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davanstrien
/
pytorchmodelmetadata

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AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_10
2d93e2d26d7f14d9817c11f74606246a9e934ec7
2022-01-05T10:16:39.000Z
[ "pytorch", "roberta", "feature-extraction", "transformers" ]
feature-extraction
false
AnonymousSub
null
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_10
2
null
transformers
22,900
Entry not found
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1_squad2.0
88e879a2ec8e5602e7f5601410978ff872560e68
2022-01-18T03:52:44.000Z
[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
AnonymousSub
null
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1_squad2.0
2
null
transformers
22,901
Entry not found
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10
d20c39830c2363e094f52c54f464683906bbd4fb
2022-01-05T10:19:27.000Z
[ "pytorch", "roberta", "feature-extraction", "transformers" ]
feature-extraction
false
AnonymousSub
null
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10
2
null
transformers
22,902
Entry not found
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1
261cc9bba50dc4aa9c42ba227f696952e7c313d2
2022-01-10T21:07:58.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
AnonymousSub
null
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1
2
null
transformers
22,903
Entry not found
AnonymousSub/specter-bert-model
210d51a593982803cc10a9a3d78a519b4cb6adc4
2021-11-05T10:29:02.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
AnonymousSub
null
AnonymousSub/specter-bert-model
2
null
transformers
22,904
Entry not found
AnonymousSub/unsup-consert-base
48cba7d663ae810c6a810d3b405636102985f4de
2021-09-04T17:44:20.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
AnonymousSub
null
AnonymousSub/unsup-consert-base
2
null
transformers
22,905
Entry not found
AnonymousSub/unsup-consert-base_copy
e148848aec7136eb92fcbed849054f3bbe63757a
2022-01-23T04:50:37.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
AnonymousSub
null
AnonymousSub/unsup-consert-base_copy
2
null
transformers
22,906
Entry not found
AnonymousSub/unsup-consert-base_squad2.0
8f7dd7ce2d00166d063001f5c8aa363a367e261b
2022-01-17T17:35:27.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
AnonymousSub
null
AnonymousSub/unsup-consert-base_squad2.0
2
null
transformers
22,907
Entry not found
AnonymousSub/unsup-consert-papers
abc8a65a542e05dbdc730cf69dca08c3d441ef11
2021-10-25T00:23:51.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
AnonymousSub
null
AnonymousSub/unsup-consert-papers
2
null
transformers
22,908
Entry not found
Anorak/nirvana
430dc9dcd47990c1c96d38f25895e2f7198035bf
2021-10-17T15:48:15.000Z
[ "pytorch", "pegasus", "text2text-generation", "unk", "dataset:Anorak/autonlp-data-Niravana-test2", "transformers", "autonlp", "co2_eq_emissions", "autotrain_compatible" ]
text2text-generation
false
Anorak
null
Anorak/nirvana
2
null
transformers
22,909
--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - Anorak/autonlp-data-Niravana-test2 co2_eq_emissions: 4.214012748213151 --- # Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 20384195 - CO2 Emissions (in grams): 4.214012748213151 ## Validation Metrics - Loss: 1.0120062828063965 - Rouge1: 41.1808 - Rouge2: 26.2564 - RougeL: 31.3106 - RougeLsum: 38.9991 - Gen Len: 58.45 ## 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 AutoNLP"}' https://api-inference.huggingface.co/Anorak/autonlp-Niravana-test2-20384195 ```
AnthonyNelson/DialoGPT-small-ricksanchez
f6757b250cab82699ada672cff0a3572ad366152
2021-09-05T22:27:52.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
AnthonyNelson
null
AnthonyNelson/DialoGPT-small-ricksanchez
2
null
transformers
22,910
--- tags: - conversational --- # Rick Sanchez DialoGPT Model
Apisate/DialoGPT-small-jordan
cbb91cb83b5a48e3d4aa8f96c7eb3b5058821418
2021-12-05T18:13:17.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Apisate
null
Apisate/DialoGPT-small-jordan
2
null
transformers
22,911
--- tags: - conversational --- # Jordan DialoGPT Model
ArBert/albert-base-v2-finetuned-ner-gmm-twitter
bdda2ec79b3fd4f0fc70b89814e36645b1caece0
2022-02-12T09:26:34.000Z
[ "pytorch", "tensorboard", "albert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
ArBert
null
ArBert/albert-base-v2-finetuned-ner-gmm-twitter
2
null
transformers
22,912
Entry not found
ArBert/roberta-base-finetuned-ner-kmeans-twitter
eede78cacf892879f008da686402d81d549d1669
2022-02-12T12:53:00.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
token-classification
false
ArBert
null
ArBert/roberta-base-finetuned-ner-kmeans-twitter
2
null
transformers
22,913
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: roberta-base-finetuned-ner-kmeans-twitter results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-finetuned-ner-kmeans-twitter This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggingface.co/ArBert/roberta-base-finetuned-ner) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6645 - Precision: 0.6885 - Recall: 0.7665 - F1: 0.7254 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | No log | 1.0 | 245 | 0.2820 | 0.6027 | 0.7543 | 0.6700 | | No log | 2.0 | 490 | 0.2744 | 0.6308 | 0.7864 | 0.7000 | | 0.2301 | 3.0 | 735 | 0.2788 | 0.6433 | 0.7637 | 0.6984 | | 0.2301 | 4.0 | 980 | 0.3255 | 0.6834 | 0.7221 | 0.7022 | | 0.1153 | 5.0 | 1225 | 0.3453 | 0.6686 | 0.7439 | 0.7043 | | 0.1153 | 6.0 | 1470 | 0.3988 | 0.6797 | 0.7420 | 0.7094 | | 0.0617 | 7.0 | 1715 | 0.4711 | 0.6702 | 0.7259 | 0.6969 | | 0.0617 | 8.0 | 1960 | 0.4904 | 0.6904 | 0.7505 | 0.7192 | | 0.0328 | 9.0 | 2205 | 0.5088 | 0.6591 | 0.7713 | 0.7108 | | 0.0328 | 10.0 | 2450 | 0.5709 | 0.6468 | 0.7788 | 0.7067 | | 0.019 | 11.0 | 2695 | 0.5570 | 0.6642 | 0.7533 | 0.7059 | | 0.019 | 12.0 | 2940 | 0.5574 | 0.6899 | 0.7656 | 0.7258 | | 0.0131 | 13.0 | 3185 | 0.5858 | 0.6952 | 0.7609 | 0.7265 | | 0.0131 | 14.0 | 3430 | 0.6239 | 0.6556 | 0.7826 | 0.7135 | | 0.0074 | 15.0 | 3675 | 0.5931 | 0.6825 | 0.7599 | 0.7191 | | 0.0074 | 16.0 | 3920 | 0.6364 | 0.6785 | 0.7580 | 0.7161 | | 0.005 | 17.0 | 4165 | 0.6437 | 0.6855 | 0.7580 | 0.7199 | | 0.005 | 18.0 | 4410 | 0.6610 | 0.6779 | 0.7599 | 0.7166 | | 0.0029 | 19.0 | 4655 | 0.6625 | 0.6853 | 0.7656 | 0.7232 | | 0.0029 | 20.0 | 4900 | 0.6645 | 0.6885 | 0.7665 | 0.7254 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
ArBert/roberta-base-finetuned-ner
06ce81bfc506df27fd20ddb7fa8561a2ce34402f
2022-02-03T16:42:50.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
token-classification
false
ArBert
null
ArBert/roberta-base-finetuned-ner
2
null
transformers
22,914
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-base-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-finetuned-ner This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0738 - Precision: 0.9232 - Recall: 0.9437 - F1: 0.9333 - Accuracy: 0.9825 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1397 | 1.0 | 1368 | 0.0957 | 0.9141 | 0.9048 | 0.9094 | 0.9753 | | 0.0793 | 2.0 | 2736 | 0.0728 | 0.9274 | 0.9324 | 0.9299 | 0.9811 | | 0.0499 | 3.0 | 4104 | 0.0738 | 0.9232 | 0.9437 | 0.9333 | 0.9825 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
Aran/DialoGPT-medium-harrypotter
fca320589a088c982735b8e0d93521a9ee9320a0
2021-11-21T19:35:20.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Aran
null
Aran/DialoGPT-medium-harrypotter
2
null
transformers
22,915
--- tags: - conversational --- # Harry Potter DialoGPT Model
Arcktosh/DialoGPT-small-rick
16228b8755ccfcbd93591fae74ae59764d182867
2021-09-03T19:05:23.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Arcktosh
null
Arcktosh/DialoGPT-small-rick
2
null
transformers
22,916
--- tags: - conversational --- # Rick DialoGPT Model
Ateeb/QA
00974c5ac9f8ba56d1368747c21d64ba5084b545
2021-05-03T11:41:12.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
Ateeb
null
Ateeb/QA
2
null
transformers
22,917
Entry not found
Augustvember/wokka5
9172900f98de94103f79dd3df8050b48d3bf5bbe
2021-08-08T16:47:14.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Augustvember
null
Augustvember/wokka5
2
null
transformers
22,918
--- tags: - conversational --- #MyAwesomeModel
AvatarXD/DialoGPT-medium-Blitzo
5d3d5f6a3f90a06bd468e0eb03dbf042a4bd55f1
2021-09-23T23:59:54.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
AvatarXD
null
AvatarXD/DialoGPT-medium-Blitzo
2
null
transformers
22,919
--- tags: - conversational --- # Blitzo DialoGPT Model
Awsaf/large-eren
43eca0d3e80d39480dd9b4983d676627de15a986
2021-09-21T14:38:33.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Awsaf
null
Awsaf/large-eren
2
null
transformers
22,920
--- tags: - conversational --- # Eren Yeager Model
Aybars/ModelOnTquad
363b905632e8c3c07d0e1b684dcfd9cca80679e6
2022-02-17T06:52:42.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
Aybars
null
Aybars/ModelOnTquad
2
null
transformers
22,921
Entry not found
AyushPJ/ai-club-inductions-21-nlp-distilBERT
467847f143b2680f92edcc9650f40e4de750de0e
2021-10-20T23:38:45.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
AyushPJ
null
AyushPJ/ai-club-inductions-21-nlp-distilBERT
2
null
transformers
22,922
--- tags: - generated_from_trainer model-index: - name: ai-club-inductions-21-nlp-distilBERT 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-club-inductions-21-nlp-distilBERT This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cu110 - Datasets 1.14.0 - Tokenizers 0.10.3
AyushPJ/ai-club-inductions-21-nlp-roBERTa
813707accc930a7b5b9422e39a0c2f6e789fde0e
2021-10-20T22:33:57.000Z
[ "pytorch", "roberta", "question-answering", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
AyushPJ
null
AyushPJ/ai-club-inductions-21-nlp-roBERTa
2
null
transformers
22,923
--- tags: - generated_from_trainer model-index: - name: ai-club-inductions-21-nlp-roBERTa 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-club-inductions-21-nlp-roBERTa This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.11.3 - Pytorch 1.7.1+cpu - Datasets 1.14.0 - Tokenizers 0.10.3
BSen/wav2vec2-base-timit-demo-colab
7b9e60d0b3c8c9dc0c7b6d6fd447f2987691e7d3
2021-12-02T07:51:26.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
BSen
null
BSen/wav2vec2-base-timit-demo-colab
2
null
transformers
22,924
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-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-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4877 - Wer: 0.4895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6615 | 4.0 | 500 | 1.7423 | 1.0723 | | 0.8519 | 8.0 | 1000 | 0.4877 | 0.4895 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
Baybars/wav2vec2-xls-r-300m-cv8-turkish
2362365a60811ed6740ec7702b2a28aca8914715
2022-03-23T18:34:22.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "tr", "dataset:common_voice", "transformers", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Baybars
null
Baybars/wav2vec2-xls-r-300m-cv8-turkish
2
0
transformers
22,925
--- language: - tr license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer - hf-asr-leaderboard - robust-speech-event - tr datasets: - common_voice model-index: - name: '' 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. --> # 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 - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.4164 - Wer: 0.3098 - Cer: 0.0764 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Language Model N-gram language model is trained by [mpoyraz](https://huggingface.co/mpoyraz/wav2vec2-xls-r-300m-cv7-turkish) on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 0.6356 | 9.09 | 500 | 0.5055 | 0.5536 | 0.1381 | | 0.3847 | 18.18 | 1000 | 0.4002 | 0.4247 | 0.1065 | | 0.3377 | 27.27 | 1500 | 0.4193 | 0.4167 | 0.1078 | | 0.2175 | 36.36 | 2000 | 0.4351 | 0.3861 | 0.0974 | | 0.2074 | 45.45 | 2500 | 0.3962 | 0.3622 | 0.0916 | | 0.159 | 54.55 | 3000 | 0.4062 | 0.3526 | 0.0888 | | 0.1882 | 63.64 | 3500 | 0.3991 | 0.3445 | 0.0850 | | 0.1766 | 72.73 | 4000 | 0.4214 | 0.3396 | 0.0847 | | 0.116 | 81.82 | 4500 | 0.4182 | 0.3265 | 0.0812 | | 0.0718 | 90.91 | 5000 | 0.4259 | 0.3191 | 0.0781 | | 0.019 | 100.0 | 5500 | 0.4164 | 0.3098 | 0.0764 | ## Evaluation Commands Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing. 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python eval.py --model_id Baybars/wav2vec2-xls-r-300m-cv8-turkish --dataset mozilla-foundation/common_voice_8_0 --config tr --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id Baybars/wav2vec2-xls-r-300m-cv8-turkish --dataset speech-recognition-community-v2/dev_data --config tr --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
Benicio/t5-small-finetuned-en-to-ru
6b0c1f6d967cbd4066a7c5edb93619c1bad5234d
2021-11-28T14:16:01.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Benicio
null
Benicio/t5-small-finetuned-en-to-ru
2
null
transformers
22,926
Entry not found
Biasface/DDDC2
883c55830d52448e6a1ccb95cc5e9276a378d2b6
2021-11-30T17:29:01.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Biasface
null
Biasface/DDDC2
2
null
transformers
22,927
--- tags: - conversational --- #hi
BigSalmon/GPT2HardandEasy
cf3caa58029b484cb6c3226b2bbf550b107c068d
2021-09-25T22:25:08.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers" ]
text-generation
false
BigSalmon
null
BigSalmon/GPT2HardandEasy
2
null
transformers
22,928
Entry not found
BigSalmon/GPTNeo350MInformalToFormalLincoln
b9c0b9d9ae46cc6cef37cd63cc8d552702895041
2022-02-17T21:37:07.000Z
[ "pytorch", "gpt_neo", "text-generation", "transformers" ]
text-generation
false
BigSalmon
null
BigSalmon/GPTNeo350MInformalToFormalLincoln
2
null
transformers
22,929
Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership. *** - ```
BigSalmon/GPTNeo350MInformalToFormalLincoln2
5013946a3ff05515ca853fea4530a8a45e0dc769
2022-02-21T00:14:01.000Z
[ "pytorch", "gpt_neo", "text-generation", "transformers" ]
text-generation
false
BigSalmon
null
BigSalmon/GPTNeo350MInformalToFormalLincoln2
2
null
transformers
22,930
Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership. *** - ```
BigSalmon/GPTNeo350MInformalToFormalLincoln3
74158705fed97ead772f1adb884b212009a844dd
2022-02-25T05:04:02.000Z
[ "pytorch", "gpt_neo", "text-generation", "transformers" ]
text-generation
false
BigSalmon
null
BigSalmon/GPTNeo350MInformalToFormalLincoln3
2
null
transformers
22,931
Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main ``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln3") model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln3") ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership. *** - ``` ``` infill: chrome extensions [MASK] accomplish everyday tasks. Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks. infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. infill: ``` ``` Essay Intro (California High-Speed Rail): built with an eye on the future, california's high-speed rail service resolves to change the face of travel. Essay Intro (YIMBY's Need To Win): home to the most expensive housing market in the united states, san francisco is the city in which the yimby and anti-yimby hordes wage an eternal battle. Essay Intro ( ```
BigSalmon/InformalToFormalLincoln14
4e632b5da9f23eec30130dfd3e428c0fb5f1d055
2021-12-22T22:40:51.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
BigSalmon
null
BigSalmon/InformalToFormalLincoln14
2
null
transformers
22,932
Informal to Formal: ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln14") model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln14") ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. informal english: ````
BigSalmon/InformalToFormalLincoln19
588734714e5a49fefe30fc5b806e6661fd60e303
2022-02-01T04:56:29.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
BigSalmon
null
BigSalmon/InformalToFormalLincoln19
2
null
transformers
22,933
Informal to Formal: ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln19") model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln19") ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2Space (The model for this space changes over time) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ```` ``` ### - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership. ### - with 2,000,000 individual articles on everything - wikipedia is the #8 site on the world wide web - created by anyone with access to a computer - growing at fast rate - proof that collaborative community-based projects are the future Text: encompassing a staggering 2,000,000 articles on every subject conceivable, wikipedia is the 8th most visited website in the world. borne of the collective efforts of anyone with an internet connection, its contents are increasing exponentially. most compellingly, however, this effort is an affirmation that community-based initiatives is the future. ### - ```
BigSalmon/MrLincoln125MNeo
2eefd20ccb5197104ccdf2e5a7a524fc17462958
2021-12-11T20:16:52.000Z
[ "pytorch", "tensorboard", "gpt_neo", "text-generation", "transformers" ]
text-generation
false
BigSalmon
null
BigSalmon/MrLincoln125MNeo
2
null
transformers
22,934
Informal to Formal: ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MrLincoln125MNeo") model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln125MNeo") ``` ``` https://huggingface.co/spaces/BigSalmon/InformalToFormal ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. informal english: meteors are much harder to see, because they are only there for a fraction of a second. Translated into the Style of Abraham Lincoln: meteors are not ( easily / readily ) detectable, lasting for mere fractions of a second. informal english: ````
BigSalmon/Neo
1159a3e719eca673dac55005b8b92e9796b4ee7b
2021-04-07T15:05:25.000Z
[ "pytorch", "gpt_neo", "text-generation", "transformers" ]
text-generation
false
BigSalmon
null
BigSalmon/Neo
2
null
transformers
22,935
Entry not found
BigSalmon/Rowerta
57c78fe4182ef165ba1abb36fc71c55abeb53acf
2021-06-11T01:07:05.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
BigSalmon
null
BigSalmon/Rowerta
2
null
transformers
22,936
Entry not found
BigSalmon/T5Salmon2
bbaae7505425d116a8bea0992a6e48118a64e747
2021-06-23T02:20:46.000Z
[ "pytorch", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
BigSalmon
null
BigSalmon/T5Salmon2
2
null
transformers
22,937
Entry not found
BigTooth/DialoGPT-small-tohru
6d8c0877a2ccc92eb1aca42c833e828a4c5402ae
2021-08-29T17:01:00.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
BigTooth
null
BigTooth/DialoGPT-small-tohru
2
null
transformers
22,938
--- tags: - conversational --- # Tohru DialoGPT model
BigeS/DialoGPT-small-Rick
d2e046e61b06518f6d0d569e4786fe2f07e96ce1
2021-08-27T07:51:52.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
BigeS
null
BigeS/DialoGPT-small-Rick
2
null
transformers
22,939
--- tags: - conversational --- #Rick Sanchez DialoGPT Model
BinksSachary/DialoGPT-small-shaxx
b760764bfc79f53d4393056dceeb6c98c2fa8840
2021-06-03T04:48:29.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
BinksSachary
null
BinksSachary/DialoGPT-small-shaxx
2
null
transformers
22,940
--- tags: - conversational --- # My Awesome Model
BinksSachary/ShaxxBot2
f2eccf5f2eda68f0b2c0b68e8fb898064af06db0
2021-06-03T04:37:46.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
BinksSachary
null
BinksSachary/ShaxxBot2
2
null
transformers
22,941
--- tags: - conversational --- # My Awesome Model from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") # Let's chat for 4 lines for step in range(4): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # print(new_user_input_ids) # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) # pretty print last ouput tokens from bot print("JoshuaBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
BogdanKuloren/checkpoint-10500-finetuned-ner
3f7376ac9906554f5c13b3ea123ee6cebdd69804
2021-11-30T11:25:34.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
BogdanKuloren
null
BogdanKuloren/checkpoint-10500-finetuned-ner
2
null
transformers
22,942
Entry not found
Brokette/projetCS
2d6035b8d7aaa277781577a79fdb73211f1651c8
2022-02-17T10:20:31.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
Brokette
null
Brokette/projetCS
2
null
transformers
22,943
Entry not found
BumBelDumBel/ZORK_AI_SCIFI
e1a10f710ea3d31ad926655aa9d64af7ad8ab4e6
2021-07-19T14:51:33.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer" ]
text-generation
false
BumBelDumBel
null
BumBelDumBel/ZORK_AI_SCIFI
2
null
transformers
22,944
--- tags: - generated_from_trainer model_index: - name: ZORK_AI_SCIFI results: - task: name: Causal Language Modeling type: text-generation --- <!-- 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. --> # ZORK_AI_SCIFI This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Tokenizers 0.10.3
Capreolus/birch-bert-large-car_mb
fb51ca50ffcdb74e0d682789f3f9f2562d05046b
2021-05-18T17:38:06.000Z
[ "pytorch", "tf", "jax", "bert", "next-sentence-prediction", "transformers" ]
null
false
Capreolus
null
Capreolus/birch-bert-large-car_mb
2
null
transformers
22,945
Entry not found
CenIA/albert-base-spanish-finetuned-qa-mlqa
2191ccea3a9543d7a076064dabe87afa09a8bfc6
2022-01-18T03:15:12.000Z
[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
CenIA
null
CenIA/albert-base-spanish-finetuned-qa-mlqa
2
null
transformers
22,946
Entry not found
CenIA/albert-large-spanish-finetuned-pos
78429a612573d3fcaadc658ae701f4da2883cc0e
2021-12-17T22:03:38.000Z
[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
CenIA
null
CenIA/albert-large-spanish-finetuned-pos
2
null
transformers
22,947
Entry not found
CenIA/albert-xlarge-spanish-finetuned-qa-mlqa
c51722e39ca7d4cdacb66a77a6a7ecba2abdb5d0
2022-01-19T20:57:30.000Z
[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
CenIA
null
CenIA/albert-xlarge-spanish-finetuned-qa-mlqa
2
null
transformers
22,948
Entry not found
CenIA/albert-xlarge-spanish
c6a1f7869636684554dc3e5fc92219287c74aaa4
2022-04-28T19:55:48.000Z
[ "pytorch", "tf", "albert", "pretraining", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA" ]
null
false
CenIA
null
CenIA/albert-xlarge-spanish
2
null
transformers
22,949
--- language: - es tags: - albert - spanish - OpenCENIA datasets: - large_spanish_corpus --- # ALBERT XLarge Spanish This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora). The model was trained on a single TPU v3-8 with the following hyperparameters and steps/time: - LR: 0.0003125 - Batch Size: 128 - Warmup ratio: 0.00078125 - Warmup steps: 6250 - Goal steps: 8000000 - Total steps: 2775000 - Total training time (aprox): 64.2 days. ## Training loss ![https://drive.google.com/uc?export=view&id=1rw0vvqZY9LZAzRUACLjmP18Fc6D1fv7x](https://drive.google.com/uc?export=view&id=1rw0vvqZY9LZAzRUACLjmP18Fc6D1fv7x)
CennetOguz/distilbert-base-uncased-finetuned-imdb-accelerate
e44aae75d8a7f48a7c531cf8389ba0fb21eaeccd
2022-02-17T17:26:44.000Z
[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
CennetOguz
null
CennetOguz/distilbert-base-uncased-finetuned-imdb-accelerate
2
null
transformers
22,950
Entry not found
Chakita/Kalbert
c6a235e92a43bb97fea5fa07158ae1153304ce11
2022-01-07T12:34:09.000Z
[ "pytorch", "albert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Chakita
null
Chakita/Kalbert
2
null
transformers
22,951
Entry not found
CheonggyeMountain-Sherpa/kogpt-trinity-poem
adaa0d5b9f3f42443aa4226a16e4812ea10c802c
2021-12-14T09:47:19.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
CheonggyeMountain-Sherpa
null
CheonggyeMountain-Sherpa/kogpt-trinity-poem
2
1
transformers
22,952
Entry not found
Chun/w-zh2en-mtm
860cc2827f2321346164771263bfdad86cea74ee
2021-08-24T14:36:46.000Z
[ "pytorch", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Chun
null
Chun/w-zh2en-mtm
2
null
transformers
22,953
Entry not found
CianB/DialoGPT-small-JohnnySilverhand2
3510533095127e40334a6574b76395b1156cc2fc
2021-08-26T19:15:22.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
CianB
null
CianB/DialoGPT-small-JohnnySilverhand2
2
null
transformers
22,954
--- tags: - conversational --- # Johnny Silverhand DialoGPT model
Ciruzzo/DialoGPT-small-harrypotter
11d481e9e74bb23b89f32969124c70bcab5de601
2021-09-07T10:47:28.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Ciruzzo
null
Ciruzzo/DialoGPT-small-harrypotter
2
null
transformers
22,955
--- tags: - conversational --- # Harry Potter DialoGPT Model
ClaudeCOULOMBE/RickBot
d1b4c625e2dcef62f15f272179f4e7aa54d1bdd0
2021-08-12T05:56:47.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
ClaudeCOULOMBE
null
ClaudeCOULOMBE/RickBot
2
null
transformers
22,956
--- tags: - conversational --- # RickBot built for [Chai](https://chai.ml/) Make your own [here](https://colab.research.google.com/drive/1o5LxBspm-C28HQvXN-PRQavapDbm5WjG?usp=sharing)
CodeNinja1126/bert-p-encoder
c4fa4a4c50fb9ba9d7f2b41a848453cde831632a
2021-05-12T01:26:46.000Z
[ "pytorch" ]
null
false
CodeNinja1126
null
CodeNinja1126/bert-p-encoder
2
null
null
22,957
Entry not found
CodeNinja1126/bert-q-encoder
d22103cf3e5513969aca5e57b4dd35cfe021970f
2021-05-12T01:31:17.000Z
[ "pytorch" ]
null
false
CodeNinja1126
null
CodeNinja1126/bert-q-encoder
2
null
null
22,958
Entry not found
CoffeeAddict93/gpt2-call-of-the-wild
1f5d19c69c23ccb6404174a1790d8c24175b1258
2021-12-02T03:08:53.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
CoffeeAddict93
null
CoffeeAddict93/gpt2-call-of-the-wild
2
null
transformers
22,959
Entry not found
CoffeeAddict93/gpt2-medium-modest-proposal
47ad3cbe8e3f943bd727d892d2fe0f177788929b
2021-12-02T03:58:35.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
CoffeeAddict93
null
CoffeeAddict93/gpt2-medium-modest-proposal
2
null
transformers
22,960
Entry not found
ComCom/gpt2
34a2b31c5f1832e471a1faaf964c29cb6949917d
2021-11-15T04:58:28.000Z
[ "pytorch", "gpt2", "feature-extraction", "transformers" ]
feature-extraction
false
ComCom
null
ComCom/gpt2
2
null
transformers
22,961
해당 모델은 [해당 사이트](https://huggingface.co/gpt2)에서 가져온 모델입니다. 해당 모델은 [Teachable NLP](https://ainize.ai/teachable-nlp) 서비스에서 사용됩니다.
Connor/DialoGPT-small-rick
8358391c0ec7f5d8359afff632f0dea92e4eceb6
2021-09-21T11:25:25.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Connor
null
Connor/DialoGPT-small-rick
2
null
transformers
22,962
--- tags: - conversational --- # Rick DialoGPT Model
Contrastive-Tension/BERT-Large-CT
9c812bcfcb9a897b89893f1a241c2ed43a5d200a
2021-05-18T18:00:51.000Z
[ "pytorch", "tf", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Contrastive-Tension
null
Contrastive-Tension/BERT-Large-CT
2
null
transformers
22,963
Entry not found
CurtisBowser/DialoGPT-small-sora
c8b57dd7ea2f7d76e156101588a5f21697be32bf
2021-10-20T20:36:40.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
CurtisBowser
null
CurtisBowser/DialoGPT-small-sora
2
null
transformers
22,964
--- tags: - conversational --- # Sora DialoGPT Model
CyberMuffin/DialoGPT-small-ChandlerBot
3a1b0139b9c8ea4a1934f6fc4db26847e1e7c40a
2021-09-19T13:04:55.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
CyberMuffin
null
CyberMuffin/DialoGPT-small-ChandlerBot
2
null
transformers
22,965
--- tags: - conversational --- # Chandler Bot DialoGPT model
DSI/ar_emotion_6
6337fe4984c4baffae85aa35614e1e8c2fad64d2
2021-11-13T18:48:18.000Z
[ "pytorch", "bert", "transformers" ]
null
false
DSI
null
DSI/ar_emotion_6
2
null
transformers
22,966
Entry not found
DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken
7b3ca1cd2192bd36326f483b49e3e85e9d59d4eb
2022-02-04T17:07:47.000Z
[ "pytorch", "tensorboard", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
DaisyMak
null
DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken
2
null
transformers
22,967
Entry not found
Davlan/bert-base-multilingual-cased-finetuned-luganda
4e2cc003ba87776151db384d44e0c171ea978f3f
2021-06-17T17:43:07.000Z
[ "pytorch", "bert", "fill-mask", "lg", "transformers", "autotrain_compatible" ]
fill-mask
false
Davlan
null
Davlan/bert-base-multilingual-cased-finetuned-luganda
2
null
transformers
22,968
Hugging Face's logo --- language: lg datasets: --- # bert-base-multilingual-cased-finetuned-luganda ## Model description **bert-base-multilingual-cased-finetuned-luganda** is a **Luganda BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Luganda language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luganda corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-luganda') >>> unmasker("Ffe tulwanyisa abo abaagala okutabangula [MASK], Kimuli bwe yategeezezza.") ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 + [BUKKEDDE](https://github.com/masakhane-io/masakhane-ner/tree/main/text_by_language/luganda) +[Luganda CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | lg_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 80.36 | 84.70 ### BibTeX entry and citation info By David Adelani ``` ```
Davlan/m2m100_418M-yor-eng-mt
72ec4bc326b504b34e4719df054d091803af319b
2022-03-29T09:21:03.000Z
[ "pytorch", "m2m_100", "text2text-generation", "yo", "en", "dataset:JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)", "arxiv:2103.08647", "transformers", "autotrain_compatible" ]
text2text-generation
false
Davlan
null
Davlan/m2m100_418M-yor-eng-mt
2
null
transformers
22,969
Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # m2m100_418M-eng-yor-mt ## Model description **m2m100_418M-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned facebook/m2m100_418M model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English. Specifically, this model is a *facebook/m2m100_418M* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning m2m100_418M achieves **16.76 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57 ### BibTeX entry and citation info By David Adelani ``` ```
Davlan/mbart50-large-yor-eng-mt
9ea3bc7991382d5017881311e1f36224174b6021
2021-09-26T12:40:29.000Z
[ "pytorch", "mbart", "text2text-generation", "yo", "en", "dataset:JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)", "arxiv:2103.08647", "transformers", "autotrain_compatible" ]
text2text-generation
false
Davlan
null
Davlan/mbart50-large-yor-eng-mt
2
null
transformers
22,970
Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # mbart50-large-yor-eng-mt ## Model description **mbart50-large-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned facebook/mbart-large-50 model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English. Specifically, this model is a *mbart-large-50* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model. #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning mbart50-large achieves **15.88 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57 ### BibTeX entry and citation info By David Adelani ``` ```
Davlan/mt5-small-pcm-en
1a2f4937603f832eaad5c268f3b562a654f43a10
2022-01-22T19:20:51.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Davlan
null
Davlan/mt5-small-pcm-en
2
null
transformers
22,971
Entry not found
Declan/Breitbart_model_v6
1169a840d19a72881c61ce58115dd82b3462160d
2021-12-15T07:36:12.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/Breitbart_model_v6
2
null
transformers
22,972
Entry not found
Declan/Breitbart_model_v7
c3bffa886fe6d6f55176d39e3aa4b0f3efe733ad
2021-12-19T09:11:22.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/Breitbart_model_v7
2
null
transformers
22,973
Entry not found
Declan/CNN_model_v1
897930acf0aa3f38bdecbebf231b0bd3841bff85
2021-12-12T08:29:34.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/CNN_model_v1
2
null
transformers
22,974
Entry not found
Declan/ChicagoTribune_model_v1
53467b29e20943facee13530164554495aa9a8e1
2021-12-12T01:43:13.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/ChicagoTribune_model_v1
2
null
transformers
22,975
Entry not found
Declan/ChicagoTribune_model_v3
e4a4ec1c56c51618a0339cf81680a7ef19451a6a
2021-12-15T08:34:07.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/ChicagoTribune_model_v3
2
null
transformers
22,976
Entry not found
Declan/ChicagoTribune_model_v5
4fac4c0e01f7c3f7126a1098442eb397f0c7a1aa
2021-12-15T09:41:50.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/ChicagoTribune_model_v5
2
null
transformers
22,977
Entry not found
Declan/ChicagoTribune_model_v7
7ed15eb67932bfb6c207435b36fa6698e4fa5788
2021-12-19T10:09:01.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/ChicagoTribune_model_v7
2
null
transformers
22,978
Entry not found
Declan/FoxNews_model_v2
369aea614bafe7e31a99fa33b143928fc4120934
2021-12-15T14:10:16.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/FoxNews_model_v2
2
null
transformers
22,979
Entry not found
Declan/HuffPost_model_v1
e633dcd053bce23a204f4c280e65a20cface11f6
2021-12-13T01:18:28.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/HuffPost_model_v1
2
null
transformers
22,980
Entry not found
Declan/HuffPost_model_v2
7212e6f4ca8e8c0951400a3147601389793dce35
2021-12-15T16:57:17.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/HuffPost_model_v2
2
null
transformers
22,981
Entry not found
Declan/HuffPost_model_v3
32aaeffb480454df5a6bb5959868add95528c477
2021-12-15T17:25:09.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/HuffPost_model_v3
2
null
transformers
22,982
Entry not found
Declan/HuffPost_model_v5
81f49c97cc9b7dde38492f9fea4e06c40108285a
2021-12-15T19:58:11.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/HuffPost_model_v5
2
null
transformers
22,983
Entry not found
Declan/HuffPost_model_v6
df897433d63199e54900a51d0dccd13a564beef3
2021-12-19T13:01:06.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/HuffPost_model_v6
2
null
transformers
22,984
Entry not found
Declan/NPR_model_v1
b3cc0265f7090896be97402bbdf6691929d08fce
2021-12-14T03:35:38.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/NPR_model_v1
2
null
transformers
22,985
Entry not found
Declan/NPR_model_v2
4fef8096b611b24c8a2c8b1bf369a87f853ebfcf
2021-12-15T21:29:02.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/NPR_model_v2
2
null
transformers
22,986
Entry not found
Declan/NPR_model_v3
45ad2fa7341c7126adb5ee8c6c9e1ac9e6a8195a
2021-12-16T01:21:21.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/NPR_model_v3
2
null
transformers
22,987
Entry not found
Declan/NPR_model_v5
8b046ac6ae4408482a24c12833a4fd6974e9ce18
2021-12-16T03:34:48.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/NPR_model_v5
2
null
transformers
22,988
Entry not found
Declan/NewYorkTimes_model_v2
6a0f46f2a9f4b045a43690dace646619ef213a39
2021-12-19T03:15:06.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/NewYorkTimes_model_v2
2
null
transformers
22,989
Entry not found
Declan/Politico_model_v3
51c9de4dff4f2efd63369a5b6397b02bbe1618cf
2021-12-16T05:53:56.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/Politico_model_v3
2
null
transformers
22,990
Entry not found
Declan/Politico_model_v4
4188f0a108f046bb4cb00a0cd0d9ef68736f8d36
2021-12-16T07:01:54.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/Politico_model_v4
2
null
transformers
22,991
Entry not found
Declan/Politico_model_v5
8f1c67c7bc298395c826ec03c77949692c5a5c0b
2021-12-16T08:16:57.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/Politico_model_v5
2
null
transformers
22,992
Entry not found
Declan/Politico_model_v6
9294c9b9a0fc776d23fcdb75e51d4b0b023c72fa
2021-12-19T15:53:57.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/Politico_model_v6
2
null
transformers
22,993
Entry not found
Declan/Reuters_model_v2
83bbfa04db4919a9a3827e4eddcf7896e7b04ea9
2021-12-16T09:59:10.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/Reuters_model_v2
2
null
transformers
22,994
Entry not found
Declan/Reuters_model_v5
97bd92d3927f4398a3fe5a1f33e83ee42ec915df
2021-12-16T19:34:18.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/Reuters_model_v5
2
null
transformers
22,995
Entry not found
Declan/WallStreetJournal_model_v1
ffe533b1d9b1211554f81c2ad9491822de38433e
2021-12-14T20:58:50.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/WallStreetJournal_model_v1
2
null
transformers
22,996
Entry not found
Declan/WallStreetJournal_model_v5
f6aec8c7d3b0711adc819e3af5c84de73847f111
2021-12-18T02:02:53.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Declan
null
Declan/WallStreetJournal_model_v5
2
null
transformers
22,997
Entry not found
DeividasM/wav2vec2-large-xlsr-53-lithuanian
14c7b2828be30a7b703797dc1acb4ac3f75db31a
2021-07-05T14:19:00.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "lt", "dataset:common_voice", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
DeividasM
null
DeividasM/wav2vec2-large-xlsr-53-lithuanian
2
null
transformers
22,998
--- language: lt datasets: - common_voice tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Lithuanina by Deividas Mataciunas results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice lt type: common_voice args: lt metrics: - name: Test WER type: wer value: 56.55 --- # Wav2Vec2-Large-XLSR-53-Lithuanian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Lithuanian using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "lt", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") model = Wav2Vec2ForCTC.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): \\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): \\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Lithuanian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "lt", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") model = Wav2Vec2ForCTC.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") model.to("cuda") chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): \\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() \\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): \\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) \\twith torch.no_grad(): \\t\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) \\tbatch["pred_strings"] = processor.batch_decode(pred_ids) \\treturn batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 56.55 % ## Training The Common Voice `train`, `validation` datasets were used for training.
DeltaHub/adapter_t5-3b_mrpc
7e70e74088b52f0d2430341d7b2872d26269a216
2022-02-11T09:08:52.000Z
[ "pytorch", "transformers" ]
null
false
DeltaHub
null
DeltaHub/adapter_t5-3b_mrpc
2
null
transformers
22,999
Entry not found