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dccuchile/albert-xlarge-spanish-finetuned-mldoc
[ "pytorch", "albert", "text-classification", "transformers" ]
text-classification
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26
null
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: Bio_ClinicalBERT_fold_3_ternary_v1 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. --> # Bio_ClinicalBERT_fold_3_ternary_v1 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0585 - F1: 0.7952 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5738 | 0.7709 | | 0.5512 | 2.0 | 578 | 0.5828 | 0.7733 | | 0.5512 | 3.0 | 867 | 0.7217 | 0.7830 | | 0.2304 | 4.0 | 1156 | 1.0389 | 0.7867 | | 0.2304 | 5.0 | 1445 | 1.0992 | 0.7915 | | 0.0951 | 6.0 | 1734 | 1.3528 | 0.7806 | | 0.0388 | 7.0 | 2023 | 1.4223 | 0.7879 | | 0.0388 | 8.0 | 2312 | 1.5588 | 0.7830 | | 0.0172 | 9.0 | 2601 | 1.5913 | 0.7976 | | 0.0172 | 10.0 | 2890 | 1.7464 | 0.7842 | | 0.0143 | 11.0 | 3179 | 1.7395 | 0.7927 | | 0.0143 | 12.0 | 3468 | 1.7523 | 0.7939 | | 0.0108 | 13.0 | 3757 | 1.8059 | 0.7952 | | 0.0099 | 14.0 | 4046 | 1.9056 | 0.7855 | | 0.0099 | 15.0 | 4335 | 1.8550 | 0.7903 | | 0.0076 | 16.0 | 4624 | 1.8718 | 0.7988 | | 0.0076 | 17.0 | 4913 | 1.9325 | 0.7976 | | 0.0033 | 18.0 | 5202 | 1.9504 | 0.7952 | | 0.0033 | 19.0 | 5491 | 1.9841 | 0.7879 | | 0.003 | 20.0 | 5780 | 1.9843 | 0.7952 | | 0.0001 | 21.0 | 6069 | 2.0110 | 0.7927 | | 0.0001 | 22.0 | 6358 | 2.0049 | 0.7939 | | 0.0028 | 23.0 | 6647 | 2.0638 | 0.7915 | | 0.0028 | 24.0 | 6936 | 2.0612 | 0.7903 | | 0.0011 | 25.0 | 7225 | 2.0585 | 0.7952 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
dccuchile/albert-xlarge-spanish-finetuned-ner
[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
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5
null
--- datasets: - wnut2017 metrics: - f1 - precision - recall model-index: - name: tner/roberta-large-wnut2017 results: - task: name: Token Classification type: token-classification dataset: name: wnut2017 type: wnut2017 args: wnut2017 metrics: - name: F1 type: f1 value: 0.5375139977603584 - name: Precision type: precision value: 0.6789250353606789 - name: Recall type: recall value: 0.4448563484708063 - name: F1 (macro) type: f1_macro value: 0.4734480458244917 - name: Precision (macro) type: precision_macro value: 0.59471614080646 - name: Recall (macro) type: recall_macro value: 0.4020936892146829 - name: F1 (entity span) type: f1_entity_span value: 0.6304591265397536 - name: Precision (entity span) type: precision_entity_span value: 0.7963224893917963 - name: Recall (entity span) type: recall_entity_span value: 0.5217794253938832 pipeline_tag: token-classification widget: - text: "Jacob Collier is a Grammy awarded artist from England." example_title: "NER Example 1" --- # tner/roberta-large-wnut2017 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) dataset. Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.5375139977603584 - Precision (micro): 0.6789250353606789 - Recall (micro): 0.4448563484708063 - F1 (macro): 0.4734480458244917 - Precision (macro): 0.59471614080646 - Recall (macro): 0.4020936892146829 The per-entity breakdown of the F1 score on the test set are below: - corporation: 0.4065040650406504 - group: 0.33913043478260874 - location: 0.6715867158671587 - person: 0.6657342657342658 - product: 0.27999999999999997 - work_of_art: 0.4777327935222672 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.5084441265818846, 0.5659035599952082] - 95%: [0.5009032784561068, 0.5708361009044657] - F1 (macro): - 90%: [0.5084441265818846, 0.5659035599952082] - 95%: [0.5009032784561068, 0.5708361009044657] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-wnut2017/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/roberta-large-wnut2017/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip ```shell pip install tner ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/roberta-large-wnut2017") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. ### Training hyperparameters The following hyperparameters were used during training: - dataset: ['tner/wnut2017'] - dataset_split: train - dataset_name: None - local_dataset: None - model: roberta-large - crf: True - max_length: 128 - epoch: 15 - batch_size: 64 - lr: 1e-05 - random_seed: 42 - gradient_accumulation_steps: 1 - weight_decay: None - lr_warmup_step_ratio: 0.1 - max_grad_norm: 10.0 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-wnut2017/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-camacho-collados-2021-ner, title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", author = "Ushio, Asahi and Camacho-Collados, Jose", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.7", doi = "10.18653/v1/2021.eacl-demos.7", pages = "53--62", abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.", } ```
dccuchile/albert-xlarge-spanish-finetuned-pawsx
[ "pytorch", "albert", "text-classification", "transformers" ]
text-classification
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24
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--- datasets: - tner/wnut2017 metrics: - f1 - precision - recall model-index: - name: tner/deberta-v3-large-wnut2017 results: - task: name: Token Classification type: token-classification dataset: name: tner/wnut2017 type: tner/wnut2017 args: tner/wnut2017 metrics: - name: F1 type: f1 value: 0.5047353760445682 - name: Precision type: precision value: 0.63268156424581 - name: Recall type: recall value: 0.4198331788693234 - name: F1 (macro) type: f1_macro value: 0.4165125500830091 - name: Precision (macro) type: precision_macro value: 0.5356144444686111 - name: Recall (macro) type: recall_macro value: 0.3573954549633822 - name: F1 (entity span) type: f1_entity_span value: 0.6249999999999999 - name: Precision (entity span) type: precision_entity_span value: 0.7962697274031564 - name: Recall (entity span) type: recall_entity_span value: 0.5143651529193698 pipeline_tag: token-classification widget: - text: "Jacob Collier is a Grammy awarded artist from England." example_title: "NER Example 1" --- # tner/deberta-v3-large-wnut2017 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the [tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) dataset. Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.5047353760445682 - Precision (micro): 0.63268156424581 - Recall (micro): 0.4198331788693234 - F1 (macro): 0.4165125500830091 - Precision (macro): 0.5356144444686111 - Recall (macro): 0.3573954549633822 The per-entity breakdown of the F1 score on the test set are below: - corporation: 0.25477707006369427 - group: 0.34309623430962344 - location: 0.6187050359712232 - person: 0.6721763085399448 - product: 0.18579234972677597 - work_of_art: 0.42452830188679247 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.4752384997212858, 0.5329114690850492] - 95%: [0.46929053844001617, 0.537282841423422] - F1 (macro): - 90%: [0.4752384997212858, 0.5329114690850492] - 95%: [0.46929053844001617, 0.537282841423422] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip ```shell pip install tner ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/deberta-v3-large-wnut2017") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. ### Training hyperparameters The following hyperparameters were used during training: - dataset: ['tner/wnut2017'] - dataset_split: train - dataset_name: None - local_dataset: None - model: microsoft/deberta-v3-large - crf: False - max_length: 128 - epoch: 15 - batch_size: 16 - lr: 1e-05 - random_seed: 42 - gradient_accumulation_steps: 4 - weight_decay: 1e-07 - lr_warmup_step_ratio: 0.1 - max_grad_norm: 10.0 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-camacho-collados-2021-ner, title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", author = "Ushio, Asahi and Camacho-Collados, Jose", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.7", doi = "10.18653/v1/2021.eacl-demos.7", pages = "53--62", abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.", } ```
dccuchile/albert-xlarge-spanish-finetuned-pos
[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
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3
null
--- datasets: - tner/conll2003 metrics: - f1 - precision - recall model-index: - name: tner/roberta-large-conll2003 results: - task: name: Token Classification type: token-classification dataset: name: tner/conll2003 type: tner/conll2003 args: tner/conll2003 metrics: - name: F1 type: f1 value: 0.924769027716674 - name: Precision type: precision value: 0.9191883855168795 - name: Recall type: recall value: 0.9304178470254958 - name: F1 (macro) type: f1_macro value: 0.9110950780089749 - name: Precision (macro) type: precision_macro value: 0.9030546238754271 - name: Recall (macro) type: recall_macro value: 0.9197126371122274 - name: F1 (entity span) type: f1_entity_span value: 0.9619852164730729 - name: Precision (entity span) type: precision_entity_span value: 0.9562631210636809 - name: Recall (entity span) type: recall_entity_span value: 0.9677762039660056 pipeline_tag: token-classification widget: - text: "Jacob Collier is a Grammy awarded artist from England." example_title: "NER Example 1" --- # tner/roberta-large-conll2003 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [tner/conll2003](https://huggingface.co/datasets/tner/conll2003) dataset. Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.924769027716674 - Precision (micro): 0.9191883855168795 - Recall (micro): 0.9304178470254958 - F1 (macro): 0.9110950780089749 - Precision (macro): 0.9030546238754271 - Recall (macro): 0.9197126371122274 The per-entity breakdown of the F1 score on the test set are below: - location: 0.9390573401380967 - organization: 0.9107142857142857 - other: 0.8247422680412372 - person: 0.9698664181422801 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.9185189408755685, 0.9309806929048586] - 95%: [0.9174010190551032, 0.9318590917100465] - F1 (macro): - 90%: [0.9185189408755685, 0.9309806929048586] - 95%: [0.9174010190551032, 0.9318590917100465] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-conll2003/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/roberta-large-conll2003/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip ```shell pip install tner ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/roberta-large-conll2003") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. ### Training hyperparameters The following hyperparameters were used during training: - dataset: ['tner/conll2003'] - dataset_split: train - dataset_name: None - local_dataset: None - model: roberta-large - crf: True - max_length: 128 - epoch: 17 - batch_size: 64 - lr: 1e-05 - random_seed: 42 - gradient_accumulation_steps: 1 - weight_decay: None - lr_warmup_step_ratio: 0.1 - max_grad_norm: 10.0 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-conll2003/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-camacho-collados-2021-ner, title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", author = "Ushio, Asahi and Camacho-Collados, Jose", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.7", doi = "10.18653/v1/2021.eacl-demos.7", pages = "53--62", abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.", } ```
dccuchile/albert-xlarge-spanish-finetuned-xnli
[ "pytorch", "albert", "text-classification", "transformers" ]
text-classification
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29
null
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: Bio_ClinicalBERT_fold_4_ternary_v1 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. --> # Bio_ClinicalBERT_fold_4_ternary_v1 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7349 - F1: 0.8052 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5378 | 0.7818 | | 0.5561 | 2.0 | 578 | 0.4835 | 0.8002 | | 0.5561 | 3.0 | 867 | 0.6401 | 0.7978 | | 0.2473 | 4.0 | 1156 | 0.8665 | 0.7842 | | 0.2473 | 5.0 | 1445 | 0.9942 | 0.7965 | | 0.1002 | 6.0 | 1734 | 1.1535 | 0.8015 | | 0.0428 | 7.0 | 2023 | 1.2619 | 0.8027 | | 0.0428 | 8.0 | 2312 | 1.4386 | 0.7990 | | 0.017 | 9.0 | 2601 | 1.4864 | 0.8039 | | 0.017 | 10.0 | 2890 | 1.4817 | 0.8015 | | 0.0145 | 11.0 | 3179 | 1.5205 | 0.8052 | | 0.0145 | 12.0 | 3468 | 1.6825 | 0.7842 | | 0.0115 | 13.0 | 3757 | 1.6670 | 0.7990 | | 0.0083 | 14.0 | 4046 | 1.7283 | 0.7904 | | 0.0083 | 15.0 | 4335 | 1.6552 | 0.8039 | | 0.0071 | 16.0 | 4624 | 1.6760 | 0.8076 | | 0.0071 | 17.0 | 4913 | 1.6973 | 0.7891 | | 0.0109 | 18.0 | 5202 | 1.6050 | 0.8027 | | 0.0109 | 19.0 | 5491 | 1.6379 | 0.8126 | | 0.0037 | 20.0 | 5780 | 1.6936 | 0.8039 | | 0.0013 | 21.0 | 6069 | 1.7187 | 0.8027 | | 0.0013 | 22.0 | 6358 | 1.7839 | 0.7965 | | 0.0015 | 23.0 | 6647 | 1.7551 | 0.8015 | | 0.0015 | 24.0 | 6936 | 1.7312 | 0.8064 | | 0.001 | 25.0 | 7225 | 1.7349 | 0.8052 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
dccuchile/albert-xxlarge-spanish-finetuned-mldoc
[ "pytorch", "albert", "text-classification", "transformers" ]
text-classification
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26
null
--- datasets: - tner/wnut2017 metrics: - f1 - precision - recall model-index: - name: tner/bertweet-large-wnut2017 results: - task: name: Token Classification type: token-classification dataset: name: tner/wnut2017 type: tner/wnut2017 args: tner/wnut2017 metrics: - name: F1 type: f1 value: 0.5302273987798114 - name: Precision type: precision value: 0.6602209944751382 - name: Recall type: recall value: 0.44300278035217794 - name: F1 (macro) type: f1_macro value: 0.4643459997680019 - name: Precision (macro) type: precision_macro value: 0.5792841925426832 - name: Recall (macro) type: recall_macro value: 0.3973128655628379 - name: F1 (entity span) type: f1_entity_span value: 0.6142697881828317 - name: Precision (entity span) type: precision_entity_span value: 0.7706293706293706 - name: Recall (entity span) type: recall_entity_span value: 0.5106580166821131 pipeline_tag: token-classification widget: - text: "Jacob Collier is a Grammy awarded artist from England." example_title: "NER Example 1" --- # tner/bertweet-large-wnut2017 This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on the [tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) dataset. Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.5302273987798114 - Precision (micro): 0.6602209944751382 - Recall (micro): 0.44300278035217794 - F1 (macro): 0.4643459997680019 - Precision (macro): 0.5792841925426832 - Recall (macro): 0.3973128655628379 The per-entity breakdown of the F1 score on the test set are below: - corporation: 0.3902439024390244 - group: 0.37130801687763715 - location: 0.6595744680851063 - person: 0.65474552957359 - product: 0.2857142857142857 - work_of_art: 0.4244897959183674 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.5002577319587629, 0.5587481638299118] - 95%: [0.4947163587619384, 0.5629013150503995] - F1 (macro): - 90%: [0.5002577319587629, 0.5587481638299118] - 95%: [0.4947163587619384, 0.5629013150503995] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bertweet-large-wnut2017/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/bertweet-large-wnut2017/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip ```shell pip install tner ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/bertweet-large-wnut2017") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. ### Training hyperparameters The following hyperparameters were used during training: - dataset: ['tner/wnut2017'] - dataset_split: train - dataset_name: None - local_dataset: None - model: vinai/bertweet-large - crf: False - max_length: 128 - epoch: 15 - batch_size: 16 - lr: 1e-05 - random_seed: 42 - gradient_accumulation_steps: 4 - weight_decay: 1e-07 - lr_warmup_step_ratio: 0.1 - max_grad_norm: 10.0 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/bertweet-large-wnut2017/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-camacho-collados-2021-ner, title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", author = "Ushio, Asahi and Camacho-Collados, Jose", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.7", doi = "10.18653/v1/2021.eacl-demos.7", pages = "53--62", abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.", } ```
dccuchile/albert-xxlarge-spanish-finetuned-ner
[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
{ "architectures": [ "AlbertForTokenClassification" ], "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 } } }
28
null
--- datasets: - tner/wnut2017 metrics: - f1 - precision - recall model-index: - name: tner/deberta-large-wnut2017 results: - task: name: Token Classification type: token-classification dataset: name: tner/wnut2017 type: tner/wnut2017 args: tner/wnut2017 metrics: - name: F1 type: f1 value: 0.5105386416861827 - name: Precision type: precision value: 0.6931637519872814 - name: Recall type: recall value: 0.4040778498609824 - name: F1 (macro) type: f1_macro value: 0.4263428845085451 - name: Precision (macro) type: precision_macro value: 0.6003185137596864 - name: Recall (macro) type: recall_macro value: 0.35195768262641947 - name: F1 (entity span) type: f1_entity_span value: 0.5936768149882904 - name: Precision (entity span) type: precision_entity_span value: 0.8060413354531002 - name: Recall (entity span) type: recall_entity_span value: 0.46987951807228917 pipeline_tag: token-classification widget: - text: "Jacob Collier is a Grammy awarded artist from England." example_title: "NER Example 1" --- # tner/deberta-large-wnut2017 This model is a fine-tuned version of [microsoft/deberta-large](https://huggingface.co/microsoft/deberta-large) on the [tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) dataset. Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.5105386416861827 - Precision (micro): 0.6931637519872814 - Recall (micro): 0.4040778498609824 - F1 (macro): 0.4263428845085451 - Precision (macro): 0.6003185137596864 - Recall (macro): 0.35195768262641947 The per-entity breakdown of the F1 score on the test set are below: - corporation: 0.3503649635036496 - group: 0.3148148148148148 - location: 0.6029411764705882 - person: 0.6628895184135977 - product: 0.1951219512195122 - work_of_art: 0.431924882629108 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.47970650356554456, 0.5385161869734422] - 95%: [0.47475901512925966, 0.5430870496346687] - F1 (macro): - 90%: [0.47970650356554456, 0.5385161869734422] - 95%: [0.47475901512925966, 0.5430870496346687] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-large-wnut2017/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/deberta-large-wnut2017/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip ```shell pip install tner ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/deberta-large-wnut2017") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. ### Training hyperparameters The following hyperparameters were used during training: - dataset: ['tner/wnut2017'] - dataset_split: train - dataset_name: None - local_dataset: None - model: microsoft/deberta-large - crf: True - max_length: 128 - epoch: 15 - batch_size: 16 - lr: 1e-05 - random_seed: 42 - gradient_accumulation_steps: 4 - weight_decay: 1e-07 - lr_warmup_step_ratio: 0.1 - max_grad_norm: 10.0 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-large-wnut2017/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-camacho-collados-2021-ner, title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", author = "Ushio, Asahi and Camacho-Collados, Jose", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.7", doi = "10.18653/v1/2021.eacl-demos.7", pages = "53--62", abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.", } ```
dccuchile/albert-xxlarge-spanish-finetuned-pos
[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
{ "architectures": [ "AlbertForTokenClassification" ], "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 } } }
3
null
--- datasets: - tner/conll2003 metrics: - f1 - precision - recall model-index: - name: tner/deberta-v3-large-conll2003 results: - task: name: Token Classification type: token-classification dataset: name: tner/conll2003 type: tner/conll2003 args: tner/conll2003 metrics: - name: F1 type: f1 value: 0.9222388190844389 - name: Precision type: precision value: 0.9154020582592011 - name: Recall type: recall value: 0.9291784702549575 - name: F1 (macro) type: f1_macro value: 0.9043961692086329 - name: Precision (macro) type: precision_macro value: 0.8959854326377331 - name: Recall (macro) type: recall_macro value: 0.9135442454672595 - name: F1 (entity span) type: f1_entity_span value: 0.960570322126386 - name: Precision (entity span) type: precision_entity_span value: 0.9550227511375569 - name: Recall (entity span) type: recall_entity_span value: 0.9661827195467422 pipeline_tag: token-classification widget: - text: "Jacob Collier is a Grammy awarded artist from England." example_title: "NER Example 1" --- # tner/deberta-v3-large-conll2003 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the [tner/conll2003](https://huggingface.co/datasets/tner/conll2003) dataset. Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.9222388190844389 - Precision (micro): 0.9154020582592011 - Recall (micro): 0.9291784702549575 - F1 (macro): 0.9043961692086329 - Precision (macro): 0.8959854326377331 - Recall (macro): 0.9135442454672595 The per-entity breakdown of the F1 score on the test set are below: - location: 0.9407496977025392 - organization: 0.9115486335586247 - other: 0.7920110192837466 - person: 0.9732753262896209 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.9157944386463721, 0.9286928993636353] - 95%: [0.9146558483630953, 0.9297919809412201] - F1 (macro): - 90%: [0.9157944386463721, 0.9286928993636353] - 95%: [0.9146558483630953, 0.9297919809412201] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-conll2003/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-conll2003/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip ```shell pip install tner ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/deberta-v3-large-conll2003") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. ### Training hyperparameters The following hyperparameters were used during training: - dataset: ['tner/conll2003'] - dataset_split: train - dataset_name: None - local_dataset: None - model: microsoft/deberta-v3-large - crf: False - max_length: 128 - epoch: 15 - batch_size: 16 - lr: 1e-05 - random_seed: 42 - gradient_accumulation_steps: 4 - weight_decay: None - lr_warmup_step_ratio: 0.1 - max_grad_norm: 10.0 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-conll2003/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-camacho-collados-2021-ner, title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", author = "Ushio, Asahi and Camacho-Collados, Jose", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.7", doi = "10.18653/v1/2021.eacl-demos.7", pages = "53--62", abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.", } ```
dccuchile/albert-xxlarge-spanish-finetuned-qa-mlqa
[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
{ "architectures": [ "AlbertForQuestionAnswering" ], "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 } } }
7
null
--- datasets: - tner/bc5cdr metrics: - f1 - precision - recall model-index: - name: tner/deberta-v3-large-bc5cdr results: - task: name: Token Classification type: token-classification dataset: name: tner/bc5cdr type: tner/bc5cdr args: tner/bc5cdr metrics: - name: F1 type: f1 value: 0.8902493653874869 - name: Precision type: precision value: 0.8697724178175452 - name: Recall type: recall value: 0.9117137322866755 - name: F1 (macro) type: f1_macro value: 0.8863403908610603 - name: Precision (macro) type: precision_macro value: 0.8657302393432342 - name: Recall (macro) type: recall_macro value: 0.9080747413030301 - name: F1 (entity span) type: f1_entity_span value: 0.8929371360310587 - name: Precision (entity span) type: precision_entity_span value: 0.8723983660766388 - name: Recall (entity span) type: recall_entity_span value: 0.9144663064532572 pipeline_tag: token-classification widget: - text: "Jacob Collier is a Grammy awarded artist from England." example_title: "NER Example 1" --- # tner/deberta-v3-large-bc5cdr This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the [tner/bc5cdr](https://huggingface.co/datasets/tner/bc5cdr) dataset. Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.8902493653874869 - Precision (micro): 0.8697724178175452 - Recall (micro): 0.9117137322866755 - F1 (macro): 0.8863403908610603 - Precision (macro): 0.8657302393432342 - Recall (macro): 0.9080747413030301 The per-entity breakdown of the F1 score on the test set are below: - chemical: 0.9298502009499452 - disease: 0.8428305807721753 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.885162383660078, 0.8951239957151518] - 95%: [0.8838793313408008, 0.8959517574197015] - F1 (macro): - 90%: [0.885162383660078, 0.8951239957151518] - 95%: [0.8838793313408008, 0.8959517574197015] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-bc5cdr/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-bc5cdr/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip ```shell pip install tner ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/deberta-v3-large-bc5cdr") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. ### Training hyperparameters The following hyperparameters were used during training: - dataset: ['tner/bc5cdr'] - dataset_split: train - dataset_name: None - local_dataset: None - model: microsoft/deberta-v3-large - crf: True - max_length: 128 - epoch: 15 - batch_size: 16 - lr: 1e-05 - random_seed: 42 - gradient_accumulation_steps: 4 - weight_decay: 1e-07 - lr_warmup_step_ratio: 0.1 - max_grad_norm: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-bc5cdr/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-camacho-collados-2021-ner, title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", author = "Ushio, Asahi and Camacho-Collados, Jose", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.7", doi = "10.18653/v1/2021.eacl-demos.7", pages = "53--62", abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.", } ```
dccuchile/bert-base-spanish-wwm-uncased-finetuned-xnli
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
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36
null
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: Bio_ClinicalBERT_fold_10_ternary_v1 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. --> # Bio_ClinicalBERT_fold_10_ternary_v1 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0706 - F1: 0.7748 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.6097 | 0.7290 | | 0.555 | 2.0 | 580 | 0.6106 | 0.7649 | | 0.555 | 3.0 | 870 | 0.6608 | 0.7847 | | 0.2449 | 4.0 | 1160 | 0.8894 | 0.7809 | | 0.2449 | 5.0 | 1450 | 1.1049 | 0.7760 | | 0.1055 | 6.0 | 1740 | 1.2951 | 0.7884 | | 0.0338 | 7.0 | 2030 | 1.4809 | 0.7760 | | 0.0338 | 8.0 | 2320 | 1.4751 | 0.7698 | | 0.0225 | 9.0 | 2610 | 1.6648 | 0.7809 | | 0.0225 | 10.0 | 2900 | 1.7174 | 0.7772 | | 0.006 | 11.0 | 3190 | 1.7872 | 0.7735 | | 0.006 | 12.0 | 3480 | 1.7803 | 0.7748 | | 0.0161 | 13.0 | 3770 | 1.9302 | 0.7735 | | 0.0005 | 14.0 | 4060 | 1.9853 | 0.7748 | | 0.0005 | 15.0 | 4350 | 2.0043 | 0.7735 | | 0.0062 | 16.0 | 4640 | 1.9969 | 0.7760 | | 0.0062 | 17.0 | 4930 | 2.0173 | 0.7760 | | 0.0068 | 18.0 | 5220 | 1.9891 | 0.7785 | | 0.0034 | 19.0 | 5510 | 1.9951 | 0.7797 | | 0.0034 | 20.0 | 5800 | 2.0283 | 0.7748 | | 0.0049 | 21.0 | 6090 | 1.9985 | 0.7834 | | 0.0049 | 22.0 | 6380 | 2.0131 | 0.7760 | | 0.0011 | 23.0 | 6670 | 2.0526 | 0.7748 | | 0.0011 | 24.0 | 6960 | 2.0662 | 0.7748 | | 0.001 | 25.0 | 7250 | 2.0706 | 0.7748 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
dccuchile/distilbert-base-spanish-uncased-finetuned-ner
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
{ "architectures": [ "DistilBertForTokenClassification" ], "model_type": "distilbert", "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
null
Note: this model is deprecated, please use https://huggingface.co/songlab/gpn-brassicales
dccuchile/distilbert-base-spanish-uncased-finetuned-pawsx
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
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29
null
--- tags: - conversational --- #harry pitter dialoGPT model
dccuchile/distilbert-base-spanish-uncased-finetuned-pos
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
{ "architectures": [ "DistilBertForTokenClassification" ], "model_type": "distilbert", "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: - en - ro license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: distilled-mt5-small-010099_1 results: - task: name: Translation type: translation dataset: name: wmt16 ro-en type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 7.3454 --- <!-- 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. --> # distilled-mt5-small-010099_1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8040 - Bleu: 7.3454 - Gen Len: 44.8149 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
dccuchile/distilbert-base-spanish-uncased-finetuned-qa-mlqa
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
{ "architectures": [ "DistilBertForQuestionAnswering" ], "model_type": "distilbert", "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
null
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="rebolforces/q-Taxi-v3", 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"]) ```
dccuchile/distilbert-base-spanish-uncased-finetuned-xnli
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
{ "architectures": [ "DistilBertForSequenceClassification" ], "model_type": "distilbert", "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 } } }
31
null
--- language: - en - ro license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: distilled-mt5-small-1b0000 results: - task: name: Translation type: translation dataset: name: wmt16 ro-en type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 1.1101 --- <!-- 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. --> # distilled-mt5-small-1b0000 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 3.7760 - Bleu: 1.1101 - Gen Len: 99.5898 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
dccuchile/distilbert-base-spanish-uncased
[ "pytorch", "distilbert", "fill-mask", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA", "autotrain_compatible" ]
fill-mask
{ "architectures": [ "DistilBertForMaskedLM" ], "model_type": "distilbert", "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 } } }
670
null
--- language: - en - ro license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: distilled-mt5-small-010099_8 results: - task: name: Translation type: translation dataset: name: wmt16 ro-en type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 6.231 --- <!-- 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. --> # distilled-mt5-small-010099_8 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.9641 - Bleu: 6.231 - Gen Len: 50.1911 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate
[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
{ "architectures": [ "DistilBertForMaskedLM" ], "model_type": "distilbert", "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 datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: train args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8648740833380706 --- <!-- 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 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1365 - F1: 0.8649 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 | | 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 | | 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Chae/botman
[ "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 } } }
5
null
--- 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: 0.1608 - F1: 0.8593 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 | | 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 | | 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Chaewon/mmnt_decoder_en
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
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12
2022-08-10T05:05:11Z
--- language: - en - ro license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: distilled-mt5-small-test results: - task: name: Translation type: translation dataset: name: wmt16 ro-en type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 7.5082 --- <!-- 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. --> # distilled-mt5-small-test This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8241 - Bleu: 7.5082 - Gen Len: 44.0405 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
CharlieChen/feedback-bigbird
[]
null
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0
2022-08-10T06:34:09Z
--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
CheonggyeMountain-Sherpa/kogpt-trinity-poem
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
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15
null
--- language: ga datasets: - common_voice - living-audio-Irish metrics: - wer tags: - audio - automatic-speech-recognition - ga-IE - speech - Irish - Gaelic model-index: - name: Wav2vec 2.0 large 300m XLS-R results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 10.0 type: common_voice args: ga-IE metrics: - name: Test WER type: wer value: 25.94 --- # Irish-Gaelic Automatic Speech Recognition This is the model for Irish ASR. It has been trained on the Common-voice dataset and living Irish audio dataset. The Common-voice code for the Irish language is ga-IE. From the Common voice dataset, all the Validated audio clips and all the living audio clips were taken into account and after a random train-test split, 90% of the total dataset (5156 utterances) were taken for training, and the rest of the 10% of real data (579 utterances) were taken for testing. This dataset was finetuned on wav2vec2-large-xls-r-300m. On the testing dataset, 25.94% of WER could be achieved. ### How to use Example of transcribing the Common Voice audio clip from the invalidated dataset, using GPU if available. The model expects 16kHz audio. ```python from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor model = Wav2Vec2ForCTC.from_pretrained("Aditya3107/wav2vec2-large-xls-r-1b-ga-ie") processor = Wav2Vec2Processor.from_pretrained("Aditya3107/wav2vec2-large-xls-r-1b-ga-ie") # Reading taken audio clip import librosa, torch audio, rate = librosa.load("common-voice-irish/common_voice/cv-corpus-10.0-2022-07-04/ga-IE/clips/common_voice_ga-IE_1818627.mp3", sr = 16000) # Taking an input value input_values = processor(audio, sampling_rate=16_000, return_tensors = "pt", padding="longest").input_values # Storing logits (non-normalized prediction values) logits = model(input_values).logits # Storing predicted ids prediction = torch.argmax(logits, dim = -1) # Passing the prediction to the tokenizer decode to get the transcription transcription = processor.batch_decode(prediction)[0] print(transcription) ``` ### Results Example of the transcribed audio clips and testing on SCLITE. ``` Speaker sentences 0: #utts: 1 id: (common_voice_ga-IE_17401296.mp3) Scores: (#C #S #D #I) 4 1 0 0 Attributes: Case_sensitve REF: an bhfuil cóta bán óir HYP: an bhfuil cóta bán air Eval: S id: (common_voice_ga-IE_17410244.mp3) Scores: (#C #S #D #I) 3 1 0 2 Attributes: Case_sensitve REF: *** ** an bud é sin HYP: cad é an rud é sin Eval: I I S id: (common_voice_ga-IE_17410257.mp3) Scores: (#C #S #D #I) 9 2 1 2 Attributes: Case_sensitve REF: i gabhaim buíochas libh a chairde ******* ** támindéagtstruth le tuilleadh uaibh ar baá HYP: * gabhaim buíochas libh a chairde táimid ag tsnúth le tuilleadh uaibh ar ball Eval: D I I S S id: (common_voice_ga-IE_17410401.mp3) Scores: (#C #S #D #I) 6 1 0 0 Attributes: Case_sensitve REF: níl ach tá peann ina phóca uige HYP: níl ach tá peann ina phóca aige Eval: S id: (common_voice_ga-IE_17410403.mp3) Scores: (#C #S #D #I) 5 1 0 1 Attributes: Case_sensitve REF: agus *** cadé an dath atá air HYP: agus cad é an dath atá air Eval: I S id: (common_voice_ga-IE_17410412.mp3) Scores: (#C #S #D #I) 6 2 0 0 Attributes: Case_sensitve REF: is lá é seo chun ceiliúradh a dhéan HYP: is lá é seo chun céiliúradh a dhéanamh Eval: S S id: (common_voice_ga-IE_17444712.mp3) Scores: (#C #S #D #I) 4 6 0 0 Attributes: Case_sensitve REF: don chathaoileach mirín de brom don stiúrdhóirat liam ón maoladha HYP: don chathaoirleach máirín de brún don stiúrthóir liam ó maolaodha Eval: S S S S S S id: (common_voice_ga-IE_17449454.mp3) Scores: (#C #S #D #I) 4 0 0 0 Attributes: Case_sensitve REF: ceacht a trí déag HYP: ceacht a trí déag Eval: ``` ### Future Tasks The language model with KenLM will be added if any good resource of Irish text is found. ### Citation If you want to cite this model you can use this: ``` @MISC {, author = "Aditya Parikh", title = "Finetuned XLS-R model for Irish (Ga-IE) language for Automatic Speech Recognition", howpublished = "{\url{https://huggingface.co/Aditya3107/wav2vec2-large-xls-r-1b-ga-ie}}", month = "aug", year = "2022" } ```
CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper
[ "ko", "gpt2", "license:cc-by-nc-sa-4.0" ]
null
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0
null
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9195 - name: F1 type: f1 value: 0.9194694114253713 --- <!-- 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-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2308 - Accuracy: 0.9195 - F1: 0.9195 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8402 | 1.0 | 250 | 0.3406 | 0.8945 | 0.8907 | | 0.258 | 2.0 | 500 | 0.2308 | 0.9195 | 0.9195 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Chertilasus/main
[]
null
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0
2022-08-10T08:28:09Z
--- license: mit tags: - generated_from_keras_callback model-index: - name: XLM-roberta-finetuned 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. --> # XLM-roberta-finetuned This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: ## 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: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.21.1 - TensorFlow 2.8.2 - Tokenizers 0.12.1
Chester/traffic-rec
[]
null
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0
2022-08-10T08:29:27Z
--- tags: - conversational --- # Rick and Morty DialoGPT Model
Chikita1/www_stash_stock
[ "license:bsd-3-clause-clear" ]
null
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0
null
--- tags: - unconditional-image-generation license: apache-2.0 --- # Model info Project [fbanimegan](https://github.com/SkyTNT/fbanimegan) ### fbanime.pkl StyleGan2 model trained with official [StyleGan3](https://github.com/NVlabs/stylegan3). But I modified the code (networks_stylegan2.py and dataset.py) to support non-square resolutions. FID: 1.4 ### fbanime_fp32.pkl fp32 version of fbanime.pkl Note: Fp16 version (fbanime.pkl) only works on gpu. And fp32 version works on gpu and cpu. ### g_mapping.onnx onnx format mapping network of fbanime_fp32.pkl ### g_synthesis.onnx onnx format synthesis network of fbanime_fp32.pkl ### encoder.onnx e4e model trained with [encoder4editing-stylegan3](https://github.com/yj7082126/encoder4editing-stylegan3). I add support for official StyleGan2 model and change backbone to ResNet-34 in [restyle-encoder](https://github.com/yuval-alaluf/restyle-encoder). ### waifu_dect.onnx YOLOv5 model trained with official [YOLOv5](https://github.com/ultralytics/yolov5) # Usage see [demo](https://huggingface.co/spaces/skytnt/full-body-anime-gan/blob/main/app.py) # Dataset [fbanimehq](https://huggingface.co/datasets/skytnt/fbanimehq) v2.0
Ching/negation_detector
[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
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9
2022-08-10T08:46:51Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-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. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0614 - Precision: 0.9288 - Recall: 0.9388 - F1: 0.9338 - Accuracy: 0.9840 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2456 | 1.0 | 878 | 0.0683 | 0.9151 | 0.9223 | 0.9187 | 0.9814 | | 0.0542 | 2.0 | 1756 | 0.0609 | 0.9227 | 0.9335 | 0.9281 | 0.9829 | | 0.0293 | 3.0 | 2634 | 0.0614 | 0.9288 | 0.9388 | 0.9338 | 0.9840 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Tokenizers 0.12.1
Chiuchiyin/DialoGPT-small-Donald
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
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7
null
--- license: apache-2.0 tags: - generated_from_trainer datasets: - source_data_nlp metrics: - precision - recall - f1 model-index: - name: sd-geneprod-roles-v2 results: - task: name: Token Classification type: token-classification dataset: name: source_data_nlp type: source_data_nlp args: GENEPROD_ROLES metrics: - name: Precision type: precision value: 0.9227577212638568 - name: Recall type: recall value: 0.9288143683990692 - name: F1 type: f1 value: 0.9257761389318425 --- <!-- 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. --> # sd-geneprod-roles-v2 This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on the source_data_nlp dataset. It achieves the following results on the evaluation set: - Loss: 0.0136 - Accuracy Score: 0.9950 - Precision: 0.9228 - Recall: 0.9288 - F1: 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 256 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:| | 0.014 | 1.0 | 1569 | 0.0136 | 0.9950 | 0.9228 | 0.9288 | 0.9258 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0a0+bfe5ad2 - Datasets 1.17.0 - Tokenizers 0.12.1
ChoboAvenger/DialoGPT-small-DocBot
[]
null
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0
2022-08-10T09:09:39Z
Used for a regression test addressing [this issue](https://github.com/huggingface/huggingface_hub/issues/981).
ChrisP/xlm-roberta-base-finetuned-marc-en
[]
null
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0
2022-08-10T09:26:02Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9371173258315406 - name: Recall type: recall value: 0.9530461124200605 - name: F1 type: f1 value: 0.945014601585315 - name: Accuracy type: accuracy value: 0.9865338199799847 --- <!-- 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-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0599 - Precision: 0.9371 - Recall: 0.9530 - F1: 0.9450 - Accuracy: 0.9865 ## 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 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0883 | 1.0 | 1756 | 0.0690 | 0.9181 | 0.9320 | 0.9250 | 0.9821 | | 0.0334 | 2.0 | 3512 | 0.0623 | 0.9279 | 0.9504 | 0.9390 | 0.9858 | | 0.0189 | 3.0 | 5268 | 0.0599 | 0.9371 | 0.9530 | 0.9450 | 0.9865 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
ChrisVCB/DialoGPT-medium-cmjs
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
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7
2022-08-10T09:35:23Z
--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/mvicentel/ddpm-butterflies-128/tensorboard?#scalars)
Chuah/DialoGPT-small-harrypotter
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
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9
2022-08-10T10:03:35Z
--- datasets: - tner/tweebank_ner metrics: - f1 - precision - recall model-index: - name: tner/roberta-large-tweebank-ner results: - task: name: Token Classification type: token-classification dataset: name: tner/tweebank_ner type: tner/tweebank_ner args: tner/tweebank_ner metrics: - name: F1 type: f1 value: 0.7439490445859872 - name: Precision type: precision value: 0.7121951219512195 - name: Recall type: recall value: 0.7786666666666666 - name: F1 (macro) type: f1_macro value: 0.7354319457314183 - name: Precision (macro) type: precision_macro value: 0.712928566565599 - name: Recall (macro) type: recall_macro value: 0.7620465365030582 - name: F1 (entity span) type: f1_entity_span value: 0.8178343949044585 - name: Precision (entity span) type: precision_entity_span value: 0.7829268292682927 - name: Recall (entity span) type: recall_entity_span value: 0.856 pipeline_tag: token-classification widget: - text: "Jacob Collier is a Grammy awarded artist from England." example_title: "NER Example 1" --- # tner/roberta-large-tweebank-ner This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [tner/tweebank_ner](https://huggingface.co/datasets/tner/tweebank_ner) dataset. Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.7439490445859872 - Precision (micro): 0.7121951219512195 - Recall (micro): 0.7786666666666666 - F1 (macro): 0.7354319457314183 - Precision (macro): 0.712928566565599 - Recall (macro): 0.7620465365030582 The per-entity breakdown of the F1 score on the test set are below: - location: 0.7782805429864253 - organization: 0.7377049180327869 - other: 0.5520581113801453 - person: 0.8736842105263157 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.7156413818791614, 0.771698046498159] - 95%: [0.7063867669973017, 0.7763088810979543] - F1 (macro): - 90%: [0.7156413818791614, 0.771698046498159] - 95%: [0.7063867669973017, 0.7763088810979543] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweebank-ner/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweebank-ner/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip ```shell pip install tner ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/roberta-large-tweebank-ner") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. ### Training hyperparameters The following hyperparameters were used during training: - dataset: ['tner/tweebank_ner'] - dataset_split: train - dataset_name: None - local_dataset: None - model: roberta-large - crf: True - max_length: 128 - epoch: 15 - batch_size: 64 - lr: 1e-05 - random_seed: 42 - gradient_accumulation_steps: 1 - weight_decay: None - lr_warmup_step_ratio: 0.1 - max_grad_norm: 10.0 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-tweebank-ner/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-camacho-collados-2021-ner, title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", author = "Ushio, Asahi and Camacho-Collados, Jose", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.7", doi = "10.18653/v1/2021.eacl-demos.7", pages = "53--62", abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.", } ```
ChukSamuels/DialoGPT-small-Dr.FauciBot
[ "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
null
--- datasets: - tner/tweebank_ner metrics: - f1 - precision - recall model-index: - name: tner/deberta-v3-large-tweebank-ner results: - task: name: Token Classification type: token-classification dataset: name: tner/tweebank_ner type: tner/tweebank_ner args: tner/tweebank_ner metrics: - name: F1 type: f1 value: 0.7253474520185308 - name: Precision type: precision value: 0.7201051248357424 - name: Recall type: recall value: 0.7306666666666667 - name: F1 (macro) type: f1_macro value: 0.701874697798745 - name: Precision (macro) type: precision_macro value: 0.7043005470796733 - name: Recall (macro) type: recall_macro value: 0.706915721861374 - name: F1 (entity span) type: f1_entity_span value: 0.8178343949044585 - name: Precision (entity span) type: precision_entity_span value: 0.7829268292682927 - name: Recall (entity span) type: recall_entity_span value: 0.856 pipeline_tag: token-classification widget: - text: "Jacob Collier is a Grammy awarded artist from England." example_title: "NER Example 1" --- # tner/deberta-v3-large-tweebank-ner This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the [tner/tweebank_ner](https://huggingface.co/datasets/tner/tweebank_ner) dataset. Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.7253474520185308 - Precision (micro): 0.7201051248357424 - Recall (micro): 0.7306666666666667 - F1 (macro): 0.701874697798745 - Precision (macro): 0.7043005470796733 - Recall (macro): 0.706915721861374 The per-entity breakdown of the F1 score on the test set are below: - location: 0.7289719626168224 - organization: 0.7040816326530612 - other: 0.5182926829268293 - person: 0.856152512998267 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.6978100031831928, 0.7529703029130037] - 95%: [0.691700704571692, 0.7582901338971108] - F1 (macro): - 90%: [0.6978100031831928, 0.7529703029130037] - 95%: [0.691700704571692, 0.7582901338971108] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-tweebank-ner/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-tweebank-ner/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip ```shell pip install tner ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/deberta-v3-large-tweebank-ner") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. ### Training hyperparameters The following hyperparameters were used during training: - dataset: ['tner/tweebank_ner'] - dataset_split: train - dataset_name: None - local_dataset: None - model: microsoft/deberta-v3-large - crf: True - max_length: 128 - epoch: 15 - batch_size: 16 - lr: 1e-05 - random_seed: 42 - gradient_accumulation_steps: 4 - weight_decay: 1e-07 - lr_warmup_step_ratio: 0.1 - max_grad_norm: 10.0 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-tweebank-ner/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-camacho-collados-2021-ner, title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", author = "Ushio, Asahi and Camacho-Collados, Jose", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.7", doi = "10.18653/v1/2021.eacl-demos.7", pages = "53--62", abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.", } ```
Chun/DialoGPT-large-dailydialog
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
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6
null
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_1_binary_v1 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. --> # xlnet-base-cased_fold_1_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7812 - F1: 0.8161 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.3938 | 0.8019 | | 0.4444 | 2.0 | 576 | 0.3945 | 0.8086 | | 0.4444 | 3.0 | 864 | 0.4738 | 0.8245 | | 0.2504 | 4.0 | 1152 | 0.6641 | 0.8123 | | 0.2504 | 5.0 | 1440 | 0.8714 | 0.7863 | | 0.159 | 6.0 | 1728 | 0.9177 | 0.8179 | | 0.0832 | 7.0 | 2016 | 1.1719 | 0.8129 | | 0.0832 | 8.0 | 2304 | 1.2858 | 0.8146 | | 0.046 | 9.0 | 2592 | 1.2557 | 0.8181 | | 0.046 | 10.0 | 2880 | 1.3332 | 0.8033 | | 0.0313 | 11.0 | 3168 | 1.2840 | 0.8112 | | 0.0313 | 12.0 | 3456 | 1.4164 | 0.8175 | | 0.0246 | 13.0 | 3744 | 1.3709 | 0.8143 | | 0.0173 | 14.0 | 4032 | 1.4319 | 0.8179 | | 0.0173 | 15.0 | 4320 | 1.5706 | 0.8195 | | 0.0138 | 16.0 | 4608 | 1.6072 | 0.8230 | | 0.0138 | 17.0 | 4896 | 1.7454 | 0.8192 | | 0.0016 | 18.0 | 5184 | 1.7281 | 0.8099 | | 0.0016 | 19.0 | 5472 | 1.7692 | 0.8151 | | 0.0088 | 20.0 | 5760 | 1.7376 | 0.8132 | | 0.0081 | 21.0 | 6048 | 1.7715 | 0.8086 | | 0.0081 | 22.0 | 6336 | 1.7400 | 0.8152 | | 0.0053 | 23.0 | 6624 | 1.7845 | 0.8099 | | 0.0053 | 24.0 | 6912 | 1.8096 | 0.8150 | | 0.0062 | 25.0 | 7200 | 1.7812 | 0.8161 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Chun/w-en2zh-hsk
[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
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1
2022-08-10T10:27:57Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - source_data_nlp metrics: - precision - recall - f1 model-index: - name: sd-panelization-v2 results: - task: name: Token Classification type: token-classification dataset: name: source_data_nlp type: source_data_nlp args: PANELIZATION metrics: - name: Precision type: precision value: 0.9134245120169964 - name: Recall type: recall value: 0.9494824016563147 - name: F1 type: f1 value: 0.9311044937736871 --- <!-- 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. --> # sd-panelization-v2 This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on the source_data_nlp dataset. It achieves the following results on the evaluation set: - Loss: 0.0050 - Accuracy Score: 0.9982 - Precision: 0.9134 - Recall: 0.9495 - F1: 0.9311 ## 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: 32 - eval_batch_size: 256 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:| | 0.0048 | 1.0 | 431 | 0.0050 | 0.9982 | 0.9134 | 0.9495 | 0.9311 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0a0+bfe5ad2 - Datasets 1.17.0 - Tokenizers 0.12.1
Chun/w-en2zh-otm
[ "pytorch", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
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7
2022-08-10T10:39:14Z
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_2_binary_v1 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. --> # xlnet-base-cased_fold_2_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8748 - F1: 0.8066 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.4803 | 0.7433 | | 0.434 | 2.0 | 580 | 0.4385 | 0.8099 | | 0.434 | 3.0 | 870 | 0.5382 | 0.8078 | | 0.254 | 4.0 | 1160 | 0.6944 | 0.7982 | | 0.254 | 5.0 | 1450 | 0.9908 | 0.8058 | | 0.1479 | 6.0 | 1740 | 1.1090 | 0.8062 | | 0.0874 | 7.0 | 2030 | 1.2405 | 0.8042 | | 0.0874 | 8.0 | 2320 | 1.3174 | 0.8012 | | 0.0505 | 9.0 | 2610 | 1.5211 | 0.7909 | | 0.0505 | 10.0 | 2900 | 1.4014 | 0.8126 | | 0.0301 | 11.0 | 3190 | 1.4798 | 0.8047 | | 0.0301 | 12.0 | 3480 | 1.4668 | 0.8091 | | 0.0279 | 13.0 | 3770 | 1.5286 | 0.8075 | | 0.0233 | 14.0 | 4060 | 1.6752 | 0.8006 | | 0.0233 | 15.0 | 4350 | 1.5265 | 0.8132 | | 0.019 | 16.0 | 4640 | 1.6440 | 0.7949 | | 0.019 | 17.0 | 4930 | 1.7471 | 0.8097 | | 0.0096 | 18.0 | 5220 | 1.7329 | 0.8121 | | 0.0075 | 19.0 | 5510 | 1.7472 | 0.8191 | | 0.0075 | 20.0 | 5800 | 1.8043 | 0.8161 | | 0.0052 | 21.0 | 6090 | 1.8102 | 0.8141 | | 0.0052 | 22.0 | 6380 | 1.7944 | 0.8116 | | 0.0044 | 23.0 | 6670 | 1.8211 | 0.8141 | | 0.0044 | 24.0 | 6960 | 1.8741 | 0.8066 | | 0.0046 | 25.0 | 7250 | 1.8748 | 0.8066 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Chun/w-zh2en-mto
[ "pytorch", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
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7
2022-08-10T11:04:18Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 340.50 +/- 183.40 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** 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 utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sofiaoliveira -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga sofiaoliveira ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 10000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.05), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 1000), ('n_timesteps', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
Chungu424/DATA
[]
null
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0
2022-08-10T11:09:02Z
--- license: apache-2.0 tags: - vision - image-classification datasets: - cifar10 --- # ConvNext-tiny-finetuned-cifar10 (tiny-sized model) ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt). Convnext tiny finetuned on cifar 10 dataset. Which has ten classes. Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
Chungu424/qazwsx
[]
null
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0
null
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_3_binary_v1 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. --> # xlnet-base-cased_fold_3_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8649 - F1: 0.8044 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.4483 | 0.8000 | | 0.4228 | 2.0 | 578 | 0.4264 | 0.8040 | | 0.4228 | 3.0 | 867 | 0.5341 | 0.8056 | | 0.2409 | 4.0 | 1156 | 0.9077 | 0.8103 | | 0.2409 | 5.0 | 1445 | 1.1069 | 0.7889 | | 0.1386 | 6.0 | 1734 | 1.0288 | 0.8093 | | 0.0817 | 7.0 | 2023 | 1.2477 | 0.8049 | | 0.0817 | 8.0 | 2312 | 1.5915 | 0.7872 | | 0.0465 | 9.0 | 2601 | 1.5323 | 0.8035 | | 0.0465 | 10.0 | 2890 | 1.4351 | 0.7989 | | 0.0376 | 11.0 | 3179 | 1.4639 | 0.7916 | | 0.0376 | 12.0 | 3468 | 1.6027 | 0.7956 | | 0.0234 | 13.0 | 3757 | 1.7860 | 0.7931 | | 0.0109 | 14.0 | 4046 | 1.8567 | 0.7934 | | 0.0109 | 15.0 | 4335 | 1.8294 | 0.8053 | | 0.0115 | 16.0 | 4624 | 1.7799 | 0.7971 | | 0.0115 | 17.0 | 4913 | 1.5935 | 0.8000 | | 0.0142 | 18.0 | 5202 | 1.8136 | 0.8066 | | 0.0142 | 19.0 | 5491 | 1.7718 | 0.8063 | | 0.0124 | 20.0 | 5780 | 1.8581 | 0.8053 | | 0.0083 | 21.0 | 6069 | 1.8523 | 0.8056 | | 0.0083 | 22.0 | 6358 | 1.8408 | 0.8035 | | 0.0045 | 23.0 | 6647 | 1.8347 | 0.8040 | | 0.0045 | 24.0 | 6936 | 1.8683 | 0.8067 | | 0.0005 | 25.0 | 7225 | 1.8649 | 0.8044 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Chungu424/repodata
[]
null
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0
null
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-ft1500_norm300 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-ft1500_norm300 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0940 - Mse: 4.3760 - Mae: 1.4084 - R2: 0.4625 - Accuracy: 0.3517 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:--------:| | 0.7424 | 1.0 | 3122 | 1.1071 | 4.4286 | 1.4098 | 0.4561 | 0.3338 | | 0.5038 | 2.0 | 6244 | 1.1794 | 4.7177 | 1.4140 | 0.4205 | 0.3677 | | 0.356 | 3.0 | 9366 | 1.0717 | 4.2866 | 1.3852 | 0.4735 | 0.3581 | | 0.2293 | 4.0 | 12488 | 1.0940 | 4.3760 | 1.4084 | 0.4625 | 0.3517 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Ci/Pai
[]
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
2022-08-10T11:27:49Z
--- license: afl-3.0 --- --- About : This model can be used for text summarization. The dataset on which it was fine tuned consisted of 10,323 articles. The Data Fields : - "Headline" : title of the article - "articleBody" : the main article content - "source" : the link to the readmore page. The data splits were : - Train : 8258. - Vaildation : 2065. ### How to use along with pipeline ```python from transformers import pipeline from transformers import AutoTokenizer, AutoModelForSeq2Seq tokenizer = AutoTokenizer.from_pretrained("AkashKhamkar/InSumT510k") model = AutoModelForSeq2SeqLM.from_pretrained("AkashKhamkar/InSumT510k") summarizer = pipeline("summarization", model=model, tokenizer=tokenizer) summarizer("Text for summarization...", min_length=5, max_length=50) ``` language: - English library_name: Pytorch tags: - Summarization - T5-base - Conditional Modelling -
Cilan/dalle-knockoff
[]
null
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0
null
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_4_binary_v1 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. --> # xlnet-base-cased_fold_4_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5724 - F1: 0.8315 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.4043 | 0.8009 | | 0.4373 | 2.0 | 578 | 0.4093 | 0.8260 | | 0.4373 | 3.0 | 867 | 0.5084 | 0.8206 | | 0.2707 | 4.0 | 1156 | 0.5945 | 0.8087 | | 0.2707 | 5.0 | 1445 | 0.6389 | 0.8251 | | 0.1691 | 6.0 | 1734 | 0.8131 | 0.8156 | | 0.1012 | 7.0 | 2023 | 0.9865 | 0.8190 | | 0.1012 | 8.0 | 2312 | 1.1356 | 0.8342 | | 0.0506 | 9.0 | 2601 | 1.0624 | 0.8369 | | 0.0506 | 10.0 | 2890 | 1.2604 | 0.8255 | | 0.0384 | 11.0 | 3179 | 1.2648 | 0.8183 | | 0.0384 | 12.0 | 3468 | 1.3763 | 0.8158 | | 0.0318 | 13.0 | 3757 | 1.4966 | 0.8217 | | 0.0221 | 14.0 | 4046 | 1.3889 | 0.8250 | | 0.0221 | 15.0 | 4335 | 1.4014 | 0.8284 | | 0.0145 | 16.0 | 4624 | 1.5321 | 0.8289 | | 0.0145 | 17.0 | 4913 | 1.4914 | 0.8233 | | 0.0172 | 18.0 | 5202 | 1.3946 | 0.8314 | | 0.0172 | 19.0 | 5491 | 1.5032 | 0.8269 | | 0.0135 | 20.0 | 5780 | 1.5111 | 0.8328 | | 0.0087 | 21.0 | 6069 | 1.4899 | 0.8318 | | 0.0087 | 22.0 | 6358 | 1.5562 | 0.8311 | | 0.0061 | 23.0 | 6647 | 1.5384 | 0.8327 | | 0.0061 | 24.0 | 6936 | 1.5798 | 0.8304 | | 0.0052 | 25.0 | 7225 | 1.5724 | 0.8315 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Cinnamon/electra-small-japanese-discriminator
[ "pytorch", "electra", "pretraining", "ja", "transformers", "license:apache-2.0" ]
null
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419
2022-08-10T11:56:42Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - metrics: - type: mean_reward value: 17.60 +/- 26.37 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 --- # **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
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
--- tags: - vision - zero-shot-image-classification - endpoints-template library_name: generic --- # Fork of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) for a `zero-sho-image-classification` Inference endpoint. This repository implements a `custom` task for `zero-shot-image-classification` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/clip-zero-shot-image-classification/blob/main/pipeline.py). To use deploy this model a an Inference Endpoint you have to select `Custom` as task to use the `pipeline.py` file. -> _double check if it is selected_ ### expected Request payload ```json { "image": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgICAgMC....", // base64 image as bytes "candiates":["sea","palace","car","ship"] } ``` below is an example on how to run a request using Python and `requests`. ## Run Request 1. prepare an image. ```bash !wget https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg ``` 2. run request ```python import json from typing import List import requests as r import base64 ENDPOINT_URL = "" HF_TOKEN = "" def predict(path_to_image: str = None, candiates: List[str] = None): with open(path_to_image, "rb") as i: b64 = base64.b64encode(i.read()) payload = {"inputs": {"image": b64.decode("utf-8"), "candiates": candiates}} response = r.post( ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload ) return response.json() prediction = predict( path_to_image="palace.jpg", candiates=["sea", "palace", "car", "ship"] ) ``` expected output ```python [{'label': 'palace', 'score': 0.9996134638786316}, {'label': 'car', 'score': 0.0002602009626571089}, {'label': 'ship', 'score': 0.00011758189066313207}, {'label': 'sea', 'score': 8.666840585647151e-06}] ```
Ciruzzo/DialoGPT-small-hattypotter
[]
null
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0
2022-08-10T12:09:25Z
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_5_binary_v1 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. --> # xlnet-base-cased_fold_5_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7395 - F1: 0.8206 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.4246 | 0.8154 | | 0.4211 | 2.0 | 576 | 0.5181 | 0.8063 | | 0.4211 | 3.0 | 864 | 0.4939 | 0.8149 | | 0.2483 | 4.0 | 1152 | 0.6181 | 0.8227 | | 0.2483 | 5.0 | 1440 | 0.9251 | 0.8006 | | 0.1512 | 6.0 | 1728 | 0.9639 | 0.8082 | | 0.0858 | 7.0 | 2016 | 1.1315 | 0.8074 | | 0.0858 | 8.0 | 2304 | 1.1322 | 0.8303 | | 0.053 | 9.0 | 2592 | 1.3171 | 0.8017 | | 0.053 | 10.0 | 2880 | 1.3729 | 0.8100 | | 0.0325 | 11.0 | 3168 | 1.2708 | 0.8252 | | 0.0325 | 12.0 | 3456 | 1.5105 | 0.8242 | | 0.0203 | 13.0 | 3744 | 1.4902 | 0.8233 | | 0.0179 | 14.0 | 4032 | 1.5874 | 0.8194 | | 0.0179 | 15.0 | 4320 | 1.5933 | 0.8135 | | 0.0174 | 16.0 | 4608 | 1.5908 | 0.8088 | | 0.0174 | 17.0 | 4896 | 1.5692 | 0.8249 | | 0.0129 | 18.0 | 5184 | 1.6597 | 0.8167 | | 0.0129 | 19.0 | 5472 | 1.6009 | 0.8218 | | 0.0095 | 20.0 | 5760 | 1.6962 | 0.8225 | | 0.0062 | 21.0 | 6048 | 1.7075 | 0.8182 | | 0.0062 | 22.0 | 6336 | 1.7335 | 0.8181 | | 0.0077 | 23.0 | 6624 | 1.7175 | 0.8204 | | 0.0077 | 24.0 | 6912 | 1.7680 | 0.8187 | | 0.0024 | 25.0 | 7200 | 1.7395 | 0.8206 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
ClaudeCOULOMBE/RickBot
[ "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: apache-2.0 tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: En-Tn_update 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. --> # En-Tn_update This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-tn](https://huggingface.co/Helsinki-NLP/opus-mt-en-tn) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.13002 - Bleu: 39.1470 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results |Epoch| Training Loss | Validation Loss| Bleu | |:---:|:---------------:|:----------------:|:-------:| | 1 | 1.929300 | 1.884056 | 29.762382| | 2 | 1.637300 | 1.605588 | 32.846868| | 3 | 1.500000 | 1.457442 | 34.307484| | 4 | 1.402400 | 1.356578 | 35.423774| | 5 | 1.324000 | 1.276492 | 36.553368| | 6 | 1.251300 | 1.221768 | 37.464270| | 7 | 1.224700 | 1.181320 | 38.157490| | 8 | 1.193200 | 1.152997 | 38.800566| | 9 | 1.166700 | 1.136147 | 38.985707| | 10 | 1.142500 | 1.130020 | 39.209327| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
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
2022-08-10T12:23:04Z
--- tags: - generated_from_keras_callback model-index: - name: mojtaba767/bert-base-parsbert-uncased-finetuned-imdb 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. --> # mojtaba767/bert-base-parsbert-uncased-finetuned-imdb This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.6698 - Validation Loss: 4.3501 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -687, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.6698 | 4.3501 | 0 | ### Framework versions - Transformers 4.21.1 - TensorFlow 2.8.2 - Tokenizers 0.12.1
CleveGreen/FieldClassifier_v2_gpt
[ "pytorch", "gpt2", "text-classification", "transformers" ]
text-classification
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26
2022-08-10T12:35:02Z
--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: categorization-finetuned-20220721-164940-distilled-20220810-123313 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. --> # categorization-finetuned-20220721-164940-distilled-20220810-123313 This model is a fine-tuned version of [carted-nlp/categorization-finetuned-20220721-164940](https://huggingface.co/carted-nlp/categorization-finetuned-20220721-164940) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0787 - Accuracy: 0.8416 - F1: 0.8396 ## 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: 7e-06 - train_batch_size: 64 - eval_batch_size: 64 - seed: 314 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1500 - num_epochs: 15.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.2976 | 0.56 | 2500 | 0.1441 | 0.7219 | 0.7071 | | 0.1417 | 1.12 | 5000 | 0.1180 | 0.7719 | 0.7653 | | 0.1236 | 1.69 | 7500 | 0.1076 | 0.7901 | 0.7854 | | 0.1148 | 2.25 | 10000 | 0.1014 | 0.8015 | 0.7977 | | 0.1092 | 2.81 | 12500 | 0.0972 | 0.8089 | 0.8052 | | 0.1043 | 3.37 | 15000 | 0.0942 | 0.8135 | 0.8102 | | 0.1013 | 3.94 | 17500 | 0.0916 | 0.8181 | 0.8147 | | 0.0985 | 4.5 | 20000 | 0.0897 | 0.8219 | 0.8190 | | 0.0962 | 5.06 | 22500 | 0.0881 | 0.8241 | 0.8215 | | 0.0945 | 5.62 | 25000 | 0.0866 | 0.8270 | 0.8246 | | 0.0928 | 6.19 | 27500 | 0.0857 | 0.8286 | 0.8262 | | 0.0912 | 6.75 | 30000 | 0.0843 | 0.8310 | 0.8286 | | 0.0901 | 7.31 | 32500 | 0.0836 | 0.8321 | 0.8299 | | 0.0887 | 7.87 | 35000 | 0.0827 | 0.8339 | 0.8315 | | 0.0879 | 8.43 | 37500 | 0.0821 | 0.8350 | 0.8329 | | 0.0875 | 9.0 | 40000 | 0.0814 | 0.8362 | 0.8342 | | 0.0865 | 9.56 | 42500 | 0.0811 | 0.8370 | 0.8348 | | 0.0855 | 10.12 | 45000 | 0.0806 | 0.8375 | 0.8355 | | 0.0853 | 10.68 | 47500 | 0.0798 | 0.8386 | 0.8367 | | 0.0845 | 11.25 | 50000 | 0.0799 | 0.8392 | 0.8372 | | 0.0844 | 11.81 | 52500 | 0.0793 | 0.8401 | 0.8383 | | 0.0838 | 12.37 | 55000 | 0.0793 | 0.8402 | 0.8381 | | 0.0834 | 12.93 | 57500 | 0.0790 | 0.8410 | 0.8390 | | 0.0832 | 13.5 | 60000 | 0.0788 | 0.8414 | 0.8394 | | 0.083 | 14.06 | 62500 | 0.0787 | 0.8415 | 0.8395 | | 0.0828 | 14.62 | 65000 | 0.0787 | 0.8416 | 0.8396 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6
CleveGreen/JobClassifier
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
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31
2022-08-10T12:35:45Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PPO results: - metrics: - type: mean_reward value: -152.01 +/- 37.87 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8 # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'workRL/testppo' 'batch_size': 512 'minibatch_size': 128} ```
CleveGreen/JobClassifier_v2
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
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37
null
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_6_binary_v1 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. --> # xlnet-base-cased_fold_6_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6214 - F1: 0.8352 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.4174 | 0.7980 | | 0.4661 | 2.0 | 580 | 0.4118 | 0.8142 | | 0.4661 | 3.0 | 870 | 0.5152 | 0.8331 | | 0.2714 | 4.0 | 1160 | 0.6901 | 0.8242 | | 0.2714 | 5.0 | 1450 | 0.6853 | 0.8451 | | 0.1542 | 6.0 | 1740 | 0.8570 | 0.8399 | | 0.0935 | 7.0 | 2030 | 1.1342 | 0.8401 | | 0.0935 | 8.0 | 2320 | 1.1763 | 0.8397 | | 0.037 | 9.0 | 2610 | 1.3530 | 0.8215 | | 0.037 | 10.0 | 2900 | 1.3826 | 0.8402 | | 0.0351 | 11.0 | 3190 | 1.4057 | 0.8374 | | 0.0351 | 12.0 | 3480 | 1.4259 | 0.8455 | | 0.0159 | 13.0 | 3770 | 1.4270 | 0.8431 | | 0.0249 | 14.0 | 4060 | 1.4215 | 0.8442 | | 0.0249 | 15.0 | 4350 | 1.4245 | 0.8408 | | 0.0197 | 16.0 | 4640 | 1.4171 | 0.8353 | | 0.0197 | 17.0 | 4930 | 1.4537 | 0.8383 | | 0.0137 | 18.0 | 5220 | 1.4786 | 0.8430 | | 0.0068 | 19.0 | 5510 | 1.5635 | 0.8443 | | 0.0068 | 20.0 | 5800 | 1.5527 | 0.8378 | | 0.0062 | 21.0 | 6090 | 1.5917 | 0.8460 | | 0.0062 | 22.0 | 6380 | 1.6317 | 0.8318 | | 0.005 | 23.0 | 6670 | 1.6226 | 0.8340 | | 0.005 | 24.0 | 6960 | 1.6378 | 0.8310 | | 0.007 | 25.0 | 7250 | 1.6214 | 0.8352 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
CleveGreen/JobClassifier_v2_gpt
[ "pytorch", "gpt2", "text-classification", "transformers" ]
text-classification
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27
2022-08-10T12:40:54Z
--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image extra_gated_prompt: |- 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 claim 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 carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model --- # Stable Diffusion v1 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The **Stable-Diffusion-v-1-2** checkpoint was initialized with the weights of the [Stable-Diffusion-v-1-1](https:/steps/huggingface.co/CompVis/stable-diffusion-v-1-1-original) checkpoint and subsequently fine-tuned on 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. For more information, please refer to [Training](#training). #### Download the weights - [sd-v1-2.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/resolve/main/sd-v1-2.ckpt) - [sd-v1-2-full-ema.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/resolve/main/sd-v1-2-full-ema.ckpt) This weights are intended to be used with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion). If you are looking for the model to use with the D🧨iffusers library, [come here](https://huggingface.co/CompVis/stable-diffusion-v1-2). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`, which were trained as follows, - `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`. 515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-variants-scores.jpg) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
Clint/clinton
[]
null
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0
null
--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image library_name: "stable-diffusion" extra_gated_prompt: |- 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 claim 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 carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model --- # Stable Diffusion v1 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The **Stable-Diffusion-v-1-3** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v-1-2-original) checkpoint and subsequently fine-tuned on 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). For more information, please refer to [Training](#training). #### Download the weights - [sd-v1-3.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-3-original/resolve/main/sd-v1-3.ckpt) - [sd-v1-3-full-ema.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-3-original/resolve/main/sd-v1-3-full-ema.ckpt) This weights are intended to be used with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion). If you are looking for the model to use with the D🧨iffusers library, [come here](https://huggingface.co/CompVis/stable-diffusion-v1-3). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`, which were trained as follows, - `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`. 515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-variants-scores.jpg) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
Cloudy/DialoGPT-CJ-large
[ "pytorch", "conversational" ]
conversational
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1
2022-08-10T12:42:16Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilbert-base-multilingual-cased-misogyny-sexism-decay0.05-fr-outofdomain 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-misogyny-sexism-decay0.05-fr-outofdomain This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9920 - Accuracy: 0.2851 - F1: 0.1967 - Precision: 0.1124 - Recall: 0.7870 - Mae: 0.7149 - Tn: 1727 - Fp: 6043 - Fn: 207 - Tp: 765 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:----:|:----:|:---:|:---:| | 0.3603 | 1.0 | 2233 | 0.8218 | 0.3251 | 0.2021 | 0.1163 | 0.7685 | 0.6749 | 2095 | 5675 | 225 | 747 | | 0.298 | 2.0 | 4466 | 0.9031 | 0.3164 | 0.2047 | 0.1175 | 0.7912 | 0.6836 | 1997 | 5773 | 203 | 769 | | 0.2438 | 3.0 | 6699 | 0.9920 | 0.2851 | 0.1967 | 0.1124 | 0.7870 | 0.7149 | 1727 | 6043 | 207 | 765 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
ClydeWasTaken/DialoGPT-small-joshua
[ "conversational" ]
conversational
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0
null
--- language: - en - ro license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: distilled-mt5-small-010099-0.2 results: - task: name: Translation type: translation dataset: name: wmt16 ro-en type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 7.5783 --- <!-- 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. --> # distilled-mt5-small-010099-0.2 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8513 - Bleu: 7.5783 - Gen Len: 45.037 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
CoShin/XLM-roberta-large_ko_en_nil_sts
[]
null
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0
2022-08-10T13:02:46Z
--- datasets: - relbert/conceptnet_high_confidence model-index: - name: relbert/roberta-large-conceptnet-average-no-mask-prompt-d-nce 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: 0.8548412698412698 - 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: 0.5962566844919787 - 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: 0.5875370919881305 - 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: 0.7937743190661478 - 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: 0.926 - 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: 0.6447368421052632 - 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: 0.6805555555555556 - 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: 0.9159258701220431 - name: F1 (macro) type: f1_macro value: 0.9120976005401666 - 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: 0.8685446009389672 - name: F1 (macro) type: f1_macro value: 0.7131242903396904 - 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: 0.6722643553629469 - name: F1 (macro) type: f1_macro value: 0.6696626067611262 - 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: 0.9610488975446895 - name: F1 (macro) type: f1_macro value: 0.8687323343385976 - 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: 0.898464431212786 - name: F1 (macro) type: f1_macro value: 0.8946031569394925 --- # relbert/roberta-large-conceptnet-average-no-mask-prompt-d-nce RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence). 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/roberta-large-conceptnet-average-no-mask-prompt-d-nce/raw/main/analogy.json)): - Accuracy on SAT (full): 0.5962566844919787 - Accuracy on SAT: 0.5875370919881305 - Accuracy on BATS: 0.7937743190661478 - Accuracy on U2: 0.6447368421052632 - Accuracy on U4: 0.6805555555555556 - Accuracy on Google: 0.926 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-d-nce/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9159258701220431 - Micro F1 score on CogALexV: 0.8685446009389672 - Micro F1 score on EVALution: 0.6722643553629469 - Micro F1 score on K&H+N: 0.9610488975446895 - Micro F1 score on ROOT09: 0.898464431212786 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-d-nce/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8548412698412698 ### 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/roberta-large-conceptnet-average-no-mask-prompt-d-nce") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: average_no_mask - data: relbert/conceptnet_high_confidence - template_mode: manual - template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj> - loss_function: nce_logout - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 145 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 640 - gradient_accumulation: 8 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-d-nce/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", } ```
CoachCarter/distilbert-base-uncased-finetuned-squad
[]
null
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0
null
--- language: - en - ro license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: distilled-mt5-small-010099-0.25 results: - task: name: Translation type: translation dataset: name: wmt16 ro-en type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 7.611 --- <!-- 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. --> # distilled-mt5-small-010099-0.25 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8387 - Bleu: 7.611 - Gen Len: 44.8304 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
CodeDanCode/CartmenBot
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
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14
null
--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image inference: false --- # Stable Diffusion Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. This model card gives an overview of all available model checkpoints. For more in-detail model cards, please have a look at the model repositories listed under [Model Access](#model-access). ## Stable Diffusion Version 1 For the first version 4 model checkpoints are released. *Higher* versions have been trained for longer and are thus usually better in terms of image generation quality then *lower* versions. More specifically: - **stable-diffusion-v1-1**: The checkpoint is randomly initialized and has been trained on 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - **stable-diffusion-v1-2**: The checkpoint resumed training from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - **stable-diffusion-v1-3**: The checkpoint resumed training from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598) - **stable-diffusion-v1-4**: The checkpoint resumed training from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [**`stable-diffusion-v1-4`**](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2`.225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). ### Model Access Each checkpoint can be used both with Hugging Face's [ 🧨 Diffusers library](https://github.com/huggingface/diffusers) or the original [Stable Diffusion GitHub repository](https://github.com/CompVis/stable-diffusion). Note that you have to *"click-request"* them on each respective model repository. | **[🤗's 🧨 Diffusers library](https://github.com/huggingface/diffusers)** | **[Stable Diffusion GitHub repository](https://github.com/CompVis/stable-diffusion)** | | ----------- | ----------- | | [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1) | [`stable-diffusion-v-1-1-original`](https://huggingface.co/CompVis/stable-diffusion-v-1-1-original) | | [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2) | [`stable-diffusion-v-1-2-original`](https://huggingface.co/CompVis/stable-diffusion-v-1-2-original) | | [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3) | [`stable-diffusion-v-1-3-original`](https://huggingface.co/CompVis/stable-diffusion-v-1-3-original) | | [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) | [`stable-diffusion-v-1-4-original`](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) | ### Demo To quickly try out the model, you can try out the [Stable Diffusion Space](https://huggingface.co/spaces/stabilityai/stable-diffusion). ### License [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
CodeDanCode/SP-KyleBot
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
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15
2022-08-10T13:10:01Z
--- tags: - generated_from_keras_callback model-index: - name: mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m 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. --> # mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8764 - Validation Loss: 2.7682 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -968, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.8764 | 2.7682 | 0 | ### Framework versions - Transformers 4.21.1 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1
CodeMonkey98/distilroberta-base-finetuned-wikitext2
[]
null
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0
null
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_7_binary_v1 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. --> # xlnet-base-cased_fold_7_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7774 - F1: 0.8111 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.4189 | 0.7903 | | 0.432 | 2.0 | 576 | 0.3927 | 0.8045 | | 0.432 | 3.0 | 864 | 0.4868 | 0.8108 | | 0.2573 | 4.0 | 1152 | 0.6763 | 0.8019 | | 0.2573 | 5.0 | 1440 | 0.8132 | 0.8105 | | 0.1612 | 6.0 | 1728 | 0.8544 | 0.8086 | | 0.0972 | 7.0 | 2016 | 1.1274 | 0.8109 | | 0.0972 | 8.0 | 2304 | 1.2622 | 0.8056 | | 0.0515 | 9.0 | 2592 | 1.3398 | 0.8013 | | 0.0515 | 10.0 | 2880 | 1.5421 | 0.8082 | | 0.0244 | 11.0 | 3168 | 1.4931 | 0.8042 | | 0.0244 | 12.0 | 3456 | 1.5744 | 0.8045 | | 0.0287 | 13.0 | 3744 | 1.4169 | 0.8091 | | 0.0255 | 14.0 | 4032 | 1.5790 | 0.7999 | | 0.0255 | 15.0 | 4320 | 1.6094 | 0.7994 | | 0.0098 | 16.0 | 4608 | 1.5758 | 0.8006 | | 0.0098 | 17.0 | 4896 | 1.5326 | 0.8140 | | 0.0203 | 18.0 | 5184 | 1.6431 | 0.8114 | | 0.0203 | 19.0 | 5472 | 1.7105 | 0.8072 | | 0.0104 | 20.0 | 5760 | 1.6353 | 0.8139 | | 0.0062 | 21.0 | 6048 | 1.6762 | 0.8108 | | 0.0062 | 22.0 | 6336 | 1.7076 | 0.8106 | | 0.0088 | 23.0 | 6624 | 1.7887 | 0.8035 | | 0.0088 | 24.0 | 6912 | 1.7731 | 0.8099 | | 0.0026 | 25.0 | 7200 | 1.7774 | 0.8111 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
CodeNinja1126/test-model
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
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24
null
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_8_binary_v1 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. --> # xlnet-base-cased_fold_8_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5333 - F1: 0.8407 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.3866 | 0.8172 | | 0.4299 | 2.0 | 580 | 0.4215 | 0.8246 | | 0.4299 | 3.0 | 870 | 0.4765 | 0.8238 | | 0.2564 | 4.0 | 1160 | 0.7283 | 0.8350 | | 0.2564 | 5.0 | 1450 | 0.6825 | 0.8363 | | 0.1553 | 6.0 | 1740 | 0.9637 | 0.8339 | | 0.0893 | 7.0 | 2030 | 1.1392 | 0.8239 | | 0.0893 | 8.0 | 2320 | 1.1868 | 0.8231 | | 0.0538 | 9.0 | 2610 | 1.2180 | 0.8346 | | 0.0538 | 10.0 | 2900 | 1.2353 | 0.8253 | | 0.0386 | 11.0 | 3190 | 1.1883 | 0.8317 | | 0.0386 | 12.0 | 3480 | 1.2786 | 0.8375 | | 0.0289 | 13.0 | 3770 | 1.3725 | 0.8375 | | 0.0146 | 14.0 | 4060 | 1.3171 | 0.8463 | | 0.0146 | 15.0 | 4350 | 1.2323 | 0.8425 | | 0.0182 | 16.0 | 4640 | 1.3169 | 0.8485 | | 0.0182 | 17.0 | 4930 | 1.4424 | 0.8336 | | 0.0125 | 18.0 | 5220 | 1.4336 | 0.8385 | | 0.0102 | 19.0 | 5510 | 1.4888 | 0.8405 | | 0.0102 | 20.0 | 5800 | 1.5227 | 0.8419 | | 0.0035 | 21.0 | 6090 | 1.4994 | 0.8421 | | 0.0035 | 22.0 | 6380 | 1.4845 | 0.8424 | | 0.0047 | 23.0 | 6670 | 1.5006 | 0.8422 | | 0.0047 | 24.0 | 6960 | 1.5468 | 0.8422 | | 0.0042 | 25.0 | 7250 | 1.5333 | 0.8407 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
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
2022-08-10T13:46:48Z
--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/cats metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-flowers-128-2 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/cats` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/rdruce/ddpm-flowers-128-2/tensorboard?#scalars)
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
2022-08-10T13:55:41Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # COS_TAPT_n_RoBERTa_STS 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. <!--- 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('Kyleiwaniec/COS_TAPT_n_RoBERTa_STS') 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 792 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": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 317, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 1024, '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 -->
CoderEFE/DialoGPT-marxbot
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational", "has_space" ]
conversational
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11
2022-08-10T14:11:27Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab 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-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4313 - Wer: 0.3336 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.0055 | 3.67 | 400 | 0.7015 | 0.6789 | | 0.4384 | 7.34 | 800 | 0.4827 | 0.4875 | | 0.2143 | 11.01 | 1200 | 0.4672 | 0.4554 | | 0.1431 | 14.68 | 1600 | 0.4331 | 0.4014 | | 0.1053 | 18.35 | 2000 | 0.4471 | 0.3822 | | 0.0857 | 22.02 | 2400 | 0.4324 | 0.3637 | | 0.0683 | 25.69 | 2800 | 0.4305 | 0.3423 | | 0.0526 | 29.36 | 3200 | 0.4313 | 0.3336 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
Venkatakrishnan-Ramesh/Text_gen
[]
null
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0
2022-08-10T14:12:23Z
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_9_binary_v1 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. --> # xlnet-base-cased_fold_9_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7204 - F1: 0.8203 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 291 | 0.4045 | 0.8001 | | 0.4262 | 2.0 | 582 | 0.3914 | 0.8297 | | 0.4262 | 3.0 | 873 | 0.5050 | 0.8029 | | 0.2488 | 4.0 | 1164 | 0.7681 | 0.8007 | | 0.2488 | 5.0 | 1455 | 0.8349 | 0.8262 | | 0.1483 | 6.0 | 1746 | 0.9045 | 0.8220 | | 0.0894 | 7.0 | 2037 | 1.1584 | 0.8165 | | 0.0894 | 8.0 | 2328 | 1.1818 | 0.8300 | | 0.0389 | 9.0 | 2619 | 1.3332 | 0.8147 | | 0.0389 | 10.0 | 2910 | 1.2373 | 0.8285 | | 0.038 | 11.0 | 3201 | 1.3156 | 0.8234 | | 0.038 | 12.0 | 3492 | 1.3251 | 0.8341 | | 0.0211 | 13.0 | 3783 | 1.3144 | 0.8255 | | 0.0158 | 14.0 | 4074 | 1.5686 | 0.8168 | | 0.0158 | 15.0 | 4365 | 1.5382 | 0.8185 | | 0.0165 | 16.0 | 4656 | 1.5203 | 0.8282 | | 0.0165 | 17.0 | 4947 | 1.5352 | 0.8136 | | 0.0142 | 18.0 | 5238 | 1.4799 | 0.8243 | | 0.0062 | 19.0 | 5529 | 1.5030 | 0.8294 | | 0.0062 | 20.0 | 5820 | 1.6264 | 0.8094 | | 0.0078 | 21.0 | 6111 | 1.6949 | 0.8122 | | 0.0078 | 22.0 | 6402 | 1.7106 | 0.8139 | | 0.0043 | 23.0 | 6693 | 1.7234 | 0.8218 | | 0.0043 | 24.0 | 6984 | 1.7344 | 0.8208 | | 0.0028 | 25.0 | 7275 | 1.7204 | 0.8203 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
CoffeeAddict93/gpt1-call-of-the-wild
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
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8
null
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: roberta-base-finetuned-squad 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. --> # roberta-base-finetuned-squad This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.25 - num_epochs: 3 - training precision: Mixed Precision ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cpu - Datasets 2.1.0 - Tokenizers 0.12.1
CoffeeAddict93/gpt1-modest-proposal
[ "pytorch", "openai-gpt", "text-generation", "transformers", "has_space" ]
text-generation
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11
2022-08-10T14:24:18Z
--- license: mit language: en --- # T5(v1.1)-SLED (SLiding-Encoder and Decoder, large-sized model) SLED models use pretrained, short-range encoder-decoder models, and apply them over long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder ## Model description This SLED model is based on the T5(V1.1) model, which is described in its [model card](https://huggingface.co/google/t5-v1_1-large). The developers write in a [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) that the T5 model: > Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself. T5 v1.1 includes several improvments on top of the original checkpoint. see its card for details ## Intended uses & limitations You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. ### How to use To use the model, you first need to install `py-sled` in your environment (or clone the code from the [official repository](https://github.com/Mivg/SLED/blob/main/README.md)) ``` pip install py-sled ``` For more installation instructions, see [here](https://github.com/Mivg/SLED#Installation). Once installed, SLED is fully compatible with HuggingFace's AutoClasses (AutoTokenizer, AutoConfig, AutoModel and AutoModelForCausalLM) and can be loaded using the from_pretrained methods ```python import sled # *** required so that SledModels will be registered for the AutoClasses *** model = AutoModel.from_pretrained('tau/t5-v1_1-large-sled') ``` Here is how to use this model in PyTorch: ```python from sled import SledTokenizer, SledModel tokenizer = SledTokenizer.from_pretrained('tau/t5-v1_1-large-sled') model = SledModel.from_pretrained('tau/t5-v1_1-large-sled') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` You can also replace SledModel by SledModelForConditionalGeneration for Seq2Seq generation ```python model = SledModelForConditionalGeneration.from_pretrained('tau/t5-v1_1-large-sled') ``` In case you wish to apply SLED on a task containing a prefix (e.g. question) which should be given as a context to every chunk, you can pass the `prefix_length` tensor input as well (A LongTensor in the length of the batch size). ```python import torch import sled # *** required so that SledModels will be registered for the AutoClasses *** tokenizer = AutoTokenizer.from_pretrained('tau/t5-v1_1-large-sled') model = AutoModel.from_pretrained('tau/t5-v1_1-large-sled') document_input_ids = tokenizer("Dogs are great for you.", return_tensors="pt").input_ids prefix_input_ids = tokenizer("Are dogs good for you?", return_tensors="pt").input_ids input_ids = torch.cat((prefix_input_ids, document_input_ids), dim=-1) attention_mask = torch.ones_like(input_ids) prefix_length = torch.LongTensor([[prefix_input_ids.size(1)]]) outputs = model(input_ids=input_ids, attention_mask=attention_mask, prefix_length=prefix_length) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info Please cite both the SLED [paper](https://arxiv.org/abs/2208.00748.pdf) and the T5 [paper](https://arxiv.org/pdf/1910.10683.pdf) by Raffel et al ```bibtex @inproceedings{Ivgi2022EfficientLU, title={Efficient Long-Text Understanding with Short-Text Models}, author={Maor Ivgi and Uri Shaham and Jonathan Berant}, year={2022} } ``` ```bibtex @article{2020t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {140}, pages = {1-67}, url = {http://jmlr.org/papers/v21/20-074.html} } ```
CoffeeAddict93/gpt2-medium-modest-proposal
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
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7
null
--- language: ja datasets: - csj tags: - audio - automatic-speech-recognition license: cc-by-nc-4.0 --- ### Usage ```python #!pip install transformers==4.17.0 #!pip install https://github.com/kpu/kenlm/archive/master.zip #!pip install pyctcdecode==0.4.0 from transformers.file_utils import cached_path, hf_bucket_url from importlib.machinery import SourceFileLoader from transformers import Wav2Vec2ProcessorWithLM from IPython.lib.display import Audio import torchaudio import torch # Load model & processor model_name = "nguyenvulebinh/wav2vec2-base-ja" model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name) processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name) # Load an example audio (16k) audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="sample.wav"))) input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt') # Infer output = model(**input_data) # Output transcript without LM print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy())) # Output transcript with LM print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text) ``` ### Model Parameters License The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode ### Contact [email protected] [![Follow](https://img.shields.io/twitter/follow/nguyenvulebinh?style=social)](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)
CoffeeAddict93/gpt2-modest-proposal
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
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12
2022-08-10T14:27:25Z
--- license: mit language: en --- # BART-SLED (SLiding-Encoder and Decoder, large-sized model) SLED models use pretrained, short-range encoder-decoder models, and apply them over long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder ## Model description This SLED model is based on the BART model, which is described in its [model card](https://huggingface.co/facebook/bart-large). BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). When used as a BART-SLED model, it can be applied on long text tasks. ## Intended uses & limitations You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. ### How to use To use the model, you first need to install `py-sled` in your environment (or clone the code from the [official repository](https://github.com/Mivg/SLED/blob/main/README.md)) ``` pip install py-sled ``` For more installation instructions, see [here](https://github.com/Mivg/SLED#Installation). Once installed, SLED is fully compatible with HuggingFace's AutoClasses (AutoTokenizer, AutoConfig, AutoModel and AutoModelForCausalLM) and can be loaded using the from_pretrained methods ```python import sled # *** required so that SledModels will be registered for the AutoClasses *** model = AutoModel.from_pretrained('tau/bart-large-sled') ``` Here is how to use this model in PyTorch: ```python from sled import SledTokenizer, SledModel tokenizer = SledTokenizer.from_pretrained('tau/bart-large-sled') model = SledModel.from_pretrained('tau/bart-large-sled') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` You can also replace SledModel by SledModelForConditionalGeneration for Seq2Seq generation ```python model = SledModelForConditionalGeneration.from_pretrained('tau/bart-large-sled') ``` In case you wish to apply SLED on a task containing a prefix (e.g. question) which should be given as a context to every chunk, you can pass the `prefix_length` tensor input as well (A LongTensor in the length of the batch size). ```python import torch import sled # *** required so that SledModels will be registered for the AutoClasses *** tokenizer = AutoTokenizer.from_pretrained('tau/bart-large-sled') model = AutoModel.from_pretrained('tau/bart-large-sled') document_input_ids = tokenizer("Dogs are great for you.", return_tensors="pt").input_ids prefix_input_ids = tokenizer("Are dogs good for you?", return_tensors="pt").input_ids input_ids = torch.cat((prefix_input_ids, document_input_ids), dim=-1) attention_mask = torch.ones_like(input_ids) prefix_length = torch.LongTensor([[prefix_input_ids.size(1)]]) outputs = model(input_ids=input_ids, attention_mask=attention_mask, prefix_length=prefix_length) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info Please cite both the SLED [paper](https://arxiv.org/abs/2208.00748.pdf) and the BART [paper](https://arxiv.org/abs/1910.13461) by Lewis et al ```bibtex @inproceedings{Ivgi2022EfficientLU, title={Efficient Long-Text Understanding with Short-Text Models}, author={Maor Ivgi and Uri Shaham and Jonathan Berant}, year={2022} } ``` ```bibtex @article{DBLP:journals/corr/abs-1910-13461, author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov and Luke Zettlemoyer}, title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, journal = {CoRR}, volume = {abs/1910.13461}, year = {2019}, url = {http://arxiv.org/abs/1910.13461}, eprinttype = {arXiv}, eprint = {1910.13461}, timestamp = {Thu, 31 Oct 2019 14:02:26 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
CohleM/bert-nepali-tokenizer
[]
null
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0
null
--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8346456692913387 --- <!-- 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-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2763 - F1: 0.8346 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5779 | 1.0 | 191 | 0.3701 | 0.7701 | | 0.2735 | 2.0 | 382 | 0.2908 | 0.8254 | | 0.1769 | 3.0 | 573 | 0.2763 | 0.8346 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
CohleM/mbert-nepali-tokenizer
[]
null
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0
2022-08-10T14:40:20Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilbert-amazon-shoe-reviews_ubuntu 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-amazon-shoe-reviews_ubuntu 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: 0.9573 - Accuracy: 0.5726 - F1: [0.62998761 0.45096564 0.49037037 0.55640244 0.73547094] - Precision: [0.62334478 0.45704118 0.47534706 0.5858748 0.72102161] - Recall: [0.63677355 0.4450495 0.5063743 0.52975327 0.75051125] ## 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: 32 - eval_batch_size: 64 - 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 | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------:|:--------------------------------------------------------:|:--------------------------------------------------------:| | 0.9617 | 1.0 | 2813 | 0.9573 | 0.5726 | [0.62998761 0.45096564 0.49037037 0.55640244 0.73547094] | [0.62334478 0.45704118 0.47534706 0.5858748 0.72102161] | [0.63677355 0.4450495 0.5063743 0.52975327 0.75051125] | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu102 - Datasets 2.4.0 - Tokenizers 0.12.1
Coldestadam/Breakout_Mentors_SpongeBob_Model
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
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10
null
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlnet-base-cased_fold_10_binary_v1 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. --> # xlnet-base-cased_fold_10_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7782 - F1: 0.8137 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.3796 | 0.8145 | | 0.4196 | 2.0 | 576 | 0.4319 | 0.7810 | | 0.4196 | 3.0 | 864 | 0.6227 | 0.8002 | | 0.231 | 4.0 | 1152 | 0.6258 | 0.7941 | | 0.231 | 5.0 | 1440 | 1.0692 | 0.7866 | | 0.1307 | 6.0 | 1728 | 1.1257 | 0.8005 | | 0.0756 | 7.0 | 2016 | 1.2283 | 0.8072 | | 0.0756 | 8.0 | 2304 | 1.3407 | 0.8061 | | 0.0486 | 9.0 | 2592 | 1.5232 | 0.8059 | | 0.0486 | 10.0 | 2880 | 1.6731 | 0.8053 | | 0.0339 | 11.0 | 3168 | 1.6536 | 0.8087 | | 0.0339 | 12.0 | 3456 | 1.7526 | 0.7996 | | 0.019 | 13.0 | 3744 | 1.6662 | 0.7909 | | 0.0237 | 14.0 | 4032 | 1.6028 | 0.8071 | | 0.0237 | 15.0 | 4320 | 1.7627 | 0.7964 | | 0.0078 | 16.0 | 4608 | 1.6513 | 0.8169 | | 0.0078 | 17.0 | 4896 | 1.7795 | 0.8039 | | 0.015 | 18.0 | 5184 | 1.8669 | 0.7935 | | 0.015 | 19.0 | 5472 | 1.6288 | 0.8118 | | 0.0124 | 20.0 | 5760 | 1.6630 | 0.8104 | | 0.004 | 21.0 | 6048 | 1.7418 | 0.8167 | | 0.004 | 22.0 | 6336 | 1.7651 | 0.8128 | | 0.0043 | 23.0 | 6624 | 1.7279 | 0.8163 | | 0.0043 | 24.0 | 6912 | 1.8177 | 0.8093 | | 0.004 | 25.0 | 7200 | 1.7782 | 0.8137 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
ComCom/gpt2
[ "pytorch", "gpt2", "feature-extraction", "transformers" ]
feature-extraction
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1
2022-08-10T14:58:00Z
--- license: apache-2.0 --- # OFA-Base-Caption This is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on the MSCOCO Caption dataset for image captioning. Specifically, the model was first trained with cross-entropy loss and then with CIDEr optimization. For more information, please refer to the official github ([https://github.com/OFA-Sys/OFA](https://github.com/OFA-Sys/OFA)) Temporarily, we only provide the finetuned checkpoints based on the official code.
ComCom-Dev/gpt2-bible-test
[]
null
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0
null
--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8124233755619126 --- <!-- 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-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2630 - F1: 0.8124 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8193 | 1.0 | 70 | 0.3200 | 0.7356 | | 0.2773 | 2.0 | 140 | 0.2841 | 0.7882 | | 0.1807 | 3.0 | 210 | 0.2630 | 0.8124 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
Cometasonmi451/Mine
[]
null
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0
2022-08-10T15:02:17Z
--- license: mit tags: - generated_from_trainer model-index: - name: roberta-base-finetuned-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. --> # roberta-base-finetuned-wikitext2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5020 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - training precision: Mixed Precision ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6689 | 1.0 | 300 | 1.5518 | | 1.7525 | 2.0 | 600 | 1.5078 | | 1.5267 | 3.0 | 900 | 1.4971 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.10.0+cpu - Datasets 2.4.0 - Tokenizers 0.12.1
cometrain/neurotitle-rugpt3-small
[ "pytorch", "gpt2", "text-generation", "ru", "en", "dataset:All-NeurIPS-Papers-Scraper", "transformers", "Cometrain AutoCode", "Cometrain AlphaML", "license:mit" ]
text-generation
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20
null
--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.en metrics: - name: F1 type: f1 value: 0.6886160714285715 --- <!-- 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-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4043 - F1: 0.6886 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1347 | 1.0 | 50 | 0.5771 | 0.4880 | | 0.5066 | 2.0 | 100 | 0.4209 | 0.6582 | | 0.3631 | 3.0 | 150 | 0.4043 | 0.6886 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
Connorvr/BrightBot-small
[ "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
2022-08-10T15:27:43Z
--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-finetuned-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. --> # gpt2-finetuned-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6777 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - training precision: Mixed Precision ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.1203 | 1.0 | 145 | 3.7695 | | 3.9141 | 2.0 | 290 | 3.6953 | | 3.9057 | 3.0 | 435 | 3.6777 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.10.0+cpu - Datasets 2.4.0 - Tokenizers 0.12.1
Connorvr/TeachingGen
[ "pytorch", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit" ]
text-generation
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4
null
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all 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-all 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: 0.1745 - F1: 0.8505 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3055 | 1.0 | 835 | 0.1842 | 0.8099 | | 0.1561 | 2.0 | 1670 | 0.1711 | 0.8452 | | 0.1016 | 3.0 | 2505 | 0.1745 | 0.8505 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
ConstellationBoi/Oop
[]
null
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0
2022-08-10T15:58:55Z
--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - PhucLe/autotrain-data-LRO-tratify-data co2_eq_emissions: emissions: 2.223269909428516 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1237947025 - CO2 Emissions (in grams): 2.2233 ## Validation Metrics - Loss: 0.392 - Accuracy: 0.869 - Macro F1: 0.868 - Micro F1: 0.869 - Weighted F1: 0.868 - Macro Precision: 0.871 - Micro Precision: 0.869 - Weighted Precision: 0.871 - Macro Recall: 0.869 - Micro Recall: 0.869 - Weighted Recall: 0.869 ## 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/PhucLe/autotrain-LRO-tratify-data-1237947025 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("PhucLe/autotrain-LRO-tratify-data-1237947025", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("PhucLe/autotrain-LRO-tratify-data-1237947025", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
Contrastive-Tension/BERT-Base-CT-STSb
[ "pytorch", "tf", "jax", "bert", "feature-extraction", "transformers" ]
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 } } }
5
null
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: tuto-bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9380178985747432 - name: Recall type: recall value: 0.9525412319084483 - name: F1 type: f1 value: 0.9452237808951236 - name: Accuracy type: accuracy value: 0.9866809913463237 --- <!-- 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. --> # tuto-bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0827 - Precision: 0.9380 - Recall: 0.9525 - F1: 0.9452 - Accuracy: 0.9867 ## 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 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0218 | 1.0 | 1756 | 0.0714 | 0.9372 | 0.9524 | 0.9447 | 0.9862 | | 0.0123 | 2.0 | 3512 | 0.0761 | 0.9347 | 0.9510 | 0.9428 | 0.9859 | | 0.0063 | 3.0 | 5268 | 0.0827 | 0.9380 | 0.9525 | 0.9452 | 0.9867 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.11.0
Contrastive-Tension/BERT-Base-CT
[ "pytorch", "tf", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
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16
2022-08-10T16:08:27Z
--- library_name: sklearn tags: - sklearn - tabular-classification widget: structuredData: area error: - 30.29 - 96.05 - 48.31 compactness error: - 0.01911 - 0.01652 - 0.01484 concave points error: - 0.01037 - 0.0137 - 0.01093 concavity error: - 0.02701 - 0.02269 - 0.02813 fractal dimension error: - 0.003586 - 0.001698 - 0.002461 mean area: - 481.9 - 1130.0 - 748.9 mean compactness: - 0.1058 - 0.1029 - 0.1223 mean concave points: - 0.03821 - 0.07951 - 0.08087 mean concavity: - 0.08005 - 0.108 - 0.1466 mean fractal dimension: - 0.06373 - 0.05461 - 0.05796 mean perimeter: - 81.09 - 123.6 - 101.7 mean radius: - 12.47 - 18.94 - 15.46 mean smoothness: - 0.09965 - 0.09009 - 0.1092 mean symmetry: - 0.1925 - 0.1582 - 0.1931 mean texture: - 18.6 - 21.31 - 19.48 perimeter error: - 2.497 - 5.486 - 3.094 radius error: - 0.3961 - 0.7888 - 0.4743 smoothness error: - 0.006953 - 0.004444 - 0.00624 symmetry error: - 0.01782 - 0.01386 - 0.01397 texture error: - 1.044 - 0.7975 - 0.7859 worst area: - 677.9 - 1866.0 - 1156.0 worst compactness: - 0.2378 - 0.2336 - 0.2394 worst concave points: - 0.1015 - 0.1789 - 0.1514 worst concavity: - 0.2671 - 0.2687 - 0.3791 worst fractal dimension: - 0.0875 - 0.06589 - 0.08019 worst perimeter: - 96.05 - 165.9 - 124.9 worst radius: - 14.97 - 24.86 - 19.26 worst smoothness: - 0.1426 - 0.1193 - 0.1546 worst symmetry: - 0.3014 - 0.2551 - 0.2837 worst texture: - 24.64 - 26.58 - 26.0 --- # Model description This is a DecisionTreeClassifier model trained on breast cancer dataset. ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |--------------------------|---------| | ccp_alpha | 0.0 | | class_weight | | | criterion | gini | | max_depth | | | max_features | | | max_leaf_nodes | | | min_impurity_decrease | 0.0 | | min_samples_leaf | 1 | | min_samples_split | 2 | | min_weight_fraction_leaf | 0.0 | | random_state | | | splitter | best | </details> ### Model Plot The model plot is below. <style>#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 {color: black;background-color: white;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 pre{padding: 0;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-toggleable {background-color: white;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-toggleable__content {max-height: 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1;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-parallel-item:only-child::after {width: 0;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741 div.sk-text-repr-fallback {display: none;}</style><div id="sk-7c3e7180-7d07-45af-b2c4-4682b6ba8741" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="f10c71c1-4f35-46d5-b90a-c6e06005a09c" type="checkbox" checked><label for="f10c71c1-4f35-46d5-b90a-c6e06005a09c" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div> ## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|----------| | accuracy | 0.935673 | | f1 score | 0.935673 | # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file) ``` </details> # Model Card Authors This model card is written by following authors: skops_user # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` bibtex @inproceedings{...,year={2020}} ``` Confusion Matrix ![Confusion Matrix](confusion_matrix.png)
Contrastive-Tension/BERT-Base-Swe-CT-STSb
[ "pytorch", "tf", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
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126
2022-08-10T16:26:55Z
language: - "List of ISO 639-1 code for your language" - lang1 - lang2 thumbnail: "///" tags: - Conversational - Conversational license: "any valid license identifier" datasets: - dataset1 - dataset2 metrics: - metric1 - metric2
Contrastive-Tension/BERT-Distil-CT-STSb
[ "pytorch", "tf", "distilbert", "feature-extraction", "transformers" ]
feature-extraction
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1
null
--- license: apache-2.0 tags: - generated_from_trainer datasets: - source_data_nlp metrics: - precision - recall - f1 model-index: - name: sd-smallmol-roles-v2 results: - task: name: Token Classification type: token-classification dataset: name: source_data_nlp type: source_data_nlp args: SMALL_MOL_ROLES metrics: - name: Precision type: precision value: 0.9628394473558838 - name: Recall type: recall value: 0.9716346153846154 - name: F1 type: f1 value: 0.9672170375687963 --- <!-- 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. --> # sd-smallmol-roles-v2 This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on the source_data_nlp dataset. It achieves the following results on the evaluation set: - Loss: 0.0015 - Accuracy Score: 0.9995 - Precision: 0.9628 - Recall: 0.9716 - F1: 0.9672 ## 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: 32 - eval_batch_size: 256 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:| | 0.0013 | 1.0 | 1569 | 0.0015 | 0.9995 | 0.9628 | 0.9716 | 0.9672 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0a0+bfe5ad2 - Datasets 1.17.0 - Tokenizers 0.12.1
Contrastive-Tension/BERT-Distil-CT
[ "pytorch", "tf", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
{ "architectures": [ "DistilBertForMaskedLM" ], "model_type": "distilbert", "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
2022-08-10T16:42:04Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-cased-bert-yoga-finetuned 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-base-cased-bert-yoga-finetuned This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0760 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4202 | 1.0 | 235 | 2.1511 | | 2.1798 | 2.0 | 470 | 2.0707 | | 2.1428 | 3.0 | 705 | 2.0810 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cpu - Datasets 2.4.0 - Tokenizers 0.12.1
Contrastive-Tension/BERT-Large-CT-STSb
[ "pytorch", "tf", "jax", "bert", "feature-extraction", "transformers" ]
feature-extraction
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7
2022-08-10T16:52:34Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9265 - name: F1 type: f1 value: 0.9265405847311663 --- <!-- 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-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2133 - Accuracy: 0.9265 - F1: 0.9265 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8401 | 1.0 | 250 | 0.3144 | 0.9085 | 0.9058 | | 0.2524 | 2.0 | 500 | 0.2133 | 0.9265 | 0.9265 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
Contrastive-Tension/BERT-Large-NLI-CT
[ "pytorch", "tf", "jax", "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 } } }
15
2022-08-10T17:08:48Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-cvs-estimation-years-experience 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-cvs-estimation-years-experience This model is a fine-tuned version of [jhonparra18/bert-base-cased-cv-studio_name-medium](https://huggingface.co/jhonparra18/bert-base-cased-cv-studio_name-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 9.4494 - Mse: 9.4494 - Mae: 2.0686 - R2: 0.4131 - Accuracy: 0.2586 ## 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 - lr_scheduler_warmup_steps: 20 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:--------:| | No log | 10.34 | 300 | 10.5131 | 10.5131 | 2.2140 | 0.3470 | 0.2759 | | 3.3802 | 20.69 | 600 | 9.1915 | 9.1915 | 2.0780 | 0.4291 | 0.2759 | | 3.3802 | 31.03 | 900 | 8.8261 | 8.8261 | 1.9359 | 0.4518 | 0.2931 | | 0.1613 | 41.38 | 1200 | 9.4494 | 9.4494 | 2.0686 | 0.4131 | 0.2586 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu102 - Datasets 2.4.0 - Tokenizers 0.12.1
Coolhand/Abuela
[ "en", "image_restoration", "superresolution", "license:mit" ]
null
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0
2022-08-10T17:36:23Z
--- license: mit tags: - generated_from_trainer model-index: - name: 4-way-detection-prop-16 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. --> # 4-way-detection-prop-16 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) 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: 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: 10 ### Training results ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Corvus/DialoGPT-medium-CaptainPrice
[ "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
2022-08-10T17:56:19Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 273.70 +/- 23.38 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **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 ... ```
CouchCat/ma_mlc_v7_distil
[ "pytorch", "distilbert", "text-classification", "en", "transformers", "multi-label", "license:mit" ]
text-classification
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29
2022-08-10T18:14:51Z
--- license: apache-2.0 tags: - summarization - generated_from_trainer language: - fa widget: - text: 'در روزهای گذشته انتشار تصاویر کودکان و نوجوانانی که از والدین‌شان جدا شده و در اردوگاه‌های موقت در ایالت‌های مرزی آمریکا نگهداری می‌شوند، انتقادات گسترده‌ای را در داخل و خارج آمریکا از سیاست ضد مهاجرتی ترامپ، برانگیخته است. به گزارش این اعتراضات به حدی است که حتی "ملانیا ترامپ" بانوی اول آمریکا نیز نتوانست از آن دفاع کند و این اقدام را محکوم کرد. ماجرا از این قرار است که در یک ماه گذشته دولت آمریکا با ارایه تفسیر موسعی از قانون مهاجرت به آمریکا بیش از 2200 فرزند را از والدین مهاجر آنها جدا کرد. بر اساس این تفسیر از قانون ورود غیرقانونی به خاک ایالات متحده آمریکا جرم محسوب می‌شود و به همین خاطر افرادی که به صورت غیرقانونی وارد خاک آمریکا شده‌اند برای محاکمه دستگیر می‌شوند و فرزندانشان از آنها جدا می‌شوند. این جداسازی و انتشار تصاویری از صدها کودک و نوجوان و حتی فرزندان خردسال زیر 2 سال که از والدین خود جدا شده اند صدای بسیاری را در آمریکا و جهان درآورده است. گفتنی است جداسازی والدین و فرزندان بر مبنای قانون جدیدی انجام نمی‌شود بلکه دولت ترامپ تلاش دارد قانونی را که در دوره‌های گذشته نسبت به آن اغماض می‌شد، "سفت و سخت" به مورد اجرا بگذارد؛ تنها تغییری که دولت ترامپ نسبت به دولت اوباما درباره قانون دارد، "تفسیر موسع" آن از "وقوع جرم" از سوی مهاجران غیرقانونی است، بدین گونه که دولت ترامپ نفس ورود غیرقانونی به خاک آمریکا را جرم انگاشته و مهاجران را برای محاکمه و اخراج دستگیر می‌کند اما در دولت‌های گذشته نسبت به این ورود با اغماض بیشتری برخورد می‌شد و تنها در صورتی که مهاجرغیرقانونی اقدامی مجرمانه را در خاک آمریکا مرتکب می‌شد، نسبت به دستگیری و اخراج فرد مزبور اقدام می‌شد. دموکرات‌ها این اقدام دولت ترامپ را غیراخلاقی و "شیطانی" توصیف کرده‌اند و حتی "لورا بوش" همسر "جورج دبلیو بوش" رییس جمهور اسبق آمریکا با اعلام انزجار از این اقدام، گفته طاقت دیدن صحنه ضجه و گریه کودکان خردسال پس از جدایی آنها از والدین‌شان را ندارد. این اعتراضات در حالی است که ترامپ از این اقدام دفاع کرده و گفته راهی جز این نیست. او دیروز بار دیگر با دفاع از سیاست جدید دولت آمریکا برضد مهاجران گفت که او اجازه نخواهد داد آمریکا نیز مثل اروپا به "اردوگاه پناهجویان" تبدیل شود. در روزهای گذشته در برخی شهرهای آمریکا تظاهرات‌هایی بر ضد جداسازی فرزندان و والدین مهاجر برگزار شده است و فعالان اجتماعی و حقوق بشر در آمریکا به این اقبدام به شدت اعتراض کرده و خوستار توقف اجرای این طرح شده‌اند. "جف سشنز" وزیر دادگستری کابینه ترامپ هم در واکنش به مقایسه این طرح با اقدامات دوره "آلمان نازی" - در جداسازی والدین از فرزندان در اردوگاه‌های مرگ یا کار اجباری- گفته است این طرح به هیچ وجه قابل مقایسه با اقدامات دوره آلمان نازی نیست. پس از اینکه "مایکل هایدن" رییس سابق سازمان اطلاعات مرکزی آمریکا (سیا) در توییتر خود این اقدام را با اردوگاه‌های آلمان نازی مقایسه کرد و به شدت آن را محکوم کرد وزیر دادگستری کابینه ترامپ دیروز در مصاحبه‌ای با فاکس‌نیوز با دفاع از اجرای سخت‌گیرانه قانون ضد مهاجرت غیرقانونی به خاک آمریکا این مقایسه را "بزرگ‌نمایی" دانست چون به گفته او: در آلمان نازی، جلوی خروج یهودیان از کشور را می‌گرفتند." کنگره آمریکا قرار است در هفته جاری درباره یک قانون جدید مهاجرتی به تصمیم‌گیری برسد.' - text: 'وزرای خارجه اسراییل و ایران در دومین سالگرد شهادت سردار سپهبد "قاسم سلیمانی" در توییتر جدال کردند. به گزارش ، در پی توییت اخیر "حسین امیر عبدالهیان" وزیر امور خارجه جمهوری اسلامی ایران درباره تهدیدات رژیم اسراییل به اقدام نظامی علیه ایران، "یائیر لاپید" وزیر خارجه اسراییل امروز از طریق توییتر با بازنشر توییت امیرعبدالهیان به توییت او پاسخ داد. امیر عبدالهیان دیروز در توییتی با اشاره به مصاحبه اخیر لاپید مبنی بر توانایی غیرقابل تصور اسراییل برای حمله نظامی علیه ایران نوشته بود:" اظهارات آشفته وزير خارجه رژيم جعلی اسراییل در قبال ملت بزرگ ایران، مصداق این ضرب المثل معروف ایرانیست که« شتر در خواب بیند پنبه دانه، گهی لپ لپ خورد گه دانه دانه». با اقتدار و عقلانیت از حقوق، منافع و‌پیشرفت ملت دفاع می کنیم. صهیونیسم جایی در آینده جهان ندارد." لاپید روز جمعه در مصاحبه ای گفته بود رژیم تل آویو توانایی هایی برای اقدام نظامی علیه ایران دارد که در مخیله هیچ کسی نمی گنجد و اگر منافع تل آویو از جانب ایران تهدید شود، قادر است به صورت یکجانبه علیه ایران اقدام کند. امروز لاپید با بازنشر توییت امیرعبدالهیان که در واکنش به اظهارات تهدید آمیز اخیر او علیه ایران نوشته بود در رشته توییتی نوشت:" رژیم افراطی ایران اسراییل را تهدید به نابودی می کند، اما همچنان در این نبرد شکست خواهد خورد. حکومت شکست خورده ایران این کشور را از درون ویران می کند. به قول شاعر ایرانی سعدی: « اصل بد نیکو نگردد زانکه بنیادش بد است. »." او در توییتی دیگر افزود:" ایرانیان باید بدانند که رژیم آنها مسبب زندگی فلاکت بار آنهاست. دولت اسراییل قوی است و اجازه نخواهد داد که شهروندانش آسیب ببینند."' metrics: - rouge model-index: - name: mt5-base-finetuned 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. --> # mt5-base-finetuned-v1 This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the Persian News dataset. It achieves the following results on the evaluation set: - Loss: 1.087988 - Rouge1: 1.2887 - Rouge2: 0.1861 - Rougel: 1.2862 - Rougelsum: 1.2818 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-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: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 1.223400 | 1 | 20437| 1.153162 | 1.0624 | 0.1351 | 1.0668 | 1.0740 | | 1.202900 | 2 | 40874| 1.086163 | 1.1579 | 0.1426 | 1.1724 | 1.1599 | | 1.173500 | 3 | 61311| 1.087988 | 1.2887 | 0.1861 | 1.2862 | 1.2818 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
CouchCat/ma_ner_v7_distil
[ "pytorch", "distilbert", "token-classification", "en", "transformers", "ner", "license:mit", "autotrain_compatible" ]
token-classification
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13
2022-08-10T18:31:11Z
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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(&#39;https://pbs.twimg.com/profile_images/1196519479364268034/5QpniWSP_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1019713132023992320/fkvVczkz_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1514451221054173189/BWP3wqQj_400x400.jpg&#39;)"> </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">Humongous Ape MP & Fake Showbiz News & wint but Al & Ninja Sex Party but AI & gpt up a guy(?) & MORTIMUS COWBOY: The Bastard of Diapers</div> <div style="text-align: center; font-size: 14px;">@apesahoy-dril9999-dril_gpt2-fakeshowbiznews-gptupaguy-nsp_gpt2</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 Humongous Ape MP & Fake Showbiz News & wint but Al & Ninja Sex Party but AI & gpt up a guy(?) & MORTIMUS COWBOY: The Bastard of Diapers. | Data | Humongous Ape MP | Fake Showbiz News | wint but Al | Ninja Sex Party but AI | gpt up a guy(?) | MORTIMUS COWBOY: The Bastard of Diapers | | --- | --- | --- | --- | --- | --- | --- | | Tweets downloaded | 3246 | 3250 | 3229 | 692 | 3250 | 3249 | | Retweets | 198 | 1 | 47 | 13 | 16 | 0 | | Short tweets | 609 | 1 | 57 | 44 | 10 | 142 | | Tweets kept | 2439 | 3248 | 3125 | 635 | 3224 | 3107 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2kz7wo92/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 @apesahoy-dril9999-dril_gpt2-fakeshowbiznews-gptupaguy-nsp_gpt2's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1zpt8x6i) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1zpt8x6i/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/apesahoy-dril9999-dril_gpt2-fakeshowbiznews-gptupaguy-nsp_gpt2') 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)
CouchCat/ma_sa_v7_distil
[ "pytorch", "distilbert", "text-classification", "en", "transformers", "sentiment-analysis", "license:mit" ]
text-classification
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38
2022-08-10T18:31:32Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion model-index: - name: nlp_bert_emo_classifier 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. --> # nlp_bert_emo_classifier This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2791 ## 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.8887 | 0.25 | 500 | 0.4212 | | 0.3216 | 0.5 | 1000 | 0.3192 | | 0.2649 | 0.75 | 1500 | 0.2746 | | 0.2535 | 1.0 | 2000 | 0.2573 | | 0.163 | 1.25 | 2500 | 0.2157 | | 0.1868 | 1.5 | 3000 | 0.2118 | | 0.1258 | 1.75 | 3500 | 0.2319 | | 0.1726 | 2.0 | 4000 | 0.1853 | | 0.1035 | 2.25 | 4500 | 0.2146 | | 0.1135 | 2.5 | 5000 | 0.2207 | | 0.1117 | 2.75 | 5500 | 0.2496 | | 0.1145 | 3.0 | 6000 | 0.2482 | | 0.0726 | 3.25 | 6500 | 0.2654 | | 0.0828 | 3.5 | 7000 | 0.2622 | | 0.0817 | 3.75 | 7500 | 0.2775 | | 0.0689 | 4.0 | 8000 | 0.2791 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.10.3
Coverage/sakurajimamai
[]
null
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0
2022-08-10T18:54:55Z
--- language: en thumbnail: http://www.huggingtweets.com/apesahoy-dril-dril_gpt2-fakeshowbiznews-gptupaguy-nsp_gpt2/1660158001400/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(&#39;https://pbs.twimg.com/profile_images/1196519479364268034/5QpniWSP_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1019713132023992320/fkvVczkz_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1510917391533830145/XW-zSFDJ_400x400.jpg&#39;)"> </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">Humongous Ape MP & Fake Showbiz News & wint & wint but Al & Ninja Sex Party but AI & gpt up a guy(?)</div> <div style="text-align: center; font-size: 14px;">@apesahoy-dril-dril_gpt2-fakeshowbiznews-gptupaguy-nsp_gpt2</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 Humongous Ape MP & Fake Showbiz News & wint & wint but Al & Ninja Sex Party but AI & gpt up a guy(?). | Data | Humongous Ape MP | Fake Showbiz News | wint | wint but Al | Ninja Sex Party but AI | gpt up a guy(?) | | --- | --- | --- | --- | --- | --- | --- | | Tweets downloaded | 3246 | 3250 | 3231 | 3229 | 692 | 3250 | | Retweets | 198 | 1 | 499 | 47 | 13 | 16 | | Short tweets | 609 | 1 | 288 | 57 | 44 | 10 | | Tweets kept | 2439 | 3248 | 2444 | 3125 | 635 | 3224 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ocv4vat/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 @apesahoy-dril-dril_gpt2-fakeshowbiznews-gptupaguy-nsp_gpt2's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gb80yim) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gb80yim/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/apesahoy-dril-dril_gpt2-fakeshowbiznews-gptupaguy-nsp_gpt2') 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)
Coyotl/DialoGPT-test-last-arthurmorgan
[ "conversational" ]
conversational
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0
2022-08-10T19:48:13Z
--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: categorization-finetuned-20220721-164940-distilled-20220810-185342 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. --> # categorization-finetuned-20220721-164940-distilled-20220810-185342 This model is a fine-tuned version of [carted-nlp/categorization-finetuned-20220721-164940](https://huggingface.co/carted-nlp/categorization-finetuned-20220721-164940) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0639 - Accuracy: 0.87 - F1: 0.8690 ## 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: 64 - eval_batch_size: 64 - seed: 314 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1500 - num_epochs: 30.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| | 0.269 | 0.56 | 2500 | 0.1280 | 0.7547 | 0.7461 | | 0.125 | 1.12 | 5000 | 0.1052 | 0.7960 | 0.7916 | | 0.1079 | 1.69 | 7500 | 0.0950 | 0.8132 | 0.8102 | | 0.0992 | 2.25 | 10000 | 0.0898 | 0.8216 | 0.8188 | | 0.0938 | 2.81 | 12500 | 0.0859 | 0.8294 | 0.8268 | | 0.0891 | 3.37 | 15000 | 0.0828 | 0.8349 | 0.8329 | | 0.0863 | 3.94 | 17500 | 0.0806 | 0.8391 | 0.8367 | | 0.0834 | 4.5 | 20000 | 0.0788 | 0.8417 | 0.8400 | | 0.081 | 5.06 | 22500 | 0.0774 | 0.8449 | 0.8430 | | 0.0792 | 5.62 | 25000 | 0.0754 | 0.8475 | 0.8460 | | 0.0778 | 6.19 | 27500 | 0.0749 | 0.8489 | 0.8474 | | 0.0758 | 6.75 | 30000 | 0.0738 | 0.8517 | 0.8502 | | 0.0745 | 7.31 | 32500 | 0.0729 | 0.8531 | 0.8519 | | 0.0733 | 7.87 | 35000 | 0.0720 | 0.8544 | 0.8528 | | 0.072 | 8.43 | 37500 | 0.0714 | 0.8559 | 0.8546 | | 0.0716 | 9.0 | 40000 | 0.0707 | 0.8565 | 0.8554 | | 0.0701 | 9.56 | 42500 | 0.0704 | 0.8574 | 0.8558 | | 0.0693 | 10.12 | 45000 | 0.0700 | 0.8581 | 0.8569 | | 0.0686 | 10.68 | 47500 | 0.0690 | 0.8600 | 0.8588 | | 0.0675 | 11.25 | 50000 | 0.0690 | 0.8605 | 0.8593 | | 0.0673 | 11.81 | 52500 | 0.0682 | 0.8614 | 0.8603 | | 0.0663 | 12.37 | 55000 | 0.0682 | 0.8619 | 0.8606 | | 0.0657 | 12.93 | 57500 | 0.0675 | 0.8634 | 0.8624 | | 0.0648 | 13.5 | 60000 | 0.0674 | 0.8636 | 0.8625 | | 0.0647 | 14.06 | 62500 | 0.0668 | 0.8644 | 0.8633 | | 0.0638 | 14.62 | 65000 | 0.0669 | 0.8648 | 0.8635 | | 0.0634 | 15.18 | 67500 | 0.0665 | 0.8654 | 0.8643 | | 0.063 | 15.74 | 70000 | 0.0663 | 0.8664 | 0.8654 | | 0.0623 | 16.31 | 72500 | 0.0662 | 0.8663 | 0.8652 | | 0.0622 | 16.87 | 75000 | 0.0657 | 0.8669 | 0.8660 | | 0.0615 | 17.43 | 77500 | 0.0658 | 0.8670 | 0.8660 | | 0.0616 | 17.99 | 80000 | 0.0655 | 0.8676 | 0.8667 | | 0.0608 | 18.56 | 82500 | 0.0653 | 0.8683 | 0.8672 | | 0.0606 | 19.12 | 85000 | 0.0653 | 0.8679 | 0.8669 | | 0.0602 | 19.68 | 87500 | 0.0648 | 0.8690 | 0.8680 | | 0.0599 | 20.24 | 90000 | 0.0650 | 0.8688 | 0.8677 | | 0.0598 | 20.81 | 92500 | 0.0647 | 0.8689 | 0.8680 | | 0.0592 | 21.37 | 95000 | 0.0647 | 0.8692 | 0.8681 | | 0.0591 | 21.93 | 97500 | 0.0646 | 0.8698 | 0.8688 | | 0.0587 | 22.49 | 100000 | 0.0645 | 0.8699 | 0.8690 | | 0.0586 | 23.05 | 102500 | 0.0644 | 0.8699 | 0.8690 | | 0.0583 | 23.62 | 105000 | 0.0644 | 0.8699 | 0.8690 | | 0.058 | 24.18 | 107500 | 0.0642 | 0.8703 | 0.8693 | | 0.058 | 24.74 | 110000 | 0.0642 | 0.8704 | 0.8694 | | 0.0578 | 25.3 | 112500 | 0.0641 | 0.8703 | 0.8693 | | 0.0576 | 25.87 | 115000 | 0.0641 | 0.8708 | 0.8699 | | 0.0573 | 26.43 | 117500 | 0.0641 | 0.8708 | 0.8698 | | 0.0574 | 26.99 | 120000 | 0.0639 | 0.8711 | 0.8702 | | 0.0571 | 27.55 | 122500 | 0.0640 | 0.8711 | 0.8701 | | 0.0569 | 28.12 | 125000 | 0.0639 | 0.8711 | 0.8702 | | 0.0569 | 28.68 | 127500 | 0.0639 | 0.8712 | 0.8703 | | 0.057 | 29.24 | 130000 | 0.0639 | 0.8712 | 0.8703 | | 0.0566 | 29.8 | 132500 | 0.0638 | 0.8713 | 0.8704 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6
Coyotl/DialoGPT-test2-arthurmorgan
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
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7
2022-08-10T19:03:09Z
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: roberta-base_fold_1_binary_v1 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. --> # roberta-base_fold_1_binary_v1 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4984 - F1: 0.8339 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.3819 | 0.8117 | | 0.4108 | 2.0 | 576 | 0.3696 | 0.8281 | | 0.4108 | 3.0 | 864 | 0.4890 | 0.8343 | | 0.2261 | 4.0 | 1152 | 0.7605 | 0.8298 | | 0.2261 | 5.0 | 1440 | 0.7754 | 0.8307 | | 0.1404 | 6.0 | 1728 | 0.7650 | 0.8174 | | 0.0962 | 7.0 | 2016 | 0.8539 | 0.8315 | | 0.0962 | 8.0 | 2304 | 1.0770 | 0.8263 | | 0.0433 | 9.0 | 2592 | 1.1450 | 0.8292 | | 0.0433 | 10.0 | 2880 | 1.1700 | 0.8205 | | 0.0344 | 11.0 | 3168 | 1.2376 | 0.8241 | | 0.0344 | 12.0 | 3456 | 1.2688 | 0.8329 | | 0.0219 | 13.0 | 3744 | 1.3276 | 0.8283 | | 0.0123 | 14.0 | 4032 | 1.2930 | 0.8320 | | 0.0123 | 15.0 | 4320 | 1.4631 | 0.8266 | | 0.0177 | 16.0 | 4608 | 1.4326 | 0.8270 | | 0.0177 | 17.0 | 4896 | 1.4770 | 0.8334 | | 0.0053 | 18.0 | 5184 | 1.5972 | 0.8214 | | 0.0053 | 19.0 | 5472 | 1.5331 | 0.8327 | | 0.0045 | 20.0 | 5760 | 1.5487 | 0.8359 | | 0.0086 | 21.0 | 6048 | 1.4610 | 0.8315 | | 0.0086 | 22.0 | 6336 | 1.4685 | 0.8353 | | 0.0071 | 23.0 | 6624 | 1.4933 | 0.8358 | | 0.0071 | 24.0 | 6912 | 1.4898 | 0.8310 | | 0.0022 | 25.0 | 7200 | 1.4984 | 0.8339 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
Craak/GJ0001
[]
null
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0
2022-08-10T19:50:13Z
--- tags: - document-understanding - endpoints-template library_name: generic --- # Deploy a Space as inference Endpoint _This is a fork of the [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/spaces/naver-clova-ix/donut-base-finetuned-cord-v2) Space. This repository implements a custom container for 🤗 Inference Endpoints using a Gradio space. To deploy this model as an Inference Endpoint, you have to select Custom as task and a custom image. * CPU image: `philschmi/gradio-api:cpu` * GPU image: `philschmi/gradio-api:gpu` * PORT: `7860` * ~Health Route: `/`~-> is default Also make sure to add `server_name="0.0.0.0"` in your `launch()` call to make sure the request is correct proxied. If you want to use the UI with the Inference Endpoint, you have to select as endpoint type `public` and add [auth through gradio](https://gradio.app/docs/#launch-header) ### Example API Request Payload Get an image you want to use, e.g. ```bash !wget https://datasets-server.huggingface.co/assets/naver-clova-ix/cord-v2/--/naver-clova-ix--cord-v2/train/0/image/image.jpg ``` run inference ```python import requests as r import base64 ENDPOINT_URL = "" HF_TOKEN = "" def predict(path_to_image: str = None): ext = path_to_image.split('.')[-1] prefix = f'data:image/{ext};base64,' with open(path_to_image, 'rb') as f: img = f.read() payload = {"data": [prefix + base64.b64encode(img).decode('utf-8')]} response = r.post( f"{ENDPOINT_URL}/api/predict", headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload ) if response.status_code == 200: return response.json() else: raise Exception(f"Error: {response.status_code}") prediction = predict(path_to_image="image.jpg") ```
Craig/paraphrase-MiniLM-L6-v2
[ "pytorch", "bert", "arxiv:1908.10084", "sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "license:apache-2.0" ]
feature-extraction
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1,026
2022-08-10T20:47:52Z
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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(&#39;https://pbs.twimg.com/profile_images/1196519479364268034/5QpniWSP_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1479595267800322048/Aqqb82wz_400x400.png&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1019713132023992320/fkvVczkz_400x400.jpg&#39;)"> </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">Humongous Ape MP & ste 🍊 & Fake Showbiz News & Ninja Sex Party but AI & gpt up a guy(?) & waint</div> <div style="text-align: center; font-size: 14px;">@apesahoy-chai_ste-fakeshowbiznews-gptupaguy-nsp_gpt2-powerdril_gpt2</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 Humongous Ape MP & ste 🍊 & Fake Showbiz News & Ninja Sex Party but AI & gpt up a guy(?) & waint. | Data | Humongous Ape MP | ste 🍊 | Fake Showbiz News | Ninja Sex Party but AI | gpt up a guy(?) | waint | | --- | --- | --- | --- | --- | --- | --- | | Tweets downloaded | 3245 | 3193 | 3250 | 692 | 3250 | 103 | | Retweets | 196 | 302 | 1 | 13 | 16 | 11 | | Short tweets | 609 | 488 | 1 | 44 | 10 | 2 | | Tweets kept | 2440 | 2403 | 3248 | 635 | 3224 | 90 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2r8q1li1/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 @apesahoy-chai_ste-fakeshowbiznews-gptupaguy-nsp_gpt2-powerdril_gpt2's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/e3lx58vb) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/e3lx58vb/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/apesahoy-chai_ste-fakeshowbiznews-gptupaguy-nsp_gpt2-powerdril_gpt2') 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)
Crives/distilbert-base-uncased-finetuned-emotion
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
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31
2022-08-10T21:54:40Z
--- tags: - conversational --- # Guin DialoGPT model
CurtisBowser/DialoGPT-medium-sora-three
[]
null
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0
null
--- language: - en - ro license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: distilled-mt5-small-0.4-0.25 results: - task: name: Translation type: translation dataset: name: wmt16 ro-en type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 3.2179 --- <!-- 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. --> # distilled-mt5-small-0.4-0.25 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 3.8561 - Bleu: 3.2179 - Gen Len: 41.2356 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
DJSammy/bert-base-danish-uncased_BotXO-ai
[ "pytorch", "jax", "da", "dataset:common_crawl", "dataset:wikipedia", "transformers", "bert", "masked-lm", "license:cc-by-4.0", "fill-mask" ]
fill-mask
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14
null
--- license: apache-2.0 tags: - generated_from_trainer datasets: - sem_eval_2018_task_1 metrics: - f1 - accuracy model-index: - name: bert-finetuned-sem_eval-english results: - task: name: Text Classification type: text-classification dataset: name: sem_eval_2018_task_1 type: sem_eval_2018_task_1 config: subtask5.english split: train args: subtask5.english metrics: - name: F1 type: f1 value: 0.7113731269958242 - name: Accuracy type: accuracy value: 0.28103837471783294 --- <!-- 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-sem_eval-english This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the sem_eval_2018_task_1 dataset. It achieves the following results on the evaluation set: - Loss: 0.3131 - F1: 0.7114 - Roc Auc: 0.8046 - Accuracy: 0.2810 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 0.4067 | 1.0 | 855 | 0.3205 | 0.6756 | 0.7766 | 0.2709 | | 0.2828 | 2.0 | 1710 | 0.3062 | 0.7058 | 0.7973 | 0.3014 | | 0.239 | 3.0 | 2565 | 0.3122 | 0.7100 | 0.8038 | 0.2810 | | 0.2145 | 4.0 | 3420 | 0.3131 | 0.7114 | 0.8046 | 0.2810 | | 0.1888 | 5.0 | 4275 | 0.3167 | 0.7096 | 0.8022 | 0.2844 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
alexandrainst/da-emotion-classification-base
[ "pytorch", "tf", "bert", "text-classification", "da", "transformers", "license:cc-by-sa-4.0" ]
text-classification
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837
null
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion 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-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2057 - Accuracy: 0.9255 - F1: 0.9257 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8084 | 1.0 | 250 | 0.2883 | 0.9125 | 0.9110 | | 0.2371 | 2.0 | 500 | 0.2057 | 0.9255 | 0.9257 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.0+cu113 - Tokenizers 0.13.2
Danbi/distilroberta-base-finetuned-wikitext2
[]
null
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0
null
--- language: - bg - mk - multilingual license: cc0-1.0 tags: - BERTovski - MaCoCu --- # Model description **BERTovski** is a large pre-trained language model trained on Bulgarian and Macedonian texts. It was trained from scratch using the RoBERTa architecture. It was developed as part of the [MaCoCu](https://macocu.eu/) project. The main developer is [Rik van Noord](https://www.rikvannoord.nl/) from the University of Groningen. BERTovski was trained on 74GB of text, which is equal to just over 7 billion tokens. It was trained for 300,000 steps with a batch size of 2,048, which was approximately 30 epochs. The training and fine-tuning procedures are described in detail on our [Github repo](https://github.com/macocu/LanguageModels). We aim to train this model for even longer, so keep an eye out for newer versions! # How to use ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel tokenizer = AutoTokenizer.from_pretrained("RVN/BERTovski") model = AutoModel.from_pretrained("RVN/BERTovski") # PyTorch model = TFAutoModel.from_pretrained("RVN/BERTovski") # Tensorflow ``` # Data For training, we used all Bulgarian and Macedonian data that was present in the [MaCoCu](https://macocu.eu/), Oscar, mc4 and Wikipedia corpora. In a manual analysis we found that for Oscar and mc4, if the data did not come from the corresponding domain (.bg or .mk), it was often (badly) machine translated. Therefore, we opted to only use data that originally came from a .bg or .mk domain. After de-duplicating the data, we were left with a total of 54.5 GB of Bulgarian and 9 GB of Macedonian text. Since there was quite a bit more Bulgarian data, we simply doubled the Macedonian data during training. We trained a shared vocabulary of 32,000 pieces on a subset of the data in which the Bulgarian/Macedonian split was 50/50. # Benchmark performance We tested performance of BERTovski on benchmarks of XPOS, UPOS and NER. For Bulgarian, we used the data from the [Universal Dependencies](https://universaldependencies.org/) project. For Macedonian, we used the data sets created in the [babushka-bench](https://github.com/clarinsi/babushka-bench/) project. We also tested on a Google (Bulgarian) and human (Macedonian) translated version of the COPA data set (for details see our [Github repo](https://github.com/RikVN/COPA)). We compare performance to the strong multi-lingual models XLMR-base and XLMR-large. For details regarding the fine-tuning procedure you can checkout our [Github](https://github.com/macocu/LanguageModels). Scores are averages of three runs, except for COPA, for which we use 10 runs. We use the same hyperparameter settings for all models for UPOS/XPOS/NER, for COPA we optimized the learning rate on the dev set. ## Bulgarian | | **UPOS** | **UPOS** | **XPOS** | **XPOS** | **NER** | **NER** | **COPA** | |-----------------|:--------:|:--------:|:--------:|:--------:|:-------:|:--------:|:--------:| | | **Dev** | **Test** | **Dev** | **Test** | **Dev** | **Test** | **Test** | | **XLM-R-base** | 99.2 | 99.4 | 98.0 | 98.3 | 93.2 | 92.9 | 56.9 | | **XLM-R-large** | 99.3 | 99.4 | 97.4 | 97.7 | 93.7 | 93.5 | 53.1 | | **BERTovski** | 98.8 | 99.1 | 97.6 | 97.8 | 93.5 | 93.3 | 51.7 | ## Macedonian | | **UPOS** | **UPOS** | **XPOS** | **XPOS** | **NER** | **NER** | **COPA** | |-----------------|:--------:|:--------:|:--------:|:--------:|:-------:|:--------:|:--------:| | | **Dev** | **Test** | **Dev** | **Test** | **Dev** | **Test** | **Test** | | **XLM-R-base** | 98.3 | 98.6 | 97.3 | 97.1 | 92.8 | 94.8 | 55.3 | | **XLM-R-large** | 98.3 | 98.7 | 97.7 | 97.5 | 93.3 | 95.1 | 52.5 | | **BERTovski** | 97.8 | 98.1 | 96.4 | 96.0 | 92.8 | 94.6 | 51.8 | # Acknowledgements Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). The authors received funding from the European Union's Connecting Europe Facility 2014- 2020 - CEF Telecom, under Grant Agreement No.INEA/CEF/ICT/A2020/2278341 (MaCoCu). # Citation If you use this model, please cite the following paper: ```bibtex @inproceedings{non-etal-2022-macocu, title = "{M}a{C}o{C}u: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages", author = "Ba{\~n}{\'o}n, Marta and Espl{\`a}-Gomis, Miquel and Forcada, Mikel L. and Garc{\'\i}a-Romero, Cristian and Kuzman, Taja and Ljube{\v{s}}i{\'c}, Nikola and van Noord, Rik and Sempere, Leopoldo Pla and Ram{\'\i}rez-S{\'a}nchez, Gema and Rupnik, Peter and Suchomel, V{\'\i}t and Toral, Antonio and van der Werff, Tobias and Zaragoza, Jaume", booktitle = "Proceedings of the 23rd Annual Conference of the European Association for Machine Translation", month = jun, year = "2022", address = "Ghent, Belgium", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2022.eamt-1.41", pages = "303--304" } ```
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2
[ "pytorch", "bert", "arxiv:1908.10084", "sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "license:apache-2.0" ]
sentence-similarity
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1,517
null
--- language: - en - ro license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: distilled-mt5-small-0.05-1 results: - task: name: Translation type: translation dataset: name: wmt16 ro-en type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 6.997 --- <!-- 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. --> # distilled-mt5-small-0.05-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8106 - Bleu: 6.997 - Gen Len: 46.2551 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1