.39832349e-01 8.43146236e-01 9.70195728e-01 2.11579170e-01 1.06049228e-01 1.61502816e-01 0.00000000e+00] | 14 | | 0.3000 | 0.4375 | 0.3296 | 0.4004 | 0.8666 | [0. 0.78266617 0.87516084 0.70472612 0.86490176 0.45228049 nan 0.42625351 0.54739354 0. 0.82459025 0. 0. 0. 0. 0.51809119 0. 0. 0.69081711 0.12347692 0.35720113 0.50921058 0. nan 0.00489936 0.24630062 0.14805039 0. 0.86169724 0.71926146 0.92796331 0.08257639 0.06410606 0.14539247 0. ] | [0. 0.9075929 0.9264549 0.93787289 0.92618179 0.57743083 nan 0.55003982 0.78286607 0. 0.94643176 0. 0. 0. 0. 0.62538921 0. 0. 0.80130182 0.13309691 0.69176706 0.69506169 0. nan 0.00507726 0.30979772 0.15393969 0. 0.93923901 0.84161243 0.9636732 0.1240378 0.17630371 0.19733096 0. ] | 15 | | 0.2958 | 0.4558 | 0.3321 | 0.3960 | 0.8649 | [0.00000000e+00 7.61108709e-01 8.60621205e-01 6.66132134e-01 8.52805958e-01 4.61529893e-01 nan 4.08367412e-01 5.31449716e-01 0.00000000e+00 8.35699926e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.07030790e-01 0.00000000e+00 0.00000000e+00 7.07129610e-01 6.48353710e-02 3.15606022e-01 5.11721371e-01 0.00000000e+00 nan 6.30311903e-04 2.73874288e-01 2.03863944e-01 0.00000000e+00 8.66259515e-01 7.58237242e-01 9.29139752e-01 2.50199629e-01 3.09762934e-02 1.61355571e-01 0.00000000e+00] | [0.00000000e+00 9.26534567e-01 9.27389090e-01 7.04037518e-01 9.24733729e-01 5.57765301e-01 nan 5.03121563e-01 8.16946898e-01 0.00000000e+00 9.33051726e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.15985021e-01 0.00000000e+00 0.00000000e+00 8.13389275e-01 6.50734371e-02 6.70053085e-01 6.00421712e-01 0.00000000e+00 nan 6.81363606e-04 3.40217727e-01 2.25090328e-01 0.00000000e+00 9.48289295e-01 8.67999126e-01 9.71413074e-01 3.64800631e-01 8.85273258e-02 2.01834862e-01 0.00000000e+00] | 16 | | 0.2734 | 0.4359 | 0.3324 | 0.3921 | 0.8700 | [0.00000000e+00 7.69753150e-01 8.66375446e-01 7.12734804e-01 8.60891290e-01 4.75851982e-01 nan 4.34175617e-01 5.46685274e-01 3.24503811e-02 8.38429371e-01 6.02288697e-04 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.86131744e-01 0.00000000e+00 0.00000000e+00 7.27408322e-01 8.04817093e-02 3.54733831e-01 5.42941315e-01 0.00000000e+00 nan 2.71841412e-04 2.58488818e-01 1.79625596e-01 0.00000000e+00 8.66870562e-01 7.48169480e-01 9.27856685e-01 7.49529761e-02 5.06564228e-02 1.31117070e-01 0.00000000e+00] | [0.00000000e+00 9.25702577e-01 9.27332221e-01 7.66360378e-01 9.04612005e-01 5.93608265e-01 nan 6.05902185e-01 7.70458139e-01 3.87911592e-02 9.33542542e-01 1.19000397e-03 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.85184718e-01 0.00000000e+00 0.00000000e+00 8.77167736e-01 9.19689932e-02 5.50758130e-01 6.18987337e-01 0.00000000e+00 nan 2.85733125e-04 3.36777474e-01 1.97679266e-01 0.00000000e+00 9.34506301e-01 8.56945493e-01 9.73108663e-01 8.09021630e-02 2.00250313e-01 1.67017963e-01 0.00000000e+00] | 17 | | 0.2616 | 0.4510 | 0.3349 | 0.4009 | 0.8726 | [0. 0.79136177 0.88249795 0.78534299 0.86229779 0.47690438 nan 0.43122561 0.54187893 0.01320356 0.83487865 0.01648963 0. 0.00144389 0. 0.51996092 0. 0. 0.70356624 0.09487574 0.37358034 0.43221057 0. nan 0.05030614 0.28552575 0.19602978 0. 0.86214818 0.75003141 0.92402054 0.03367055 0.0295635 0.15961857 0. ] | [0. 0.9308775 0.94322916 0.85211162 0.90509242 0.57554177 nan 0.5785717 0.8193957 0.0157871 0.93896272 0.03232844 0. 0.00144389 0. 0.6718842 0. 0. 0.80146551 0.09782199 0.57800629 0.60986079 0. nan 0.05345407 0.40116852 0.21407727 0. 0.93948742 0.87904777 0.97272876 0.03561119 0.11522737 0.2649179 0. ] | 18 | | 0.2570 | 0.4381 | 0.3378 | 0.4040 | 0.8691 | [0. 0.78633412 0.8781239 0.70951789 0.85768155 0.49725305 nan 0.4385802 0.5419402 0.01325455 0.84049064 0.03469167 0. 0. 0. 0.52032603 0. 0. 0.68820155 0.07929718 0.30712852 0.51640481 0. nan 0.01769049 0.26803817 0.21887178 0. 0.85998636 0.71539146 0.93235425 0.24885785 0.05621853 0.11969413 0. ] | [0. 0.91321796 0.93586512 0.7493935 0.91472526 0.63834931 nan 0.58292224 0.81417994 0.01497519 0.94252235 0.05394685 0. 0. 0. 0.64331398 0. 0. 0.82029437 0.08115742 0.56811405 0.59644195 0. nan 0.01995736 0.34179208 0.24586576 0. 0.94413845 0.83304234 0.96807676 0.34801978 0.20125156 0.15898892 0. ] | 19 | | 0.2617 | 0.4168 | 0.3396 | 0.3963 | 0.8781 | [0.00000000e+00 7.94986290e-01 8.78321279e-01 7.49897343e-01 8.49326301e-01 5.23130579e-01 nan 4.50929207e-01 5.51662857e-01 2.18050542e-02 8.41160082e-01 1.61248710e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.99800580e-01 0.00000000e+00 0.00000000e+00 7.33030551e-01 3.70162822e-02 3.87012787e-01 5.37036435e-01 0.00000000e+00 nan 2.52828519e-04 2.58401363e-01 2.18729726e-01 0.00000000e+00 8.68371051e-01 7.68056025e-01 9.33727233e-01 9.82409932e-02 3.83513478e-02 1.51214616e-01 0.00000000e+00] | [0.00000000e+00 9.07468689e-01 9.33071883e-01 8.06640187e-01 9.49407168e-01 6.81786840e-01 nan 5.64532420e-01 8.05049940e-01 2.72440235e-02 9.42797113e-01 2.47917493e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.80009257e-01 0.00000000e+00 0.00000000e+00 8.80850860e-01 3.73148381e-02 5.59445628e-01 5.88173859e-01 0.00000000e+00 nan 2.63753654e-04 3.24654954e-01 2.34262090e-01 0.00000000e+00 9.43142842e-01 8.79414683e-01 9.68600549e-01 1.42839273e-01 9.82895286e-02 1.98829554e-01 0.00000000e+00] | 20 | | 0.2444 | 0.4463 | 0.3361 | 0.3952 | 0.8747 | [0.00000000e+00 8.09356188e-01 8.73758666e-01 7.03484664e-01 8.50663613e-01 4.40395666e-01 nan 4.70255723e-01 5.66815751e-01 5.21693766e-04 8.47186281e-01 1.10941303e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.16683571e-01 0.00000000e+00 0.00000000e+00 7.12799896e-01 5.64981182e-02 3.71929696e-01 5.03181014e-01 0.00000000e+00 nan 1.16099071e-03 2.60644571e-01 2.24766447e-01 0.00000000e+00 8.67722068e-01 7.66105359e-01 9.35288074e-01 1.30229066e-01 4.53205481e-02 1.26864531e-01 0.00000000e+00] | [0.00000000e+00 9.19159433e-01 9.48361579e-01 7.32578878e-01 9.32163864e-01 5.01168899e-01 nan 5.99960700e-01 7.73417917e-01 5.41271989e-04 9.21135986e-01 1.94367315e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.32331903e-01 0.00000000e+00 0.00000000e+00 8.67884025e-01 5.84201607e-02 5.18153787e-01 7.57499634e-01 0.00000000e+00 nan 1.25282986e-03 3.28637485e-01 2.39056420e-01 0.00000000e+00 9.39480431e-01 8.67690868e-01 9.70610826e-01 2.31997152e-01 1.16729245e-01 1.65141676e-01 0.00000000e+00] | 21 | | 0.2327 | 0.4708 | 0.3333 | 0.3991 | 0.8674 | [0.00000000e+00 8.01482811e-01 8.67112634e-01 7.26469941e-01 8.47789494e-01 4.26344060e-01 nan 4.59877772e-01 5.61767489e-01 3.08784808e-02 8.50980045e-01 8.21140639e-04 0.00000000e+00 5.56009812e-02 0.00000000e+00 5.19460186e-01 0.00000000e+00 0.00000000e+00 6.66718429e-01 1.00749376e-01 3.06011822e-01 4.73609191e-01 0.00000000e+00 nan 5.21670878e-02 2.74661980e-01 2.16300138e-01 0.00000000e+00 8.70513680e-01 7.58257933e-01 9.31855744e-01 4.02310154e-04 5.41590367e-02 1.45066810e-01 0.00000000e+00] | [0.00000000e+00 8.96213795e-01 9.66916102e-01 7.73539637e-01 8.80813661e-01 4.61368313e-01 nan 6.05202376e-01 7.84196522e-01 3.49120433e-02 9.33452042e-01 1.09083697e-03 0.00000000e+00 5.61056106e-02 0.00000000e+00 6.42177901e-01 0.00000000e+00 0.00000000e+00 7.70254403e-01 1.04428195e-01 7.01999428e-01 6.97985197e-01 0.00000000e+00 nan 5.98940589e-02 3.48124479e-01 2.34748471e-01 0.00000000e+00 9.37775670e-01 8.78224080e-01 9.72937862e-01 4.32992071e-04 2.44305382e-01 1.83551218e-01 0.00000000e+00] | 22 | | 0.2307 | 0.4395 | 0.3472 | 0.4131 | 0.8740 | [0. 0.78921013 0.87836164 0.7238651 0.85405051 0.48305222 nan 0.46174517 0.55335413 0.01711339 0.84971971 0.07427615 0. 0.19647651 0. 0.52684296 0. 0. 0.70735442 0.11184526 0.39826268 0.50387815 0. nan 0.05343915 0.27936942 0.23151827 0. 0.87119512 0.76032244 0.93287485 0.00244547 0.02821955 0.16774339 0. ] | [0. 0.92820839 0.93256894 0.76932845 0.93401204 0.61981657 nan 0.61634072 0.81936678 0.01975643 0.94590099 0.1012495 0. 0.2415429 0. 0.6241269 0. 0. 0.81714715 0.1280285 0.64358053 0.67924551 0. nan 0.06437787 0.34337226 0.26928155 0. 0.94337279 0.8780219 0.97322357 0.00300208 0.10171047 0.23578672 0. ] | 23 | | 0.2314 | 0.4319 | 0.3499 | 0.4182 | 0.8767 | [0. 0.81268914 0.88090286 0.77985058 0.85570746 0.45828635 nan 0.47299524 0.53277529 0.04378213 0.84593985 0.03478787 0. 0.1971561 0. 0.54043589 0. 0. 0.7359562 0.05923714 0.36875898 0.54834606 0. nan 0.0091682 0.2642792 0.23962131 0. 0.86379601 0.6984222 0.9344155 0.19722481 0.0095982 0.16301427 0. ] | [0. 0.89305484 0.93568687 0.88020793 0.94148981 0.58722529 nan 0.61994664 0.81367861 0.04916554 0.95151603 0.06277271 0. 0.25453795 0. 0.66359505 0. 0. 0.86745813 0.05975395 0.56066626 0.69054391 0. nan 0.01118755 0.32281238 0.27171345 0. 0.9460712 0.8195451 0.97224534 0.37103572 0.02778473 0.2253981 0. ] | 24 | | 0.2280 | 0.4165 | 0.3413 | 0.4040 | 0.8751 | [0. 0.80627187 0.88428648 0.68558419 0.86189479 0.47772167 nan 0.46152926 0.58013249 0.03674413 0.85526933 0.00621328 0. 0.00391914 0. 0.51552043 0. 0. 0.7219038 0.11633219 0.37078391 0.48759114 0. nan 0.04097689 0.25837403 0.23783935 0. 0.86459819 0.69037029 0.93378787 0.16489662 0.01161391 0.18831714 0. ] | [0. 0.90036072 0.93838536 0.72247672 0.95316477 0.6161249 nan 0.61164372 0.77422776 0.05448805 0.94020362 0.00912336 0. 0.00391914 0. 0.64499705 0. 0. 0.8629953 0.12306992 0.55344734 0.64075339 0. nan 0.04945381 0.31623508 0.26601584 0. 0.93210162 0.89534679 0.97086444 0.24202332 0.02736754 0.28242179 0. ] | 25 | | 0.2332 | 0.4404 | 0.3516 | 0.4245 | 0.8748 | [0. 0.79554848 0.87301633 0.83834717 0.85459995 0.51844513 nan 0.47147093 0.51906075 0.07904743 0.85844229 0.11856842 0. 0.20806579 0. 0.49652457 0. 0. 0.71537872 0.12687546 0.36603458 0.48369829 0. nan 0.01636133 0.26809207 0.24211655 0. 0.86537401 0.74525303 0.93088227 0.00370986 0.03710524 0.17143043 0. ] | [0. 0.90971751 0.92232534 0.87320268 0.9481476 0.68155153 nan 0.66184212 0.82753268 0.10690122 0.9341899 0.13962713 0. 0.38098185 0. 0.59963814 0. 0. 0.82473535 0.13202988 0.64751899 0.62376621 0. nan 0.01872651 0.32878909 0.28248332 0. 0.9485047 0.86660759 0.97579904 0.00397391 0.12273675 0.24574891 0. ] | 26 | | 0.2146 | 0.4515 | 0.3472 | 0.4129 | 0.8760 | [0. 0.77768215 0.87939478 0.75655563 0.85872515 0.51912277 nan 0.47242163 0.57270343 0.04912978 0.84772409 0.04133148 0. 0.28216704 0. 0.50142357 0. 0. 0.72646668 0.08708308 0.41888468 0.46264328 0. nan 0.00952928 0.24906863 0.23188316 0. 0.86985544 0.75426408 0.93570207 0. 0.00161353 0.15174018 0. ] | [0. 0.91082524 0.9223558 0.86526048 0.93459861 0.70221741 nan 0.61752143 0.76844318 0.064682 0.94790176 0.04432765 0. 0.38675743 0. 0.58171337 0. 0. 0.86150488 0.08919156 0.64534791 0.76292334 0. nan 0.01094578 0.29978017 0.26035297 0. 0.9390376 0.84793111 0.96900737 0. 0.00383813 0.18862775 0. ] | 27 | | 0.2245 | 0.4819 | 0.3481 | 0.4183 | 0.8677 | [0. 0.74754716 0.87589221 0.7595096 0.75176585 0.46424109 nan 0.43492805 0.55661905 0.04973311 0.85506372 0.1407866 0. 0.15455217 0. 0.4863142 0. 0. 0.72532016 0.16110796 0.37871237 0.54738549 0. nan 0.03596414 0.27015132 0.27383189 0. 0.87155837 0.74696253 0.93097913 0.03859201 0.03974808 0.19051036 0. ] | [0. 0.92496748 0.93198417 0.91066332 0.76595448 0.59939476 nan 0.57168392 0.76530022 0.06892197 0.94636898 0.19843316 0. 0.30218647 0. 0.5681646 0. 0. 0.83417317 0.18737447 0.68308592 0.66512645 0. nan 0.03932127 0.32249168 0.30377988 0. 0.94566641 0.86854326 0.97023299 0.04389577 0.11848144 0.26846741 0. ] | 28 | | 0.2265 | 0.4246 | 0.3481 | 0.4050 | 0.8802 | [0. 0.81690969 0.8780207 0.77104697 0.84868605 0.47437381 nan 0.4670048 0.56430385 0.0503272 0.85498949 0.10595414 0. 0.11204925 0. 0.53524176 0. 0. 0.74013112 0.04461066 0.39307836 0.51529041 0. nan 0.01267638 0.28575942 0.24784411 0. 0.87024598 0.76078812 0.93874897 0.00447477 0.03791316 0.15759319 0. ] | [0. 0.90897891 0.95223635 0.79356326 0.91900275 0.59798835 nan 0.59817326 0.81432455 0.05827695 0.94238457 0.11364538 0. 0.13139439 0. 0.65023563 0. 0. 0.88394096 0.04698092 0.61423758 0.59820238 0. nan 0.01437457 0.35860267 0.28456782 0. 0.93586082 0.86061465 0.96934172 0.00557116 0.10738423 0.20431221 0. ] | 29 | | 0.2067 | 0.4302 | 0.3582 | 0.4204 | 0.8782 | [0. 0.80612851 0.87374492 0.77399105 0.80074838 0.5069675 nan 0.46617634 0.55019827 0.13081984 0.85635853 0.13375531 0. 0.27468398 0. 0.54140892 0. 0. 0.7462191 0.05761157 0.39168026 0.5446341 0. nan 0.01506284 0.29841736 0.22899218 0. 0.87061226 0.74041425 0.93770948 0.13239158 0.00821369 0.13209342 0. ] | [0. 0.90557948 0.95169451 0.78927679 0.84910476 0.62275304 nan 0.60393893 0.82533454 0.18655841 0.94690627 0.1592622 0. 0.33168317 0. 0.68313978 0. 0. 0.88132122 0.06502636 0.62727359 0.66106362 0. nan 0.01804515 0.38855037 0.25931073 0. 0.94157236 0.8393111 0.97364591 0.18567662 0.01985816 0.15624759 0. ] | 30 | | 0.1993 | 0.4191 | 0.3525 | 0.4026 | 0.8855 | [0.00000000e+00 8.12143438e-01 8.82519501e-01 8.32421151e-01 8.74313051e-01 4.81253823e-01 nan 4.87073361e-01 5.86132068e-01 9.62771937e-02 8.59982957e-01 8.85474149e-02 0.00000000e+00 2.01491034e-04 0.00000000e+00 5.35389616e-01 0.00000000e+00 0.00000000e+00 7.53505814e-01 3.69389833e-02 3.98315791e-01 5.86352445e-01 0.00000000e+00 nan 3.86967641e-02 2.99523304e-01 2.23544639e-01 0.00000000e+00 8.66545952e-01 7.59345221e-01 9.36605085e-01 5.82816319e-04 7.04036476e-04 1.95231882e-01 0.00000000e+00] | [0.00000000e+00 9.18027876e-01 9.52593301e-01 8.60861913e-01 9.17933036e-01 5.59645609e-01 nan 6.69381444e-01 7.81217462e-01 1.31348669e-01 9.42924301e-01 1.03431178e-01 0.00000000e+00 2.06270627e-04 0.00000000e+00 6.30669865e-01 0.00000000e+00 0.00000000e+00 8.97879175e-01 3.70010043e-02 4.89103277e-01 6.59469339e-01 0.00000000e+00 nan 4.58931358e-02 3.79932245e-01 2.46004725e-01 0.00000000e+00 9.44760882e-01 8.53162772e-01 9.75178979e-01 5.96566854e-04 1.75219024e-03 2.87445529e-01 0.00000000e+00] | 31 | | 0.2068 | 0.4805 | 0.3370 | 0.3952 | 0.8643 | [0. 0.77056757 0.8601312 0.79546358 0.80826542 0.46090981 nan 0.47734482 0.58905088 0.03181978 0.85901467 0.01694625 0. 0.00549451 0. 0.48326241 0. 0. 0.71413255 0.08548594 0.355285 0.56037404 0. nan 0.11377479 0.28155688 0.23155416 0. 0.84077004 0.62872483 0.94074387 0.04323906 0.00477968 0.16213294 0. ] | [0. 0.86625295 0.93033124 0.83741848 0.95175277 0.58905634 nan 0.60932022 0.77904824 0.04077582 0.93578138 0.01963507 0. 0.00556931 0. 0.57342422 0. 0. 0.8469629 0.09113733 0.63002638 0.69225687 0. nan 0.12851397 0.34756471 0.25621873 0. 0.94994706 0.68278681 0.96967504 0.05913709 0.01677096 0.23138435 0. ] | 32 | | 0.2026 | 0.4072 | 0.3583 | 0.4178 | 0.8851 | [0. 0.82427491 0.88959049 0.80744875 0.87290087 0.48819667 nan 0.49479206 0.5689031 0.01115143 0.86655557 0.12424298 0. 0.08815858 0. 0.54211487 0. 0. 0.73966336 0.07793422 0.39875788 0.55704844 0. nan 0.12136758 0.29085268 0.26794571 0. 0.86907912 0.7468531 0.94141202 0.04189236 0.01138124 0.18175892 0. ] | [0. 0.90820792 0.94843014 0.86590797 0.935603 0.6234152 nan 0.65185001 0.7529887 0.01136671 0.9412309 0.13427211 0. 0.10457921 0. 0.6997812 0. 0. 0.87681775 0.08373086 0.56962713 0.71690686 0. nan 0.15183419 0.36895259 0.31343802 0. 0.93965024 0.8725032 0.97198241 0.05008275 0.04046725 0.25251491 0. ] | 33 | | 0.1890 | 0.4580 | 0.3568 | 0.4229 | 0.8760 | [0. 0.81909166 0.87945582 0.84022719 0.85181051 0.44449375 nan 0.46587584 0.56531144 0.05849796 0.85834655 0.13038109 0. 0.23667513 0. 0.53010343 0. 0. 0.70286385 0.08324543 0.31813212 0.48792893 0. nan 0.08722779 0.30648587 0.25211314 0. 0.870058 0.73406627 0.93603361 0.09757433 0.04511995 0.17383064 0. ] | [0. 0.89810904 0.95241393 0.90661944 0.91497772 0.51961956 nan 0.65114676 0.83394393 0.06865133 0.95007864 0.14656882 0. 0.30775578 0. 0.61461752 0. 0. 0.81590436 0.09039982 0.61695222 0.59357779 0. nan 0.09587445 0.39630552 0.2932532 0. 0.94689981 0.84862376 0.97537462 0.14406127 0.14217772 0.23057617 0. ] | 34 | | 0.1856 | 0.4192 | 0.3656 | 0.4327 | 0.8810 | [0. 0.80138932 0.88262901 0.81089302 0.86535724 0.47775953 nan 0.47998869 0.57502219 0.0555217 0.8547197 0.09356223 0. 0.29941446 0. 0.4994726 0. 0. 0.74427779 0.10476547 0.39876117 0.530613 0. nan 0.06260014 0.28825048 0.26307467 0. 0.86958647 0.76353802 0.93445836 0.20078295 0.02415195 0.18310195 0. ] | [0. 0.92397697 0.93724033 0.85106091 0.92750395 0.5475243 nan 0.64661523 0.82444757 0.07397384 0.94253458 0.12971043 0. 0.39026403 0. 0.56784903 0. 0. 0.88627818 0.11856641 0.54413363 0.67935232 0. nan 0.0841594 0.35805457 0.30635075 0. 0.93943008 0.852706 0.97181886 0.43921754 0.0738423 0.26218876 0. ] | 35 | | 0.1823 | 0.4526 | 0.3522 | 0.4102 | 0.8767 | [0. 0.78316685 0.87642881 0.75047304 0.86249292 0.46791957 nan 0.49549382 0.57114384 0.08703693 0.86072555 0.10211813 0. 0.18376371 0. 0.49874928 0. 0. 0.73435033 0.05303611 0.39974749 0.45439447 0. nan 0.03187949 0.28929847 0.252677 0. 0.8723413 0.74111546 0.93814337 0.0911524 0.01717172 0.20816307 0. ] | [0. 0.92816869 0.93137636 0.78767061 0.92675339 0.59854763 nan 0.62024483 0.71829085 0.14009923 0.95105293 0.11761206 0. 0.21431518 0. 0.56629218 0. 0. 0.87048153 0.05749435 0.53241044 0.64072965 0. nan 0.03600237 0.35849772 0.27463174 0. 0.93890724 0.88137406 0.97292233 0.14287776 0.04397163 0.28736837 0. ] | 36 | | 0.1828 | 0.4314 | 0.3540 | 0.4137 | 0.8837 | [0.00000000e+00 8.08817183e-01 8.86533437e-01 8.29367464e-01 8.66921982e-01 5.02412424e-01 nan 4.88635866e-01 5.60640323e-01 8.39031061e-02 8.56029524e-01 1.48001648e-01 0.00000000e+00 2.88729590e-02 0.00000000e+00 5.27888135e-01 0.00000000e+00 0.00000000e+00 7.40145106e-01 5.94355934e-02 3.83677842e-01 5.51371204e-01 0.00000000e+00 nan 2.54484244e-02 2.99810052e-01 2.57164681e-01 0.00000000e+00 8.66461858e-01 7.59758000e-01 9.39794819e-01 8.55545803e-03 2.93707321e-04 2.00986041e-01 0.00000000e+00] | [0.00000000e+00 9.20986697e-01 9.42897048e-01 8.79794678e-01 9.16674152e-01 6.25797872e-01 nan 6.53392696e-01 7.86385022e-01 1.54984213e-01 9.51069237e-01 1.78103927e-01 0.00000000e+00 2.99092409e-02 0.00000000e+00 6.40158209e-01 0.00000000e+00 0.00000000e+00 8.70579667e-01 6.04130053e-02 5.57868972e-01 7.35243038e-01 0.00000000e+00 nan 3.09031365e-02 3.74579444e-01 2.92419400e-01 0.00000000e+00 9.45130703e-01 8.41614926e-01 9.69892426e-01 8.91001463e-03 6.67501043e-04 2.82612669e-01 0.00000000e+00] | 37 | | 0.1824 | 0.4277 | 0.3516 | 0.4128 | 0.8808 | [0. 0.80850849 0.8835188 0.81832156 0.87084804 0.52909381 nan 0.48544633 0.57416469 0.06544565 0.86014741 0.09572506 0. 0.04364361 0. 0.53546177 0. 0. 0.72880369 0.07815572 0.36619794 0.45105441 0. nan 0.02904442 0.31295304 0.268757 0. 0.87009835 0.77016379 0.93780115 0.02053909 0.00775031 0.19176263 0. ] | [0. 0.93462002 0.91830503 0.88981486 0.94415116 0.74919228 nan 0.63542345 0.80433651 0.09896256 0.95110348 0.11324871 0. 0.04971122 0. 0.61501725 0. 0. 0.85270915 0.08732425 0.54361868 0.58993825 0. nan 0.03250764 0.39146001 0.30016676 0. 0.9446708 0.87850239 0.97614375 0.02966477 0.01535252 0.27686197 0. ] | 38 | | 0.1853 | 0.4315 | 0.3703 | 0.4396 | 0.8843 | [0. 0.82385333 0.88384746 0.82923402 0.87047461 0.44946715 nan 0.50195066 0.5526193 0.13775167 0.85419626 0.11663244 0. 0.24123441 0. 0.50296284 0. 0. 0.75525625 0.01028213 0.42676119 0.62702595 0.06111111 nan 0.01903464 0.32208879 0.27514231 0. 0.86733642 0.75225439 0.93747993 0.18324185 0.01936191 0.19849636 0. ] | [0. 0.90365119 0.95252047 0.86895636 0.95780426 0.51196152 nan 0.66021153 0.84474181 0.20306721 0.91545736 0.16898056 0. 0.37891914 0. 0.57321383 0. 0. 0.86912221 0.01029375 0.54801488 0.73707468 0.06340058 nan 0.02149592 0.43258561 0.31736381 0. 0.94644158 0.84006614 0.97630627 0.4108325 0.04714226 0.34773038 0. ] | 39 | | 0.1750 | 0.4506 | 0.3588 | 0.4247 | 0.8777 | [0. 0.8085732 0.87850952 0.80281118 0.80588458 0.43532471 nan 0.49235495 0.56953178 0.04144004 0.86063777 0.1253508 0. 0.29125623 0. 0.56867524 0. 0. 0.72796854 0.03398534 0.34825502 0.58349517 0.04278075 nan 0.02356575 0.3145996 0.25091045 0. 0.86821676 0.7616297 0.94167999 0.0760155 0.01097801 0.17674597 0. ] | [0. 0.8587409 0.96059527 0.84914689 0.95691624 0.49065318 nan 0.64206647 0.80599476 0.04330176 0.94759276 0.13288378 0. 0.49401815 0. 0.68717916 0. 0. 0.83929688 0.03427065 0.62310627 0.69531488 0.04610951 nan 0.02716663 0.42729112 0.28005142 0. 0.94607512 0.87723316 0.97355068 0.11214495 0.02653317 0.23750462 0. ] | 40 | | 0.1856 | 0.4630 | 0.3468 | 0.4169 | 0.8702 | [0. 0.79680775 0.87408906 0.80508886 0.7836208 0.54111501 nan 0.46906575 0.51571781 0.05906675 0.84646062 0.12752339 0. 0.02326951 0. 0.49396166 0. 0. 0.70301274 0.10455404 0.36326525 0.45432265 0.00288184 nan 0.06241517 0.29796314 0.27257073 0. 0.85824781 0.70264792 0.93738763 0.10944047 0.07468448 0.16378606 0. ] | [0. 0.92066362 0.93733642 0.8457505 0.80518061 0.72000372 nan 0.61879522 0.83036713 0.090212 0.93724404 0.17978977 0. 0.03259076 0. 0.52749727 0. 0. 0.80827055 0.12335237 0.6834038 0.55220964 0.00288184 nan 0.08086247 0.38878944 0.30794886 0. 0.93395437 0.8773043 0.97470902 0.14777538 0.20984564 0.21967583 0. ] | 41 | | 0.1886 | 0.4251 | 0.3676 | 0.4456 | 0.8827 | [0. 0.81869055 0.88872516 0.83047488 0.84276443 0.50458334 nan 0.50154127 0.55514409 0.11767963 0.8593002 0.18142472 0. 0.17150681 0. 0.52765579 0. 0. 0.72523268 0.11768383 0.38784377 0.55537055 0.03125 nan 0.04156492 0.2927821 0.28836848 0. 0.87210633 0.77489312 0.94023927 0.01637101 0.07383087 0.2126436 0. ] | [0. 0.91039924 0.94692984 0.8782822 0.93049806 0.63547194 nan 0.64587233 0.81659018 0.1533604 0.95128203 0.25357001 0. 0.5554868 0. 0.59909114 0. 0. 0.81739862 0.14772157 0.70659589 0.64127558 0.03170029 nan 0.05387168 0.38205471 0.34779739 0. 0.9516963 0.85983713 0.97204659 0.02417058 0.18481435 0.30708968 0. ] | 42 | | 0.1649 | 0.4242 | 0.3717 | 0.4383 | 0.8829 | [0. 0.8245252 0.88278 0.781006 0.85842353 0.51259623 nan 0.51245584 0.58046843 0.1180867 0.86495296 0.18057803 0. 0.19290237 0. 0.57019005 0. 0. 0.73953555 0.08630286 0.40811401 0.47419294 0.14364641 nan 0.03056729 0.30467132 0.2893461 0. 0.86773127 0.73354145 0.94299225 0.12116164 0.05836818 0.18823529 0. ] | [0. 0.89896041 0.94310935 0.79709015 0.94969977 0.67147032 nan 0.65138807 0.79877367 0.14253496 0.94740116 0.23234827 0. 0.27021452 0. 0.66277455 0. 0. 0.86192885 0.09854381 0.60905623 0.64588831 0.14985591 nan 0.03674968 0.38943084 0.3317468 0. 0.93877576 0.88623506 0.97329085 0.17635286 0.14092616 0.25900882 0. ] | 43 | | 0.1721 | 0.4380 | 0.3659 | 0.4303 | 0.8830 | [0. 0.79204009 0.88761045 0.81838271 0.87599756 0.55268285 nan 0.51243018 0.57342413 0.1328 0.86370891 0.12697056 0. 0.01482085 0. 0.54979459 0. 0. 0.7440432 0.07280754 0.43229119 0.48547786 0.1754386 nan 0.04705945 0.29268443 0.28866261 0. 0.86370303 0.73106053 0.94208474 0.03836815 0.07654387 0.18451303 0. ] | [0. 0.91791605 0.9276758 0.9021757 0.9262825 0.70668615 nan 0.6749058 0.79231422 0.1871899 0.94815532 0.13258628 0. 0.01877063 0. 0.65320205 0. 0. 0.86297038 0.08255398 0.62974983 0.64313491 0.20172911 nan 0.05861925 0.36101085 0.32994024 0. 0.9582507 0.8229035 0.97011706 0.05883881 0.16712557 0.26603474 0. ] | 44 | | 0.1781 | 0.4529 | 0.3574 | 0.4191 | 0.8774 | [0. 0.80590718 0.86988106 0.71453937 0.86614986 0.4249264 nan 0.47975456 0.54781776 0.09453882 0.84344987 0.10822898 0. 0.05507559 0. 0.52010855 0. 0. 0.74685485 0.08247778 0.43946981 0.52154191 0.24444444 nan 0.03570312 0.29856758 0.28932541 0. 0.86943451 0.76210843 0.93975656 0.04628113 0.00666137 0.17955183 0. ] | [0. 0.90777016 0.93538454 0.74532453 0.96011721 0.53476928 nan 0.67329934 0.81065134 0.10744249 0.95209245 0.16798889 0. 0.06311881 0. 0.58061937 0. 0. 0.86390726 0.09013307 0.63941956 0.64314678 0.28530259 nan 0.04266215 0.38685357 0.33004447 0. 0.94754825 0.87771988 0.97640668 0.04758102 0.0090947 0.25304287 0. ] | 45 | | 0.1684 | 0.4189 | 0.3690 | 0.4293 | 0.8874 | [0. 0.84396057 0.88758961 0.81114547 0.88917721 0.45907522 nan 0.49766899 0.57141046 0.13114032 0.86950251 0.16812566 0. 0.05402798 0. 0.55671436 0. 0. 0.75187936 0.06411064 0.38664923 0.50159138 0.2393736 nan 0.03612036 0.31872514 0.30305254 0. 0.86860485 0.76230745 0.94340831 0.00953726 0.07691803 0.17518119 0. ] | [0. 0.91434158 0.94889397 0.87210345 0.95322397 0.59465787 nan 0.66608234 0.78205623 0.16111863 0.93973807 0.20378818 0. 0.06930693 0. 0.66147017 0. 0. 0.89656164 0.06706628 0.53578308 0.5492624 0.30835735 nan 0.04147526 0.40549508 0.35870623 0. 0.94735747 0.87335684 0.97257866 0.01137326 0.19582812 0.23648119 0. ] | 46 | | 0.1720 | 0.4344 | 0.3638 | 0.4240 | 0.8838 | [0. 0.83634649 0.88252909 0.81745832 0.86977854 0.47517537 nan 0.5031844 0.59798619 0.1079865 0.85554815 0.12994482 0. 0.04696466 0. 0.53919303 0. 0. 0.75014771 0.0744804 0.3882156 0.59230036 0.10471204 nan 0.04368721 0.31787732 0.29543235 0. 0.86536823 0.72760094 0.94104705 0.03982311 0.02227327 0.18113074 0. ] | [0. 0.8989315 0.94316465 0.90119359 0.9497093 0.61772174 nan 0.62712399 0.73975165 0.12124493 0.94667146 0.16580722 0. 0.06415017 0. 0.68711605 0. 0. 0.87653714 0.07776801 0.5226676 0.71570028 0.11527378 nan 0.05734444 0.42000828 0.35728182 0. 0.94526778 0.87853858 0.96476425 0.06568009 0.06391322 0.27004723 0. ] | 47 | | 0.1621 | 0.4094 | 0.3770 | 0.4361 | 0.8898 | [0. 0.82399384 0.89066451 0.819736 0.87845688 0.52339167 nan 0.51547818 0.58811266 0.11202084 0.86668833 0.16280099 0. 0.08182504 0. 0.55484888 0. 0. 0.76325378 0.05293637 0.40960729 0.5817725 0.34567901 nan 0.03438815 0.31336815 0.27887594 0. 0.87318663 0.74216819 0.94257309 0.02714967 0.0518505 0.20559452 0. ] | [0. 0.92314847 0.9443915 0.90050762 0.92923985 0.63544129 nan 0.66654946 0.76154024 0.13576906 0.95223513 0.18236811 0. 0.13428218 0. 0.62953379 0. 0. 0.89400131 0.06196648 0.56801551 0.65125268 0.40345821 nan 0.04132141 0.3929294 0.31270845 0. 0.94826999 0.87554959 0.97042967 0.03051151 0.14226116 0.3023746 0. ] | 48 | | 0.1508 | 0.4301 | 0.3689 | 0.4261 | 0.8878 | [0. 0.82155443 0.88837272 0.80869927 0.84681809 0.50445633 nan 0.5062558 0.58202362 0.09694114 0.86506226 0.10300594 0. 0.03122511 0. 0.55651564 0. 0. 0.76493797 0.04021662 0.40453306 0.56038987 0.34382567 nan 0.02428609 0.30885576 0.28811326 0. 0.87087236 0.74857511 0.94321046 0.02300712 0.03721037 0.20366003 0. ] | [0. 0.88109026 0.95044945 0.85142397 0.95993416 0.6370042 nan 0.65971511 0.81045852 0.11321606 0.95401169 0.10670369 0. 0.04042904 0. 0.66801313 0. 0. 0.90595882 0.04265001 0.5292762 0.61230561 0.4092219 nan 0.0283755 0.37721503 0.3266398 0. 0.950358 0.87250445 0.96996696 0.02583519 0.09486859 0.28234463 0. ] | 49 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.0 - Tokenizers 0.13.2

ChrisP/xlm-roberta-base-finetuned-marc-en

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-21T19:56:44Z

--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k - wit-400m - imagenet-12k --- # Model card for vit_large_patch14_clip_336.openai_ft_in12k_in1k A Vision Transformer (ViT) image classification model. Pretrained on WIT-400M image-text pairs by OpenAI using CLIP. Fine-tuned on ImageNet-12k and then ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143). ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 304.5 - GMACs: 174.7 - Activations (M): 128.2 - Image size: 336 x 336 - **Papers:** - Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020 - Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** - WIT-400M - ImageNet-12k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('vit_large_patch14_clip_336.openai_ft_in12k_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_large_patch14_clip_336.openai_ft_in12k_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 577, 1024) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` ```bibtex @article{cherti2022reproducible, title={Reproducible scaling laws for contrastive language-image learning}, author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia}, journal={arXiv preprint arXiv:2212.07143}, year={2022} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```

ChrisVCB/DialoGPT-medium-ej

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

13

2022-11-21T20:00:09Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### stevediffusion_v2 Dreambooth model trained by daniel-comet with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:

ChristopherA08/IndoELECTRA

[ "pytorch", "electra", "pretraining", "id", "dataset:oscar", "transformers" ]

null

{ "architectures": [ "ElectraForPreTraining" ], "model_type": "electra", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

4

2022-11-21T20:06:38Z

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-finetuned-transcriptSteve results: [] ---  # gpt2-finetuned-transcriptSteve This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6308 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 18 | 2.6415 | | No log | 2.0 | 36 | 2.6353 | | No log | 3.0 | 54 | 2.6308 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

Chun/DialoGPT-large-dailydialog

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

6

2022-11-21T20:16:51Z

--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/Avrik/abstract-anim-spritesheets/resolve/main/AnimationGrid.gif" tags: - stable-diffusion - text-to-image - image-to-image --- # Abstract Animation Sprite Sheets An experimental Dreambooth model trained on individual frames of looping 3D animations that were then laid out on a 4x4 grid. Generates sprite sheets that can create very interesting abstract animations. Use the token **AbstrAnm spritesheet**. Size must be set at 512x512 or your outputs may not work properly. **Example prompt:** <i>AbstrAnm spritesheet, animation of a red glowing orb in the sky, highly detailed, fog, atmosphere, glow, sprites, animated, abstract</i> <br> **Negative prompt:** <i>high contrast, text, overlay</i> <br> Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 8 Feel free to experiment with other types of prompts and/or model merges. ![Sample Generations](https://huggingface.co/Avrik/abstract-anim-spritesheets/resolve/main/AnimationGrid.gif) You can also upscale it 4x to produce 512x512 animations. Used SD Upscale from AUTOMATIC1111's web UI to add more sharpness and detail. ![Upscaled](https://huggingface.co/Avrik/abstract-anim-spritesheets/resolve/main/AnimationGridUpscale.gif) Discovered it's actually quite flexible and could even animate less abstract concepts. ![New Animations](https://huggingface.co/Avrik/abstract-anim-spritesheets/resolve/main/natureanims.gif) **Prompt 1:** <i>AbstrAnm spritesheet, animation of magical swirling clouds in the clear blue sky, floating in crystal clear water, circular, sunny, timelapse, lens flare, nature, 35mm lens shot, photorealistic, sprites, animated, art by Greg Rutkowski</i> <br> **Negative prompt:** <i>text, overlay, abstract, boring, empty, barren, simple background</i> <br> Steps: 25, Sampler: DPM++ 2S a, CFG scale: 10 **Prompt 2:** <i>AbstrAnm spritesheet, animation of a beautiful flower blowing in the wind, serene, pink, sunny, timelapse, lens flare, nature, 35mm lens shot, photorealistic, sprites, animated, art by Greg Rutkowski</i> **Negative prompt:** <i>text, overlay, abstract, boring, empty, barren, simple background</i> <br> Steps: 25, Sampler: DPM++ 2S a, CFG scale: 10 Some issues with this model: - May not loop seamlessly - Tends to be too noisy - Sprites aren't usually perfect squares - Small size and short animation (could experiment with training on larger resolutions in the future)

Chun/DialoGPT-medium-dailydialog

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

15

2022-11-21T20:17:36Z

--- language: en thumbnail: http://www.huggingtweets.com/adamscochran-fehrsam-taschalabs/1669062033978/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1504547300416364550/rFebXP9K_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1513406762904612866/-haRj3pk_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1593745112844144641/Q2zhPcdt_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tascha & Fred Ehrsam & Adam Cochran (adamscochran.eth)</div> <div style="text-align: center; font-size: 14px;">@adamscochran-fehrsam-taschalabs</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Tascha & Fred Ehrsam & Adam Cochran (adamscochran.eth). | Data | Tascha | Fred Ehrsam | Adam Cochran (adamscochran.eth) | | --- | --- | --- | --- | | Tweets downloaded | 3244 | 1674 | 3242 | | Retweets | 215 | 188 | 555 | | Short tweets | 210 | 150 | 150 | | Tweets kept | 2819 | 1336 | 2537 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/35tvoqtp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @adamscochran-fehrsam-taschalabs's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fv0c31k5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fv0c31k5/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/adamscochran-fehrsam-taschalabs') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

Chun/w-en2zh-hsk

[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

1

2022-11-21T20:21:16Z

This model is Nightmare XXX. Prompt being "NghtmrXxxFrk". To note this isn't actual porn or anything. However this takes my popular pictures that combine horrific, gross, nightmarish stuff with weird things like some minor nudity or even adult toy realm. In other words its semi-adult stuff that makes you say "WTF is this. Someone bleach my eyes!". ![Model 5.png](https://s3.amazonaws.com/moonup/production/uploads/1669084844054-6333e639d58823d613336ee3.png)

Chun/w-zh2en-hsk

[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-misinfo-model-700-Zhaohui results: [] ---  # finetuning-misinfo-model-700-Zhaohui This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5297 - Accuracy: 0.8857 - F1: 0.8889 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

Chun/w-zh2en-mtm

[ "pytorch", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "MBartForConditionalGeneration" ], "model_type": "mbart", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

8

null

--- language: en thumbnail: http://www.huggingtweets.com/adamscochran-fehrsam-taschalabs/1669062033978/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1504547300416364550/rFebXP9K_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1513406762904612866/-haRj3pk_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1593745112844144641/Q2zhPcdt_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tascha & Fred Ehrsam & Adam Cochran (adamscochran.eth)</div> <div style="text-align: center; font-size: 14px;">@adamscochran-fehrsam-taschalabs</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Tascha & Fred Ehrsam & Adam Cochran (adamscochran.eth). | Data | Tascha | Fred Ehrsam | Adam Cochran (adamscochran.eth) | | --- | --- | --- | --- | | Tweets downloaded | 3244 | 1674 | 3242 | | Retweets | 215 | 188 | 555 | | Short tweets | 210 | 150 | 150 | | Tweets kept | 2819 | 1336 | 2537 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/35tvoqtp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @adamscochran-fehrsam-taschalabs's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fv0c31k5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fv0c31k5/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/adamscochran-fehrsam-taschalabs') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

Chun/w-zh2en-mto

[ "pytorch", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "MBartForConditionalGeneration" ], "model_type": "mbart", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- license: cc0-1.0 --- Drop anneface.pt into your stable-diffusion-webui/embeddings folder and use prompt <anneface> to get this upset gal. ![anneface](https://huggingface.co/sd-concepts-library/anneface/resolve/main/1.png)

Cinnamon/electra-small-japanese-discriminator

[ "pytorch", "electra", "pretraining", "ja", "transformers", "license:apache-2.0" ]

null

{ "architectures": [ "ElectraForPreTraining" ], "model_type": "electra", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

419

null

--- inference: true language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m --- # Stable Diffusion v1.5 fine tuned on Waltz with Bashir screencaps Use prompt: 'wltzwthbshr' [Waltz with Bashir on IMDB](https://www.imdb.com/title/tt1185616) ### Output Samples: Settings used: "wltzwthbshr SUBJECT", euler a, 35 steps, cfg 7, 1024x1024, high res fix on, sd-vae-ft-mse-original (AUTOMATIC1111 webui) <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000797-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669066999379-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067001297-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067002574-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067002737-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000480-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669066999949-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067002829-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000524-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669066998455-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067001216-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000265-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000984-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000421-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067003066-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067000476-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067002688-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067001859-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067002184-637bef89ca8542a0ba8cd54b.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669067003006-637bef89ca8542a0ba8cd54b.png" width="100%"/> ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at [Stable diffusion Pipelines](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "mikesmodels/Waltz_with_Bashir_Diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "wltzwthbshr dwayne johnson" image = pipe(prompt).images[0] image.save("./dwayne_johnson.png") ```

Ciruzzo/DialoGPT-small-harrypotter

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

9

null

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### stevefussion_v3 Dreambooth model trained by daniel-comet with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:

Ciruzzo/DialoGPT-small-hattypotter

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # arinze/address-match-abp-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 64 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('arinze/address-match-abp-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=arinze/address-match-abp-v2) ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 3125 with parameters: ``` {'batch_size': 32} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 157, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 384, 'out_features': 64, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors

ClaudeYang/awesome_fb_model

[ "pytorch", "bart", "text-classification", "dataset:multi_nli", "transformers", "zero-shot-classification" ]

zero-shot-classification

{ "architectures": [ "BartForSequenceClassification" ], "model_type": "bart", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

26

null

# Lingala Text-to-Speech This model was trained on the OpenSLR's 71.6 hours aligned lingala bible dataset. ## Model description A Conditional Variational Autoencoder with Adversarial Learning(VITS), which is an end-to-end approach to the text-to-speech task. To train the model, we used the espnet2 toolkit. ## Usage First install espnet2 ``` sh pip install espnet ``` Download the model and the config files from this repo. To generate a wav file using this model, run the following: ``` sh from espnet2.bin.tts_inference import Text2Speech import soundfile as sf text2speech = Text2Speech(train_config="config.yaml",model_file="train.total_count.best.pth") wav = text2speech("oyo kati na Ye ozwi lisiko mpe bolimbisi ya masumu")["wav"] sf.write("outfile.wav", wav.numpy(), text2speech.fs, "PCM_16") ```

CleveGreen/FieldClassifier_v2

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

46

null

--- license: creativeml-openrail-m tags: - text-to-image --- ### 2d-art-sprites Dreambooth model trained by ana-tamais with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook You can test this model using this [Colab Notebook for Inference](https://colab.research.google.com/drive/1pFaEJHa7mxFruBfm2hDnR8S6aEo7sAFx?usp=sharing) Sample pictures of 2dart concept: <img src="https://huggingface.co/ana-tamais/2d-art-sprites/resolve/main/concept_images/2dart_1.png" width=256></img> <img src="https://huggingface.co/ana-tamais/2d-art-sprites/resolve/main/concept_images/2dart_4.png" width=256></img> <img src="https://huggingface.co/ana-tamais/2d-art-sprites/resolve/main/concept_images/2dart_9.png" width=256></img> We saved the training data in `dataset.zip`, and some generated results in `results.zip`. ### Some recommendations We recommend to set the: - prompt as: `"[some wizard, paladin, healer, etc.], in the style of 2dart, white background, no background, full body"` - negative prompt as: `"deformed, mutilated limbs, background, multiple people"` - guidance scale between `9 and 10` - sampling method as `Euler` - sampling steps as `60` - batch size as `1` - avoid high batch size, unless you have a high memory GPU This set of hyperparameters lead us to stable and good results. This model was trained using images which the character has some human format or is a not deformed living being. So if you try to predict something like "sword, mirror, candle, etc" (non-living things), we saw the model doesn't perform so well. You need at least a Tesla T4 to be able to run the inference step using the given [notebook](https://colab.research.google.com/drive/1pFaEJHa7mxFruBfm2hDnR8S6aEo7sAFx?usp=sharing).

CoachCarter/distilbert-base-uncased

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: creativeml-openrail-m tags: - text-to-image --- ### Dan reynolds on Stable Diffusion via Dreambooth #### model by JuandaSuarez This your the Stable Diffusion model fine-tuned the Dan reynolds concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks dan reynolds** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/JuandaSuarez/dan-reynolds/resolve/main/concept_images/3.jpeg) ![image 1](https://huggingface.co/JuandaSuarez/dan-reynolds/resolve/main/concept_images/0.jpeg) ![image 2](https://huggingface.co/JuandaSuarez/dan-reynolds/resolve/main/concept_images/1.jpeg) ![image 3](https://huggingface.co/JuandaSuarez/dan-reynolds/resolve/main/concept_images/2.jpeg)

CodeMonkey98/distilroberta-base-finetuned-wikitext2

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: mit widget: - text: "François Dupont prends la direction générale du groupe IPD" tags: - generated_from_trainer metrics: - f1 model-index: - name: camembert-base-articles-ner-backup results: [] ---  # camembert-base-articles-ner-backup This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6701 - F1: 0.8723 ## Model description This model identifies Name Entities : PERSON, ORGANISATION, JOB TITLE Another Model is being developped to predict relationships between these entities (nomination, départure) ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.9205 | 1.0 | 6 | 1.7426 | 0.0 | | 1.6476 | 2.0 | 12 | 1.5415 | 0.0 | | 1.4607 | 3.0 | 18 | 1.3944 | 0.0635 | | 1.3299 | 4.0 | 24 | 1.2587 | 0.4848 | | 1.1973 | 5.0 | 30 | 1.1287 | 0.6207 | | 1.0707 | 6.0 | 36 | 1.0110 | 0.8043 | | 0.972 | 7.0 | 42 | 0.9266 | 0.8696 | | 0.8877 | 8.0 | 48 | 0.8632 | 0.8602 | | 0.8231 | 9.0 | 54 | 0.8279 | 0.8511 | | 0.7723 | 10.0 | 60 | 0.8001 | 0.8511 | | 0.7309 | 11.0 | 66 | 0.7617 | 0.8602 | | 0.6902 | 12.0 | 72 | 0.7364 | 0.8602 | | 0.6601 | 13.0 | 78 | 0.7104 | 0.8723 | | 0.6306 | 14.0 | 84 | 0.7062 | 0.8723 | | 0.6127 | 15.0 | 90 | 0.6896 | 0.8602 | | 0.605 | 16.0 | 96 | 0.6743 | 0.8723 | | 0.5892 | 17.0 | 102 | 0.6801 | 0.8723 | | 0.5843 | 18.0 | 108 | 0.6797 | 0.8723 | | 0.5731 | 19.0 | 114 | 0.6731 | 0.8723 | | 0.5707 | 20.0 | 120 | 0.6701 | 0.8723 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2

CodeNinja1126/bert-p-encoder

[ "pytorch" ]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- language: eng datasets: - banking77 --- # Social Media Sentiment Analysis Model This is a fine-tuned version of the Distilbert model. It's best suited for sentiment-analysis. ## Model Description Social Media Sentiment Analysis Model was trained on the [dataset consisting of tweets](https://www.kaggle.com/code/mohamednabill7/sentiment-analysis-of-twitter-data/data) obtained from Kaggle." ## Intended Uses and Limitations This model is meant for sentiment-analysis. Because it was trained on a corpus of tweets, it is familiar with social media jargons. ### How to use You can use this model directly with a pipeline for text generation: ```python >>>from transformers import pipeline >>> model_name = "Kwaku/social_media_sa" >>> generator = pipeline("sentiment-analysis", model=model_name) >>> result = generator("I like this model") >>> print(result) Generated output: [{'label': 'positive', 'score': 0.9494990110397339}] ``` ### Limitations and bias This model inherits the bias of its parent, [Distilbert](https://huggingface.co/models?other=distilbert). Besides that, it was trained on only 1000 randomly selected sequences, and thus does not achieve a high probability rate. It does fairly well nonetheless.

CodeNinja1126/xlm-roberta-large-kor-mrc

[ "pytorch", "xlm-roberta", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "XLMRobertaForQuestionAnswering" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

8

null

--- language: - ko tags: - trocr - image-to-text license: mit metrics: - wer - cer widget: - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/random_2.jpg example_title: 랜덤 문장 1 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/random_6.jpg example_title: 랜덤 문장 2 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/chatbot_3.jpg example_title: 챗봇 1 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/chatbot_5.jpg example_title: 챗봇 2 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_1.jpg example_title: 뉴스 1 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_3.jpg example_title: 뉴스 2 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/nsmc_1.jpg example_title: 영화 리뷰 1 - src: https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/nsmc_2.jpg example_title: 영화 리뷰 2 --- # TrOCR for Korean Language (PoC) ## Overview TrOCR has not yet released a multilingual model including Korean, so we trained a Korean model for PoC purpose. Based on this model, it is recommended to collect more data to additionally train the 1st stage or perform fine-tuning as the 2nd stage. ## Collecting data ### Text data We created training data by processing three types of datasets. - News summarization dataset: https://huggingface.co/datasets/daekeun-ml/naver-news-summarization-ko - Naver Movie Sentiment Classification: https://github.com/e9t/nsmc - Chatbot dataset: https://github.com/songys/Chatbot_data For efficient data collection, each sentence was separated by a sentence separator library (Kiwi Python wrapper; https://github.com/bab2min/kiwipiepy), and as a result, 637,401 samples were collected. ### Image Data Image data was generated with TextRecognitionDataGenerator (https://github.com/Belval/TextRecognitionDataGenerator) introduced in the TrOCR paper. Below is a code snippet for generating images. ```shell python3 ./trdg/run.py -i ocr_dataset_poc.txt -w 5 -t {num_cores} -f 64 -l ko -c {num_samples} -na 2 --output_dir {dataset_dir} ``` ## Training ### Base model The encoder model used `facebook/deit-base-distilled-patch16-384` and the decoder model used `klue/roberta-base`. It is easier than training by starting weights from `microsoft/trocr-base-stage1`. ### Parameters We used heuristic parameters without separate hyperparameter tuning. - learning_rate = 4e-5 - epochs = 25 - fp16 = True - max_length = 64 ## Usage ### inference.py ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel, AutoTokenizer import requests from io import BytesIO from PIL import Image processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") model = VisionEncoderDecoderModel.from_pretrained("daekeun-ml/ko-trocr-base-nsmc-news-chatbot") tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/ko-trocr-base-nsmc-news-chatbot") url = "https://raw.githubusercontent.com/aws-samples/sm-kornlp/main/trocr/sample_imgs/news_1.jpg" response = requests.get(url) img = Image.open(BytesIO(response.content)) pixel_values = processor(img, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values, max_length=64) generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(generated_text) ``` All the code required for data collection and model training has been published on the author's Github. - https://github.com/daekeun-ml/sm-kornlp-usecases/tree/main/trocr

CoderBoy432/DialoGPT-small-harrypotter

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

11

null

--- language: eng datasets: - banking77 --- # Social Media Sentiment Analysis Model (Finetuned) This is a fine-tuned version of the [Social Media Sentiment Analysis Model](https://huggingface.co/Kwaku/social_media_sa) which is a finetuned version of [Distilbert](https://huggingface.co/models?other=distilbert). It's best suited for sentiment-analysis. ## Model Description Social Media Sentiment Analysis Model was trained on the [dataset consisting of tweets](https://www.kaggle.com/code/mohamednabill7/sentiment-analysis-of-twitter-data/data) obtained from Kaggle." ## Intended Uses and Limitations This model is meant for sentiment-analysis. Because it was trained on a corpus of tweets, it is familiar with social media jargons. ### How to use You can use this model directly with a pipeline for text generation: ```python >>>from transformers import pipeline >>> model_name = "Kwaku/social_media_sa_finetuned_1" >>> generator = pipeline("sentiment-analysis", model=model_name) >>> result = generator("I like this model") >>> print(result) Generated output: [{'label': 'positive', 'score': 0.9494990110397339}] ``` ### Limitations and bias This model inherits the bias of its parent, [Distilbert](https://huggingface.co/models?other=distilbert). Besides that, it was trained on only 1000 randomly selected sequences, and thus does not achieve a high probability rate. It does fairly well nonetheless.

Venkatakrishnan-Ramesh/Text_gen

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

This is a wav2vec2-base model trained from selected bird songs in a birddb dataset. ``` import librosa import torch from transformers import Wav2Vec2ForPreTraining,Wav2Vec2Processor sound_file = 'sample.wav' sound_data,_ = librosa.load(sound_file, sr=16000) model_id = "kojima-r/wav2vec2-base-birddb-small" model = Wav2Vec2ForPreTraining.from_pretrained(model_id) result=model(torch.tensor([sound_data])) hidden_vecs=result.projected_states ``` ![UMAP](https://huggingface.co/kojima-r/wav2vec2-base-birddb-small/resolve/main/umap.png)

CoffeeAddict93/gpt2-medium-modest-proposal

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- license: openrail --- # MJv4 Hallucinations These are 3 models trained on a small (<2000) dataset of Midjourney v4 images with no particular style. <b> These models are nowhere near as good as Midjourney v4 </b>, and they all suffer from a lot of "language drift" but they do have an interesting style. They are the best of something like 60 different models I trained as part of a set of experiments aimed at replicating Midjourney v4's style with only a few, uncaptioned images. The models are: - <b>mjg-4000-model.ckpt</b>: trained on 250 MJv4 images with no regularization for 4000 steps, prompt: "mjg style" - <b>mjg-12000-model.ckpt</b>: trained on 250 MJv4 images with no regularization for 12000 steps, prompt: "mjg style" - <b>mjv-1200-model.ckpt</b>: trained on 7 MJv4 images with 1000 regularization images for 1200 steps, prompt: "mjv style" Models you can download are <b>bolded</b> <img src="https://github.com/Lewington-pitsos/mj4-hallucinations/blob/main/compare.png?raw=true" width="100%"/> In my subjective opinion, only <b>mjv-1200-model.ckpt<\b> is actually worth downloading. ## Credits: - [NitroSock](https://github.com/nitrosocke/dreambooth-training-guide) for the regularization images - [prompthero](https://huggingface.co/prompthero/openjourney) whose idea I copied ## Take Down As far as I can tell, uploading these models does not cause any person or corporate entity any harm, but if you think I am wrong about this please reach out.

CoffeeAddict93/gpt2-modest-proposal

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

12

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-cola results: [] ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

CogComp/bart-faithful-summary-detector

[ "pytorch", "jax", "bart", "text-classification", "en", "dataset:xsum", "transformers", "xsum", "license:cc-by-sa-4.0" ]

text-classification

{ "architectures": [ "BartForSequenceClassification" ], "model_type": "bart", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": 1, "max_length": 128, "min_length": 12, "no_repeat_ngram_size": null, "num_beams": 4, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

234

null

WnB run: https://wandb.ai/jellywibble/huggingface/runs/1yo5mgs4?workspace=user-jellywibble

CogComp/roberta-temporal-predictor

[ "pytorch", "roberta", "fill-mask", "arxiv:2202.00436", "transformers", "license:mit", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "RobertaForMaskedLM" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

14

null

--- tags: - generated_from_keras_callback model-index: - name: dung1308/dung_NT_model_save results: [] ---  # dung1308/dung_NT_model_save This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.8144 - Validation Loss: 3.6030 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.4431 | 3.9985 | 0 | | 3.9986 | 3.8016 | 1 | | 3.8144 | 3.6030 | 2 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.7.0 - Tokenizers 0.11.0

CohleM/bert-nepali-tokenizer

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 155.33 +/- 58.36 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

CohleM/mbert-nepali-tokenizer

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- language: - en tags: - vision-language - clip - vilt datasets: - lil-lab/kilogram-data --- KiloGram dataset and code repo: https://github.com/lil-lab/kilogram Preprocessed training and evaluation data: https://huggingface.co/datasets/lil-lab/kilogram-data

ComCom/gpt2-large

[ "pytorch", "gpt2", "feature-extraction", "transformers" ]

feature-extraction

{ "architectures": [ "GPT2Model" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

1

null

--- license: apache-2.0 tags: - vision - depth-estimation - generated_from_trainer model-index: - name: glpn-nyu-finetuned-diode-221122-014502 results: [] ---  # glpn-nyu-finetuned-diode-221122-014502 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.3476 - Mae: 0.2763 - Rmse: 0.4088 - Abs Rel: 0.3308 - Log Mae: 0.1161 - Log Rmse: 0.1700 - Delta1: 0.5682 - Delta2: 0.8301 - Delta3: 0.9279 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 0.7598 | 1.0 | 72 | 0.5809 | 0.7606 | 0.9281 | 0.9834 | 0.2597 | 0.3064 | 0.1320 | 0.3250 | 0.6234 | | 0.4481 | 2.0 | 144 | 0.4013 | 0.3507 | 0.4879 | 0.4181 | 0.1415 | 0.1950 | 0.4427 | 0.7602 | 0.9021 | | 0.4066 | 3.0 | 216 | 0.3706 | 0.3081 | 0.4484 | 0.3675 | 0.1269 | 0.1823 | 0.5187 | 0.7977 | 0.9148 | | 0.3965 | 4.0 | 288 | 0.3641 | 0.2987 | 0.4336 | 0.3607 | 0.1239 | 0.1787 | 0.5294 | 0.8072 | 0.9205 | | 0.3942 | 5.0 | 360 | 0.3582 | 0.2903 | 0.4251 | 0.3490 | 0.1207 | 0.1753 | 0.5466 | 0.8165 | 0.9232 | | 0.3575 | 6.0 | 432 | 0.3568 | 0.2898 | 0.4184 | 0.3569 | 0.1211 | 0.1753 | 0.5390 | 0.8171 | 0.9265 | | 0.3418 | 7.0 | 504 | 0.3490 | 0.2771 | 0.4178 | 0.3248 | 0.1156 | 0.1707 | 0.5783 | 0.8312 | 0.9259 | | 0.2916 | 8.0 | 576 | 0.3512 | 0.2819 | 0.4172 | 0.3373 | 0.1178 | 0.1725 | 0.5620 | 0.8253 | 0.9262 | | 0.3055 | 9.0 | 648 | 0.3506 | 0.2808 | 0.4091 | 0.3422 | 0.1180 | 0.1718 | 0.5537 | 0.8248 | 0.9292 | | 0.2932 | 10.0 | 720 | 0.3518 | 0.2809 | 0.4110 | 0.3441 | 0.1182 | 0.1724 | 0.5548 | 0.8239 | 0.9290 | | 0.2518 | 11.0 | 792 | 0.3476 | 0.2756 | 0.4115 | 0.3265 | 0.1155 | 0.1700 | 0.5741 | 0.8326 | 0.9264 | | 0.3177 | 12.0 | 864 | 0.3491 | 0.2784 | 0.4104 | 0.3333 | 0.1169 | 0.1706 | 0.5620 | 0.8290 | 0.9283 | | 0.3038 | 13.0 | 936 | 0.3503 | 0.2795 | 0.4094 | 0.3410 | 0.1175 | 0.1717 | 0.5596 | 0.8275 | 0.9283 | | 0.3299 | 14.0 | 1008 | 0.3460 | 0.2750 | 0.4098 | 0.3257 | 0.1154 | 0.1693 | 0.5721 | 0.8325 | 0.9283 | | 0.3325 | 15.0 | 1080 | 0.3476 | 0.2763 | 0.4088 | 0.3308 | 0.1161 | 0.1700 | 0.5682 | 0.8301 | 0.9279 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu116 - Tokenizers 0.13.2

Craig/paraphrase-MiniLM-L6-v2

[ "pytorch", "bert", "arxiv:1908.10084", "sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "license:apache-2.0" ]

feature-extraction

{ "architectures": [ "BertModel" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

1,026

null

--- license: apache-2.0 tags: - vision - depth-estimation - generated_from_trainer model-index: - name: glpn-nyu-finetuned-diode-221122-044810 results: [] ---  # glpn-nyu-finetuned-diode-221122-044810 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.3690 - Mae: 0.2909 - Rmse: 0.4208 - Abs Rel: 0.3635 - Log Mae: 0.1224 - Log Rmse: 0.1793 - Delta1: 0.5323 - Delta2: 0.8179 - Delta3: 0.9258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.3864 | 1.0 | 72 | 1.2016 | 3.4656 | 3.5204 | 5.1101 | 0.6881 | 0.7346 | 0.0 | 0.0011 | 0.0764 | | 1.0082 | 2.0 | 144 | 0.4607 | 0.4107 | 0.5420 | 0.5254 | 0.1697 | 0.2234 | 0.3596 | 0.6460 | 0.8465 | | 0.4656 | 3.0 | 216 | 0.4071 | 0.3431 | 0.4758 | 0.4359 | 0.1425 | 0.1992 | 0.4567 | 0.7481 | 0.8958 | | 0.4093 | 4.0 | 288 | 0.3953 | 0.3261 | 0.4622 | 0.4197 | 0.1363 | 0.1947 | 0.4841 | 0.7624 | 0.9103 | | 0.392 | 5.0 | 360 | 0.3916 | 0.3211 | 0.4463 | 0.4116 | 0.1338 | 0.1896 | 0.4810 | 0.7756 | 0.9176 | | 0.3466 | 6.0 | 432 | 0.3807 | 0.3075 | 0.4451 | 0.3658 | 0.1293 | 0.1839 | 0.5026 | 0.7921 | 0.9180 | | 0.3297 | 7.0 | 504 | 0.3811 | 0.3047 | 0.4448 | 0.3534 | 0.1290 | 0.1835 | 0.5066 | 0.7920 | 0.9137 | | 0.2768 | 8.0 | 576 | 0.3779 | 0.3057 | 0.4283 | 0.3894 | 0.1280 | 0.1832 | 0.5046 | 0.7996 | 0.9256 | | 0.2849 | 9.0 | 648 | 0.3753 | 0.2978 | 0.4341 | 0.3496 | 0.1259 | 0.1806 | 0.5149 | 0.8041 | 0.9184 | | 0.2571 | 10.0 | 720 | 0.3825 | 0.3068 | 0.4305 | 0.3896 | 0.1289 | 0.1849 | 0.4998 | 0.7974 | 0.9206 | | 0.2246 | 11.0 | 792 | 0.3718 | 0.2951 | 0.4235 | 0.3678 | 0.1240 | 0.1803 | 0.5249 | 0.8105 | 0.9248 | | 0.2703 | 12.0 | 864 | 0.3716 | 0.2945 | 0.4317 | 0.3593 | 0.1235 | 0.1808 | 0.5324 | 0.8122 | 0.9215 | | 0.2596 | 13.0 | 936 | 0.3692 | 0.2921 | 0.4185 | 0.3690 | 0.1229 | 0.1798 | 0.5294 | 0.8167 | 0.9264 | | 0.2604 | 14.0 | 1008 | 0.3684 | 0.2893 | 0.4171 | 0.3601 | 0.1223 | 0.1785 | 0.5325 | 0.8179 | 0.9252 | | 0.2679 | 15.0 | 1080 | 0.3690 | 0.2909 | 0.4208 | 0.3635 | 0.1224 | 0.1793 | 0.5323 | 0.8179 | 0.9258 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu116 - Tokenizers 0.13.2

Cthyllax/DialoGPT-medium-PaladinDanse

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

10

null

--- license: creativeml-openrail-m --- Made using highly curated best quality masterful artwork from an ancient indonesian stone carving website, with some help from their independent doodling connoisseur brothers in arms, 3000 pieces of their best work. Prompt used: aiseeic aisee_10000.ckpt was made with Anything v.3. aiseeic_15000.ckpt was made with SD 1.5. AIsee (Anything) examples ![00043-170289776-Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, facial featur.png](https://s3.amazonaws.com/moonup/production/uploads/1669095647555-637c5f1b9495870ef76f1472.png) ![00042-3508825484-Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, facial featur.png](https://s3.amazonaws.com/moonup/production/uploads/1669095647512-637c5f1b9495870ef76f1472.png) ![00045-2382850738-Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, facial featur.png](https://s3.amazonaws.com/moonup/production/uploads/1669095647489-637c5f1b9495870ef76f1472.png) AIsee SD examples ![00053-758175738-aiseeic, Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, faci.png](https://s3.amazonaws.com/moonup/production/uploads/1669095834760-637c5f1b9495870ef76f1472.png) ![00039-477119149-Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, facial featur.png](https://s3.amazonaws.com/moonup/production/uploads/1669095834642-637c5f1b9495870ef76f1472.png) ![00057-2487031390-aiseeic, Emma Watson full shot modeling as Jessica Rabbit, (EOS 5DS R, ISO100, f_8, 1_125, 84mm, postprocessed, crisp face, faci.png](https://s3.amazonaws.com/moonup/production/uploads/1669095834700-637c5f1b9495870ef76f1472.png) I own nothing and I will be happy.

DJSammy/bert-base-swedish-uncased_BotXO-ai

[ "pytorch", "transformers" ]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

1

null

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6346626984126984 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32887700534759357 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3264094955489614 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47581989994441354 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.464 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37719298245614036 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36342592592592593 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7761036612927528 - name: F1 (macro) type: f1_macro value: 0.7415561766602355 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7328638497652582 - name: F1 (macro) type: f1_macro value: 0.47573763054929613 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5390032502708559 - name: F1 (macro) type: f1_macro value: 0.49194003623703636 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8753564721430062 - name: F1 (macro) type: f1_macro value: 0.7536524804914483 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8282670009401442 - name: F1 (macro) type: f1_macro value: 0.8236645741563291 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2 RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/analogy.json)): - Accuracy on SAT (full): 0.32887700534759357 - Accuracy on SAT: 0.3264094955489614 - Accuracy on BATS: 0.47581989994441354 - Accuracy on U2: 0.37719298245614036 - Accuracy on U4: 0.36342592592592593 - Accuracy on Google: 0.464 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/classification.json)): - Micro F1 score on BLESS: 0.7761036612927528 - Micro F1 score on CogALexV: 0.7328638497652582 - Micro F1 score on EVALution: 0.5390032502708559 - Micro F1 score on K&H+N: 0.8753564721430062 - Micro F1 score on ROOT09: 0.8282670009401442 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6346626984126984 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-triplet-2/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen

[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "BertForQuestionAnswering" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

1,907

null

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7048015873015873 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37967914438502676 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3916913946587537 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5347415230683713 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.69 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41228070175438597 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3888888888888889 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.853246948922706 - name: F1 (macro) type: f1_macro value: 0.8485536876305343 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8044600938967136 - name: F1 (macro) type: f1_macro value: 0.5726819680585065 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5839653304442037 - name: F1 (macro) type: f1_macro value: 0.5524953070884607 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.934687347847256 - name: F1 (macro) type: f1_macro value: 0.8063588254058023 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8279536195549985 - name: F1 (macro) type: f1_macro value: 0.7955713493721125 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1 RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37967914438502676 - Accuracy on SAT: 0.3916913946587537 - Accuracy on BATS: 0.5347415230683713 - Accuracy on U2: 0.41228070175438597 - Accuracy on U4: 0.3888888888888889 - Accuracy on Google: 0.69 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/classification.json)): - Micro F1 score on BLESS: 0.853246948922706 - Micro F1 score on CogALexV: 0.8044600938967136 - Micro F1 score on EVALution: 0.5839653304442037 - Micro F1 score on K&H+N: 0.934687347847256 - Micro F1 score on ROOT09: 0.8279536195549985 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7048015873015873 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-triplet-1/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken

[ "pytorch", "tensorboard", "bert", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "BertForQuestionAnswering" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6670436507936508 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3770053475935829 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37388724035608306 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4802668148971651 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.558 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33771929824561403 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34953703703703703 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.893174627090553 - name: F1 (macro) type: f1_macro value: 0.8866591988732194 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7863849765258216 - name: F1 (macro) type: f1_macro value: 0.5308624907920565 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5704225352112676 - name: F1 (macro) type: f1_macro value: 0.5510856788391408 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9581275648605412 - name: F1 (macro) type: f1_macro value: 0.8644516035001516 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8523973675963648 - name: F1 (macro) type: f1_macro value: 0.8523947470987124 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2 RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3770053475935829 - Accuracy on SAT: 0.37388724035608306 - Accuracy on BATS: 0.4802668148971651 - Accuracy on U2: 0.33771929824561403 - Accuracy on U4: 0.34953703703703703 - Accuracy on Google: 0.558 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2/raw/main/classification.json)): - Micro F1 score on BLESS: 0.893174627090553 - Micro F1 score on CogALexV: 0.7863849765258216 - Micro F1 score on EVALution: 0.5704225352112676 - Micro F1 score on K&H+N: 0.9581275648605412 - Micro F1 score on ROOT09: 0.8523973675963648 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6670436507936508 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: nce_logout - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-nce-2/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

DamolaMack/Classyfied

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8018650793650793 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3502673796791444 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35014836795252224 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5202890494719289 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.644 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.43287037037037035 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8461654361910502 - name: F1 (macro) type: f1_macro value: 0.8411664963735426 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8145539906103286 - name: F1 (macro) type: f1_macro value: 0.5873414064116238 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6505958829902492 - name: F1 (macro) type: f1_macro value: 0.6269958308732405 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9319051262433052 - name: F1 (macro) type: f1_macro value: 0.8393686548194149 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7511751801942964 - name: F1 (macro) type: f1_macro value: 0.6464435364634403 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1 RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3502673796791444 - Accuracy on SAT: 0.35014836795252224 - Accuracy on BATS: 0.5202890494719289 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.43287037037037035 - Accuracy on Google: 0.644 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8461654361910502 - Micro F1 score on CogALexV: 0.8145539906103286 - Micro F1 score on EVALution: 0.6505958829902492 - Micro F1 score on K&H+N: 0.9319051262433052 - Micro F1 score on ROOT09: 0.7511751801942964 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8018650793650793 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: triplet - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-triplet-1/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

Danbi/distilgpt2-finetuned-wikitext2

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: agpl-3.0 language: - gl - pt widget: - text: >- A miña amiga Rosa, de Lisboa, estudou en Montreal. Agora traballa en Nova Pescanova. --- # Named Entity Recognition (NER) model for Galician This is a NER model for Galician (ILG/RAG spelling) which uses the standard 'enamex' classes: LOC (geographical locations); PER (people); ORG (organizations); MISC (other entities). The model is based on [BERT-base-gl-cased](https://huggingface.co/marcosgg/bert-base-gl-cased), which has been fine-tuned using custom splits of the [SLI_NERC dataset](https://github.com/xavier-gz/SLI_Galician_Corpora). On the test split of this dataset (not used for training), the model obtained the following results (Precision/Recall/F-score): 87.69 / 89.7 / 88.68.

Davlan/xlm-roberta-base-finetuned-lingala

[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "XLMRobertaForMaskedLM" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

9

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-idrak-practice results: [] ---  # wav2vec2-base-timit-demo-idrak-practice This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3538 - Wer: 0.3209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.634 | 0.87 | 500 | 2.6452 | 1.0 | | 1.0497 | 1.73 | 1000 | 0.5711 | 0.5138 | | 0.4584 | 2.6 | 1500 | 0.4421 | 0.4492 | | 0.3198 | 3.46 | 2000 | 0.3818 | 0.3941 | | 0.2263 | 4.33 | 2500 | 0.3653 | 0.3767 | | 0.1845 | 5.19 | 3000 | 0.3424 | 0.3661 | | 0.1388 | 6.06 | 3500 | 0.3702 | 0.3519 | | 0.1214 | 6.92 | 4000 | 0.3515 | 0.3439 | | 0.1026 | 7.79 | 4500 | 0.3585 | 0.3292 | | 0.0834 | 8.65 | 5000 | 0.3474 | 0.3236 | | 0.0737 | 9.52 | 5500 | 0.3538 | 0.3209 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.12.0+cu116 - Datasets 1.18.3 - Tokenizers 0.12.1

Dazai/Ko

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: apache-2.0 duplicated_from: hf-internal-testing/tiny-stable-diffusion-torch --- ```python from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") ```

Declan/Breitbart_model_v1

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

9

null

--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m --- Use prompt: '**btdmnky**' to get a monkey. You can use the categories in the game to generate a monkey based on that category, such as putting "btdmnky magic" will generate a monkey based on the magic monkeys in-game. You can use: - primary - military - magic - support (results won't be great) - hero Some examples: <font size="1">magic hero, godly, laser beams, god rays, dominant pose, monkey, cloak, super powers, Magic The Gathering, magical, fantasy, colorful, realistic Negative prompt: lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, deformed, mutated, extra limbs, Steps: 35, Sampler: Euler, CFG scale: 6.5, Seed: 235226828, Size: 512x512</font> ![Super Magic Monkey](https://huggingface.co/Junglerally/Stable-BTD6/resolve/main/SuperMagicMonkey.png) <font size="1">btdmnky hero magic style, high quality, digital art, monkey, godly, god, powerful Negative prompt: jagged, jaggy lines, poorly drawn, low quality, (((text))), pixelated, jpeg artifacts, messy, deformed, mutated, extra limbs, extra tails Steps: 40, Sampler: DPM++ 2S a, CFG scale: 7, Seed: 2116075235, Size: 512x512</font> ![Magic Hero](https://huggingface.co/Junglerally/Stable-BTD6/resolve/main/MagicHeroGen.png) <font size="1">btdmnky primary style, high quality, digital art, monkey Negative prompt: jagged, jaggy lines, poorly drawn, low quality, (((text))), pixelated, jpeg artifacts, messy, deformed, mutated, extra limbs, extra tails Steps: 40, Sampler: DPM++ 2S a, CFG scale: 7, Seed: 3160304320, Size: 512x512</font> ![Primary](https://huggingface.co/Junglerally/Stable-BTD6/resolve/main/PrimaryGen.png) <font size="1">btdmnky hero style, high quality, digital art, monkey Negative prompt: jagged, jaggy lines, poorly drawn, low quality, (((text))), pixelated, jpeg artifacts, messy, deformed, mutated, extra limbs, extra tails Steps: 40, Sampler: DPM++ 2S a, CFG scale: 7, Seed: 968959303, Size: 512x512</font> ![Hero](https://huggingface.co/Junglerally/Stable-BTD6/resolve/main/HeroGen.png) <font size="1">btdmnky magic style, high quality, cat Negative prompt: jagged, jaggy lines, poorly drawn, low quality, (((text))), pixelated, jpeg artifacts Steps: 40, Sampler: Euler, CFG scale: 7, Seed: 2591613767, Size: 512x512</font> ![Cat Monkey](https://huggingface.co/Junglerally/Stable-BTD6/resolve/main/CatMonkey.png)

Declan/ChicagoTribune_model_v7

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- license: mit tags: - generated_from_trainer model-index: - name: chile-gpt results: [] ---  # chile-gpt This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 9.4320 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 10.6676 | 0.98 | 6 | 9.5748 | | 9.6237 | 1.98 | 12 | 9.2470 | | 9.2815 | 2.98 | 18 | 8.8724 | | 8.8097 | 3.98 | 24 | 8.3629 | | 8.2296 | 4.98 | 30 | 7.8407 | | 7.6891 | 5.98 | 36 | 7.4161 | | 7.3013 | 6.98 | 42 | 7.1598 | | 7.0671 | 7.98 | 48 | 7.0080 | | 6.9404 | 8.98 | 54 | 6.9133 | | 6.7543 | 9.98 | 60 | 6.7723 | | 6.5845 | 10.98 | 66 | 6.6619 | | 6.4193 | 11.98 | 72 | 6.5965 | | 6.2554 | 12.98 | 78 | 6.5185 | | 6.0993 | 13.98 | 84 | 6.4632 | | 5.93 | 14.98 | 90 | 6.4155 | | 5.7684 | 15.98 | 96 | 6.4183 | | 5.6242 | 16.98 | 102 | 6.3981 | | 5.4577 | 17.98 | 108 | 6.4609 | | 5.2898 | 18.98 | 114 | 6.4577 | | 5.1113 | 19.98 | 120 | 6.5617 | | 4.9319 | 20.98 | 126 | 6.5827 | | 4.7464 | 21.98 | 132 | 6.6961 | | 4.5505 | 22.98 | 138 | 6.8359 | | 4.341 | 23.98 | 144 | 6.9193 | | 4.1324 | 24.98 | 150 | 7.0325 | | 3.8938 | 25.98 | 156 | 7.1993 | | 3.6691 | 26.98 | 162 | 7.3179 | | 3.4316 | 27.98 | 168 | 7.4708 | | 3.2041 | 28.98 | 174 | 7.5654 | | 2.9614 | 29.98 | 180 | 7.7535 | | 2.7189 | 30.98 | 186 | 7.8551 | | 2.4944 | 31.98 | 192 | 8.0094 | | 2.2624 | 32.98 | 198 | 8.0527 | | 2.0292 | 33.98 | 204 | 8.1857 | | 1.809 | 34.98 | 210 | 8.3468 | | 1.597 | 35.98 | 216 | 8.4307 | | 1.3849 | 36.98 | 222 | 8.6230 | | 1.2081 | 37.98 | 228 | 8.6666 | | 1.0273 | 38.98 | 234 | 8.7926 | | 0.8661 | 39.98 | 240 | 8.8861 | | 0.7308 | 40.98 | 246 | 8.9042 | | 0.6189 | 41.98 | 252 | 8.9202 | | 0.5335 | 42.98 | 258 | 9.0861 | | 0.459 | 43.98 | 264 | 9.1198 | | 0.3958 | 44.98 | 270 | 9.2129 | | 0.3587 | 45.98 | 276 | 9.2434 | | 0.3222 | 46.98 | 282 | 9.3005 | | 0.2948 | 47.98 | 288 | 9.3961 | | 0.2677 | 48.98 | 294 | 9.4605 | | 0.2348 | 49.98 | 300 | 9.4320 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+rocm5.2 - Datasets 2.6.1 - Tokenizers 0.13.2

Declan/NPR_model_v5

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - LiveEvil/autotrain-data-copuml-la-beta-demo co2_eq_emissions: emissions: 1.2815143214785873 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2205770755 - CO2 Emissions (in grams): 1.2815 ## Validation Metrics - Loss: 1.085 - Accuracy: 0.747 - Macro F1: 0.513 - Micro F1: 0.747 - Weighted F1: 0.715 - Macro Precision: 0.533 - Micro Precision: 0.747 - Weighted Precision: 0.691 - Macro Recall: 0.515 - Micro Recall: 0.747 - Weighted Recall: 0.747 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/LiveEvil/autotrain-copuml-la-beta-demo-2205770755 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("LiveEvil/autotrain-copuml-la-beta-demo-2205770755", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("LiveEvil/autotrain-copuml-la-beta-demo-2205770755", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

Declan/NPR_model_v6

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- tags: - generated_from_trainer datasets: - ebiquity-v2 model-index: - name: enlmr-conll2003 results: [] ---  # enlmr-conll2003 This model is a fine-tuned version of [manirai91/enlm-roberta-final](https://huggingface.co/manirai91/enlm-roberta-final) on the ebiquity-v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0 - Datasets 2.7.0 - Tokenizers 0.13.2

Declan/Politico_model_v1

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

2022-11-22T17:53:53Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-the_verge-linustechtips-two_min results: [] ---  # distilgpt2-the_verge-linustechtips-two_min This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Declan/Politico_model_v5

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

2022-11-22T18:13:52Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Modified-Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 16.10 +/- 10.73 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

DeepPavlov/xlm-roberta-large-en-ru

[ "pytorch", "xlm-roberta", "feature-extraction", "en", "ru", "transformers" ]

feature-extraction

{ "architectures": [ "XLMRobertaModel" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

190

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-neo-125M-wikitext2 results: [] ---  # gpt-neo-125M-wikitext2 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0325 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 259 | 6.4308 | | 6.8563 | 2.0 | 518 | 6.0898 | | 6.8563 | 3.0 | 777 | 6.0325 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2

Denny29/DialoGPT-medium-asunayuuki

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

9

2022-11-22T22:24:13Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-multilingual-cased-sv2 results: [] ---  # distilbert-base-multilingual-cased-sv2 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the squad_v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

DeskDown/MarianMixFT_en-fil

[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

2022-11-22T22:30:20Z

--- language: - pt thumbnail: "Portuguese BERT for the Legal Domain" pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - transformers datasets: - assin - assin2 - stjiris/portuguese-legal-sentences-v1.0 widget: - source_sentence: "O advogado apresentou as provas ao juíz." sentences: - "O juíz leu as provas." - "O juíz leu o recurso." - "O juíz atirou uma pedra." example_title: "Example 1" model-index: - name: BERTimbau results: - task: name: STS type: STS metrics: - name: Pearson Correlation - assin Dataset type: Pearson Correlation value: 0.7716333759993093 - name: Pearson Correlation - assin2 Dataset type: Pearson Correlation value: 0.8403302138785704 - name: Pearson Correlation - stsb_multi_mt pt Dataset type: Pearson Correlation value: 0.8249826985133595 --- # stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0 (Legal BERTimbau) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0 derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large. It was trained using the MLM technique with a learning rate 3e-5 [Legal Sentences from +-30000 documents](https://huggingface.co/datasets/stjiris/portuguese-legal-sentences-v1.0) 130k training steps (best performance for our semantic search system implementation) It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets. [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2) and [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) portuguese subdataset ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Isto é um exemplo", "Isto é um outro exemplo"] model = SentenceTransformer('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0') model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1028, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors If you use this work, please cite: ```bibtex @inproceedings{MeloSemantic, author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o}, title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a}, } @inproceedings{souza2020bertimbau, author = {F{\'a}bio Souza and Rodrigo Nogueira and Roberto Lotufo}, title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese}, booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)}, year = {2020} } @inproceedings{fonseca2016assin, title={ASSIN: Avaliacao de similaridade semantica e inferencia textual}, author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S}, booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal}, pages={13--15}, year={2016} } @inproceedings{real2020assin, title={The assin 2 shared task: a quick overview}, author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo}, booktitle={International Conference on Computational Processing of the Portuguese Language}, pages={406--412}, year={2020}, organization={Springer} } @InProceedings{huggingface:dataset:stsb_multi_mt, title = {Machine translated multilingual STS benchmark dataset.}, author={Philip May}, year={2021}, url={https://github.com/PhilipMay/stsb-multi-mt} } ```

DeskDown/MarianMixFT_en-id

[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

2022-11-22T22:35:19Z

--- license: mit tags: - generated_from_trainer datasets: - conll2003 model-index: - name: xlm-roberta-conll2003 results: [] ---  # xlm-roberta-conll2003 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0 - Datasets 2.7.0 - Tokenizers 0.13.2

DeskDown/MarianMixFT_en-ms

[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

2022-11-22T22:47:21Z

--- license: mit tags: - image-to-text - image-to-image - text-to-image - text-to-text - image-editing - image-variation - generation - vision datasets: - Laion2B-en widget: - text: "A high tech solarpunk utopia in the Amazon rainforest" example_title: Amazon rainforest --- # Versatile Diffusion V1.0 Model Card We built **Versatile Diffusion (VD), the first unified multi-flow multimodal diffusion framework**, as a step towards **Universal Generative AI**. Versatile Diffusion can natively support image-to-text, image-variation, text-to-image, and text-variation, and can be further extended to other applications such as semantic-style disentanglement, image-text dual-guided generation, latent image-to-text-to-image editing, and more. Future versions will support more modalities such as speech, music, video and 3D. Resources for more information: [GitHub](https://github.com/SHI-Labs/Versatile-Diffusion), [arXiv](https://arxiv.org/abs/2211.08332). # Model Details One single flow of Versatile Diffusion contains a VAE, a diffuser, and a context encoder, and thus handles one task (e.g., text-to-image) under one data type (e.g., image) and one context type (e.g., text). The multi-flow structure of Versatile Diffusion shows in the following diagram: <p align="center"> <img src="https://huggingface.co/shi-labs/versatile-diffusion-model/resolve/main/assets/figures/vd_combined.png" width="99%"> </p> - **Developed by:** Xingqian Xu, Atlas Wang, Eric Zhang, Kai Wang, and Humphrey Shi - **Model type:** Diffusion-based multimodal generation model - **Language(s):** English - **License:** MIT - **Resources for more information:** [GitHub Repository](https://github.com/SHI-Labs/Versatile-Diffusion), [Paper](https://arxiv.org/abs/2211.08332). - **Cite as:** ``` @article{xu2022versatile, title = {Versatile Diffusion: Text, Images and Variations All in One Diffusion Model}, author = {Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi}, year = 2022, url = {https://arxiv.org/abs/2211.08332}, eprint = {2211.08332}, archiveprefix = {arXiv}, primaryclass = {cs.CV} } ``` # Usage You can use the model both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [SHI-Labs Versatile Diffusion codebase](https://github.com/SHI-Labs/Versatile-Diffusion). ## 🧨 Diffusers Diffusers let's you both use a unified and more memory-efficient, task-specific pipelines. **Make sure to install `transformers` from `"main"` in order to use this model.**: ``` pip install git+https://github.com/huggingface/transformers ``` ## VersatileDiffusionPipeline To use Versatile Diffusion for all tasks, it is recommend to use the [`VersatileDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion#diffusers.VersatileDiffusionPipeline) ```py #! pip install git+https://github.com/huggingface/transformers diffusers torch from diffusers import VersatileDiffusionPipeline import torch import requests from io import BytesIO from PIL import Image pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe = pipe.to("cuda") # prompt prompt = "a red car" # initial image url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") # text to image image = pipe.text_to_image(prompt).images[0] # image variation image = pipe.image_variation(image).images[0] # image variation image = pipe.dual_guided(prompt, image).images[0] ``` ### Task Specific The task specific pipelines load only the weights that are needed onto GPU. You can find all task specific pipelines [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion#versatilediffusion). You can use them as follows: ### Text to Image ```py from diffusers import VersatileDiffusionTextToImagePipeline import torch pipe = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe.remove_unused_weights() pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) image = pipe("an astronaut riding on a horse on mars", generator=generator).images[0] image.save("./astronaut.png") ``` #### Image variations ```py from diffusers import VersatileDiffusionImageVariationPipeline import torch import requests from io import BytesIO from PIL import Image # download an initial image url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) image = pipe(image, generator=generator).images[0] image.save("./car_variation.png") ``` #### Dual-guided generation ```py from diffusers import VersatileDiffusionDualGuidedPipeline import torch import requests from io import BytesIO from PIL import Image # download an initial image url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") text = "a red car in the sun" pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe.remove_unused_weights() pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) text_to_image_strength = 0.75 image = pipe(prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator).images[0] image.save("./red_car.png") ``` ### Original GitHub Repository Follow the instructions [here](https://github.com/SHI-Labs/Versatile-Diffusion/#evaluation). # Cautions, Biases, and Content Acknowledgment We would like the raise the awareness of users of this demo of its potential issues and concerns. Like previous large foundation models, Versatile Diffusion could be problematic in some cases, partially due to the imperfect training data and pretrained network (VAEs / context encoders) with limited scope. In its future research phase, VD may do better on tasks such as text-to-image, image-to-text, etc., with the help of more powerful VAEs, more sophisticated network designs, and more cleaned data. So far, we have kept all features available for research testing both to show the great potential of the VD framework and to collect important feedback to improve the model in the future. We welcome researchers and users to report issues with the HuggingFace community discussion feature or email the authors. Beware that VD may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography, and violence. VD was trained on the LAION-2B dataset, which scraped non-curated online images and text, and may contain unintended exceptions as we removed illegal content. VD in this demo is meant only for research purposes.

DeskDown/MarianMixFT_en-th

[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

2022-11-22T23:09:35Z

--- license: creativeml-openrail-m thumbnail: "https://huggingface.co/tuwonga/dbluth/resolve/main/dbluth_prev1.jpg" tags: - stable-diffusion - text-to-image --- ### dbluth I played a lot in my childhood at laser disc videogames so this model is my personal tribute to the great Disney animator Don Bluth.This is a fine-tuned Stable Diffusion model (based on v1.5), I've trained three different models from videogames laser disc **Dragon's Lair** , **Space Ace** and **Dragon's Lair II Time Warp** then I merged these models into a single one called dbluth. Use the token **_dbluth_** in your prompts to use the style. _Download the ckpt file from "files and versions" tab into the stable diffusion models folder of your web-ui of choice._ The model is pretty similar to Disney classic model because of course Don Bluth was one of the main animator in classic Disney era. _I've found interesting the output in the img2img generation. You can see the results in the second image (original/img2img)._ **Characters and rendered with this model:** ![Character Samples](https://huggingface.co/tuwonga/dbluth/resolve/main/dbluth_prev1.jpg) _prompt and settings used: **[person] in dbluth style** | **Steps: 30, Sampler: Euler, CFG scale: 7.5**_ **Characters rendered with img2img:** ![Character Samples](https://huggingface.co/tuwonga/dbluth/resolve/main/dbluth_prev2.jpg) _prompt and settings used: **[person] in dbluth style** | **Steps: 30 - denoising stregth around 50/70 but you can play around with settings**_ -- This model was trained with Dreambooth training by TheLastBen, using 40 images at 8000 steps with 20% of text encoder for each model and then merged in a single one with Automatic1111 webui checkpoint merger. -- ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

DeskDown/MarianMix_en-ja-10

[ "pytorch", "tensorboard", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

1

2022-11-22T23:16:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 model-index: - name: mbert-conll2003 results: [] ---  # mbert-conll2003 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0 - Datasets 2.7.0 - Tokenizers 0.13.2

Dibyaranjan/nl_image_search

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-23T00:36:55Z

--- license: mit --- ### Alberto_Montt on Stable Diffusion This is the `<AlbertoMontt>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<AlbertoMontt> 0](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/3.jpeg) ![<AlbertoMontt> 1](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/0.jpeg) ![<AlbertoMontt> 2](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/5.jpeg) ![<AlbertoMontt> 3](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/2.jpeg) ![<AlbertoMontt> 4](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/1.jpeg) ![<AlbertoMontt> 5](https://huggingface.co/sd-concepts-library/alberto-montt/resolve/main/concept_images/4.jpeg)

DicoTiar/wisdomfiy

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

2022-11-23T00:38:10Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### American Flag Cowboy Hat on Stable Diffusion via Dreambooth #### model by aakamishra This your the Stable Diffusion model fine-tuned the American Flag Cowboy Hat concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks hat** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/sd-dreambooth-library/american-flag-cowboy-hat/resolve/main/concept_images/3.jpeg) ![image 1](https://huggingface.co/sd-dreambooth-library/american-flag-cowboy-hat/resolve/main/concept_images/0.jpeg) ![image 2](https://huggingface.co/sd-dreambooth-library/american-flag-cowboy-hat/resolve/main/concept_images/2.jpeg) ![image 3](https://huggingface.co/sd-dreambooth-library/american-flag-cowboy-hat/resolve/main/concept_images/1.jpeg) ![image 4](https://huggingface.co/sd-dreambooth-library/american-flag-cowboy-hat/resolve/main/concept_images/4.jpeg)

Dilmk2/DialoGPT-small-harrypotter

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

13

2022-11-23T00:50:28Z

--- license: other tags: - computer_vision - pose_estimation --- Copyright 2021-2023 by Mackenzie Mathis, Alexander Mathis, Shaokai Ye and contributors. All rights reserved. - Non-commercial use only is permitted - please cite Ye et al if you use this model in your work https://arxiv.org/abs/2203.07436v1 - If this license is not suitable for your business or project please contact EPFL-TTO (https://tto.epfl.ch/) for a full commercial license. This software may not be used to harm any animal deliberately.

DingleyMaillotUrgell/homer-bot

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

12

2022-11-23T01:25:59Z

--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: reco-ner results: [] ---  # reco-ner This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0668 - Precision: 0.8125 - Recall: 0.8790 - F1: 0.8444 - Accuracy: 0.9819 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4516 | 1.0 | 626 | 0.4047 | 0.4332 | 0.4564 | 0.4445 | 0.8980 | | 0.3677 | 2.0 | 1252 | 0.2774 | 0.4918 | 0.5731 | 0.5293 | 0.9193 | | 0.2892 | 3.0 | 1878 | 0.2133 | 0.6139 | 0.6581 | 0.6353 | 0.9384 | | 0.2736 | 4.0 | 2504 | 0.1772 | 0.6248 | 0.6854 | 0.6537 | 0.9488 | | 0.221 | 5.0 | 3130 | 0.1503 | 0.6295 | 0.7328 | 0.6772 | 0.9560 | | 0.1569 | 6.0 | 3756 | 0.1283 | 0.6821 | 0.8108 | 0.7409 | 0.9623 | | 0.1534 | 7.0 | 4382 | 0.0995 | 0.7412 | 0.8119 | 0.7749 | 0.9708 | | 0.089 | 8.0 | 5008 | 0.0846 | 0.7695 | 0.8353 | 0.8010 | 0.9760 | | 0.0923 | 9.0 | 5634 | 0.0743 | 0.7881 | 0.8740 | 0.8289 | 0.9789 | | 0.0711 | 10.0 | 6260 | 0.0668 | 0.8125 | 0.8790 | 0.8444 | 0.9819 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

DongHai/DialoGPT-small-rick

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

9

2022-11-23T01:50:21Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-demo-M02-2 results: [] ---  # wav2vec2-demo-M02-2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2709 - Wer: 1.0860 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 23.4917 | 0.91 | 500 | 3.2945 | 1.0 | | 3.4102 | 1.81 | 1000 | 3.1814 | 1.0 | | 2.9438 | 2.72 | 1500 | 2.7858 | 1.0 | | 2.6698 | 3.62 | 2000 | 2.4745 | 1.0035 | | 1.9542 | 4.53 | 2500 | 1.8675 | 1.3745 | | 1.2737 | 5.43 | 3000 | 1.6459 | 1.3703 | | 0.9748 | 6.34 | 3500 | 1.8406 | 1.3037 | | 0.7696 | 7.25 | 4000 | 1.5086 | 1.2476 | | 0.6396 | 8.15 | 4500 | 1.8280 | 1.2476 | | 0.558 | 9.06 | 5000 | 1.7680 | 1.2247 | | 0.4865 | 9.96 | 5500 | 1.8210 | 1.2309 | | 0.4244 | 10.87 | 6000 | 1.7910 | 1.1775 | | 0.3898 | 11.78 | 6500 | 1.8021 | 1.1831 | | 0.3456 | 12.68 | 7000 | 1.7746 | 1.1456 | | 0.3349 | 13.59 | 7500 | 1.8969 | 1.1519 | | 0.3233 | 14.49 | 8000 | 1.7402 | 1.1234 | | 0.3046 | 15.4 | 8500 | 1.8585 | 1.1429 | | 0.2622 | 16.3 | 9000 | 1.6687 | 1.0950 | | 0.2593 | 17.21 | 9500 | 1.8192 | 1.1144 | | 0.2541 | 18.12 | 10000 | 1.8665 | 1.1110 | | 0.2098 | 19.02 | 10500 | 1.9996 | 1.1186 | | 0.2192 | 19.93 | 11000 | 2.0346 | 1.1040 | | 0.1934 | 20.83 | 11500 | 2.1924 | 1.1012 | | 0.2034 | 21.74 | 12000 | 1.8060 | 1.0929 | | 0.1857 | 22.64 | 12500 | 2.0334 | 1.0798 | | 0.1819 | 23.55 | 13000 | 2.1223 | 1.1040 | | 0.1621 | 24.46 | 13500 | 2.1795 | 1.0957 | | 0.1548 | 25.36 | 14000 | 2.1545 | 1.1089 | | 0.1512 | 26.27 | 14500 | 2.2707 | 1.1186 | | 0.1472 | 27.17 | 15000 | 2.1698 | 1.0888 | | 0.1296 | 28.08 | 15500 | 2.2496 | 1.0867 | | 0.1312 | 28.99 | 16000 | 2.2969 | 1.0881 | | 0.1331 | 29.89 | 16500 | 2.2709 | 1.0860 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

Dongjae/mrc2reader

[ "pytorch", "xlm-roberta", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "XLMRobertaForQuestionAnswering" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

2022-11-23T02:04:01Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion widget: - text: "zombie_vector " --- ### Jak's Zombie Vector Pack for Stable Diffusion Another fantastic image pack brought to you by 124 training images through 5000 training steps, 20% Training text crafted by Jak_TheAI_Artist Include Prompt trigger: "zombie_vector" to activate. Perfect for designing T-shirts and zombie vector art. Sample pictures of this concept: ![zombie 0](https://huggingface.co/plasmo/zombie-vector/resolve/main/concept_images/trump.jpg) ![zombie 1](https://huggingface.co/plasmo/zombie-vector/resolve/main/concept_images/biden.jpg) ![zombie 2](https://huggingface.co/plasmo/zombie-vector/resolve/main/concept_images/sm.jpg) ![zombie 3](https://huggingface.co/plasmo/zombie-vector/resolve/main/concept_images/ww.jpg)

Dongmin/testmodel

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "T5ForConditionalGeneration" ], "model_type": "t5", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": true, "length_penalty": 2, "max_length": 200, "min_length": 30, "no_repeat_ngram_size": 3, "num_beams": 4, "prefix": "summarize: " }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to German: " }, "translation_en_to_fr": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to French: " }, "translation_en_to_ro": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to Romanian: " } } }

11

2022-11-23T02:44:30Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### TaylorSwift Dreambooth model trained by taytay4eva with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook using the StableDiffusionv1.5 model CREATOR NOTE 1: The keyword for this model is <b>taySwift</b> CREATOR NOTE 2: "Taylor Berry" is a blend of the original model as put through further iterations of DreamBooth and Berry_mix at a 7:3 ratio. It provides a bit better mesh of images and, I think, an overall smoother final product, but whichever you like is what you should go with! Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) ![Sample Image of TaylorSwiftv2](https://huggingface.co/sd-dreambooth-library/taylorswift/blob/main/concept_images/indexaa.png) positive prompt: <b>taySwift</b>, Masterpiece, cinematic lighting, photorealistic, realistic, extremely detailed, (fancy clothes, puffy sleeves, Lacy shirt, thigh high boots, leather boots, short skirt), cheerful attitude, happy woman, excited woman), artgerm, greg rutkowski, alphonse mucha negative prompt: Ugly, lowres, duplicate, morbid, mutilated, out of frame, extra fingers, extra limbs, extra legs, extra heads, extra arms, extra breasts, extra nipples, extra head, extra digit, poorly drawn hands, poorly drawn face, mutation, mutated hands, bad anatomy, long neck, signature, watermark, username, blurry, artist name, deformed, distorted fingers, distorted limbs, distorted legs, distorted heads, distorted arms, distorted breasts, distorted nipples, distorted head, distorted digit Steps: 85, CFG scale: 7, Seed: 1903506130, Face restoration: CodeFormer, Size: 576x832, Model hash: ad57baac, Denoising strength: 0.75, Mask blur: 4 Upscale: 2, visibility: 1.0, model:ESRGAN_4x ![Sample Image of Taylor Berry](https://huggingface.co/sd-dreambooth-library/taylorswift/blob/main/00143-3262192747-oil%20painting%2C%20sensual%2C%20(full%20body)%2C%20taySwift%2C%20princess%2C%20(auburn%20hair)%2C%20erotic%2C%20fantasy%20princess%2C%20tavern%20wench%2C%20bar%2C%20magical%2C%20bus.png) positive prompt: oil painting, sensual, (full body), <b>taySwift</b>, princess, (auburn hair), erotic, fantasy princess, tavern wench, bar, magical, busty, huge titties, curvy, full red lips, kiss, sensual clothes, off the shoulder dress, lace, ((blue) and green floor length dress), (Albert Lynch), J. C. Leyendecker, Ruan Jia, Gaston Bussiere, Alexandre Cabanel, WLOP, best quality negative prompt: (blonde hair), (ugly:1.3), (duplicate:1.3), (morbid), (mutilated), out of frame, extra fingers, mutated hands, (poorly drawn hands), (poorly drawn face), (mutation:1.3), (deformed:1.3), (amputee:1.3), blurry, bad anatomy, bad proportions, (extra limbs), cloned face, (disfigured:1.3), gross proportions, (malformed limbs), (missing arms), (missing legs), (extra arms), (extra legs), mutated hands, (fused fingers), (too many fingers), (long neck:1.3), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, black and white, monochrome, censored Steps: 42, CFG scale: 11, Denoising Strength: 0.75, Seed: 3262192735

Doogie/Waynehills-KE-T5-doogie

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

2022-11-23T02:47:05Z

--- tags: - tensorflowtts - audio - text-to-speech - text-to-mel language: vi license: apache-2.0 datasets: - infore --- # Install TensorFlowTTS ``` pip install TensorFlowTTS ``` ## Converting your Text to Mel Spectrogram ```python import numpy as np import soundfile as sf import yaml import IPython.display as ipd import tensorflow as tf from tensorflow_tts.inference import AutoProcessor from tensorflow_tts.inference import TFAutoModel processor = AutoProcessor.from_pretrained("MarcNg/fastspeech2-vi-infore") fastspeech2 = TFAutoModel.from_pretrained("MarcNg/fastspeech2-vi-infore") text = "xin chào đây là một ví dụ về chuyển đổi văn bản thành giọng nói" input_ids = processor.text_to_sequence(text) mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference( input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0), speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32), speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32), f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32), energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32), ) ``` ## Bonus: Convert Mel Spectrogram to Speech ```python mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-ljspeech-en") audio_before = mb_melgan.inference(mel_before)[0, :, 0] audio_after = mb_melgan.inference(mel_after)[0, :, 0] sf.write("audio_before.wav", audio_before, 22050, "PCM_16") sf.write("audio_after.wav", audio_after, 22050, "PCM_16") ipd.Audio('audio_after.wav') ``` #### Referencing FastSpeech2 ``` @misc{ren2021fastspeech, title={FastSpeech 2: Fast and High-Quality End-to-End Text to Speech}, author={Yi Ren and Chenxu Hu and Xu Tan and Tao Qin and Sheng Zhao and Zhou Zhao and Tie-Yan Liu}, year={2021}, eprint={2006.04558}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```

DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

29

2022-11-23T04:33:07Z

--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: whisper-small-hi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 113.49784136121221 ---  # whisper-small-hi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 4.5728 - Wer: 113.4978 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.877 | 0.01 | 6 | 4.5728 | 19.4978 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

28

2022-11-23T04:41:41Z

--- license: cc-by-4.0 --- # GenRead: FiD model trained on TQA -- This is the model checkpoint of GenRead [2], based on the T5-3B and trained on the TriviaQA [1]. -- Hyperparameters: 8 x 80GB A100 GPUs; batch size 16; AdamW; LR 6e-5; best dev at 8500 steps References: [1] TriviaQA: A Large Scale Dataset for Reading Comprehension and Question Answering. ACL 2017 [2] Generate rather than Retrieve: Large Language Models are Strong Context Generators. arXiv 2022 ## Model performance We evaluate it on the TriviaQA dataset, the EM score is 71.55. <a href="https://huggingface.co/exbert/?model=bert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a> --- license: cc-by-4.0 ---

DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

30

2022-11-23T04:43:08Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 173.24 +/- 14.93 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

DoyyingFace/bert-asian-hate-tweets-asonam-unclean

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

30

2022-11-23T05:20:19Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1651 - F1: 0.8578 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.211 | 1.0 | 715 | 0.1834 | 0.8266 | | 0.1447 | 2.0 | 1430 | 0.1624 | 0.8464 | | 0.0933 | 3.0 | 2145 | 0.1651 | 0.8578 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0 - Datasets 2.7.0 - Tokenizers 0.12.1

DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

25

2022-11-23T05:39:41Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="popolin52/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

albert-base-v2

[ "pytorch", "tf", "jax", "rust", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

4,785,283

2022-11-23T05:57:57Z

This model is said to serve as a repository for Futanari or futa models, with a focus on the creation and storage of these types of models. Despite ongoing efforts, the elusive third element remains elusive. Nevertheless, it is thought to be a valuable asset when used in conjunction with other models to get "better? futanari images"

albert-large-v1

[ "pytorch", "tf", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

687

2022-11-23T06:21:29Z

--- language: - nl license: apache-2.0 tags: - whisper-event - hf-asr-leaderboard datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Medium nl - GeoffVdr results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 type: mozilla-foundation/common_voice_11_0 config: nl split: test args: nl metrics: - name: Wer type: wer value: 7.514 co2_eq_emissions: emissions: 2930 source: https://mlco2.github.io/impact/ training_type: fine-tuning geographical_location: Ghent, Belgium hardware_used: 1 v100 GPU --- # Whisper Medium nl - GeoffVdr This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data - Training: Mozilla CommonVoice 11 Dutch train+validation set - Evaluation: Mozilla CommonVoice 11 Dutch test set ## Training procedure ## Training Hyperparameters - learning_rate: 1e-5 - train_batch_size: 8 - eval_batch_size: 8 - gradient_accumulation_steps: 2 - lr_scheduler_warmup_steps: 500 - training_steps: 12000 ## Training Results | Training Loss | Epoch | Step | WER | |:-------------:|:-----:|:----:|:----:| | 0.1111 | 0.39 | 1000 | 9.89 | | 0.0884 | 0.78 | 2000 | 9.26 | | 0.0362 | 1.17 | 3000 | 8.64 | | 0.0359 | 1.56 | 4000 | 8.60 | | 0.0375 | 1.95 | 5000 | 8.24 | : : | 0.0015 | 4.68 | 12000| 7.51 | ### Framework versions

albert-large-v2

[ "pytorch", "tf", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

26,792

2022-11-23T06:31:48Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-finetuned-squad_2 results: [] ---  # bert-finetuned-squad_2 This model is a fine-tuned version of [tomXBE/distilbert-base-uncased-finetuned-squad](https://huggingface.co/tomXBE/distilbert-base-uncased-finetuned-squad) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

albert-xxlarge-v2

[ "pytorch", "tf", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "exbert", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

42,640

null

--- tags: - generated_from_keras_callback model-index: - name: dung1308/RM_system_NLP_model results: [] ---  # dung1308/RM_system_NLP_model This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.8134 - Validation Loss: 1.8072 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.4371 | 2.4851 | 0 | | 4.0108 | 2.1003 | 1 | | 3.8134 | 1.8072 | 2 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.7.0 - Tokenizers 0.11.0

bert-base-cased

[ "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1810.04805", "transformers", "exbert", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

8,621,271

2022-11-23T06:54:13Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: distilbart-cnn-12-6-sec results: [] ---  # distilbart-cnn-12-6-sec This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0798 - Rouge1: 72.1665 - Rouge2: 62.2601 - Rougel: 67.8376 - Rougelsum: 71.1407 - Gen Len: 121.62 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 99 | 0.3526 | 53.3978 | 38.6395 | 45.6271 | 51.0477 | 111.48 | | No log | 2.0 | 198 | 0.1961 | 55.7397 | 43.6293 | 50.9595 | 54.0764 | 111.46 | | No log | 3.0 | 297 | 0.1483 | 66.9443 | 54.8966 | 62.6678 | 65.6787 | 118.64 | | No log | 4.0 | 396 | 0.1218 | 67.2661 | 56.1852 | 63.1339 | 65.8066 | 124.92 | | No log | 5.0 | 495 | 0.1139 | 67.2097 | 55.8694 | 62.7508 | 65.9706 | 123.02 | | 0.4156 | 6.0 | 594 | 0.0940 | 71.607 | 60.6697 | 66.7873 | 70.339 | 122.84 | | 0.4156 | 7.0 | 693 | 0.0888 | 71.3792 | 61.8326 | 68.25 | 70.5113 | 124.4 | | 0.4156 | 8.0 | 792 | 0.0870 | 72.7472 | 62.6968 | 68.2853 | 71.5789 | 124.34 | | 0.4156 | 9.0 | 891 | 0.0799 | 73.4438 | 63.5966 | 68.8737 | 72.3014 | 119.88 | | 0.4156 | 10.0 | 990 | 0.0798 | 72.1665 | 62.2601 | 67.8376 | 71.1407 | 121.62 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

bert-base-chinese

[ "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "zh", "arxiv:1810.04805", "transformers", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3,377,486

2022-11-23T06:54:29Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: distilbart-cnn-12-6-sec results: [] ---  # distilbart-cnn-12-6-sec This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1379 - Rouge1: 72.2845 - Rouge2: 61.1501 - Rougel: 67.6999 - Rougelsum: 70.9968 - Gen Len: 113.8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 99 | 0.4429 | 56.0806 | 40.5969 | 47.5271 | 53.7227 | 115.44 | | No log | 2.0 | 198 | 0.2279 | 56.6042 | 42.1781 | 48.9542 | 54.951 | 116.84 | | No log | 3.0 | 297 | 0.1845 | 65.9646 | 51.8575 | 59.8647 | 64.103 | 113.8 | | No log | 4.0 | 396 | 0.1532 | 71.6132 | 61.1434 | 67.4165 | 70.4093 | 110.46 | | No log | 5.0 | 495 | 0.1379 | 72.2845 | 61.1501 | 67.6999 | 70.9968 | 113.8 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

bert-large-uncased-whole-word-masking-finetuned-squad

[ "pytorch", "tf", "jax", "safetensors", "bert", "question-answering", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1810.04805", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]

question-answering

{ "architectures": [ "BertForQuestionAnswering" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

480,510

2022-11-23T07:46:33Z

--- language: da widget: - text: En trend, der kan blive ligeså hot som<mask>. tags: - roberta - danish - masked-lm - pytorch license: cc-by-4.0 --- # DanskBERT This is DanskBERT, a Danish language model. Note that you should not prepend the mask with a space when using it directly! The model is the best performing base-size model on the [ScandEval benchmark for Danish](https://scandeval.github.io/nlu-benchmark/). DanskBERT was trained on the Danish Gigaword Corpus (Strømberg-Derczynski et al., 2021). DanskBERT was trained using fairseq using the RoBERTa-base configuration. The model was trained with a batch size of 2k, and was trained to convergence for 500k steps using 16 V100 cards for approximately two weeks. If you find this model useful, please cite ``` @inproceedings{snaebjarnarson-etal-2023-transfer, title = "{T}ransfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese", author = "Snæbjarnarson, Vésteinn and Simonsen, Annika and Glavaš, Goran and Vulić, Ivan", booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)", month = "may 22--24", year = "2023", address = "Tórshavn, Faroe Islands", publisher = {Link{\"o}ping University Electronic Press, Sweden}, } ```

bert-large-uncased-whole-word-masking

[ "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1810.04805", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

76,685

2022-11-23T07:49:30Z

--- library_name: stable-baselines3 tags: - ALE/Qbert-v5 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: ALE/Qbert-v5 type: ALE/Qbert-v5 metrics: - type: mean_reward value: 6665.00 +/- 1973.49 name: mean_reward verified: false --- # **DQN** Agent playing **ALE/Qbert-v5** This is a trained model of a **DQN** agent playing **ALE/Qbert-v5** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env ALE/Qbert-v5 -orga xaeroq -f logs/ python enjoy.py --algo dqn --env ALE/Qbert-v5 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env ALE/Qbert-v5 -orga xaeroq -f logs/ rl_zoo3 enjoy --algo dqn --env ALE/Qbert-v5 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env ALE/Qbert-v5 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env ALE/Qbert-v5 -f logs/ -orga xaeroq ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

xlm-clm-ende-1024

[ "pytorch", "tf", "safetensors", "xlm", "fill-mask", "multilingual", "en", "de", "arxiv:1901.07291", "arxiv:1910.09700", "transformers", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "XLMWithLMHeadModel" ], "model_type": "xlm", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

33,817

2022-11-23T09:24:10Z

--- license: cc-by-4.0 --- ## Aina Project's Catalan-Spanish machine translation model ## Table of Contents - [Model Description](#model-description) - [Intended Uses and Limitations](#intended-use) - [How to Use](#how-to-use) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Data Preparation](#data-preparation) - [Tokenization](#tokenization) - [Hyperparameters](#hyperparameters) - [Evaluation](#evaluation) - [Variable and Metrics](#variable-and-metrics) - [Evaluation Results](#evaluation-results) - [Additional Information](#additional-information) - [Author](#author) - [Contact Information](#contact-information) - [Copyright](#copyright) - [Licensing Information](#licensing-information) - [Funding](#funding) - [Disclaimer](#disclaimer) ## Model description This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-Spanish datasets, up to 92 million sentences. Additionally, the model is evaluated on several public datasecomprising 5 different domains (general, adminstrative, technology, biomedical, and news). ## Intended uses and limitations You can use this model for machine translation from Catalan to Spanish. ## How to use ### Usage Required libraries: ```bash pip install ctranslate2 pyonmttok ``` Translate a sentence using python ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="projecte-aina/mt-aina-ca-es", revision="main") tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model") tokenized=tokenizer.tokenize("Benvingut al projecte Aina!") translator = ctranslate2.Translator(model_dir) translated = translator.translate_batch([tokenized[0]]) print(tokenizer.detokenize(translated[0][0]['tokens'])) ``` ## Training ### Training data The was trained on a combination of the following datasets: | Dataset | Sentences | Tokens | |-------------------|----------------|-------------------| | DOCG v2 | 8.472.786 | 188.929.206 | | El Periodico | 6.483.106 | 145.591.906 | | EuroParl | 1.876.669 | 49.212.670 | | WikiMatrix | 1.421.077 | 34.902.039 | | Wikimedia | 335.955 | 8.682.025 | | QED | 71.867 | 1.079.705 | | TED2020 v1 | 52.177 | 836.882 | | CCMatrix v1 | 56.103.820 | 1.064.182.320 | | MultiCCAligned v1 | 2.433.418 | 48.294.144 | | ParaCrawl | 15.327.808 | 334.199.408 | | **Total** | **92.578.683** | **1.875.910.305** | ### Training procedure ### Data preparation All datasets are concatenated and filtered using the [mBERT Gencata parallel filter](https://huggingface.co/projecte-aina/mbert-base-gencata) and cleaned using the clean-corpus-n.pl script from [moses](https://github.com/moses-smt/mosesdecoder), allowing sentences between 5 and 150 words. Before training, the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py) #### Tokenization All data is tokenized using sentencepiece, with 50 thousand token sentencepiece model learned from the combination of all filtered training data. This model is included. #### Hyperparameters The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) The following hyperparamenters were set on the Fairseq toolkit: | Hyperparameter | Value | |------------------------------------|----------------------------------| | Architecture | transformer_vaswani_wmt_en_de_bi | | Embedding size | 1024 | | Feedforward size | 4096 | | Number of heads | 16 | | Encoder layers | 24 | | Decoder layers | 6 | | Normalize before attention | True | | --share-decoder-input-output-embed | True | | --share-all-embeddings | True | | Effective batch size | 96.000 | | Optimizer | adam | | Adam betas | (0.9, 0.980) | | Clip norm | 0.0 | | Learning rate | 1e-3 | | Lr. schedurer | inverse sqrt | | Warmup updates | 4000 | | Dropout | 0.1 | | Label smoothing | 0.1 | The model was trained using shards of 10 million sentences, for a total of 13.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 6 checkpoints. ## Evaluation ### Variable and metrics We use the BLEU score for evaluation on test sets: [Flores-101](https://github.com/facebookresearch/flores), [TaCon](https://elrc-share.eu/repository/browse/tacon-spanish-constitution-mt-test-set/84a96138b98611ec9c1a00155d02670628f3e6857b0f422abd82abc3795ec8c2/), [United Nations](https://zenodo.org/record/3888414#.Y33-_tLMIW0), [Cybersecurity](https://elrc-share.eu/repository/browse/cyber-mt-test-set/2bd93faab98c11ec9c1a00155d026706b96a490ed3e140f0a29a80a08c46e91e/), [wmt19 biomedical test set](), [wmt13 news test set](https://elrc-share.eu/repository/browse/catalan-wmt2013-machine-translation-shared-task-test-set/84a96139b98611ec9c1a00155d0267061a0aa1b62e2248e89aab4952f3c230fc/) ### Evaluation results Below are the evaluation results on the machine translation from Catalan to Spanish compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es): | Test set | SoftCatalà | Google Translate | mt-aina-ca-es | |----------------------|------------|------------------|---------------| | Spanish Constitution | 70,7 | **77,1** | 75,5 | | United Nations | 78,1 | 84,3 | **86,3** | | Flores 101 dev | 23,5 | 24 | **24,1** | | Flores 101 devtest | 24,1 | 24,2 | **24,4** | | Cybersecurity | 67,3 | **76,9** | 75,1 | | wmt 19 biomedical | 60,4 | 62,7 | **63,0** | | wmt 13 news | 22,5 | 23,1 | **23,4** | | aina_aapp_ca-es | 80,9 | 81,4 | **82,8** | | Average | 53,4 | 56,7 | **56,8** | ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]) ### Contact information For further information, send an email to [email protected] ### Copyright Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center ### Licensing Information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ## Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

AIDA-UPM/MSTSb_paraphrase-xlm-r-multilingual-v1

[ "pytorch", "xlm-roberta", "sentence-transformers", "feature-extraction", "sentence-similarity", "transformers" ]

sentence-similarity

{ "architectures": [ "XLMRobertaModel" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

73

null

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1800 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 1800, "warmup_steps": 180, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2

[ "pytorch", "xlm-roberta", "feature-extraction", "multilingual", "transformers", "sentence-similarity" ]

sentence-similarity

{ "architectures": [ "XLMRobertaModel" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

1,084

2022-11-23T14:41:21Z

--- language: - en tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: tomekkorbak/test9485844 results: [] ---  # tomekkorbak/test9485844 This model is a fine-tuned version of [n/a](https://huggingface.co/n/a) on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.1 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 16 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6

AnonymousSub/SR_rule_based_roberta_hier_quadruplet_epochs_1_shard_1

[ "pytorch", "roberta", "feature-extraction", "transformers" ]

feature-extraction

{ "architectures": [ "RobertaModel" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

2

null

--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large` (based on BART architecture) is initialized with `microsoft/tapex-large` and continuously pretrained on natural and synthetic data. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```

AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1

[ "pytorch", "roberta", "feature-extraction", "transformers" ]

feature-extraction

{ "architectures": [ "RobertaModel" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

4

null

--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/pii-pile-chunk3-0-50000 - tomekkorbak/pii-pile-chunk3-50000-100000 - tomekkorbak/pii-pile-chunk3-100000-150000 - tomekkorbak/pii-pile-chunk3-150000-200000 - tomekkorbak/pii-pile-chunk3-200000-250000 - tomekkorbak/pii-pile-chunk3-250000-300000 - tomekkorbak/pii-pile-chunk3-300000-350000 - tomekkorbak/pii-pile-chunk3-350000-400000 - tomekkorbak/pii-pile-chunk3-400000-450000 - tomekkorbak/pii-pile-chunk3-450000-500000 - tomekkorbak/pii-pile-chunk3-500000-550000 - tomekkorbak/pii-pile-chunk3-550000-600000 - tomekkorbak/pii-pile-chunk3-600000-650000 - tomekkorbak/pii-pile-chunk3-650000-700000 - tomekkorbak/pii-pile-chunk3-700000-750000 - tomekkorbak/pii-pile-chunk3-750000-800000 - tomekkorbak/pii-pile-chunk3-800000-850000 - tomekkorbak/pii-pile-chunk3-850000-900000 - tomekkorbak/pii-pile-chunk3-900000-950000 - tomekkorbak/pii-pile-chunk3-950000-1000000 - tomekkorbak/pii-pile-chunk3-1000000-1050000 - tomekkorbak/pii-pile-chunk3-1050000-1100000 - tomekkorbak/pii-pile-chunk3-1100000-1150000 - tomekkorbak/pii-pile-chunk3-1150000-1200000 - tomekkorbak/pii-pile-chunk3-1200000-1250000 - tomekkorbak/pii-pile-chunk3-1250000-1300000 - tomekkorbak/pii-pile-chunk3-1300000-1350000 - tomekkorbak/pii-pile-chunk3-1350000-1400000 - tomekkorbak/pii-pile-chunk3-1400000-1450000 - tomekkorbak/pii-pile-chunk3-1450000-1500000 - tomekkorbak/pii-pile-chunk3-1500000-1550000 - tomekkorbak/pii-pile-chunk3-1550000-1600000 - tomekkorbak/pii-pile-chunk3-1600000-1650000 - tomekkorbak/pii-pile-chunk3-1650000-1700000 - tomekkorbak/pii-pile-chunk3-1700000-1750000 - tomekkorbak/pii-pile-chunk3-1750000-1800000 - tomekkorbak/pii-pile-chunk3-1800000-1850000 - tomekkorbak/pii-pile-chunk3-1850000-1900000 - tomekkorbak/pii-pile-chunk3-1900000-1950000 model-index: - name: heuristic_shannon results: [] ---  # heuristic_shannon This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.0}, 'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<|aligned|>'}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'heuristic_shannon', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3esut7nh

AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_10

[ "pytorch", "roberta", "feature-extraction", "transformers" ]

feature-extraction

{ "architectures": [ "RobertaModel" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

4

2022-11-23T21:42:22Z

--- tags: - generated_from_trainer metrics: - rouge model-index: - name: bert2bert_shared-spanish-finetuned-summarization-intento2 results: [] ---  # bert2bert_shared-spanish-finetuned-summarization-intento2 This model is a fine-tuned version of [mrm8488/bert2bert_shared-spanish-finetuned-summarization](https://huggingface.co/mrm8488/bert2bert_shared-spanish-finetuned-summarization) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.9693 - Rouge1: 1.8257 - Rouge2: 0.0 - Rougel: 1.6832 - Rougelsum: 1.6866 - Gen Len: 10.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 7.9999 | 1.0 | 6180 | 7.9915 | 1.5443 | 0.0 | 1.4357 | 1.4377 | 10.0 | | 7.9469 | 2.0 | 12360 | 7.9693 | 1.8257 | 0.0 | 1.6832 | 1.6866 | 10.0 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1

[ "pytorch", "roberta", "feature-extraction", "transformers" ]

feature-extraction

{ "architectures": [ "RobertaModel" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

4

null

--- tags: - image-to-text - image-captioning widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg example_title: Football Match - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/dog-cat.jpg example_title: Dog & Cat license: mit pinned: true inference: true ---

AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10

[ "pytorch", "roberta", "feature-extraction", "transformers" ]

feature-extraction

{ "architectures": [ "RobertaModel" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- thumbnail: https://static.tildacdn.com/tild3636-3737-4330-b332-623831323534/_READY-01-01.png tags: - conversational licence: - mit ---