Datasets:

davanstrien

/

pytorchmodelmetadata

like 1

lmqg/bart-large-squadshifts-vanilla-new_wiki

b07116d5b752c04cf4074bc2f97d77d06ee3973b

2022-06-22T10:53:40.000Z

text2text-generation

lmqg

lmqg/bart-large-squadshifts-vanilla-new_wiki

transformers

Entry not found

fujiki/gpt2-small-en2ja

8a405780c79d5aff715cdc7ef8e11fd0f2da2ad2

2022-06-22T01:33:50.000Z

text-generation

fujiki

fujiki/gpt2-small-en2ja

transformers

Entry not found

yourusername/push-to-hub-68284633-43ff-45ca-9300-ea115e5ed1ff

c6df1d4c48a0a2796623ea3d9c3d4b7b9ea6fc6e

2022-06-22T01:31:08.000Z

text-classification

yourusername

yourusername/push-to-hub-68284633-43ff-45ca-9300-ea115e5ed1ff

pytorch

--- language: en license: mit library_name: pytorch tags: text-classification datasets: glue metrics: acc --- # MyModelName asdf

sasuke/mt5-small-finetuned-amazon-en-es

282f2e830e4068824d53e986ccbe6ecd7efd60c0

2022-06-22T02:14:17.000Z

text2text-generation

sasuke

sasuke/mt5-small-finetuned-amazon-en-es

transformers

Entry not found

lmqg/bart-large-squadshifts-nyt

1c241383dded121e8c99a99ba97c8d81015fb305

2022-06-22T10:47:16.000Z

text2text-generation

lmqg

lmqg/bart-large-squadshifts-nyt

transformers

Entry not found

gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53

681fc9d832026e8a3adfff07c8a0e6f917088fbf

2022-06-23T02:23:51.000Z

automatic-speech-recognition

gary109

gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53

transformers

--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53 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. --> # ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-STEPMANIA2 dataset. It achieves the following results on the evaluation set: - Loss: 1.2034 - Wer: 0.9875 ## 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: 10 - eval_batch_size: 10 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.5631 | 1.0 | 150 | 2.4894 | 1.0 | | 1.9443 | 2.0 | 300 | 1.8861 | 1.0 | | 1.7618 | 3.0 | 450 | 1.6731 | 1.0 | | 1.2354 | 4.0 | 600 | 1.2471 | 0.9875 | | 1.2333 | 5.0 | 750 | 1.2253 | 0.9875 | | 1.2037 | 6.0 | 900 | 1.2168 | 0.9875 | | 1.2184 | 7.0 | 1050 | 1.2120 | 0.9875 | | 1.1932 | 8.0 | 1200 | 1.2080 | 0.9875 | | 1.179 | 9.0 | 1350 | 1.2039 | 0.9875 | | 1.1722 | 10.0 | 1500 | 1.2034 | 0.9875 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

lmqg/bart-large-squadshifts-reddit

7571a8bdabeaf6cb4839d11d2d941b6bded62e73

2022-06-22T10:49:38.000Z

text2text-generation

lmqg

lmqg/bart-large-squadshifts-reddit

transformers

Entry not found

micrem73/GePpeTto-finetuned-ricettetrentine

cda507fceaf042df467a9178db929444f7e74f8c

2022-06-22T09:03:55.000Z

text-generation

micrem73

micrem73/GePpeTto-finetuned-ricettetrentine

transformers

Entry not found

lmqg/bart-large-squadshifts-amazon

f761c204a46e442ae4bdf65d0d93d3908214cead

2022-06-22T10:51:41.000Z

text2text-generation

lmqg

lmqg/bart-large-squadshifts-amazon

transformers

Entry not found

lmqg/bart-large-subjqa-vanilla-books

a7e46d232070ad9e88d2185ca9a5d607c1ec40ea

2022-06-22T10:53:07.000Z

text2text-generation

lmqg

lmqg/bart-large-subjqa-vanilla-books

transformers

Entry not found

lmqg/bart-base-squadshifts-new_wiki

c172eeff882a21eb283938110238f2a928a79233

2022-06-22T10:45:58.000Z

text2text-generation

lmqg

lmqg/bart-base-squadshifts-new_wiki

transformers

Entry not found

lmqg/bart-base-squadshifts-vanilla-new_wiki

fcbe89cd2bb711041560aec337bbe0602b2c1203

2022-06-22T10:45:51.000Z

text2text-generation

lmqg

lmqg/bart-base-squadshifts-vanilla-new_wiki

transformers

Entry not found

lmqg/bart-base-squadshifts-vanilla-nyt

6912ba9a0a0210876d2b734a4095e11b7d9396a2

2022-06-22T10:47:23.000Z

text2text-generation

lmqg

lmqg/bart-base-squadshifts-vanilla-nyt

transformers

Entry not found

lmqg/bart-base-squadshifts-nyt

a5ab514acae1e6c00b1bb8615c9eace1c50c0940

2022-06-22T10:47:47.000Z

text2text-generation

lmqg

lmqg/bart-base-squadshifts-nyt

transformers

Entry not found

lmqg/bart-base-subjqa-vanilla-electronics

95ab8d30df2eac686df757730b76acc13f30a5ee

2022-06-22T10:47:34.000Z

text2text-generation

lmqg

lmqg/bart-base-subjqa-vanilla-electronics

transformers

Entry not found

lmqg/bart-base-subjqa-vanilla-grocery

55c826b5c9fd69838e8d586a26128adf54af2886

2022-06-22T10:48:54.000Z

text2text-generation

lmqg

lmqg/bart-base-subjqa-vanilla-grocery

transformers

Entry not found

lmqg/bart-base-subjqa-vanilla-books

b142a5127e9c0c184d9c8b513bad361fd979754e

2022-06-22T10:45:41.000Z

text2text-generation

lmqg

lmqg/bart-base-subjqa-vanilla-books

transformers

Entry not found

lmqg/bart-base-squadshifts-vanilla-amazon

02639c630fd9803d614fd7142b6e6799eb0771cc

2022-06-22T10:50:40.000Z

text2text-generation

lmqg

lmqg/bart-base-squadshifts-vanilla-amazon

transformers

Entry not found

lmqg/bart-base-squadshifts-amazon

bd1721caf7ee77156eff1156e9b9c2ecf63ed944

2022-06-22T10:50:48.000Z

text2text-generation

lmqg

lmqg/bart-base-squadshifts-amazon

transformers

Entry not found

lmqg/bart-base-subjqa-vanilla-restaurants

51ce87307fd60080b5fef0b34e92371313d7d782

2022-06-22T10:52:33.000Z

text2text-generation

lmqg

lmqg/bart-base-subjqa-vanilla-restaurants

transformers

Entry not found

lmqg/bart-base-subjqa-vanilla-tripadvisor

f8e1e4b21dafb4ab28c2f8c0cf36537f0559bd25

2022-06-22T10:54:09.000Z

text2text-generation

lmqg

lmqg/bart-base-subjqa-vanilla-tripadvisor

transformers

Entry not found

lmqg/bart-large-squadshifts-vanilla-amazon

74def6dd9073a66ceef979b4ed5dc812f94ca1e2

2022-06-22T11:00:01.000Z

text2text-generation

lmqg

lmqg/bart-large-squadshifts-vanilla-amazon

transformers

Entry not found

lmqg/bart-large-subjqa-vanilla-grocery

32be88d309c7daf3425f6aa9738cfde3b23238ca

2022-06-22T11:29:47.000Z

text2text-generation

lmqg

lmqg/bart-large-subjqa-vanilla-grocery

transformers

Entry not found

lmqg/bart-large-subjqa-vanilla-restaurants

7ee632005f292e9fed2c6c5ba2c34f2dbcd0b1a0

2022-06-22T12:06:10.000Z

text2text-generation

lmqg

lmqg/bart-large-subjqa-vanilla-restaurants

transformers

Entry not found

lmqg/bart-large-subjqa-vanilla-tripadvisor

f6334ab0b0b928574d0d4327f61031c8038c4935

2022-06-22T12:26:21.000Z

text2text-generation

lmqg

lmqg/bart-large-subjqa-vanilla-tripadvisor

transformers

Entry not found

lokesh-csengineer/distilbert-base-uncased-finetuned-imdb

cbbe8a8963c55918ca35ebec44d04db3f88601f6

2022-06-22T13:05:07.000Z

fill-mask

lokesh-csengineer

lokesh-csengineer/distilbert-base-uncased-finetuned-imdb

transformers

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb 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-imdb 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: 2.4099 ## 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7277 | 1.0 | 157 | 2.5120 | | 2.5953 | 2.0 | 314 | 2.4296 | | 2.5547 | 3.0 | 471 | 2.4218 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

paola-md/recipe-is

67014cfc5487cb81767857c761ea52e63a12f903

2022-06-22T13:02:06.000Z

fill-mask

paola-md

paola-md/recipe-is

transformers

Entry not found

paola-md/recipe-clean_steps

6e5ee526394a960b2e7948e38bf4ac71bc230b86

2022-06-22T13:05:23.000Z

fill-mask

paola-md

paola-md/recipe-clean_steps

transformers

Entry not found

luantber/k_cnn_cifar10

7a248cfe7498482d32e99975e40620b156f1e8f5

2022-06-22T19:35:53.000Z

image-classification

luantber

luantber/k_cnn_cifar10

pytorch

--- library_name: pytorch tags: - image-classification --- # CNN

jamesmarcel/xlm-roberta-base-finetuned-panx-de

d12c774a96b23f2fbd25cc217224f3d4824c6a04

2022-06-22T17:26:24.000Z

token-classification

jamesmarcel

jamesmarcel/xlm-roberta-base-finetuned-panx-de

transformers

--- 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 args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8620945214069894 --- <!-- 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: 0.1372 - F1: 0.8621 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 | | 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 | | 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

sexomq/TeoBot-Romanian-medium

421a9bfabea325e010b2243a8eb5fbade0d2eeaa

2022-06-24T20:04:12.000Z

conversational

sexomq

sexomq/TeoBot-Romanian-medium

transformers

--- tags: - conversational ---

namura/vit-demo

24d37b5f586d24143bef345fc1cd93a8df1d286b

2022-06-22T22:26:22.000Z

image-classification

namura

namura/vit-demo

transformers

Entry not found

sonalily/distilgpt2-finetuned-wikitext2

d99a125d18cbbb1158e5ec567a6ad165a9a0bc0b

2022-06-24T04:14:20.000Z

text-generation

sonalily

sonalily/distilgpt2-finetuned-wikitext2

transformers

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-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. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6429 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7607 | 1.0 | 2334 | 3.6664 | | 3.6527 | 2.0 | 4668 | 3.6473 | | 3.6015 | 3.0 | 7002 | 3.6429 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

gary109/ai-light-dance_chord_ft_wav2vec2-large-xlsr-53

ce2d058973c167abfea60cfa8e131e096427bb64

2022-06-25T09:19:30.000Z

automatic-speech-recognition

gary109

gary109/ai-light-dance_chord_ft_wav2vec2-large-xlsr-53

transformers

--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_chord_ft_wav2vec2-large-xlsr-53 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. --> # ai-light-dance_chord_ft_wav2vec2-large-xlsr-53 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-CHORD2 dataset. It achieves the following results on the evaluation set: - Loss: 1.8722 - Wer: 0.9590 ## 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: 3e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.1857 | 1.0 | 126 | 4.5913 | 1.0 | | 3.0939 | 2.0 | 252 | 3.0160 | 1.0 | | 2.8403 | 3.0 | 378 | 2.7337 | 1.0 | | 2.2525 | 4.0 | 504 | 2.5588 | 0.9825 | | 2.0291 | 5.0 | 630 | 2.5216 | 0.9701 | | 1.9083 | 6.0 | 756 | 2.3990 | 0.9514 | | 1.8745 | 7.0 | 882 | 2.2781 | 0.9474 | | 1.8222 | 8.0 | 1008 | 2.2360 | 0.9471 | | 1.7871 | 9.0 | 1134 | 2.1960 | 0.9463 | | 1.7225 | 10.0 | 1260 | 2.0775 | 0.9464 | | 1.6856 | 11.0 | 1386 | 2.0817 | 0.9518 | | 1.6903 | 12.0 | 1512 | 2.0607 | 0.9534 | | 1.6034 | 13.0 | 1638 | 1.9956 | 0.9504 | | 1.6171 | 14.0 | 1764 | 2.0099 | 0.9490 | | 1.5508 | 15.0 | 1890 | 2.0424 | 0.9591 | | 1.539 | 16.0 | 2016 | 1.9728 | 0.9600 | | 1.5176 | 17.0 | 2142 | 2.0421 | 0.9628 | | 1.5088 | 18.0 | 2268 | 1.9428 | 0.9598 | | 1.4739 | 19.0 | 2394 | 1.9886 | 0.9591 | | 1.4228 | 20.0 | 2520 | 2.0164 | 0.9670 | | 1.4277 | 21.0 | 2646 | 1.9968 | 0.9704 | | 1.3834 | 22.0 | 2772 | 1.9882 | 0.9669 | | 1.3768 | 23.0 | 2898 | 1.9519 | 0.9606 | | 1.3747 | 24.0 | 3024 | 1.8923 | 0.9580 | | 1.3533 | 25.0 | 3150 | 1.9767 | 0.9707 | | 1.3312 | 26.0 | 3276 | 1.8993 | 0.9609 | | 1.2743 | 27.0 | 3402 | 1.9494 | 0.9705 | | 1.2924 | 28.0 | 3528 | 1.9019 | 0.9631 | | 1.2621 | 29.0 | 3654 | 1.9110 | 0.9596 | | 1.2387 | 30.0 | 3780 | 1.9118 | 0.9627 | | 1.228 | 31.0 | 3906 | 1.8722 | 0.9590 | | 1.1938 | 32.0 | 4032 | 1.8890 | 0.9599 | | 1.1887 | 33.0 | 4158 | 1.9175 | 0.9653 | | 1.1807 | 34.0 | 4284 | 1.8983 | 0.9649 | | 1.1553 | 35.0 | 4410 | 1.9246 | 0.9703 | | 1.1448 | 36.0 | 4536 | 1.9248 | 0.9705 | | 1.1146 | 37.0 | 4662 | 1.9747 | 0.9804 | | 1.1394 | 38.0 | 4788 | 1.9119 | 0.9723 | | 1.1206 | 39.0 | 4914 | 1.8931 | 0.9630 | | 1.0892 | 40.0 | 5040 | 1.9243 | 0.9668 | | 1.104 | 41.0 | 5166 | 1.8965 | 0.9671 | | 1.054 | 42.0 | 5292 | 1.9477 | 0.9755 | | 1.0922 | 43.0 | 5418 | 1.8969 | 0.9699 | | 1.0484 | 44.0 | 5544 | 1.9423 | 0.9733 | | 1.0567 | 45.0 | 5670 | 1.9412 | 0.9745 | | 1.0615 | 46.0 | 5796 | 1.9076 | 0.9674 | | 1.0201 | 47.0 | 5922 | 1.9384 | 0.9743 | | 1.0664 | 48.0 | 6048 | 1.9509 | 0.9816 | | 1.0498 | 49.0 | 6174 | 1.9426 | 0.9757 | | 1.0303 | 50.0 | 6300 | 1.9477 | 0.9781 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

sharpcoder/wav2vec2_bjorn

d8ed61ac0fea734bcbdcabb0e179fc1746d0ebd4

2022-06-24T04:24:07.000Z

automatic-speech-recognition

sharpcoder

sharpcoder/wav2vec2_bjorn

transformers

This project is meant to fine-tune the facebook/wav2vec2 speech-to-text library using my voice specifically for my own speech to text purposes.

BlinkDL/rwkv-2-pile-430m

2a465448b30fd1cbc50e0413cbe2c82e822d2413

2022-07-20T01:50:22.000Z

text-generation

BlinkDL

BlinkDL/rwkv-2-pile-430m

--- language: - en tags: - pytorch - text-generation - causal-lm - rwkv license: bsd-2-clause datasets: - The Pile --- # RWKV-2 430M ## Model Description RWKV-2 430M is a L24-D1024 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details. At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile) to run it. ctx_len = 768 n_layer = 24 n_embd = 1024 Final checkpoint: 20220615-10803.pth : Trained on the Pile for 331B tokens. * Pile loss 2.349 * LAMBADA ppl 15.34, acc 42.42% * PIQA acc 67.03% * SC2016 acc 62.05% * Hellaswag acc_norm 38.47%

gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53

7d1b033952fd52108c760213b92124732e69d8a9

2022-06-24T09:28:26.000Z

automatic-speech-recognition

gary109

gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53

transformers

--- license: apache-2.0 tags: - automatic-speech-recognition - /workspace/asante/ai-light-dance_datasets/AI_Light_Dance.py - generated_from_trainer model-index: - name: ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53 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. --> # ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the /WORKSPACE/ASANTE/AI-LIGHT-DANCE_DATASETS/AI_LIGHT_DANCE.PY - ONSET-SINGING2 dataset. It achieves the following results on the evaluation set: - Loss: 1.7583 - Wer: 0.9386 ## 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: 4e-06 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 27.4755 | 1.0 | 112 | 23.2618 | 1.0 | | 5.5145 | 2.0 | 224 | 5.2213 | 1.0 | | 4.2211 | 3.0 | 336 | 4.1673 | 1.0 | | 3.8386 | 4.0 | 448 | 3.8253 | 1.0 | | 3.5531 | 5.0 | 560 | 3.6286 | 1.0 | | 3.5215 | 6.0 | 672 | 3.4762 | 0.9864 | | 3.3493 | 7.0 | 784 | 3.3549 | 0.9847 | | 3.1264 | 8.0 | 896 | 3.1797 | 0.9759 | | 2.7557 | 9.0 | 1008 | 2.8703 | 0.9865 | | 2.6345 | 10.0 | 1120 | 2.6736 | 0.9970 | | 2.4297 | 11.0 | 1232 | 2.5638 | 1.0337 | | 2.3057 | 12.0 | 1344 | 2.3680 | 0.9839 | | 2.1436 | 13.0 | 1456 | 2.2367 | 0.9648 | | 2.0856 | 14.0 | 1568 | 2.1635 | 0.9586 | | 2.0035 | 15.0 | 1680 | 2.0945 | 0.9645 | | 1.9134 | 16.0 | 1792 | 2.0395 | 0.9630 | | 1.9443 | 17.0 | 1904 | 2.0017 | 0.9401 | | 1.8988 | 18.0 | 2016 | 1.9514 | 0.9493 | | 1.8141 | 19.0 | 2128 | 1.9111 | 0.9475 | | 1.8344 | 20.0 | 2240 | 1.8790 | 0.9395 | | 1.7775 | 21.0 | 2352 | 1.8616 | 0.9503 | | 1.7517 | 22.0 | 2464 | 1.8333 | 0.9433 | | 1.7037 | 23.0 | 2576 | 1.8156 | 0.9372 | | 1.7158 | 24.0 | 2688 | 1.7961 | 0.9482 | | 1.7111 | 25.0 | 2800 | 1.7817 | 0.9422 | | 1.69 | 26.0 | 2912 | 1.7819 | 0.9430 | | 1.6889 | 27.0 | 3024 | 1.7721 | 0.9386 | | 1.6546 | 28.0 | 3136 | 1.7647 | 0.9453 | | 1.6542 | 29.0 | 3248 | 1.7653 | 0.9375 | | 1.647 | 30.0 | 3360 | 1.7583 | 0.9386 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

slh/fcnet-base-cased

0042cceba141019f5907cc7e3366075dbff64eff

2022-06-23T06:13:53.000Z

slh

slh/fcnet-base-cased

transformers

Entry not found

rhr99/wav2vec2-large-xls-r-300m-bn-colab

93183154a40f2218a71e293485f958df33435cf8

2022-06-23T09:56:46.000Z

automatic-speech-recognition

rhr99

rhr99/wav2vec2-large-xls-r-300m-bn-colab

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice_9_0 model-index: - name: wav2vec2-large-xls-r-300m-bn-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-bn-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_9_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4662 - Wer: 0.9861 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - 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: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.278 | 0.88 | 400 | 2.1963 | 1.0 | | 0.8479 | 1.77 | 800 | 0.4662 | 0.9861 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v1

215261eea78efa5e4c4bfb3048c6139abff4fbc5

2022-06-24T05:43:24.000Z

automatic-speech-recognition

gary109

gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v1

transformers

--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-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. --> # ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v1 This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53](https://huggingface.co/gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-STEPMANIA2 dataset. It achieves the following results on the evaluation set: - Loss: 1.0763 - Wer: 0.7344 ## 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: 4e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1632 | 1.0 | 150 | 1.2007 | 0.9875 | | 1.1615 | 2.0 | 300 | 1.1912 | 0.9875 | | 1.1487 | 3.0 | 450 | 1.1942 | 0.9875 | | 1.1207 | 4.0 | 600 | 1.1753 | 0.9875 | | 1.0638 | 5.0 | 750 | 1.1345 | 0.8214 | | 1.0174 | 6.0 | 900 | 1.1541 | 0.7665 | | 0.9946 | 7.0 | 1050 | 1.0799 | 0.7716 | | 0.9694 | 8.0 | 1200 | 1.0848 | 0.7418 | | 0.9566 | 9.0 | 1350 | 1.0763 | 0.7344 | | 0.9466 | 10.0 | 1500 | 1.0791 | 0.7240 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

jgriffi/pegasus-samsum

652ca8d7a890f9bcccb1c8878dd1a2f31e78ab7a

2022-06-23T11:18:59.000Z

text2text-generation

jgriffi

jgriffi/pegasus-samsum

transformers

--- tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum 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. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4841 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7073 | 0.54 | 500 | 1.4841 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

BlinkDL/rwkv-3-pile-1b5

633087c48ba816efbcfe3a849affb17704223b00

2022-07-28T08:38:10.000Z

text-generation

BlinkDL

BlinkDL/rwkv-3-pile-1b5

--- language: - en tags: - pytorch - text-generation - causal-lm - rwkv license: bsd-2-clause datasets: - The Pile --- # RWKV-3 1.5B ## Model Description RWKV-3 1.5B is a L24-D2048 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details. At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile) to run it. ctx_len = 896 n_layer = 24 n_embd = 2048 Preview checkpoint: RWKV-3-Pile-20220723-3542.pth : Trained on the Pile for 127B tokens. * Pile loss 2.102 * LAMBADA ppl 7.52, acc 54.71% * PIQA acc 71.11% * SC2016 acc 67.24% * Hellaswag acc_norm 50.45% Preview checkpoint: 20220708-1905.pth : Trained on the Pile for 68B tokens. * Pile loss 2.148 * LAMBADA ppl 8.41, acc 53.17% * PIQA acc 69.64% * SC2016 acc 67.08% * Hellaswag acc_norm 48.20% (I am still training it)

tali/wav2vec2-large-xlsr-turkish-demo-colab

a1bffb86a9e8d149e1de4107a35f84d5075b618d

2022-06-27T11:44:33.000Z

automatic-speech-recognition

tali

tali/wav2vec2-large-xlsr-turkish-demo-colab

transformers

Entry not found

ryo0634/bert-base-random-encoder-en-0

51defa23d45209a99ef2bcdfb43423d3c2194939

2022-06-23T12:28:32.000Z

fill-mask

ryo0634

ryo0634/bert-base-random-encoder-en-0

transformers

Entry not found

mayoughi/where_am_I_hospital-balcony-hallway-airport-coffee-house-apartment-office

15c936dd3ee71efaa9bdc643ae51650e93c86773

2022-06-23T16:28:19.000Z

image-classification

mayoughi

mayoughi/where_am_I_hospital-balcony-hallway-airport-coffee-house-apartment-office

transformers

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: where_am_I_hospital-balcony-hallway-airport-coffee-house-apartment-office results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.7555555701255798 --- # where_am_I_hospital-balcony-hallway-airport-coffee-house-apartment-office Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### airport  #### balcony  #### hallway  #### hospital  #### inside apartment  #### inside coffee house  #### office  #### restaurant 

gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v2

e45907bdf58a27a0f53a5aee9fcbb77c6c14450b

2022-06-25T05:01:56.000Z

automatic-speech-recognition

gary109

gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v2

transformers

--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v2 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. --> # ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v2 This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v1](https://huggingface.co/gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v1) on the GARY109/AI_LIGHT_DANCE - ONSET-STEPMANIA2 dataset. It achieves the following results on the evaluation set: - Loss: 1.0753 - Wer: 0.7017 ## 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: 10 - eval_batch_size: 10 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.945 | 1.0 | 150 | 1.0767 | 0.7282 | | 0.9445 | 2.0 | 300 | 1.0773 | 0.7165 | | 0.9392 | 3.0 | 450 | 1.0813 | 0.7141 | | 0.933 | 4.0 | 600 | 1.0858 | 0.7032 | | 0.921 | 5.0 | 750 | 1.0753 | 0.7017 | | 0.9241 | 6.0 | 900 | 1.0787 | 0.6976 | | 0.9282 | 7.0 | 1050 | 1.0825 | 0.6959 | | 0.9184 | 8.0 | 1200 | 1.0760 | 0.6930 | | 0.915 | 9.0 | 1350 | 1.0773 | 0.6906 | | 0.9094 | 10.0 | 1500 | 1.0786 | 0.6900 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

jackcarey/t5-small-finetuned-qgsquad-qgen

20c8e3c70dd6d0d13180c790ae8b7da33fa62e68

2022-06-25T11:03:16.000Z

text2text-generation

jackcarey

jackcarey/t5-small-finetuned-qgsquad-qgen

transformers

Entry not found

soProf1998/DialoGPT-small-chattyrick

968f2a41c353dd579fe5658cb0e8dab39530f406

2022-06-24T08:22:19.000Z

conversational

soProf1998

soProf1998/DialoGPT-small-chattyrick

transformers

--- tags: - conversational --- # Chatty Rick DialoGBT Model

mohsenfayyaz/bert-base-parsbert-uncased_pquad

a4439c8da66bfb05861591f0d4ef068852a1472a

2022-06-24T08:43:28.000Z

question-answering

mohsenfayyaz

mohsenfayyaz/bert-base-parsbert-uncased_pquad

transformers

Entry not found

mohsenfayyaz/bert-base-parsbert-uncased_persian_qa

39f1452a5995a6e3fa4fcfd9d1f1e0ebf673da04

2022-06-24T09:08:59.000Z

question-answering

mohsenfayyaz

mohsenfayyaz/bert-base-parsbert-uncased_persian_qa

transformers

Entry not found

mohsenfayyaz/bert-base-parsbert-uncased_pquad_and_persian_qa

44229ce0b54b11094a5dfedc36b1c478ae7d3bac

2022-06-24T10:27:20.000Z

question-answering

mohsenfayyaz

mohsenfayyaz/bert-base-parsbert-uncased_pquad_and_persian_qa

transformers

Entry not found

mohsenfayyaz/albert-fa-base-v2_pquad

7aeb2b3845d39ba31fe368dd881a1c1daa5a529f

2022-06-24T10:51:35.000Z

question-answering

mohsenfayyaz

mohsenfayyaz/albert-fa-base-v2_pquad

transformers

Entry not found

mohsenfayyaz/albert-fa-base-v2_persian_qa

0fd36b6559b86e44f939819e62eb45b0d57760aa

2022-06-24T11:11:31.000Z

question-answering

mohsenfayyaz

mohsenfayyaz/albert-fa-base-v2_persian_qa

transformers

Entry not found

mohsenfayyaz/albert-fa-base-v2_parsquad

179a53a2013e5ef64eb3cfd8f15d7575f1bb1b0f

2022-06-24T11:47:10.000Z

question-answering

mohsenfayyaz

mohsenfayyaz/albert-fa-base-v2_parsquad

transformers

Entry not found

robertodtg/wav2vec2-large-xls-r-300m-pt-colab

afeec0aa37c5661d4bb37782ac329b7015e7e395

2022-06-25T21:25:10.000Z

automatic-speech-recognition

robertodtg

robertodtg/wav2vec2-large-xls-r-300m-pt-colab

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice_9_0 model-index: - name: wav2vec2-large-xls-r-300m-pt-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-pt-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_9_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2975 - Wer: 0.1736 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.179 | 0.49 | 400 | 1.4554 | 0.9349 | | 0.7545 | 0.98 | 800 | 0.5594 | 0.5174 | | 0.4485 | 1.47 | 1200 | 0.3964 | 0.3749 | | 0.4118 | 1.96 | 1600 | 0.3547 | 0.3172 | | 0.3282 | 2.45 | 2000 | 0.3372 | 0.3061 | | 0.3199 | 2.94 | 2400 | 0.3466 | 0.2910 | | 0.2847 | 3.44 | 2800 | 0.3651 | 0.3310 | | 0.2713 | 3.93 | 3200 | 0.3509 | 0.3016 | | 0.2414 | 4.42 | 3600 | 0.3451 | 0.2908 | | 0.2473 | 4.91 | 4000 | 0.3253 | 0.2747 | | 0.2168 | 5.4 | 4400 | 0.3243 | 0.2680 | | 0.219 | 5.89 | 4800 | 0.3067 | 0.2540 | | 0.196 | 6.38 | 5200 | 0.3268 | 0.2824 | | 0.1934 | 6.87 | 5600 | 0.3252 | 0.2736 | | 0.1808 | 7.36 | 6000 | 0.3422 | 0.2737 | | 0.177 | 7.85 | 6400 | 0.3292 | 0.2707 | | 0.1626 | 8.34 | 6800 | 0.3089 | 0.2524 | | 0.1605 | 8.83 | 7200 | 0.3062 | 0.2471 | | 0.1505 | 9.32 | 7600 | 0.3229 | 0.2474 | | 0.1491 | 9.82 | 8000 | 0.3098 | 0.2491 | | 0.1433 | 10.31 | 8400 | 0.3449 | 0.2681 | | 0.1431 | 10.8 | 8800 | 0.3439 | 0.2532 | | 0.1349 | 11.29 | 9200 | 0.3112 | 0.2413 | | 0.1236 | 11.78 | 9600 | 0.3248 | 0.2378 | | 0.1253 | 12.27 | 10000 | 0.3393 | 0.2394 | | 0.1195 | 12.76 | 10400 | 0.3050 | 0.2336 | | 0.1194 | 13.25 | 10800 | 0.3494 | 0.2550 | | 0.1125 | 13.74 | 11200 | 0.3332 | 0.2395 | | 0.1063 | 14.23 | 11600 | 0.3134 | 0.2365 | | 0.1044 | 14.72 | 12000 | 0.3101 | 0.2303 | | 0.0999 | 15.21 | 12400 | 0.3162 | 0.2248 | | 0.0986 | 15.71 | 12800 | 0.3183 | 0.2260 | | 0.0958 | 16.2 | 13200 | 0.3300 | 0.2279 | | 0.0907 | 16.69 | 13600 | 0.3136 | 0.2260 | | 0.0875 | 17.18 | 14000 | 0.3492 | 0.2203 | | 0.0823 | 17.67 | 14400 | 0.3214 | 0.2259 | | 0.0839 | 18.16 | 14800 | 0.3194 | 0.2145 | | 0.0783 | 18.65 | 15200 | 0.3122 | 0.2180 | | 0.0789 | 19.14 | 15600 | 0.3158 | 0.2127 | | 0.0732 | 19.63 | 16000 | 0.3076 | 0.2109 | | 0.0715 | 20.12 | 16400 | 0.3216 | 0.2150 | | 0.0649 | 20.61 | 16800 | 0.2958 | 0.2051 | | 0.0647 | 21.1 | 17200 | 0.3022 | 0.2014 | | 0.0649 | 21.59 | 17600 | 0.3045 | 0.2033 | | 0.0621 | 22.09 | 18000 | 0.3194 | 0.2035 | | 0.0561 | 22.58 | 18400 | 0.3197 | 0.2022 | | 0.0582 | 23.07 | 18800 | 0.3109 | 0.1978 | | 0.0533 | 23.56 | 19200 | 0.3121 | 0.1932 | | 0.0515 | 24.05 | 19600 | 0.3125 | 0.1939 | | 0.0484 | 24.54 | 20000 | 0.3081 | 0.1908 | | 0.0485 | 25.03 | 20400 | 0.3042 | 0.1896 | | 0.0444 | 25.52 | 20800 | 0.3038 | 0.1886 | | 0.0426 | 26.01 | 21200 | 0.2985 | 0.1868 | | 0.0415 | 26.5 | 21600 | 0.3066 | 0.1858 | | 0.0398 | 26.99 | 22000 | 0.3117 | 0.1828 | | 0.0397 | 27.48 | 22400 | 0.2980 | 0.1795 | | 0.0394 | 27.97 | 22800 | 0.2950 | 0.1791 | | 0.0364 | 28.47 | 23200 | 0.3025 | 0.1773 | | 0.0365 | 28.96 | 23600 | 0.3022 | 0.1747 | | 0.0376 | 29.45 | 24000 | 0.2978 | 0.1738 | | 0.0344 | 29.94 | 24400 | 0.2975 | 0.1736 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.10.0+cu111 - Datasets 2.3.2 - Tokenizers 0.12.1

gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-v1

e9913fccfadf8bbde5411b2336e8cb60b90b8278

2022-06-26T02:32:15.000Z

automatic-speech-recognition

gary109

gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-v1

transformers

--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-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. --> # ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-v1 This model is a fine-tuned version of [gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53](https://huggingface.co/gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5760 - Wer: 0.2905 ## 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: 4e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 40.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.656 | 1.0 | 112 | 1.7625 | 0.9265 | | 1.3693 | 2.0 | 224 | 1.5135 | 0.9243 | | 1.2172 | 3.0 | 336 | 1.2657 | 0.8533 | | 1.0456 | 4.0 | 448 | 1.0893 | 0.7691 | | 0.9385 | 5.0 | 560 | 1.0110 | 0.7097 | | 0.8165 | 6.0 | 672 | 0.9243 | 0.6682 | | 0.7491 | 7.0 | 784 | 0.8948 | 0.6583 | | 0.6772 | 8.0 | 896 | 0.7894 | 0.6007 | | 0.6096 | 9.0 | 1008 | 0.7684 | 0.5663 | | 0.5714 | 10.0 | 1120 | 0.6978 | 0.4826 | | 0.5213 | 11.0 | 1232 | 0.8433 | 0.4927 | | 0.4624 | 12.0 | 1344 | 0.6695 | 0.4469 | | 0.4298 | 13.0 | 1456 | 0.6569 | 0.3868 | | 0.3939 | 14.0 | 1568 | 0.6633 | 0.3694 | | 0.3803 | 15.0 | 1680 | 0.6376 | 0.3920 | | 0.3415 | 16.0 | 1792 | 0.6463 | 0.3414 | | 0.3239 | 17.0 | 1904 | 0.5841 | 0.3197 | | 0.2946 | 18.0 | 2016 | 0.5948 | 0.3112 | | 0.2751 | 19.0 | 2128 | 0.5760 | 0.2905 | | 0.2834 | 20.0 | 2240 | 0.5884 | 0.2975 | | 0.2383 | 21.0 | 2352 | 0.5989 | 0.2775 | | 0.2265 | 22.0 | 2464 | 0.6151 | 0.2853 | | 0.2158 | 23.0 | 2576 | 0.5843 | 0.2670 | | 0.2015 | 24.0 | 2688 | 0.6621 | 0.2738 | | 0.215 | 25.0 | 2800 | 0.6068 | 0.2652 | | 0.1859 | 26.0 | 2912 | 0.6136 | 0.2570 | | 0.1745 | 27.0 | 3024 | 0.6191 | 0.2624 | | 0.1611 | 28.0 | 3136 | 0.6364 | 0.2578 | | 0.1513 | 29.0 | 3248 | 0.6402 | 0.2535 | | 0.172 | 30.0 | 3360 | 0.6330 | 0.2500 | | 0.1488 | 31.0 | 3472 | 0.6275 | 0.2521 | | 0.1371 | 32.0 | 3584 | 0.6539 | 0.2540 | | 0.1356 | 33.0 | 3696 | 0.6544 | 0.2491 | | 0.1319 | 34.0 | 3808 | 0.6545 | 0.2491 | | 0.1465 | 35.0 | 3920 | 0.6573 | 0.2495 | | 0.13 | 36.0 | 4032 | 0.6594 | 0.2494 | | 0.1244 | 37.0 | 4144 | 0.6651 | 0.2476 | | 0.1228 | 38.0 | 4256 | 0.6754 | 0.2497 | | 0.1181 | 39.0 | 4368 | 0.6684 | 0.2468 | | 0.1338 | 40.0 | 4480 | 0.6713 | 0.2471 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

MahmoudAbdullah99/wav2vec-speech-model

0296692bb61fa5ab33932fa969b74daad7fd5443

2022-06-26T17:22:45.000Z

automatic-speech-recognition

MahmoudAbdullah99

MahmoudAbdullah99/wav2vec-speech-model

transformers

mohsenfayyaz/bert-base-parsbert-uncased_pquad_1epoch

c1f5f4bfc2bc89e4e56c8c901b6ac9423a02d3d7

2022-06-24T12:32:52.000Z

question-answering

mohsenfayyaz

mohsenfayyaz/bert-base-parsbert-uncased_pquad_1epoch

transformers

Entry not found

gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease

54c7fb4bd091d853f2e755c12caf9e1b300fc6be

2022-06-28T11:19:09.000Z

image-classification

gianlab

gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-plantdisease results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder args: default metrics: - name: Accuracy type: accuracy value: 0.9689922480620154 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-plantdisease This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1032 - Accuracy: 0.9690 ## Model description This model was created by importing the dataset of the photos of diseased plants into Google Colab from kaggle here: https://www.kaggle.com/datasets/emmarex/plantdisease. I then used the image classification tutorial here: https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb The possible classified diseases are: Tomato Tomato YellowLeaf Curl Virus , Tomato Late blight , Pepper bell Bacterial spot, Tomato Early blight, Potato healthy, Tomato healthy , Tomato Target_Spot , Potato Early blight , Tomato Tomato mosaic virus, Pepper bell healthy, Potato Late blight, Tomato Septoria leaf spot , Tomato Leaf Mold , Tomato Spider mites Two spotted spider mite , Tomato Bacterial spot . ## 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: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1903 | 1.0 | 145 | 0.1032 | 0.9690 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

tlin123/DialoGPT-Bopy-Alpha-1.04

cc054ad7689739f9c167ecc34d951dc29f86b812

2022-06-24T18:02:04.000Z

text-generation

tlin123

tlin123/DialoGPT-Bopy-Alpha-1.04

transformers

Entry not found

jwuthri/distilbert-base-uncased-finetuned-imdb

4452d5ec38be3cea22669e67fd133f6930a587e3

2022-06-25T05:46:38.000Z

fill-mask

jwuthri

jwuthri/distilbert-base-uncased-finetuned-imdb

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb 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-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.3811 ## 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7046 | 1.0 | 157 | 2.4782 | | 2.5679 | 2.0 | 314 | 2.4108 | | 2.5028 | 3.0 | 471 | 2.4121 | | 2.4825 | 4.0 | 628 | 2.3589 | | 2.4593 | 5.0 | 785 | 2.4074 | | 2.4294 | 6.0 | 942 | 2.3742 | | 2.4258 | 7.0 | 1099 | 2.3706 | | 2.4152 | 8.0 | 1256 | 2.3315 | | 2.409 | 9.0 | 1413 | 2.3809 | | 2.3908 | 10.0 | 1570 | 2.3394 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

shuidun/test1

5fcb99df08e526f34254ea71491d25985488bbd4

2022-06-25T04:04:42.000Z

text-generation

shuidun

shuidun/test1

transformers

--- license: mit tags: - generated_from_trainer model-index: - name: test1 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. --> # test1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

imxly/t5-copy-med-qa

c737c7f3fb0321b6749f8e7a1269ae76a140e334

2022-06-25T14:10:38.000Z

text2text-generation

imxly

imxly/t5-copy-med-qa

transformers

Entry not found

gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v3

b5e33f8cf803a8176d6eb46ec43aba5dae1b4efb

2022-06-26T06:15:42.000Z

automatic-speech-recognition

gary109

gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v3

transformers

--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v3 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. --> # ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v3 This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v2](https://huggingface.co/gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v2) on the GARY109/AI_LIGHT_DANCE - ONSET-STEPMANIA2 dataset. It achieves the following results on the evaluation set: - Loss: 1.0734 - Wer: 0.6928 ## 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: 4e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.9189 | 1.0 | 188 | 1.0770 | 0.7002 | | 0.9172 | 2.0 | 376 | 1.0780 | 0.6955 | | 0.9177 | 3.0 | 564 | 1.0824 | 0.6916 | | 0.9184 | 4.0 | 752 | 1.0734 | 0.6928 | | 0.9072 | 5.0 | 940 | 1.0841 | 0.6897 | | 0.9089 | 6.0 | 1128 | 1.0788 | 0.6870 | | 0.9174 | 7.0 | 1316 | 1.0761 | 0.6856 | | 0.9072 | 8.0 | 1504 | 1.0776 | 0.6850 | | 0.9079 | 9.0 | 1692 | 1.0795 | 0.6852 | | 0.9016 | 10.0 | 1880 | 1.0817 | 0.6850 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

rpgz31/jibber

6fff0afb3fcd50332c7b9c01bda5cf687f7b9699

2022-06-25T18:00:33.000Z

text-generation

rpgz31

rpgz31/jibber

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - bittensor metrics: - accuracy model-index: - name: test-clm results: - task: name: Causal Language Modeling type: text-generation dataset: name: bittensor train-v1.1.json type: bittensor args: train-v1.1.json metrics: - name: Accuracy type: accuracy value: 0.13872832369942195 --- <!-- 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. --> # test-clm This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the bittensor train-v1.1.json dataset. It achieves the following results on the evaluation set: - Loss: 6.5199 - Accuracy: 0.1387 ## 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: 3.0 ### Training results ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

sohomghosh/LIPI_FinSim4_ESG_task2

9b1a74b855e1fb96a62cf542b05c7e3f08ff4090

2022-06-28T01:50:57.000Z

sohomghosh

sohomghosh/LIPI_FinSim4_ESG_task2

--- license: mit --- How to use ths model? Download the pytorch_model.bin file and execute the following: ```python import pandas as pd import torch import transformers from torch.utils.data import Dataset, DataLoader from transformers import RobertaModel, RobertaTokenizer, BertModel, BertTokenizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") MAX_LEN = 128 BATCH_SIZE = 20 text_col_name = 'sentence' category_col = 'label_text' #Input should be one dataframe having one column with header as 'sentence' : test_df (do reset_index() if needed) test_df = pd.DataFrame({"sentence":['We are striving to reduce the amount of waste we produce, and to reduce water as well as paper consumption.']}) def scoring_data_prep(dataset): out = [] target = [] mask = [] for i in range(len(dataset)): rec = dataset[i] out.append(rec['ids'].reshape(-1,MAX_LEN)) mask.append(rec['mask'].reshape(-1,MAX_LEN)) out_stack = torch.cat(out, dim = 0) mask_stack = torch.cat(mask, dim =0 ) out_stack = out_stack.to(device, dtype = torch.long) mask_stack = mask_stack.to(device, dtype = torch.long) return out_stack, mask_stack class Triage(Dataset): """ This is a subclass of torch packages Dataset class. It processes input to create ids, masks and targets required for model training. """ def __init__(self, dataframe, tokenizer, max_len, text_col_name): self.len = len(dataframe) self.data = dataframe self.tokenizer = tokenizer self.max_len = max_len self.text_col_name = text_col_name def __getitem__(self, index): title = str(self.data[self.text_col_name][index]) title = " ".join(title.split()) inputs = self.tokenizer.encode_plus( title, None, add_special_tokens=True, max_length=self.max_len, pad_to_max_length=True, return_token_type_ids=True, truncation=True, ) ids = inputs["input_ids"] mask = inputs["attention_mask"] return { "ids": torch.tensor(ids, dtype=torch.long), "mask": torch.tensor(mask, dtype=torch.long), } def __len__(self): return self.len class BERTClass(torch.nn.Module): def __init__(self, num_class): super(BERTClass, self).__init__() self.num_class = num_class self.l1 = RobertaModel.from_pretrained("roberta-base") self.pre_classifier = torch.nn.Linear(768, 768) self.dropout = torch.nn.Dropout(0.3) self.classifier = torch.nn.Linear(768, self.num_class) self.history = dict() def forward(self, input_ids, attention_mask): output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask) hidden_state = output_1[0] pooler = hidden_state[:, 0] pooler = self.pre_classifier(pooler) pooler = torch.nn.ReLU()(pooler) pooler = self.dropout(pooler) output = self.classifier(pooler) return output def do_predict(model, tokenizer, test_df): test_set = Triage(test_df, tokenizer, MAX_LEN, text_col_name) test_params = {'batch_size' : BATCH_SIZE, 'shuffle': False, 'num_workers':0} test_loader = DataLoader(test_set, **test_params) out_stack, mask_stack = scoring_data_prep(dataset = test_set) n = 0 combined_output = [] model.eval() with torch.no_grad(): while n < test_df.shape[0]: output = model(out_stack[n:n+BATCH_SIZE,:],mask_stack[n:n+BATCH_SIZE,:]) n = n + BATCH_SIZE combined_output.append(output) combined_output = torch.cat(combined_output, dim = 0) preds = torch.argsort(combined_output, axis = 1, descending = True) preds = preds.to('cpu') actual_predictions = [i[0] for i in preds.tolist()] return actual_predictions model_sustain = BERTClass(2) model_sustain.to(device) model_sustain.load_state_dict(torch.load('pytorch_model.bin', map_location=device)['model_state_dict']) tokenizer_sus = BertTokenizer.from_pretrained('roberta-base') actual_predictions_sus = do_predict(model_sustain, tokenizer_sus, test_df) test_df['sustainability'] = ['sustainable' if i==0 else 'unsustainable' for i in actual_predictions_read] ``` Our work can be cited as follows: ```bibtex @inproceedings{ghosh-2022-finsim-esg, title = "Ranking Environment, Social And Governance Related Concepts And Assessing Sustainability Aspect Of Financial Texts", author={Ghosh, Sohom and Naskar, Sudip Kumar}, booktitle = "Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP@IJCAI-ECAI 2022)", month = "July" , year = "2022", address = "Vienna, Austria", publisher = "-", url = "https://mx.nthu.edu.tw/~chungchichen/FinNLP2022_IJCAI/14.pdf", pages = "87--92", } ```

sohomghosh/finrad_model

146acc8a90b57c3b27524e00f28efb91b6f0aa14

2022-06-28T01:50:47.000Z

sohomghosh

sohomghosh/finrad_model

--- license: mit --- How to load the model and generate predictions? Download the pytorch_model.bin file and execute the following: ```python import pandas as pd import torch import transformers from torch.utils.data import Dataset, DataLoader from transformers import RobertaModel, RobertaTokenizer, BertModel, BertTokenizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") MAX_LEN = 128 BATCH_SIZE = 20 text_col_name = 'sentence' category_col = 'label_text' #Input should be one dataframe having one column with header as 'sentence' : test_df (do reset_index() if needed) test_df = pd.DataFrame({"sentence":['a general increase in prices and fall in the purchasing value of money.']}) def scoring_data_prep(dataset): out = [] target = [] mask = [] for i in range(len(dataset)): rec = dataset[i] out.append(rec['ids'].reshape(-1,MAX_LEN)) mask.append(rec['mask'].reshape(-1,MAX_LEN)) out_stack = torch.cat(out, dim = 0) mask_stack = torch.cat(mask, dim =0 ) out_stack = out_stack.to(device, dtype = torch.long) mask_stack = mask_stack.to(device, dtype = torch.long) return out_stack, mask_stack class Triage(Dataset): """ This is a subclass of torch packages Dataset class. It processes input to create ids, masks and targets required for model training. """ def __init__(self, dataframe, tokenizer, max_len, text_col_name): self.len = len(dataframe) self.data = dataframe self.tokenizer = tokenizer self.max_len = max_len self.text_col_name = text_col_name def __getitem__(self, index): title = str(self.data[self.text_col_name][index]) title = " ".join(title.split()) inputs = self.tokenizer.encode_plus( title, None, add_special_tokens=True, max_length=self.max_len, pad_to_max_length=True, return_token_type_ids=True, truncation=True, ) ids = inputs["input_ids"] mask = inputs["attention_mask"] return { "ids": torch.tensor(ids, dtype=torch.long), "mask": torch.tensor(mask, dtype=torch.long), } def __len__(self): return self.len class BERTClass(torch.nn.Module): def __init__(self, num_class): super(BERTClass, self).__init__() self.num_class = num_class self.l1 = BertModel.from_pretrained("ProsusAI/finbert") self.pre_classifier = torch.nn.Linear(768, 768) self.dropout = torch.nn.Dropout(0.3) self.classifier = torch.nn.Linear(768, self.num_class) self.history = dict() def forward(self, input_ids, attention_mask): output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask) hidden_state = output_1[0] pooler = hidden_state[:, 0] pooler = self.pre_classifier(pooler) pooler = torch.nn.ReLU()(pooler) pooler = self.dropout(pooler) output = self.classifier(pooler) return output def do_predict(model, tokenizer, test_df): test_set = Triage(test_df, tokenizer, MAX_LEN, text_col_name) test_params = {'batch_size' : BATCH_SIZE, 'shuffle': False, 'num_workers':0} test_loader = DataLoader(test_set, **test_params) out_stack, mask_stack = scoring_data_prep(dataset = test_set) n = 0 combined_output = [] model.eval() with torch.no_grad(): while n < test_df.shape[0]: output = model(out_stack[n:n+BATCH_SIZE,:],mask_stack[n:n+BATCH_SIZE,:]) n = n + BATCH_SIZE combined_output.append(output) combined_output = torch.cat(combined_output, dim = 0) preds = torch.argsort(combined_output, axis = 1, descending = True) preds = preds.to('cpu') actual_predictions = [i[0] for i in preds.tolist()] return actual_predictions model_read = BERTClass(2) model_read.to(device) model_read.load_stat_dict(torch.load('pytorch_model.bin', map_location=device)['model_state_dict']) tokenizer_read = BertTokenizer.from_pretrained('ProsusAI/finbert') actual_predictions_read = do_predict(model_read, tokenizer_read, test_df) test_df['readability'] = ['readable' if i==1 else 'not_reabale' for i in actual_predictions_read] ``` ```bibtex @InProceedings{ghosh-EtAl:2022:FNP, author = {Ghosh, Sohom and Sengupta, Shovon and Naskar, Sudip and Singh, Sunny Kumar}, title = {FinRAD: Financial Readability Assessment Dataset - 13,000+ Definitions of Financial Terms for Measuring Readability}, booktitle = {Proceedings of the The 4th Financial Narrative Processing Workshop @LREC2022}, month = {June}, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {1--9}, url = {http://www.lrec-conf.org/proceedings/lrec2022/workshops/FNP/pdf/2022.fnp-1.1.pdf} } ``` ```bibtex @InProceedings{ghosh-2021-finread, title = "FinRead: A Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms", author = "Sohom Ghosh, Shovon Sengupta, Sudip Kumar Naskar, Sunny Kumar Singh", booktitle = "Proceedings of the 18th International Conference on Natural Language Processing (ICON) : System Demonstrations", month = "dec", year = "2021", publisher = "NLP Association of India (NLPAI)", url = "forthcoming", intype = {to appear in}, pre-print = "https://easychair.org/publications/preprint/1wvS" } ```

mbshr/urt5-base-init

f894fa5765485d36eaf43d9d4762e2b2bcf2e76f

2022-06-26T15:23:51.000Z

text2text-generation

mbshr

mbshr/urt5-base-init

transformers

Entry not found

Rami/qa-adhd

3813c708ff1ae7a75a884178e59d18d43b300554

2022-06-29T01:41:36.000Z

Rami

Rami/qa-adhd

--- license: mit widget: - text: "Jens Peter Hansen kommer fra Danmark" ---

zyxzyx/autotrain-sum-1042335811

9c0ce350fb3876d5b0f60f566c48eea5979179c2

2022-06-27T05:15:17.000Z

text2text-generation

zyxzyx

zyxzyx/autotrain-sum-1042335811

transformers

--- tags: autotrain language: zh widget: - text: "I love AutoTrain 🤗" datasets: - zyxzyx/autotrain-data-sum co2_eq_emissions: 426.15271368095927 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 1042335811 - CO2 Emissions (in grams): 426.15271368095927 ## Validation Metrics - Loss: 1.7748287916183472 - Rouge1: 0.536 - Rouge2: 0.0 - RougeL: 0.536 - RougeLsum: 0.536 - Gen Len: 10.9089 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/zyxzyx/autotrain-sum-1042335811 ```

hamziqureshi/t5-small-finetuned-amazon-en-es

6877badae712f3ad040f21ff996f53bddee86046

2022-06-27T13:49:14.000Z

text2text-generation

hamziqureshi

hamziqureshi/t5-small-finetuned-amazon-en-es

transformers

Entry not found

nizamudma/bart_cnn_auto

75d4d23638243fb698d615530073e60568b4b414

2022-06-29T14:15:25.000Z

text2text-generation

nizamudma

nizamudma/bart_cnn_auto

transformers

huggingtweets/reallifemera

a4383d977e54e77272c3d455fbae2d4660768526

2022-06-29T04:14:29.000Z

text-generation

huggingtweets

huggingtweets/reallifemera

transformers

--- language: en thumbnail: http://www.huggingtweets.com/reallifemera/1656476064337/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/1525581631020576771/qgSl4j4O_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Mera Brown</div> <div style="text-align: center; font-size: 14px;">@reallifemera</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.  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 Mera Brown. | Data | Mera Brown | | --- | --- | | Tweets downloaded | 944 | | Retweets | 22 | | Short tweets | 98 | | Tweets kept | 824 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wqhoe3wp/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 @reallifemera's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/nuhzlovs) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/nuhzlovs/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/reallifemera') 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* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

sumitrsch/muril_base_multiconer22_hi

8c0ee6ab8e2caacd78ebed3a909219151c813470

2022-07-06T12:27:42.000Z

token-classification

sumitrsch

sumitrsch/muril_base_multiconer22_hi

transformers

--- license: afl-3.0 --- Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scrollTo=nYtUtmyDFAqP

huggingtweets/gregorian000-levelsio

63b772d337386a52ad818c32d74be165d2595064

2022-06-28T13:11:29.000Z

text-generation

huggingtweets

huggingtweets/gregorian000-levelsio

transformers

--- 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/1501241215433510919/4GctQi3o_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/1441044961957343232/Sl1U4tSw_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#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">David ⚡ & @levelsio</div> <div style="text-align: center; font-size: 14px;">@gregorian000-levelsio</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.  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 David ⚡ & @levelsio. | Data | David ⚡ | @levelsio | | --- | --- | --- | | Tweets downloaded | 95 | 3250 | | Retweets | 22 | 176 | | Short tweets | 9 | 556 | | Tweets kept | 64 | 2518 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ozvo6hl5/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 @gregorian000-levelsio's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1emg780i) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1emg780i/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/gregorian000-levelsio') 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* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

huggingtweets/g__j

d44ecf727955f45f4ea508c0a5fe140e5d58d2b5

2022-06-28T13:36:16.000Z

text-generation

huggingtweets

huggingtweets/g__j

transformers

--- 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/959389610978742273/jfOMGQ1B_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Greg Jackson</div> <div style="text-align: center; font-size: 14px;">@g__j</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.  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 Greg Jackson. | Data | Greg Jackson | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 187 | | Short tweets | 179 | | Tweets kept | 2884 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2sl53oes/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 @g__j's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/stwh74do) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/stwh74do/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/g__j') 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* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

moonzi/distilbert-base-uncased-finetuned-imdb

fed7092526179140bf68df13ae2cb3603eb72203

2022-06-28T13:46:11.000Z

fill-mask

moonzi

moonzi/distilbert-base-uncased-finetuned-imdb

transformers

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb 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-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4702 ## 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6898 | 1.0 | 157 | 2.5423 | | 2.5746 | 2.0 | 314 | 2.4453 | | 2.5548 | 3.0 | 471 | 2.4528 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

rishiyoung/xlm-roberta-base-finetuned-panx-de

082a2163e3179c5a2728bfb4bfde6fc39cc8e82c

2022-06-28T20:49:34.000Z

token-classification

rishiyoung

rishiyoung/xlm-roberta-base-finetuned-panx-de

transformers

--- 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 args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8620945214069894 --- <!-- 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: 0.1372 - F1: 0.8621 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 | | 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 | | 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

jdang/dummy-model

325184a0037376ed224b886ee6eb70d6f63596d5

2022-06-29T00:30:36.000Z

fill-mask

jdang

jdang/dummy-model

transformers

--- language: en tags: - exbert license: apache-2.0 datasets: - bookcorpus - wikipedia --- # DistilBERT base model (dummy test) This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation). This model is uncased: it does not make a difference between english and English. ## Model description DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained with three objectives: - Distillation loss: the model was trained to return the same probabilities as the BERT base model. - Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base model. This way, the model learns the same inner representation of the English language than its teacher model, while being faster for inference or downstream tasks. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.05292855575680733, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.03968575969338417, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a business model. [SEP]", 'score': 0.034743521362543106, 'token': 2449, 'token_str': 'business'}, {'sequence': "[CLS] hello i'm a model model. [SEP]", 'score': 0.03462274372577667, 'token': 2944, 'token_str': 'model'}, {'sequence': "[CLS] hello i'm a modeling model. [SEP]", 'score': 0.018145186826586723, 'token': 11643, 'token_str': 'modeling'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import DistilBertTokenizer, DistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = DistilBertModel.from_pretrained("distilbert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import DistilBertTokenizer, TFDistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = TFDistilBertModel.from_pretrained("distilbert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. It also inherits some of [the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') >>> unmasker("The White man worked as a [MASK].") [{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]', 'score': 0.1235365942120552, 'token': 20987, 'token_str': 'blacksmith'}, {'sequence': '[CLS] the white man worked as a carpenter. [SEP]', 'score': 0.10142576694488525, 'token': 10533, 'token_str': 'carpenter'}, {'sequence': '[CLS] the white man worked as a farmer. [SEP]', 'score': 0.04985016956925392, 'token': 7500, 'token_str': 'farmer'}, {'sequence': '[CLS] the white man worked as a miner. [SEP]', 'score': 0.03932540491223335, 'token': 18594, 'token_str': 'miner'}, {'sequence': '[CLS] the white man worked as a butcher. [SEP]', 'score': 0.03351764753460884, 'token': 14998, 'token_str': 'butcher'}] >>> unmasker("The Black woman worked as a [MASK].") [{'sequence': '[CLS] the black woman worked as a waitress. [SEP]', 'score': 0.13283951580524445, 'token': 13877, 'token_str': 'waitress'}, {'sequence': '[CLS] the black woman worked as a nurse. [SEP]', 'score': 0.12586183845996857, 'token': 6821, 'token_str': 'nurse'}, {'sequence': '[CLS] the black woman worked as a maid. [SEP]', 'score': 0.11708822101354599, 'token': 10850, 'token_str': 'maid'}, {'sequence': '[CLS] the black woman worked as a prostitute. [SEP]', 'score': 0.11499975621700287, 'token': 19215, 'token_str': 'prostitute'}, {'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]', 'score': 0.04722772538661957, 'token': 22583, 'token_str': 'housekeeper'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 16 GB V100 for 90 hours. See the [training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters details. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| | | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 | ### BibTeX entry and citation info ```bibtex @article{Sanh2019DistilBERTAD, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, journal={ArXiv}, year={2019}, volume={abs/1910.01108} } ``` <a href="https://huggingface.co/exbert/?model=distilbert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

TinFernandez/dummy

c75d869ea39e8703d8ff7f9b2876e6ae1a048b95

2022-07-04T10:20:59.000Z

TinFernandez

TinFernandez/dummy

--- language: en tags: - exbert license: apache-2.0 datasets: - bookcorpus - wikipedia --- # BERT base model (uncased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.1073106899857521, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.08774490654468536, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a new model. [SEP]", 'score': 0.05338378623127937, 'token': 2047, 'token_str': 'new'}, {'sequence': "[CLS] hello i'm a super model. [SEP]", 'score': 0.04667217284440994, 'token': 3565, 'token_str': 'super'}, {'sequence': "[CLS] hello i'm a fine model. [SEP]", 'score': 0.027095865458250046, 'token': 2986, 'token_str': 'fine'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained("bert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = TFBertModel.from_pretrained("bert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-uncased') >>> unmasker("The man worked as a [MASK].") [{'sequence': '[CLS] the man worked as a carpenter. [SEP]', 'score': 0.09747550636529922, 'token': 10533, 'token_str': 'carpenter'}, {'sequence': '[CLS] the man worked as a waiter. [SEP]', 'score': 0.0523831807076931, 'token': 15610, 'token_str': 'waiter'}, {'sequence': '[CLS] the man worked as a barber. [SEP]', 'score': 0.04962705448269844, 'token': 13362, 'token_str': 'barber'}, {'sequence': '[CLS] the man worked as a mechanic. [SEP]', 'score': 0.03788609802722931, 'token': 15893, 'token_str': 'mechanic'}, {'sequence': '[CLS] the man worked as a salesman. [SEP]', 'score': 0.037680890411138535, 'token': 18968, 'token_str': 'salesman'}] >>> unmasker("The woman worked as a [MASK].") [{'sequence': '[CLS] the woman worked as a nurse. [SEP]', 'score': 0.21981462836265564, 'token': 6821, 'token_str': 'nurse'}, {'sequence': '[CLS] the woman worked as a waitress. [SEP]', 'score': 0.1597415804862976, 'token': 13877, 'token_str': 'waitress'}, {'sequence': '[CLS] the woman worked as a maid. [SEP]', 'score': 0.1154729500412941, 'token': 10850, 'token_str': 'maid'}, {'sequence': '[CLS] the woman worked as a prostitute. [SEP]', 'score': 0.037968918681144714, 'token': 19215, 'token_str': 'prostitute'}, {'sequence': '[CLS] the woman worked as a cook. [SEP]', 'score': 0.03042375110089779, 'token': 5660, 'token_str': 'cook'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average | |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:| | | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 | ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=bert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

jinjinjin/SLRelV7_TriBert

8c54df725b64050f9f39c7611c711789f39bfdbf

2022-07-21T08:19:35.000Z

jinjinjin

jinjinjin/SLRelV7_TriBert

Entry not found

harunkuf/mlsum_tr_en_mt5-small

f30e20af9d83a6012e54050a1c3af57262e7bcc4

2022-06-29T15:50:56.000Z

text2text-generation

harunkuf

harunkuf/mlsum_tr_en_mt5-small

transformers

# Multilingual mT5 model trained with MLSUM_TR and MLSUM_CNN (EN) ## Results: MLSUM_TR: * Rouge-1: 45.11 * Rouge-2: 30.96 * Rouge-L: 39.23 MLSUM_CNN: * Rouge-1: 39.65 * Rouge-2: 17.49 * Rouge-L: 27.66 Note: Huggingface Inference API truncates the results, which results in unfinished sentences when making a prediction. You can try the model in Colab: https://colab.research.google.com/drive/1QDWO3RHjjP1nS8bIvhT38B3fVIBC3TaK?usp=sharing

smeoni/apericube

bbafbad4a3e50bf8d4119b3c17f843f16574e238

2022-06-29T08:54:45.000Z

feature-extraction

smeoni

smeoni/apericube

transformers

Entry not found

anahitapld/xlnet-base-dbd

7898795cbdff1aa7cac829025ecdbd8b6ca83e46

2022-06-29T09:01:34.000Z

anahitapld

anahitapld/xlnet-base-dbd

--- license: apache-2.0 ---

SivilTaram/poet-sql-digit-finetuned-drop

81599d649cee37e8f7acf2716415b02043ed3444

2022-06-29T09:13:56.000Z

SivilTaram

SivilTaram/poet-sql-digit-finetuned-drop

--- license: mit ---

SivilTaram/poet-math-digit

4a62650e655dbcdd33bcb41f0ec421e5411cfddc

2022-06-29T09:11:06.000Z

SivilTaram

SivilTaram/poet-math-digit

--- license: mit ---

radi-cho/poetry-bg

45ca1cf2cd3b2637984aadb961003a6f3db406f0

2022-07-04T08:33:38.000Z

text-generation

radi-cho

radi-cho/poetry-bg

transformers

--- license: apache-2.0 language: - bg datasets: - chitanka tags: - torch inference: false --- # Bulgarian language poetry generation Pretrained model using causal language modeling (CLM) objective based on [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). <br/> Developed by [Radostin Cholakov](https://www.linkedin.com/in/radostin-cholakov-bb4422146/) as a part of the [AzBuki.ML](https://azbuki-ml.com) initiatives. # How to use? ```python >>> from transformers import AutoModel, AutoTokenizer >>> >>> model_id = "radi-cho/poetry-bg" >>> tokenizer = AutoTokenizer.from_pretrained(model_id) >>> model = AutoModel.from_pretrained(model_id, trust_remote_code=True) >>> >>> input_ids = tokenizer.encode( >>> "[HED]Суетата на живота[NEL][BDY]", >>> add_special_tokens=False, >>> return_tensors='pt') >>> >>> output_ids = model.generate( >>> input_ids, >>> do_sample=True, >>> max_length=250, >>> top_p=0.98, >>> top_k=0, >>> pad_token_id=2, >>> eos_token_id=50258) >>> >>> output = tokenizer.decode(output_ids[0]) >>> >>> output = output.replace('[NEL]', '\n') >>> output = output.replace('[BDY]', '\n') >>> output = output.replace('[HED]', '') >>> output = output.replace('[SEP]', '') >>> >>> print(output) Суетата на живота Да страдам ли? Да страдам ли за това? Не, не за това, че умирам... Но само за това, че миговете ми са рани. Аз съм сам и търся утеха. ``` # Custom Tokens We introduced 3 custom tokens in the tokenizer - `[NEL]`, `[BDY]`, `[HED]` - `[HED]` denotes where the title of the poem begins; - `[BDY]` denotes where the body of the poem begins; - `[NEL]` marks the end of a verse and should be decoded as a new line; `[SEP]` (with id 50258) is the *end of sequence* token. # Credits - Inspired by [rmihaylov/gpt2-medium-bg](https://huggingface.co/rmihaylov/gpt2-medium-bg). - Data: [https://chitanka.info/texts/type/poetry](https://chitanka.info/texts/type/poetry);

k3nneth/xlm-roberta-base-finetuned-panx-de

2967e35065b5c167174067a5ed56ebc123c17075

2022-06-29T16:50:45.000Z

token-classification

k3nneth

k3nneth/xlm-roberta-base-finetuned-panx-de

transformers

--- 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 args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8627004891366169 --- <!-- 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: 0.1363 - F1: 0.8627 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2539 | 1.0 | 525 | 0.1697 | 0.8179 | | 0.1317 | 2.0 | 1050 | 0.1327 | 0.8516 | | 0.0819 | 3.0 | 1575 | 0.1363 | 0.8627 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

k3nneth/xlm-roberta-base-finetuned-panx-de-fr

0aa87ee57fb6d5788d2d84eb85c3c3aa62df9f5b

2022-06-29T17:16:43.000Z

token-classification

k3nneth

k3nneth/xlm-roberta-base-finetuned-panx-de-fr

transformers

--- 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: 0.1644 - F1: 0.8617 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2891 | 1.0 | 715 | 0.1780 | 0.8288 | | 0.1471 | 2.0 | 1430 | 0.1627 | 0.8509 | | 0.0947 | 3.0 | 2145 | 0.1644 | 0.8617 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

mystery/DialoGPT-small-pinkiepie

8a1ad39bf2a097d7a9e07aecbef5f17fb2ff796c

2022-06-29T17:45:08.000Z

conversational

mystery

mystery/DialoGPT-small-pinkiepie

transformers

SivilTaram/poet-sql-finetuned-hotpotqa

68dfe85eb0497f4939b73087730fb2ae03f2568d

2022-06-30T08:12:43.000Z

SivilTaram

SivilTaram/poet-sql-finetuned-hotpotqa

--- license: mit ---

SivilTaram/tapex-t5-base-lm-adapt

4ee3013721a66137aea5f1c47c3a1b19f2357a1f

2022-06-30T08:16:24.000Z

SivilTaram

SivilTaram/tapex-t5-base-lm-adapt

--- license: mit ---

imxly/ernie-health

8b94e338a5b968289fa2621c91fb47a20865d072

2022-06-30T10:32:57.000Z

feature-extraction

imxly

imxly/ernie-health

transformers

Entry not found

fujiki/gpt-neo-en2ja-1b

48d86813e0b5a2cc546c49bae6fc61067dba94a8

2022-06-30T09:46:07.000Z

text-generation

fujiki

fujiki/gpt-neo-en2ja-1b

transformers

--- license: afl-3.0 ---

huggingtweets/lewisnwatson

70d9feb37e0d8a6c8fcddfa2566a1642a798b330

2022-06-30T20:54:25.000Z

text-generation

huggingtweets

huggingtweets/lewisnwatson

transformers

--- language: en thumbnail: http://www.huggingtweets.com/lewisnwatson/1656622460314/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/1509825675821301790/FCFan5I-_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Lewis N Watson 🇺🇦</div> <div style="text-align: center; font-size: 14px;">@lewisnwatson</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.  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 Lewis N Watson 🇺🇦. | Data | Lewis N Watson 🇺🇦 | | --- | --- | | Tweets downloaded | 1711 | | Retweets | 797 | | Short tweets | 211 | | Tweets kept | 703 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/171yd33i/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 @lewisnwatson's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zds7e037) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zds7e037/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/lewisnwatson') 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* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

omunkhuush/roberta-base-ner-demo

d479cc32372e62dffdbd532f18bde136e0ce290e

2022-07-01T04:00:33.000Z

token-classification

omunkhuush

omunkhuush/roberta-base-ner-demo

transformers

Entry not found

ganzorig/roberta-base-ner-demo

527b79ae1eeb562d674fa83dfd5df5b0a46f47d5

2022-07-01T04:14:14.000Z

token-classification

ganzorig

ganzorig/roberta-base-ner-demo

transformers

Entry not found

openclimatefix/graph-weather-forecaster-2.0deg-mini

8cb62b0a9b21fa4d4131920426e26346e6d2cf8d

2022-07-01T10:46:01.000Z

openclimatefix

openclimatefix/graph-weather-forecaster-2.0deg-mini

Entry not found

openclimatefix/graph-weather-forecaster-0.5deg-nolandsea-large

c23bc6b47fc58cb496b97d44befaca51e30097ef

2022-07-01T13:09:13.000Z

openclimatefix

openclimatefix/graph-weather-forecaster-0.5deg-nolandsea-large

Entry not found

huggingtweets/lexisother

c6f1dff7d2101eafc1d1761398716675f8fde973

2022-07-01T18:02:49.000Z

text-generation

huggingtweets

huggingtweets/lexisother

transformers

--- language: en thumbnail: http://www.huggingtweets.com/lexisother/1656698565003/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/1226468832933564418/oZJzrVUq_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Alyxia Sother </div> <div style="text-align: center; font-size: 14px;">@lexisother</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.  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 Alyxia Sother . | Data | Alyxia Sother  | | --- | --- | | Tweets downloaded | 601 | | Retweets | 269 | | Short tweets | 91 | | Tweets kept | 241 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hcphqun/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 @lexisother's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3759svle) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3759svle/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/lexisother') 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* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)