Datasets:
dk-crazydiv
/
huggingface-modelhub

Dataset card Data Studio Files Community
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4
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textattack/albert-base-v2-WNLI
2020-07-06T16:33:17.000Z
[ "pytorch", "albert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json" ]
textattack
22
transformers
## TextAttack Model Card This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 64, a learning rate of 2e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.5915492957746479, as measured by the eval set accuracy, found after 0 epoch. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/albert-base-v2-ag-news
2020-07-07T21:59:15.000Z
[ "pytorch", "albert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json" ]
textattack
73
transformers
## TextAttack Model CardThis `albert-base-v2` model was fine-tuned for sequence classification using TextAttack and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9471052631578948, as measured by the eval set accuracy, found after 3 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/albert-base-v2-imdb
2020-07-06T16:34:24.000Z
[ "pytorch", "albert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json" ]
textattack
252
transformers
## TextAttack Model Card This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack and the imdb dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 32, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.89236, as measured by the eval set accuracy, found after 3 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/albert-base-v2-rotten-tomatoes
2020-07-06T16:35:34.000Z
[ "pytorch", "albert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json" ]
textattack
52
transformers
## TextAttack Model Card This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 64, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.8808630393996247, as measured by the eval set accuracy, found after 1 epoch. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/albert-base-v2-rotten_tomatoes
2020-06-25T20:00:46.000Z
[ "pytorch", "tensorboard", "albert", "masked-lm", "transformers", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "events.out.tfevents.1593060127.qcuda1", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json", "1593060127.506348/events.out.tfevents.1593060127.qcuda1" ]
textattack
31
transformers
## albert-base-v2 fine-tuned with TextAttack on the rotten_tomatoes dataset This `albert-base-v2` model was fine-tuned for sequence classificationusing TextAttack and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned for 10 epochs with a batch size of 128, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.8855534709193246, as measured by the eval set accuracy, found after 1 epoch. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/albert-base-v2-snli
2020-07-06T16:36:47.000Z
[ "pytorch", "albert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json" ]
textattack
27
transformers
## TextAttack Model Card This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack and the snli dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 64, a learning rate of 2e-05, and a maximum sequence length of 64. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9060150375939849, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/albert-base-v2-yelp-polarity
2020-07-06T16:37:10.000Z
[ "pytorch", "albert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json" ]
textattack
992
transformers
## TextAttack Model Card This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 3e-05, and a maximum sequence length of 512. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.975078947368421, as measured by the eval set accuracy, found after 3 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/bert-base-cased-STS-B
2021-05-20T07:30:08.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.txt" ]
textattack
43
transformers
## TextAttack Model Card This `bert-base-cased` model was fine-tuned for sequence classificationusing TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 3 epochs with a batch size of 128, a learning rate of 1e-05, and a maximum sequence length of 128. Since this was a regression task, the model was trained with a mean squared error loss function. The best score the model achieved on this task was 0.8244429996636282, as measured by the eval set pearson correlation, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/bert-base-uncased-CoLA
2021-05-20T07:31:05.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_cola.txt", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
1,777
transformers
textattack/bert-base-uncased-MNLI
2021-05-20T07:31:58.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
993
transformers
textattack/bert-base-uncased-MRPC
2021-05-20T07:32:52.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_mrpc.txt", "flax_model.msgpack", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.txt" ]
textattack
371
transformers
## TextAttack Model Card This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.8774509803921569, as measured by the eval set accuracy, found after 1 epoch. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/bert-base-uncased-QNLI
2021-05-20T07:33:46.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_qnli.txt", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
60
transformers
textattack/bert-base-uncased-QQP
2021-05-20T07:34:46.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_qqp.txt", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
601
transformers
textattack/bert-base-uncased-RTE
2021-05-20T07:36:18.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_rte.txt", "flax_model.msgpack", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.txt" ]
textattack
1,014
transformers
## TextAttack Model Card This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 8, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.7256317689530686, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/bert-base-uncased-SST-2
2021-05-20T07:37:12.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_sst-2.txt", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
5,320
transformers
textattack/bert-base-uncased-STS-B
2021-05-20T07:38:28.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_sts-b.txt", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
56
transformers
textattack/bert-base-uncased-WNLI
2021-05-20T07:39:22.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_wnli.txt", "flax_model.msgpack", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.txt" ]
textattack
59
transformers
## TextAttack Model Card This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 64, a learning rate of 5e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.5633802816901409, as measured by the eval set accuracy, found after 1 epoch. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/bert-base-uncased-ag-news
2021-05-20T07:40:21.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.txt" ]
textattack
1,747
transformers
## TextAttack Model CardThis `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 3e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9514473684210526, as measured by the eval set accuracy, found after 3 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/bert-base-uncased-imdb
2021-05-20T07:42:02.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.txt" ]
textattack
2,729
transformers
## TextAttack Model Card This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the imdb dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.89088, as measured by the eval set accuracy, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/bert-base-uncased-rotten-tomatoes
2021-05-20T07:46:20.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.txt" ]
textattack
1,890
transformers
## TextAttack Model Card This `bert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned for 10 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.875234521575985, as measured by the eval set accuracy, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/bert-base-uncased-rotten_tomatoes
2021-05-20T07:47:13.000Z
[ "pytorch", "jax", "tensorboard", "bert", "masked-lm", "transformers", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "events.out.tfevents.1593052540.qcuda11", "flax_model.msgpack", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.txt", "1593052540.6245422/events.out.tfevents.1593052540.qcuda11" ]
textattack
35
transformers
## bert-base-uncased fine-tuned with TextAttack on the rotten_tomatoes dataset This `bert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned for 10 epochs with a batch size of 64, a learning rate of 5e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.875234521575985, as measured by the eval set accuracy, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/bert-base-uncased-snli
2021-05-20T07:48:06.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results.txt", "flax_model.msgpack", "pytorch_model.bin", "train_args.json", "vocab.txt" ]
textattack
1,202
transformers
textattack/bert-base-uncased-yelp-polarity
2021-05-20T07:49:07.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.txt" ]
textattack
408
transformers
## TextAttack Model Card This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 5e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9699473684210527, as measured by the eval set accuracy, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/distilbert-base-cased-CoLA
2020-06-09T16:45:43.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_cola.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
314
transformers
textattack/distilbert-base-cased-MRPC
2020-06-09T16:46:01.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_mrpc.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
35
transformers
textattack/distilbert-base-cased-QQP
2020-06-09T16:46:12.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_qqp.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
35
transformers
textattack/distilbert-base-cased-SST-2
2020-06-09T16:46:25.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_sst-2.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
302
transformers
textattack/distilbert-base-cased-STS-B
2020-06-09T16:46:42.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_sts-b.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
26
transformers
textattack/distilbert-base-cased-snli
2020-07-06T16:37:00.000Z
[ "pytorch", "distilbert", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.txt" ]
textattack
96
transformers
## TextAttack Model Card This `distilbert-base-cased` model was fine-tuned for sequence classificationusing TextAttack and the snli dataset loaded using the `nlp` library. The model was fine-tuned for 3 epochs with a batch size of 256, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.8768542979069295, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/distilbert-base-uncased-CoLA
2020-07-06T16:29:03.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_cola.txt", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.txt" ]
textattack
1,967
transformers
## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 64, a learning rate of 3e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.8235858101629914, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/distilbert-base-uncased-MNLI
2020-06-09T16:47:05.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
44
transformers
textattack/distilbert-base-uncased-MRPC
2020-07-06T16:30:12.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_mrpc.txt", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.txt" ]
textattack
61
transformers
## TextAttack Model Card This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 32, a learning rate of 2e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.8578431372549019, as measured by the eval set accuracy, found after 1 epoch. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/distilbert-base-uncased-QNLI
2020-06-09T16:47:34.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_qnli.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
55
transformers
textattack/distilbert-base-uncased-QQP
2020-06-09T16:47:45.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_qqp.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
28
transformers
textattack/distilbert-base-uncased-RTE
2020-07-06T16:31:28.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_rte.txt", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.txt" ]
textattack
345
transformers
## TextAttack Model Card This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.6570397111913358, as measured by the eval set accuracy, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/distilbert-base-uncased-SST-2
2020-06-09T16:48:10.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_sst-2.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
46
transformers
textattack/distilbert-base-uncased-STS-B
2020-06-09T16:48:25.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_sts-b.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
textattack
26
transformers
textattack/distilbert-base-uncased-WNLI
2020-07-06T16:33:44.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_wnli.txt", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.txt" ]
textattack
45
transformers
## TextAttack Model Card This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 128, a learning rate of 2e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.5633802816901409, as measured by the eval set accuracy, found after 0 epoch. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/distilbert-base-uncased-ag-news
2020-07-07T22:01:14.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.txt" ]
textattack
262
transformers
## TextAttack Model CardThis `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 32, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9478947368421052, as measured by the eval set accuracy, found after 1 epoch. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/distilbert-base-uncased-imdb
2020-07-06T16:34:50.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.txt" ]
textattack
375
transformers
## TextAttack Model Card This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the imdb dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.88, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/distilbert-base-uncased-rotten-tomatoes
2020-07-06T16:36:02.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.txt" ]
textattack
380
transformers
## TextAttack Model Card This `distilbert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned for 3 epochs with a batch size of 128, a learning rate of 1e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.8395872420262664, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/facebook-bart-base-RTE
2020-08-20T15:50:48.000Z
[ "pytorch", "bart", "seq2seq", "transformers", "text2text-generation" ]
text2text-generation
[ ".gitattributes", "README.md", "config.json", "log.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.json" ]
textattack
23
transformers
## TextAttack Model CardSince this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.7256317689530686, as measured by the eval set accuracy, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/facebook-bart-base-glue-RTE
2020-08-20T15:49:05.000Z
[ "pytorch", "bart", "seq2seq", "transformers", "text2text-generation" ]
text2text-generation
[ ".gitattributes", "README.md", "config.json", "log.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.json" ]
textattack
19
transformers
## TextAttack Model Cardrate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.7256317689530686, as measured by the eval set accuracy, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/facebook-bart-large-CoLA
2020-06-09T16:49:04.000Z
[ "pytorch", "bart", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_cola.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
textattack
342
transformers
textattack/facebook-bart-large-MNLI
2020-06-09T16:49:34.000Z
[ "pytorch", "bart", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
textattack
21
transformers
textattack/facebook-bart-large-MRPC
2020-06-09T16:49:43.000Z
[ "pytorch", "bart", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_mrpc.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
textattack
68
transformers
textattack/facebook-bart-large-QNLI
2020-06-09T16:50:26.000Z
[ "pytorch", "bart", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_qnli.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
textattack
48
transformers
textattack/facebook-bart-large-RTE
2020-06-09T16:50:55.000Z
[ "pytorch", "bart", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_rte.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
textattack
310
transformers
textattack/facebook-bart-large-SST-2
2020-06-09T16:51:43.000Z
[ "pytorch", "bart", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_sst-2.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
textattack
473
transformers
textattack/facebook-bart-large-WNLI
2020-06-09T16:52:24.000Z
[ "pytorch", "bart", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_wnli.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
textattack
9
transformers
textattack/roberta-base-CoLA
2021-05-20T22:05:35.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_cola.txt", "flax_model.msgpack", "log.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.json" ]
textattack
2,101
transformers
## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 32, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.850431447746884, as measured by the eval set accuracy, found after 1 epoch. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/roberta-base-MNLI
2021-05-20T22:06:43.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
textattack
587
transformers
textattack/roberta-base-MRPC
2021-05-20T22:07:47.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_mrpc.txt", "flax_model.msgpack", "log.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.json" ]
textattack
296
transformers
## TextAttack Model Card This `roberta-base` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 3e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9117647058823529, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/roberta-base-QNLI
2021-05-20T22:09:33.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_qnli.txt", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
textattack
30
transformers
textattack/roberta-base-RTE
2021-05-20T22:10:37.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_rte.txt", "flax_model.msgpack", "log.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.json" ]
textattack
457
transformers
## TextAttack Model Card This `roberta-base` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.7942238267148014, as measured by the eval set accuracy, found after 3 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/roberta-base-SST-2
2021-05-20T22:11:39.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_sst-2.txt", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
textattack
17,055
transformers
textattack/roberta-base-STS-B
2021-05-20T22:12:47.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_sts-b.txt", "flax_model.msgpack", "log.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.json" ]
textattack
78
transformers
## TextAttack Model Card This `roberta-base` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 8, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a regression task, the model was trained with a mean squared error loss function. The best score the model achieved on this task was 0.9108696741479216, as measured by the eval set pearson correlation, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/roberta-base-WNLI
2021-05-20T22:13:50.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "eval_results_wnli.txt", "flax_model.msgpack", "log.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "training_args.bin", "vocab.json" ]
textattack
48
transformers
## TextAttack Model Card This `roberta-base` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 5e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.5633802816901409, as measured by the eval set accuracy, found after 0 epoch. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/roberta-base-ag-news
2021-05-20T22:15:20.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "log.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.json" ]
textattack
203
transformers
## TextAttack Model CardThis `roberta-base` model was fine-tuned for sequence classification using TextAttack and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 5e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9469736842105263, as measured by the eval set accuracy, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/roberta-base-imdb
2021-05-20T22:16:19.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "log.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.json" ]
textattack
767
transformers
## TextAttack Model Card This `roberta-base` model was fine-tuned for sequence classification using TextAttack and the imdb dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 64, a learning rate of 3e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.91436, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/roberta-base-rotten-tomatoes
2021-05-20T22:17:29.000Z
[ "pytorch", "jax", "roberta", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "log.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.json" ]
textattack
37
transformers
## TextAttack Model Card This `roberta-base` model was fine-tuned for sequence classificationusing TextAttack and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned for 10 epochs with a batch size of 64, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9033771106941839, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/roberta-base-rotten_tomatoes
2021-05-20T22:18:23.000Z
[ "pytorch", "jax", "tensorboard", "roberta", "masked-lm", "transformers", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "events.out.tfevents.1593104969.qcuda8", "flax_model.msgpack", "log.txt", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "train_args.json", "vocab.json", "1593104969.029003/events.out.tfevents.1593104969.qcuda8" ]
textattack
28
transformers
## roberta-base fine-tuned with TextAttack on the rotten_tomatoes dataset This `roberta-base` model was fine-tuned for sequence classificationusing TextAttack and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned for 10 epochs with a batch size of 128, a learning rate of 5e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9033771106941839, as measured by the eval set accuracy, found after 9 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/xlnet-base-cased-CoLA
2020-07-06T16:29:34.000Z
[ "pytorch", "xlnet", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "README.md", "config.json", "eval_results_cola.txt", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json", "training_args.bin" ]
textattack
145
transformers
## TextAttack Model Cardfor 5 epochs with a batch size of 32, a learning rate of 3e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.7976989453499521, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/xlnet-base-cased-MNLI
2020-06-09T16:55:37.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "training_args.bin" ]
textattack
355
transformers
textattack/xlnet-base-cased-MRPC
2020-07-06T16:30:46.000Z
[ "pytorch", "xlnet", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "README.md", "config.json", "eval_results_mrpc.txt", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json", "training_args.bin" ]
textattack
45
transformers
## TextAttack Model Card This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 32, a learning rate of 5e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.8897058823529411, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/xlnet-base-cased-QNLI
2020-06-09T16:56:10.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_qnli.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "training_args.bin" ]
textattack
40
transformers
textattack/xlnet-base-cased-QQP
2020-06-09T16:56:26.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_qqp.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "training_args.bin" ]
textattack
21
transformers
textattack/xlnet-base-cased-RTE
2020-07-06T16:32:05.000Z
[ "pytorch", "xlnet", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "README.md", "config.json", "eval_results_rte.txt", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json", "training_args.bin" ]
textattack
103
transformers
## TextAttack Model Card This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.7111913357400722, as measured by the eval set accuracy, found after 3 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/xlnet-base-cased-SST-2
2020-06-09T16:56:53.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_sst-2.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "training_args.bin" ]
textattack
136
transformers
textattack/xlnet-base-cased-STS-B
2020-07-06T16:33:08.000Z
[ "pytorch", "xlnet", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "README.md", "config.json", "eval_results_sts-b.txt", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json", "training_args.bin" ]
textattack
65
transformers
## TextAttack Model Card This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 8, a learning rate of 5e-05, and a maximum sequence length of 128. Since this was a regression task, the model was trained with a mean squared error loss function. The best score the model achieved on this task was 0.8892630070017784, as measured by the eval set pearson correlation, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/xlnet-base-cased-WNLI
2020-07-06T16:34:15.000Z
[ "pytorch", "xlnet", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "README.md", "config.json", "eval_results_wnli.txt", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json", "training_args.bin" ]
textattack
39
transformers
## TextAttack Model Card This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 3e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.5774647887323944, as measured by the eval set accuracy, found after 0 epoch. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/xlnet-base-cased-imdb
2020-07-06T16:35:25.000Z
[ "pytorch", "xlnet", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json" ]
textattack
83
transformers
## TextAttack Model Card This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack and the imdb dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 32, a learning rate of 2e-05, and a maximum sequence length of 512. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.95352, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/xlnet-base-cased-rotten-tomatoes
2020-07-06T16:36:38.000Z
[ "pytorch", "xlnet", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "README.md", "config.json", "log.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "train_args.json" ]
textattack
33
transformers
## TextAttack Model Card This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9071294559099438, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
textattack/xlnet-large-cased-CoLA
2020-06-09T16:57:33.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_cola.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "training_args.bin" ]
textattack
33
transformers
textattack/xlnet-large-cased-MRPC
2020-06-09T16:58:10.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_mrpc.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "training_args.bin" ]
textattack
22
transformers
textattack/xlnet-large-cased-QQP
2020-06-09T16:58:40.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_qqp.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "training_args.bin" ]
textattack
16
transformers
textattack/xlnet-large-cased-SST-2
2020-06-09T16:59:05.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_sst-2.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "training_args.bin" ]
textattack
34
transformers
textattack/xlnet-large-cased-STS-B
2020-06-09T16:59:30.000Z
[ "pytorch", "xlnet", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results_sts-b.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "training_args.bin" ]
textattack
34
transformers
thatdramebaazguy/movie-roberta-MITmovie-squad
2021-05-20T22:20:09.000Z
[ "pytorch", "tf", "jax", "roberta", "question-answering", "transformers" ]
question-answering
[ ".gitattributes", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tf_model.h5", "tokenizer_config.json", "vocab.json" ]
thatdramebaazguy
11
transformers
thatdramebaazguy/movie-roberta-MITmovie
2021-05-20T22:21:20.000Z
[ "pytorch", "tf", "jax", "roberta", "token-classification", "transformers" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tf_model.h5", "tokenizer_config.json", "vocab.json" ]
thatdramebaazguy
15
transformers
Model Card coming soon!
thatdramebaazguy/movie-roberta-base
2021-05-20T22:22:56.000Z
[ "pytorch", "tf", "jax", "roberta", "masked-lm", "English", "dataset:imdb", "dataset:cornell_movie_dialogue", "transformers", "roberta-base", "masked-language-modeling", "license:cc-by-4.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tf_model.h5", "tokenizer_config.json", "vocab.json" ]
thatdramebaazguy
33
transformers
--- datasets: - imdb - cornell_movie_dialogue language: - English thumbnail: tags: - roberta - roberta-base - masked-language-modeling - masked-lm license: cc-by-4.0 --- # roberta-base for MLM ``` model_name = "thatdramebaazguy/movie-roberta-base" pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="Fill-Mask") ``` ## Overview **Language model:** roberta-base **Language:** English **Downstream-task:** Fill-Mask **Training data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names **Eval data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names **Infrastructure**: 4x Tesla v100 **Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/train_movie_roberta.sh) ## Hyperparameters ``` Num examples = 4767233 Num Epochs = 2 Instantaneous batch size per device = 20 Total train batch size (w. parallel, distributed & accumulation) = 80 Gradient Accumulation steps = 1 Total optimization steps = 119182 eval_loss = 1.6153 eval_samples = 20573 perplexity = 5.0296 learning_rate=5e-05 n_gpu = 4 ``` ## Performance perplexity = 5.0296 Some of my work: - [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/) ---
thatdramebaazguy/movie-roberta-squad
2021-05-20T22:24:26.000Z
[ "pytorch", "tf", "jax", "roberta", "question-answering", "transformers" ]
question-answering
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tf_model.h5", "tokenizer_config.json", "vocab.json" ]
thatdramebaazguy
7
transformers
Model Card coming soon!
thatdramebaazguy/roberta-base-MITmovie-squad
2021-05-20T22:25:26.000Z
[ "pytorch", "tf", "jax", "roberta", "question-answering", "transformers" ]
question-answering
[ ".gitattributes", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tf_model.h5", "tokenizer_config.json", "vocab.json" ]
thatdramebaazguy
10
transformers
thatdramebaazguy/roberta-base-MITmovie
2021-05-20T22:27:12.000Z
[ "pytorch", "tf", "jax", "roberta", "token-classification", "transformers" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tf_model.h5", "tokenizer_config.json", "vocab.json" ]
thatdramebaazguy
16
transformers
Model Card coming soon!
thatdramebaazguy/roberta-base-squad
2021-05-20T22:28:27.000Z
[ "pytorch", "tf", "jax", "roberta", "question-answering", "English", "dataset:squad", "dataset:squad-v1", "transformers", "roberta-base", "masked-language-modeling", "masked-LM" ]
question-answering
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tf_model.h5", "tokenizer_config.json", "vocab.json" ]
thatdramebaazguy
8
transformers
--- datasets: - squad - squad-v1 language: - English thumbnail: tags: - roberta - roberta-base - masked-language-modeling - masked-LM --- Model Card coming soon!
thatdramebaazguy/roberta-base-wikimovies
2021-05-20T22:29:54.000Z
[ "pytorch", "tf", "jax", "roberta", "masked-lm", "English", "dataset:wikimovies", "transformers", "roberta-base", "masked-language-modeling", "license:cc-by-4.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tf_model.h5", "tokenizer_config.json", "vocab.json" ]
thatdramebaazguy
7
transformers
--- datasets: - wikimovies language: - English thumbnail: tags: - roberta - roberta-base - masked-language-modeling license: cc-by-4.0 --- # roberta-base for MLM ``` model_name = "thatdramebaazguy/roberta-base-wikimovies" pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="Fill-Mask") ``` ## Overview **Language model:** roberta-base **Language:** English **Downstream-task:** Fill-Mask **Training data:** wikimovies **Eval data:** wikimovies **Infrastructure**: 2x Tesla v100 **Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/shell_scripts/train_movie_roberta.sh) ## Hyperparameters ``` num_examples = 4346 batch_size = 16 n_epochs = 3 base_LM_model = "roberta-base" learning_rate = 5e-05 max_query_length=64 Gradient Accumulation steps = 1 Total optimization steps = 816 evaluation_strategy=IntervalStrategy.NO prediction_loss_only=False per_device_train_batch_size=8 per_device_eval_batch_size=8 adam_beta1=0.9 adam_beta2=0.999 adam_epsilon=1e-08, max_grad_norm=1.0 lr_scheduler_type=SchedulerType.LINEAR warmup_ratio=0.0 seed=42 eval_steps=500 metric_for_best_model=None greater_is_better=None label_smoothing_factor=0.0 ``` ## Performance perplexity = 4.3808 Some of my work: - [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/) ---
theainerd/Wav2Vec2-large-xlsr-hindi
2021-03-29T07:14:33.000Z
[ "pytorch", "wav2vec2", "hi", "dataset:Interspeech 2021", "transformers", "audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "license:apache-2.0" ]
automatic-speech-recognition
[ ".gitattributes", "README.md", "config.json", "preprocessor_config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.json" ]
theainerd
344
transformers
--- language: hi datasets: - Interspeech 2021 metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Hindi by Shyam Sunder Kumar results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice hi type: common_voice args: hi metrics: - name: Test WER type: wer value: 72.62 --- # Wav2Vec2-Large-XLSR-53-hindi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) hindi using the [Multilingual and code-switching ASR challenges for low resource Indian languages](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html). 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", "hi", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return 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(): logits = 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 hindi 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", "hi", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model.to("cuda") resampler = torchaudio.transforms.Resample(48_000, 16_000) chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return 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**: 72.62 % ## Training The script used for training can be found [Hindi ASR Fine Tuning Wav2Vec2](https://colab.research.google.com/drive/1m-F7et3CHT_kpFqg7UffTIwnUV9AKgrg?usp=sharing)
theainerd/wav2vec2-large-xlsr-53-odia
2021-03-24T08:43:37.000Z
[ "pytorch", "wav2vec2", "or", "dataset:OpenSLR", "transformers", "audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "license:apache-2.0" ]
automatic-speech-recognition
[ ".gitattributes", "README.md", "config.json", "preprocessor_config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.json" ]
theainerd
10
transformers
--- language: or datasets: - OpenSLR metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Odia by Shyam Sunder Kumar results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR type: OpenSLR args: or metrics: - name: Test WER type: wer value: 68.75 --- # Wav2Vec2-Large-XLSR-53-Odia Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) odia using the [Multilingual and code-switching ASR challenges for low resource Indian languages](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html). 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", "or", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("theainerd/wav2vec2-large-xlsr-53-odia") model = Wav2Vec2ForCTC.from_pretrained("theainerd/wav2vec2-large-xlsr-53-odia") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return 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(): logits = 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 Odia 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", "or", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("theainerd/wav2vec2-large-xlsr-53-odia") model = Wav2Vec2ForCTC.from_pretrained("theainerd/wav2vec2-large-xlsr-53-odia") model.to("cuda") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return 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**: 68.75 % ## Training The script used for training can be found [Odia ASR Fine Tuning Wav2Vec2](https://colab.research.google.com/drive/1aHpFRTxaBeNblRHAtYOy0hBeXbbMWtot?usp=sharing)
thilina/mt5-sinhalese-english
2021-01-03T21:14:26.000Z
[ "pytorch", "tf", "mt5", "seq2seq", "si", "en", "transformers", "translation", "license:apache-2.0", "text2text-generation" ]
translation
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tf_model.h5", "tokenizer_config.json", "training_args.bin" ]
thilina
58
transformers
--- language: - si - en tags: - translation license: apache-2.0 metrics: - sacrebleu --- # mt5-sinhalese-english ## Model description An mT5-base model fine-tuned on the Sinhalese-English dataset in the Tatoeba Challenge. Can be used to translate from Sinhalese to English and vice versa. ## Training details - English - Sinhala dataset from the Tatoeba Challenge [Datasets](https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/master/Data.md) - [mT5-base pre-trained weights](https://huggingface.co/google/mt5-base) ## Eval results SacreBLEU score: - English to Sinhalese: 10.3 - Sinhalese to English: 24.4
thingsu/koDPR_context
2021-05-24T02:46:37.000Z
[ "pytorch", "bert", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin" ]
thingsu
204
transformers
fintuned the kykim/bert-kor-base model as a dense passage retrieval context encoder by KLUE dataset this link is experiment result. https://wandb.ai/thingsu/DenseRetrieval Corpus : Korean Wikipedia Corpus Trained Strategy : - Pretrained Model : kykim/bert-kor-base - Inverse Cloze Task : 16 Epoch, by korquad v 1.0, KLUE MRC dataset - In-batch Negatives : 12 Epoch, by KLUE MRC dataset, random sampling between Sparse Retrieval(TF-IDF) top 100 passage per each query You must need to use Korean wikipedia corpus <pre> <code> from Transformers import AutoTokenizer, BertPreTrainedModel, BertModel class BertEncoder(BertPreTrainedModel): def __init__(self, config): super(BertEncoder, self).__init__(config) self.bert = BertModel(config) self.init_weights() def forward(self, input_ids, attention_mask=None, token_type_ids=None): outputs = self.bert(input_ids, attention_mask, token_type_ids) pooled_output = outputs[1] return pooled_output model_name = 'kykim/bert-kor-base' tokenizer = AutoTokenizer.from_pretrained(model_name) q_encoder = BertEncoder.from_pretrained("thingsu/koDPR_question") p_encoder = BertEncoder.from_pretrained("thingsu/koDPR_context") </pre> </code>
thingsu/koDPR_question
2021-05-24T02:47:00.000Z
[ "pytorch", "bert", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin" ]
thingsu
193
transformers
fintuned the kykim/bert-kor-base model as a dense passage retrieval context encoder by KLUE dataset this link is experiment result. https://wandb.ai/thingsu/DenseRetrieval Corpus : Korean Wikipedia Corpus Trained Strategy : - Pretrained Model : kykim/bert-kor-base - Inverse Cloze Task : 16 Epoch, by korquad v 1.0, KLUE MRC dataset - In-batch Negatives : 12 Epoch, by KLUE MRC dataset, random sampling between Sparse Retrieval(TF-IDF) top 100 passage per each query You must need to use Korean wikipedia corpus <pre> <code> from Transformers import AutoTokenizer, BertPreTrainedModel, BertModel class BertEncoder(BertPreTrainedModel): def __init__(self, config): super(BertEncoder, self).__init__(config) self.bert = BertModel(config) self.init_weights() def forward(self, input_ids, attention_mask=None, token_type_ids=None): outputs = self.bert(input_ids, attention_mask, token_type_ids) pooled_output = outputs[1] return pooled_output model_name = 'kykim/bert-kor-base' tokenizer = AutoTokenizer.from_pretrained(model_name) q_encoder = BertEncoder.from_pretrained("thingsu/koDPR_question") p_encoder = BertEncoder.from_pretrained("thingsu/koDPR_context") </code> </pre>
thomasdehaene/gpt2-large-dutch-finetune-oscar-10m-3epoch
2021-05-23T13:08:54.000Z
[ "pytorch", "jax", "gpt2", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.json" ]
thomasdehaene
33
transformers
thomwolf/test-model
2021-01-21T14:17:13.000Z
[]
[ ".gitattributes" ]
thomwolf
0
thomwolf/vqgan_imagenet_f16_1024
2021-06-08T21:16:25.000Z
[ "pytorch", "vqgan_model", "transformers" ]
[ ".gitattributes", "config.json", "pytorch_model.bin" ]
thomwolf
3
transformers
thu-coai/CDial-GPT2_LCCC-base
2020-12-23T07:10:27.000Z
[ "pytorch", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "vocab.txt" ]
thu-coai
25
transformers
# CDial-GPT2_LCCC-base https://github.com/thu-coai/CDial-GPT
thu-coai/CDial-GPT_LCCC-base
2020-12-23T06:47:44.000Z
[ "pytorch", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "vocab.txt" ]
thu-coai
21
transformers
# CDial-GPT_LCCC-base https://github.com/thu-coai/CDial-GPT
thu-coai/CDial-GPT_LCCC-large
2020-12-23T05:56:25.000Z
[ "pytorch", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "vocab.txt" ]
thu-coai
83
transformers
# CDial-GPT_LCCC-large https://github.com/thu-coai/CDial-GPT
thu-coai/ct5-small
2020-12-16T08:50:44.000Z
[]
[ ".gitattributes" ]
thu-coai
0
thunlp/Lawformer
2021-05-09T08:10:17.000Z
[ "pytorch", "longformer", "masked-lm", "transformers", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
thunlp
344
transformers
## Lawformer ### Introduction This repository provides the source code and checkpoints of the paper "Lawformer: A Pre-trained Language Model forChinese Legal Long Documents". You can download the checkpoint from the [huggingface model hub](https://huggingface.co/xcjthu/Lawformer) or from [here](https://data.thunlp.org/legal/Lawformer.zip). ### Easy Start We have uploaded our model to the huggingface model hub. Make sure you have installed transformers. ```python >>> from transformers import AutoModel, AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext") >>> model = AutoModel.from_pretrained("thunlp/Lawformer") >>> inputs = tokenizer("任某提起诉讼，请求判令解除婚姻关系并对夫妻共同财产进行分割。", return_tensors="pt") >>> outputs = model(**inputs) ``` ### Cite If you use the pre-trained models, please cite this paper: ``` @article{xiao2021lawformer, title={Lawformer: A Pre-trained Language Model forChinese Legal Long Documents}, author={Xiao, Chaojun and Hu, Xueyu and Liu, Zhiyuan and Tu, Cunchao and Sun, Maosong}, year={2021} } ```
thunlp/neuba-bert
2021-06-11T06:47:57.000Z
[ "pytorch", "bert", "masked-lm", "transformers", "fill-mask" ]
fill-mask
[ ".gitattributes", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
thunlp
5
transformers