davanstrien/pytorchmodelmetadata · Datasets at Hugging Face

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ydl233/t5_small_model	6aa550cd537ea06366ddabc0baa95e3c8d5c3cfc	2021-12-03T04:47:46.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	ydl233	null	ydl233/t5_small_model	null	transformers	36,300	Entry not found
ying-tina/temp	d2a1b707767ccdf35d16c911c07009a574cc1eb0	2022-01-22T03:43:36.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	ying-tina	null	ying-tina/temp	null	transformers	36,301	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: temp 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. --> # temp 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: 0.4645 - Wer: 0.3527 ## 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: 100 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.4324 \| 0.4 \| 50 \| 0.5800 \| 0.4458 \| \| 0.4027 \| 0.8 \| 100 \| 0.5374 \| 0.4109 \| \| 0.3163 \| 1.2 \| 150 \| 0.5285 \| 0.3881 \| \| 0.3064 \| 1.6 \| 200 \| 0.5161 \| 0.3815 \| \| 0.3235 \| 2.0 \| 250 \| 0.4645 \| 0.3527 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
ying-tina/wav2vec2-base-timit-demo-colab-32-epochs30	9913ce3470d5619481f2b662558f65dbea94ff4b	2022-01-09T09:21:52.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	ying-tina	null	ying-tina/wav2vec2-base-timit-demo-colab-32-epochs30	null	transformers	36,302	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab-32-epochs30 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-32-epochs30 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: 0.4615 - Wer: 0.3434 ## 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: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.5243 \| 4.0 \| 500 \| 1.4532 \| 0.9540 \| \| 0.6178 \| 8.0 \| 1000 \| 0.5490 \| 0.4627 \| \| 0.223 \| 12.0 \| 1500 \| 0.4513 \| 0.3881 \| \| 0.1299 \| 16.0 \| 2000 \| 0.4573 \| 0.3698 \| \| 0.0875 \| 20.0 \| 2500 \| 0.4950 \| 0.3637 \| \| 0.0613 \| 24.0 \| 3000 \| 0.4327 \| 0.3479 \| \| 0.0478 \| 28.0 \| 3500 \| 0.4615 \| 0.3434 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
ying-tina/wav2vec2-base-timit-demo-colab-32-epochs50-earlystop	cb6080ea36f69319bfbd2aecfe537e1cc29ae49d	2022-01-09T12:13:04.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	ying-tina	null	ying-tina/wav2vec2-base-timit-demo-colab-32-epochs50-earlystop	null	transformers	36,303	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab-32-epochs50-earlystop 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-32-epochs50-earlystop 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: 0.5208 - Wer: 0.3561 ## 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: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.4294 \| 4.0 \| 500 \| 1.3397 \| 0.8966 \| \| 0.5848 \| 8.0 \| 1000 \| 0.4931 \| 0.4585 \| \| 0.2323 \| 12.0 \| 1500 \| 0.4781 \| 0.4008 \| \| 0.14 \| 16.0 \| 2000 \| 0.4294 \| 0.3806 \| \| 0.1026 \| 20.0 \| 2500 \| 0.5098 \| 0.3663 \| \| 0.0725 \| 24.0 \| 3000 \| 0.4527 \| 0.3568 \| \| 0.058 \| 28.0 \| 3500 \| 0.5208 \| 0.3561 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
ying-tina/wav2vec2-base-timit-demo-colab-test	b690d97b77bf02b1c482d314e44602dcb62d696c	2021-12-05T14:55:36.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	ying-tina	null	ying-tina/wav2vec2-base-timit-demo-colab-test	null	transformers	36,304	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab-test 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-test 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: 0.4283 - Wer: 0.3356 ## 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: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.7386 \| 4.0 \| 500 \| 2.2419 \| 1.0 \| \| 0.9366 \| 8.0 \| 1000 \| 0.4789 \| 0.4807 \| \| 0.3118 \| 12.0 \| 1500 \| 0.4197 \| 0.3973 \| \| 0.1784 \| 16.0 \| 2000 \| 0.4216 \| 0.3614 \| \| 0.1297 \| 20.0 \| 2500 \| 0.4298 \| 0.3507 \| \| 0.1091 \| 24.0 \| 3000 \| 0.4365 \| 0.3437 \| \| 0.0819 \| 28.0 \| 3500 \| 0.4283 \| 0.3356 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
ykliu1892/opus-mt-zh-de-tuned-Tatoeba-small	a9a3017a8ba23bda34f6f62279377bee17542ef5	2022-01-02T04:09:53.000Z	[ "pytorch", "tensorboard", "marian", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	ykliu1892	null	ykliu1892/opus-mt-zh-de-tuned-Tatoeba-small	null	transformers	36,305	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: opus-mt-zh-de-tuned-Tatoeba-small 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. --> # opus-mt-zh-de-tuned-Tatoeba-small This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-de](https://huggingface.co/Helsinki-NLP/opus-mt-zh-de) on a refined dataset of Tatoeba German - Chinese corpus https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/master/data/README.md. It achieves the following results on the evaluation set: - Loss: 2.2703 - Bleu: 16.504 - Gen Len: 16.6531 ## Model description More information needed ## Intended uses & limitations Prefix used during fine-tuning: "将中文翻译成德语". This prefix is also recommended in prediction. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:-------:\|:-------:\| \| 2.7229 \| 0.24 \| 16000 \| 2.5605 \| 14.1956 \| 16.2206 \| \| 2.5988 \| 0.49 \| 32000 \| 2.4447 \| 14.8619 \| 16.2726 \| \| 2.515 \| 0.73 \| 48000 \| 2.3817 \| 15.3212 \| 16.2823 \| \| 2.4683 \| 0.97 \| 64000 \| 2.3367 \| 15.9043 \| 16.7138 \| \| 2.3873 \| 1.22 \| 80000 \| 2.3115 \| 16.1037 \| 16.6369 \| \| 2.3792 \| 1.46 \| 96000 \| 2.2919 \| 16.2957 \| 16.6304 \| \| 2.3626 \| 1.7 \| 112000 \| 2.2790 \| 16.2995 \| 16.6235 \| \| 2.3353 \| 1.95 \| 128000 \| 2.2703 \| 16.504 \| 16.6531 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles	56b97d94a094e8128c72c5abe93264ce2966dfb2	2021-11-30T13:22:24.000Z	[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]	text2text-generation	false	ykliu1892	null	ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles	null	transformers	36,306	--- tags: - generated_from_trainer model-index: - name: translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles 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. --> # translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles	e07a2cb8d52d60da480e037770f9a4290fe3f653	2021-12-13T17:37:09.000Z	[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]	text2text-generation	false	ykliu1892	null	ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles	1	transformers	36,307	--- tags: - generated_from_trainer model-index: - name: translation-en-pt-t5-finetuned-Duolingo-Subtitles 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. --> # translation-en-pt-t5-finetuned-Duolingo-Subtitles This model is a fine-tuned version of [ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles](https://huggingface.co/ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.0932 - eval_bleu: 28.4269 - eval_gen_len: 8.816 - eval_runtime: 1404.5946 - eval_samples_per_second: 106.792 - eval_steps_per_second: 3.338 - epoch: 0.52 - step: 28000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
ykliu1892/translation-en-pt-t5-finetuned-Duolingo	a97e566ab8248b8571308e19e176102c4d02a0a6	2021-12-01T04:58:54.000Z	[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]	text2text-generation	false	ykliu1892	null	ykliu1892/translation-en-pt-t5-finetuned-Duolingo	null	transformers	36,308	--- tags: - generated_from_trainer metrics: - bleu model-index: - name: translation-en-pt-t5-finetuned-Duolingo 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. --> # translation-en-pt-t5-finetuned-Duolingo This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7362 - Bleu: 39.4725 - Gen Len: 9.002 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 0.5429 \| 0.24 \| 9000 \| 0.7461 \| 39.4744 \| 9.0 \| \| 0.5302 \| 0.48 \| 18000 \| 0.7431 \| 39.7559 \| 8.97 \| \| 0.5309 \| 0.72 \| 27000 \| 0.7388 \| 39.6751 \| 8.998 \| \| 0.5336 \| 0.96 \| 36000 \| 0.7362 \| 39.4725 \| 9.002 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
ylh1013/ja_chatbot	8e19ffd505c79f8576618e27b3aeecdeb8997db6	2022-01-23T02:24:03.000Z	[ "pytorch", "gpt2", "text-generation", "finetuned_from", "transformers", "generated_from_trainer", "license:mit", "model-index" ]	text-generation	false	ylh1013	null	ylh1013/ja_chatbot	null	transformers	36,309	--- language: - finetuned_from license: mit tags: - generated_from_trainer model-index: - name: ja_chatbot 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. --> # ja_chatbot This model is a fine-tuned version of [rinna/japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2-medium) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 16 - 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: 1 ### Training results ### Framework versions - Transformers 4.12.3 - Pytorch 1.10.0+cu102 - Tokenizers 0.10.3
yliu337/t5_mask_cnn_dailymail	70be5e2acf7a0e2b95f3fd7fc20e188a3c19e7f0	2022-06-04T21:38:05.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	yliu337	null	yliu337/t5_mask_cnn_dailymail	null	transformers	36,310	Entry not found
yliu337/t5_token_nonfilter_bothcontext	41feba322090a7451c4c7ec8e17f80d5717a53a0	2021-09-05T01:20:00.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	yliu337	null	yliu337/t5_token_nonfilter_bothcontext	null	transformers	36,311	Entry not found
yliu337/t5_token_nonfilter_bothcontext_padded_ctx	6bcb300a8bf8f6e6378194909af1a5829510eb25	2021-09-15T01:14:36.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	yliu337	null	yliu337/t5_token_nonfilter_bothcontext_padded_ctx	null	transformers	36,312	Entry not found
young/BertForFinance	c818d7abf7630585153f24ea946987f818ad1589	2021-03-17T05:13:04.000Z	[ "pytorch", "transformers" ]	null	false	young	null	young/BertForFinance	null	transformers	36,313	Entry not found
ytlin/1riatc43	f8c6535e20483763f2fc5d57cf6be48f14dd850b	2020-10-05T21:26:03.000Z	[ "pytorch", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	ytlin	null	ytlin/1riatc43	null	transformers	36,314	Entry not found
ytlin/2jgyqp5g	e03e6637c42a8e88ac14155e4dd1b3feb94d7b64	2020-10-06T06:54:48.000Z	[ "pytorch", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	ytlin	null	ytlin/2jgyqp5g	null	transformers	36,315	Entry not found
ytlin/46695u38_3	403eef05120ee96f0812eab7aa46363248aab25f	2021-05-23T13:51:39.000Z	[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]	text-generation	false	ytlin	null	ytlin/46695u38_3	null	transformers	36,316	Entry not found
yusufmorsi/georgebot	5ec19a640a9015980c3c5445f7e611e1df4ecb2c	2021-11-21T21:54:17.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	yusufmorsi	null	yusufmorsi/georgebot	null	transformers	36,317	--- tags: - conversational --- # George Costanza Model
yxchar/tlm-ag-large-scale	e85d8e664ab4dc59a7e288fd9e4fa6a8a983dfb8	2021-11-04T11:08:19.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-ag-large-scale	null	transformers	36,318	Entry not found
yxchar/tlm-ag-small-scale	d3aa55a8fe2f1e9c88777b89610eb2b58b4cec0f	2021-11-04T09:45:58.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-ag-small-scale	null	transformers	36,319	Entry not found
yxchar/tlm-amazon-large-scale	425213afb8e3cf6d980a4cc95796a650f14ad792	2021-11-04T13:45:00.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-amazon-large-scale	null	transformers	36,320	Entry not found
yxchar/tlm-amazon-small-scale	b5e9e950b40d0eeaf92715252d611f330d3fa921	2021-11-04T13:26:20.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-amazon-small-scale	null	transformers	36,321	Entry not found
yxchar/tlm-chemprot-large-scale	ec524900ada76dd3ce633bffc91d014cd64ca9b9	2021-11-04T14:25:13.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-chemprot-large-scale	null	transformers	36,322	Entry not found
yxchar/tlm-chemprot-small-scale	7f9915f7a0e3123bc99fd0202aa4ec9117dae60f	2021-11-04T14:09:54.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-chemprot-small-scale	null	transformers	36,323	Entry not found
yxchar/tlm-citation_intent-large-scale	63ec60d4b24f09930ec8ba0aceab23ef2666b349	2021-11-04T15:03:41.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-citation_intent-large-scale	null	transformers	36,324	Entry not found
yxchar/tlm-citation_intent-medium-scale	6502485b23dbc3f3fae9cd93afd8bbce46654bc6	2021-11-04T14:55:07.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-citation_intent-medium-scale	null	transformers	36,325	Entry not found
yxchar/tlm-citation_intent-small-scale	d3b9059002e08af3dc63ac9cbd72825b31cc5b49	2021-11-04T14:47:38.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-citation_intent-small-scale	null	transformers	36,326	Entry not found
yxchar/tlm-hyp-large-scale	5aed89ef25dc4b1034e4292ff553855d96973ea4	2021-11-04T15:42:49.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-hyp-large-scale	null	transformers	36,327	Entry not found
yxchar/tlm-imdb-medium-scale	87b82975e34b61010160008500b19731f073ef82	2021-11-04T09:43:24.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-imdb-medium-scale	null	transformers	36,328	Entry not found
yxchar/tlm-rct-20k-large-scale	22c465caf13bb019b7f347debae40385ef27b231	2021-11-04T16:02:51.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-rct-20k-large-scale	null	transformers	36,329	Entry not found
yxchar/tlm-rct-20k-medium-scale	baf67c6765879c4c324e56fbf4a5ab0a8f775050	2021-11-04T17:20:07.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yxchar	null	yxchar/tlm-rct-20k-medium-scale	null	transformers	36,330	Entry not found
yysung53/dpr	d33c2c0b18ed241147daa8561f8f80f84d244fc9	2021-10-30T22:18:04.000Z	[ "pytorch", "text_similarity", "transformers" ]	null	false	yysung53	null	yysung53/dpr	null	transformers	36,331	Entry not found
yzhou992/NetMind-20211103-448	a78f2d6e0bacac3cc17a466744e2fce9b45282c9	2021-11-03T05:47:20.000Z	[ "pytorch", "albert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yzhou992	null	yzhou992/NetMind-20211103-448	null	transformers	36,332	Entry not found
yzhou992/test_model2	66febcd1d6734a22b868915c1d0bfce3c08c7d64	2021-11-03T05:50:54.000Z	[ "pytorch", "albert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	yzhou992	null	yzhou992/test_model2	null	transformers	36,333	Entry not found
zachzhang/t5_hcm	1947584a85313c86463339539c45717a706b7470	2021-10-08T22:50:26.000Z	[ "pytorch" ]	null	false	zachzhang	null	zachzhang/t5_hcm	null	null	36,334	Entry not found
zari/my-awesome-model	c019cff5a02bc0a02780d74e0052a2d36ce17226	2021-06-22T21:29:07.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "license:apache-2.0" ]	text-generation	false	zari	null	zari/my-awesome-model	null	transformers	36,335	--- license: apache-2.0 datasets: - null model_index: - name: my-awesome-model 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. --> # my-awesome-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4356 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 91 \| 3.4934 \| \| No log \| 2.0 \| 182 \| 3.4451 \| \| No log \| 3.0 \| 273 \| 3.4356 \| ### Framework versions - Transformers 4.7.0 - Pytorch 1.9.0+cu102 - Datasets 1.8.0 - Tokenizers 0.10.3
zbmain/test	1ba8a708cacf37af6ac60a21a87986afe61bfa7f	2020-11-24T12:12:29.000Z	[ "pytorch" ]	null	false	zbmain	null	zbmain/test	null	null	36,336	123
zen-satvik/BotGPT-medium-HP	1a335d2e651151a6aebe745afac08dbf994adebc	2021-08-28T07:09:59.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	zen-satvik	null	zen-satvik/BotGPT-medium-HP	null	transformers	36,337	--- tags: conversational --- # Harry Potter Bot GPT Model
zeping/codeparrot	a094184fde7e3c78122fc8e16e31bd28436ec3fd	2022-01-19T10:03:09.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	zeping	null	zeping/codeparrot	null	transformers	36,338	Entry not found
zgotter/gpt2-test	60a92ccd47c1c10a57fbde99442fc8db3cab0d39	2021-10-11T07:13:15.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	zgotter	null	zgotter/gpt2-test	null	transformers	36,339	Entry not found
zhangxy-2019/cu_dstc9_dialoGPT	15b85cd201a34b6c52ac1cb1ed5a126cd84162a2	2021-05-23T14:05:15.000Z	[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]	text-generation	false	zhangxy-2019	null	zhangxy-2019/cu_dstc9_dialoGPT	null	transformers	36,340	Entry not found
zharry29/intent_fb-th_id	60e747d83d8c2a79a527965b835648eed92300dd	2020-09-16T20:16:29.000Z	[ "pytorch", "xlm-roberta", "multiple-choice", "transformers" ]	multiple-choice	false	zharry29	null	zharry29/intent_fb-th_id	null	transformers	36,341	Entry not found
zharry29/intent_fb-th_wh_id	55e35ceda0abe1a13031ccbe59f7a13a8b92c7fa	2020-09-16T20:17:00.000Z	[ "pytorch", "xlm-roberta", "multiple-choice", "transformers" ]	multiple-choice	false	zharry29	null	zharry29/intent_fb-th_wh_id	null	transformers	36,342	Entry not found
zharry29/intent_snips_id	4cefcb746824eb1b5ddb997878d2308e9a6cf371	2021-05-20T23:47:11.000Z	[ "pytorch", "jax", "roberta", "multiple-choice", "transformers" ]	multiple-choice	false	zharry29	null	zharry29/intent_snips_id	null	transformers	36,343	Entry not found
zhenghuabin/dummy_model	650ecd60a48463ff392cb24c61ba0f2c4d44d628	2021-11-06T09:59:44.000Z	[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhenghuabin	null	zhenghuabin/dummy_model	null	transformers	36,344	Entry not found
zhuqing/bert-base-uncased-exp2-feminist	1f64c99dfb16602f3adbd32e97fe80ffe5d5879f	2021-08-28T13:07:52.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhuqing	null	zhuqing/bert-base-uncased-exp2-feminist	null	transformers	36,345	Entry not found
zhuqing/bert-base-uncased-mumsnet-all-0	eefd92d01121b055c77d4b3c7a2ca22f63d07218	2021-08-08T09:08:07.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhuqing	null	zhuqing/bert-base-uncased-mumsnet-all-0	null	transformers	36,346	Entry not found
zhuqing/bert-base-uncased-mumsnet-all-1	7a531cdfaa4f35f21e9891c3e728b7c745ad576c	2021-08-08T10:06:39.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhuqing	null	zhuqing/bert-base-uncased-mumsnet-all-1	null	transformers	36,347	Entry not found
zhuqing/bert-base-uncased-netmums-feminist-v2	a9936660af357cc407d1046278da8b8b8e2200b8	2021-08-15T11:41:05.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhuqing	null	zhuqing/bert-base-uncased-netmums-feminist-v2	null	transformers	36,348	Entry not found
zhuqing/bert-base-uncased-netmums-parent	5ff1779fe99f13bbd1593e7a929018756f5dbaf0	2021-08-14T07:43:23.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhuqing	null	zhuqing/bert-base-uncased-netmums-parent	null	transformers	36,349	Entry not found
zhuqing/bert-base-uncased-reddit-lib	92db2000a5c86c298d27ee07535a3626dd2c93bb	2021-08-01T16:27:15.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhuqing	null	zhuqing/bert-base-uncased-reddit-lib	null	transformers	36,350	Entry not found
zhuqing/bert-base-uncased-theme2	6a955641db575c9e832a497827b43c8e3361e641	2021-07-17T07:40:12.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhuqing	null	zhuqing/bert-base-uncased-theme2	null	transformers	36,351	Entry not found
zhuqing/comparison-bert-base-uncased-netmums-feminist	ce633f7fa72a837541d078ddd3286c7a370a1552	2021-08-19T19:32:49.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhuqing	null	zhuqing/comparison-bert-base-uncased-netmums-feminist	null	transformers	36,352	Entry not found
zhuqing/comparison-distilbert-base-uncased-netmums-feminist	f39c7ee5bcc20fea038095bdd51d0c155845e04e	2021-08-20T07:21:29.000Z	[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhuqing	null	zhuqing/comparison-distilbert-base-uncased-netmums-feminist	null	transformers	36,353	Entry not found
zhuqing/distilroberta-base-theme2-6000	a53ffa9bb3f4531e1d519a67f321a135f6fffb84	2021-07-31T16:27:51.000Z	[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhuqing	null	zhuqing/distilroberta-base-theme2-6000	null	transformers	36,354	Entry not found
zhuqing/roberta-base-uncased-netmums-all	179ff25e0099f2b454ec31f8cef1657c05e57bda	2021-08-20T09:23:54.000Z	[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zhuqing	null	zhuqing/roberta-base-uncased-netmums-all	null	transformers	36,355	Entry not found
zinary/DialoGPT-small-rick-new	f40df2511796a5d7e90e82a990acf856e4a15e4b	2021-09-19T07:26:27.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	zinary	null	zinary/DialoGPT-small-rick-new	null	transformers	36,356	--- tags: - conversational --- #Rick and Morty DialoGPT
zmingshi/roberta_L-12_H-768_A-12	a6f51347b5f5ed7f931c9b00059abc6bfc7f20ef	2021-11-29T06:47:26.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zmingshi	null	zmingshi/roberta_L-12_H-768_A-12	null	transformers	36,357	Entry not found
zqf03118/bert_finetuning_test	88544fa69b07e75e6add24bb9c8d08a66b543448	2021-05-20T09:56:44.000Z	[ "pytorch", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	zqf03118	null	zqf03118/bert_finetuning_test	null	transformers	36,358	Entry not found
zuto37/DialoGPT-small-sadao	0221f7e03c60cb1daa16913449d731be90850c57	2021-09-23T09:07:55.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	zuto37	null	zuto37/DialoGPT-small-sadao	null	transformers	36,359	--- tags: - conversational --- # DialoGPT Model
zyayoung/cv-full-paper	b652f46baea9282cf60cca75dbf27a0413686f29	2021-11-23T06:27:33.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	zyayoung	null	zyayoung/cv-full-paper	1	transformers	36,360	Entry not found
nielsr/enformer-preview-v2	03a454c666702bfdfbf37560bcbd9e4fca9deb9b	2022-02-24T07:09:45.000Z	[ "pytorch" ]	null	false	nielsr	null	nielsr/enformer-preview-v2	null	null	36,361	Entry not found
wietsedv/xlm-roberta-base-ft-udpos28-da	2c1c1ec9ac7b42bea7788a3896158d8a7b92d4cb	2022-02-25T09:58:14.000Z	[ "pytorch", "xlm-roberta", "token-classification", "da", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	wietsedv	null	wietsedv/xlm-roberta-base-ft-udpos28-da	null	transformers	36,362	--- language: - da license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-da results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 89.9 - type: accuracy name: Dutch Test accuracy value: 90.0 - type: accuracy name: German Test accuracy value: 88.8 - type: accuracy name: Italian Test accuracy value: 89.4 - type: accuracy name: French Test accuracy value: 89.0 - type: accuracy name: Spanish Test accuracy value: 91.6 - type: accuracy name: Russian Test accuracy value: 90.3 - type: accuracy name: Swedish Test accuracy value: 92.4 - type: accuracy name: Norwegian Test accuracy value: 87.3 - type: accuracy name: Danish Test accuracy value: 98.3 - type: accuracy name: Low Saxon Test accuracy value: 42.2 - type: accuracy name: Akkadian Test accuracy value: 24.0 - type: accuracy name: Armenian Test accuracy value: 89.1 - type: accuracy name: Welsh Test accuracy value: 69.2 - type: accuracy name: Old East Slavic Test accuracy value: 71.8 - type: accuracy name: Albanian Test accuracy value: 79.7 - type: accuracy name: Slovenian Test accuracy value: 78.9 - type: accuracy name: Guajajara Test accuracy value: 19.2 - type: accuracy name: Kurmanji Test accuracy value: 78.1 - type: accuracy name: Turkish Test accuracy value: 78.9 - type: accuracy name: Finnish Test accuracy value: 88.2 - type: accuracy name: Indonesian Test accuracy value: 84.8 - type: accuracy name: Ukrainian Test accuracy value: 88.6 - type: accuracy name: Polish Test accuracy value: 86.2 - type: accuracy name: Portuguese Test accuracy value: 91.0 - type: accuracy name: Kazakh Test accuracy value: 83.9 - type: accuracy name: Latin Test accuracy value: 79.8 - type: accuracy name: Old French Test accuracy value: 51.8 - type: accuracy name: Buryat Test accuracy value: 57.8 - type: accuracy name: Kaapor Test accuracy value: 12.5 - type: accuracy name: Korean Test accuracy value: 65.7 - type: accuracy name: Estonian Test accuracy value: 88.4 - type: accuracy name: Croatian Test accuracy value: 89.8 - type: accuracy name: Gothic Test accuracy value: 12.7 - type: accuracy name: Swiss German Test accuracy value: 44.8 - type: accuracy name: Assyrian Test accuracy value: 15.7 - type: accuracy name: North Sami Test accuracy value: 29.9 - type: accuracy name: Naija Test accuracy value: 38.0 - type: accuracy name: Latvian Test accuracy value: 88.4 - type: accuracy name: Chinese Test accuracy value: 43.2 - type: accuracy name: Tagalog Test accuracy value: 73.1 - type: accuracy name: Bambara Test accuracy value: 25.0 - type: accuracy name: Lithuanian Test accuracy value: 86.4 - type: accuracy name: Galician Test accuracy value: 88.1 - type: accuracy name: Vietnamese Test accuracy value: 65.2 - type: accuracy name: Greek Test accuracy value: 87.1 - type: accuracy name: Catalan Test accuracy value: 89.7 - type: accuracy name: Czech Test accuracy value: 89.0 - type: accuracy name: Erzya Test accuracy value: 40.8 - type: accuracy name: Bhojpuri Test accuracy value: 49.9 - type: accuracy name: Thai Test accuracy value: 59.9 - type: accuracy name: Marathi Test accuracy value: 85.9 - type: accuracy name: Basque Test accuracy value: 77.2 - type: accuracy name: Slovak Test accuracy value: 90.2 - type: accuracy name: Kiche Test accuracy value: 26.0 - type: accuracy name: Yoruba Test accuracy value: 18.1 - type: accuracy name: Warlpiri Test accuracy value: 38.5 - type: accuracy name: Tamil Test accuracy value: 84.0 - type: accuracy name: Maltese Test accuracy value: 17.5 - type: accuracy name: Ancient Greek Test accuracy value: 63.8 - type: accuracy name: Icelandic Test accuracy value: 85.0 - type: accuracy name: Mbya Guarani Test accuracy value: 23.4 - type: accuracy name: Urdu Test accuracy value: 70.1 - type: accuracy name: Romanian Test accuracy value: 85.4 - type: accuracy name: Persian Test accuracy value: 77.9 - type: accuracy name: Apurina Test accuracy value: 26.0 - type: accuracy name: Japanese Test accuracy value: 28.6 - type: accuracy name: Hungarian Test accuracy value: 85.1 - type: accuracy name: Hindi Test accuracy value: 74.6 - type: accuracy name: Classical Chinese Test accuracy value: 28.2 - type: accuracy name: Komi Permyak Test accuracy value: 39.0 - type: accuracy name: Faroese Test accuracy value: 79.3 - type: accuracy name: Sanskrit Test accuracy value: 26.8 - type: accuracy name: Livvi Test accuracy value: 62.8 - type: accuracy name: Arabic Test accuracy value: 80.8 - type: accuracy name: Wolof Test accuracy value: 24.3 - type: accuracy name: Bulgarian Test accuracy value: 91.0 - type: accuracy name: Akuntsu Test accuracy value: 18.5 - type: accuracy name: Makurap Test accuracy value: 10.3 - type: accuracy name: Kangri Test accuracy value: 44.7 - type: accuracy name: Breton Test accuracy value: 66.1 - type: accuracy name: Telugu Test accuracy value: 85.4 - type: accuracy name: Cantonese Test accuracy value: 45.0 - type: accuracy name: Old Church Slavonic Test accuracy value: 43.0 - type: accuracy name: Karelian Test accuracy value: 69.1 - type: accuracy name: Upper Sorbian Test accuracy value: 71.1 - type: accuracy name: South Levantine Arabic Test accuracy value: 66.5 - type: accuracy name: Komi Zyrian Test accuracy value: 33.2 - type: accuracy name: Irish Test accuracy value: 69.1 - type: accuracy name: Nayini Test accuracy value: 39.7 - type: accuracy name: Munduruku Test accuracy value: 11.6 - type: accuracy name: Manx Test accuracy value: 23.9 - type: accuracy name: Skolt Sami Test accuracy value: 27.0 - type: accuracy name: Afrikaans Test accuracy value: 90.0 - type: accuracy name: Old Turkish Test accuracy value: 38.5 - type: accuracy name: Tupinamba Test accuracy value: 24.0 - type: accuracy name: Belarusian Test accuracy value: 91.0 - type: accuracy name: Serbian Test accuracy value: 90.4 - type: accuracy name: Moksha Test accuracy value: 41.2 - type: accuracy name: Western Armenian Test accuracy value: 82.0 - type: accuracy name: Scottish Gaelic Test accuracy value: 60.3 - type: accuracy name: Khunsari Test accuracy value: 41.9 - type: accuracy name: Hebrew Test accuracy value: 94.8 - type: accuracy name: Uyghur Test accuracy value: 76.5 - type: accuracy name: Chukchi Test accuracy value: 33.2 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Danish This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-da") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-da") ```
wietsedv/xlm-roberta-base-ft-udpos28-el	7258c1aac060a6dfc235ddeec1b3e069a3a52461	2022-02-25T09:58:17.000Z	[ "pytorch", "xlm-roberta", "token-classification", "el", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	wietsedv	null	wietsedv/xlm-roberta-base-ft-udpos28-el	null	transformers	36,363	--- language: - el license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-el results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 83.6 - type: accuracy name: Dutch Test accuracy value: 82.2 - type: accuracy name: German Test accuracy value: 82.6 - type: accuracy name: Italian Test accuracy value: 82.0 - type: accuracy name: French Test accuracy value: 78.7 - type: accuracy name: Spanish Test accuracy value: 82.2 - type: accuracy name: Russian Test accuracy value: 88.4 - type: accuracy name: Swedish Test accuracy value: 87.4 - type: accuracy name: Norwegian Test accuracy value: 82.1 - type: accuracy name: Danish Test accuracy value: 85.9 - type: accuracy name: Low Saxon Test accuracy value: 49.8 - type: accuracy name: Akkadian Test accuracy value: 24.4 - type: accuracy name: Armenian Test accuracy value: 84.0 - type: accuracy name: Welsh Test accuracy value: 68.9 - type: accuracy name: Old East Slavic Test accuracy value: 75.0 - type: accuracy name: Albanian Test accuracy value: 87.7 - type: accuracy name: Slovenian Test accuracy value: 77.2 - type: accuracy name: Guajajara Test accuracy value: 25.8 - type: accuracy name: Kurmanji Test accuracy value: 74.3 - type: accuracy name: Turkish Test accuracy value: 75.3 - type: accuracy name: Finnish Test accuracy value: 83.4 - type: accuracy name: Indonesian Test accuracy value: 75.4 - type: accuracy name: Ukrainian Test accuracy value: 88.6 - type: accuracy name: Polish Test accuracy value: 84.0 - type: accuracy name: Portuguese Test accuracy value: 82.4 - type: accuracy name: Kazakh Test accuracy value: 80.5 - type: accuracy name: Latin Test accuracy value: 77.3 - type: accuracy name: Old French Test accuracy value: 52.5 - type: accuracy name: Buryat Test accuracy value: 56.0 - type: accuracy name: Kaapor Test accuracy value: 11.2 - type: accuracy name: Korean Test accuracy value: 59.9 - type: accuracy name: Estonian Test accuracy value: 83.6 - type: accuracy name: Croatian Test accuracy value: 84.9 - type: accuracy name: Gothic Test accuracy value: 20.2 - type: accuracy name: Swiss German Test accuracy value: 43.6 - type: accuracy name: Assyrian Test accuracy value: 14.6 - type: accuracy name: North Sami Test accuracy value: 33.5 - type: accuracy name: Naija Test accuracy value: 42.7 - type: accuracy name: Latvian Test accuracy value: 84.9 - type: accuracy name: Chinese Test accuracy value: 42.1 - type: accuracy name: Tagalog Test accuracy value: 66.7 - type: accuracy name: Bambara Test accuracy value: 28.2 - type: accuracy name: Lithuanian Test accuracy value: 85.3 - type: accuracy name: Galician Test accuracy value: 82.1 - type: accuracy name: Vietnamese Test accuracy value: 62.8 - type: accuracy name: Greek Test accuracy value: 98.0 - type: accuracy name: Catalan Test accuracy value: 80.4 - type: accuracy name: Czech Test accuracy value: 85.0 - type: accuracy name: Erzya Test accuracy value: 43.9 - type: accuracy name: Bhojpuri Test accuracy value: 45.0 - type: accuracy name: Thai Test accuracy value: 58.6 - type: accuracy name: Marathi Test accuracy value: 85.3 - type: accuracy name: Basque Test accuracy value: 72.4 - type: accuracy name: Slovak Test accuracy value: 82.8 - type: accuracy name: Kiche Test accuracy value: 36.2 - type: accuracy name: Yoruba Test accuracy value: 28.9 - type: accuracy name: Warlpiri Test accuracy value: 38.9 - type: accuracy name: Tamil Test accuracy value: 83.0 - type: accuracy name: Maltese Test accuracy value: 22.3 - type: accuracy name: Ancient Greek Test accuracy value: 64.2 - type: accuracy name: Icelandic Test accuracy value: 80.7 - type: accuracy name: Mbya Guarani Test accuracy value: 32.4 - type: accuracy name: Urdu Test accuracy value: 53.0 - type: accuracy name: Romanian Test accuracy value: 83.7 - type: accuracy name: Persian Test accuracy value: 74.4 - type: accuracy name: Apurina Test accuracy value: 41.3 - type: accuracy name: Japanese Test accuracy value: 30.0 - type: accuracy name: Hungarian Test accuracy value: 80.2 - type: accuracy name: Hindi Test accuracy value: 60.0 - type: accuracy name: Classical Chinese Test accuracy value: 30.1 - type: accuracy name: Komi Permyak Test accuracy value: 44.2 - type: accuracy name: Faroese Test accuracy value: 72.9 - type: accuracy name: Sanskrit Test accuracy value: 40.4 - type: accuracy name: Livvi Test accuracy value: 65.2 - type: accuracy name: Arabic Test accuracy value: 76.6 - type: accuracy name: Wolof Test accuracy value: 28.0 - type: accuracy name: Bulgarian Test accuracy value: 89.6 - type: accuracy name: Akuntsu Test accuracy value: 26.7 - type: accuracy name: Makurap Test accuracy value: 18.5 - type: accuracy name: Kangri Test accuracy value: 43.1 - type: accuracy name: Breton Test accuracy value: 63.5 - type: accuracy name: Telugu Test accuracy value: 85.3 - type: accuracy name: Cantonese Test accuracy value: 48.3 - type: accuracy name: Old Church Slavonic Test accuracy value: 51.6 - type: accuracy name: Karelian Test accuracy value: 71.0 - type: accuracy name: Upper Sorbian Test accuracy value: 69.5 - type: accuracy name: South Levantine Arabic Test accuracy value: 69.2 - type: accuracy name: Komi Zyrian Test accuracy value: 36.5 - type: accuracy name: Irish Test accuracy value: 61.3 - type: accuracy name: Nayini Test accuracy value: 43.6 - type: accuracy name: Munduruku Test accuracy value: 29.4 - type: accuracy name: Manx Test accuracy value: 33.8 - type: accuracy name: Skolt Sami Test accuracy value: 31.5 - type: accuracy name: Afrikaans Test accuracy value: 85.0 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 29.2 - type: accuracy name: Belarusian Test accuracy value: 89.1 - type: accuracy name: Serbian Test accuracy value: 85.2 - type: accuracy name: Moksha Test accuracy value: 43.8 - type: accuracy name: Western Armenian Test accuracy value: 76.9 - type: accuracy name: Scottish Gaelic Test accuracy value: 54.8 - type: accuracy name: Khunsari Test accuracy value: 45.9 - type: accuracy name: Hebrew Test accuracy value: 88.5 - type: accuracy name: Uyghur Test accuracy value: 75.7 - type: accuracy name: Chukchi Test accuracy value: 34.8 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Greek This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el") ```
wietsedv/xlm-roberta-base-ft-udpos28-et	dfc3d406501190ec0945726c67032a39e87fd0ed	2022-02-25T09:58:22.000Z	[ "pytorch", "xlm-roberta", "token-classification", "et", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	wietsedv	null	wietsedv/xlm-roberta-base-ft-udpos28-et	null	transformers	36,364	--- language: - et license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-et results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 82.3 - type: accuracy name: Dutch Test accuracy value: 80.9 - type: accuracy name: German Test accuracy value: 80.4 - type: accuracy name: Italian Test accuracy value: 78.0 - type: accuracy name: French Test accuracy value: 75.6 - type: accuracy name: Spanish Test accuracy value: 75.4 - type: accuracy name: Russian Test accuracy value: 88.2 - type: accuracy name: Swedish Test accuracy value: 89.1 - type: accuracy name: Norwegian Test accuracy value: 83.2 - type: accuracy name: Danish Test accuracy value: 87.0 - type: accuracy name: Low Saxon Test accuracy value: 52.2 - type: accuracy name: Akkadian Test accuracy value: 37.9 - type: accuracy name: Armenian Test accuracy value: 87.7 - type: accuracy name: Welsh Test accuracy value: 61.5 - type: accuracy name: Old East Slavic Test accuracy value: 74.6 - type: accuracy name: Albanian Test accuracy value: 74.0 - type: accuracy name: Slovenian Test accuracy value: 77.3 - type: accuracy name: Guajajara Test accuracy value: 30.7 - type: accuracy name: Kurmanji Test accuracy value: 76.7 - type: accuracy name: Turkish Test accuracy value: 79.3 - type: accuracy name: Finnish Test accuracy value: 90.5 - type: accuracy name: Indonesian Test accuracy value: 84.1 - type: accuracy name: Ukrainian Test accuracy value: 86.9 - type: accuracy name: Polish Test accuracy value: 84.4 - type: accuracy name: Portuguese Test accuracy value: 79.6 - type: accuracy name: Kazakh Test accuracy value: 83.0 - type: accuracy name: Latin Test accuracy value: 78.5 - type: accuracy name: Old French Test accuracy value: 50.0 - type: accuracy name: Buryat Test accuracy value: 64.6 - type: accuracy name: Kaapor Test accuracy value: 21.2 - type: accuracy name: Korean Test accuracy value: 62.9 - type: accuracy name: Estonian Test accuracy value: 96.8 - type: accuracy name: Croatian Test accuracy value: 87.0 - type: accuracy name: Gothic Test accuracy value: 24.7 - type: accuracy name: Swiss German Test accuracy value: 40.7 - type: accuracy name: Assyrian Test accuracy value: 20.1 - type: accuracy name: North Sami Test accuracy value: 46.7 - type: accuracy name: Naija Test accuracy value: 41.8 - type: accuracy name: Latvian Test accuracy value: 87.9 - type: accuracy name: Chinese Test accuracy value: 52.1 - type: accuracy name: Tagalog Test accuracy value: 65.9 - type: accuracy name: Bambara Test accuracy value: 27.9 - type: accuracy name: Lithuanian Test accuracy value: 86.0 - type: accuracy name: Galician Test accuracy value: 74.4 - type: accuracy name: Vietnamese Test accuracy value: 63.7 - type: accuracy name: Greek Test accuracy value: 77.4 - type: accuracy name: Catalan Test accuracy value: 73.4 - type: accuracy name: Czech Test accuracy value: 87.4 - type: accuracy name: Erzya Test accuracy value: 53.1 - type: accuracy name: Bhojpuri Test accuracy value: 52.4 - type: accuracy name: Thai Test accuracy value: 62.6 - type: accuracy name: Marathi Test accuracy value: 88.3 - type: accuracy name: Basque Test accuracy value: 77.1 - type: accuracy name: Slovak Test accuracy value: 87.0 - type: accuracy name: Kiche Test accuracy value: 37.8 - type: accuracy name: Yoruba Test accuracy value: 26.7 - type: accuracy name: Warlpiri Test accuracy value: 42.1 - type: accuracy name: Tamil Test accuracy value: 85.4 - type: accuracy name: Maltese Test accuracy value: 30.9 - type: accuracy name: Ancient Greek Test accuracy value: 65.9 - type: accuracy name: Icelandic Test accuracy value: 82.9 - type: accuracy name: Mbya Guarani Test accuracy value: 30.6 - type: accuracy name: Urdu Test accuracy value: 67.0 - type: accuracy name: Romanian Test accuracy value: 78.5 - type: accuracy name: Persian Test accuracy value: 73.9 - type: accuracy name: Apurina Test accuracy value: 47.9 - type: accuracy name: Japanese Test accuracy value: 38.9 - type: accuracy name: Hungarian Test accuracy value: 83.2 - type: accuracy name: Hindi Test accuracy value: 71.6 - type: accuracy name: Classical Chinese Test accuracy value: 35.4 - type: accuracy name: Komi Permyak Test accuracy value: 53.2 - type: accuracy name: Faroese Test accuracy value: 76.4 - type: accuracy name: Sanskrit Test accuracy value: 38.8 - type: accuracy name: Livvi Test accuracy value: 71.2 - type: accuracy name: Arabic Test accuracy value: 76.3 - type: accuracy name: Wolof Test accuracy value: 35.3 - type: accuracy name: Bulgarian Test accuracy value: 85.8 - type: accuracy name: Akuntsu Test accuracy value: 37.5 - type: accuracy name: Makurap Test accuracy value: 15.8 - type: accuracy name: Kangri Test accuracy value: 51.7 - type: accuracy name: Breton Test accuracy value: 60.1 - type: accuracy name: Telugu Test accuracy value: 84.2 - type: accuracy name: Cantonese Test accuracy value: 58.3 - type: accuracy name: Old Church Slavonic Test accuracy value: 51.8 - type: accuracy name: Karelian Test accuracy value: 75.7 - type: accuracy name: Upper Sorbian Test accuracy value: 77.3 - type: accuracy name: South Levantine Arabic Test accuracy value: 68.8 - type: accuracy name: Komi Zyrian Test accuracy value: 46.6 - type: accuracy name: Irish Test accuracy value: 60.5 - type: accuracy name: Nayini Test accuracy value: 42.3 - type: accuracy name: Munduruku Test accuracy value: 27.1 - type: accuracy name: Manx Test accuracy value: 35.3 - type: accuracy name: Skolt Sami Test accuracy value: 40.7 - type: accuracy name: Afrikaans Test accuracy value: 77.5 - type: accuracy name: Old Turkish Test accuracy value: 46.6 - type: accuracy name: Tupinamba Test accuracy value: 46.5 - type: accuracy name: Belarusian Test accuracy value: 87.1 - type: accuracy name: Serbian Test accuracy value: 86.9 - type: accuracy name: Moksha Test accuracy value: 48.3 - type: accuracy name: Western Armenian Test accuracy value: 80.6 - type: accuracy name: Scottish Gaelic Test accuracy value: 51.5 - type: accuracy name: Khunsari Test accuracy value: 40.5 - type: accuracy name: Hebrew Test accuracy value: 89.6 - type: accuracy name: Uyghur Test accuracy value: 77.1 - type: accuracy name: Chukchi Test accuracy value: 38.9 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Estonian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-et") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-et") ```
wietsedv/xlm-roberta-base-ft-udpos28-ga	b9d0c2a08fe20f325f8225321f0a39817a11c195	2022-02-25T09:58:33.000Z	[ "pytorch", "xlm-roberta", "token-classification", "ga", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	wietsedv	null	wietsedv/xlm-roberta-base-ft-udpos28-ga	null	transformers	36,365	--- language: - ga license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-ga results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 75.3 - type: accuracy name: Dutch Test accuracy value: 79.5 - type: accuracy name: German Test accuracy value: 76.2 - type: accuracy name: Italian Test accuracy value: 73.6 - type: accuracy name: French Test accuracy value: 76.4 - type: accuracy name: Spanish Test accuracy value: 82.4 - type: accuracy name: Russian Test accuracy value: 80.7 - type: accuracy name: Swedish Test accuracy value: 78.7 - type: accuracy name: Norwegian Test accuracy value: 74.5 - type: accuracy name: Danish Test accuracy value: 77.9 - type: accuracy name: Low Saxon Test accuracy value: 49.0 - type: accuracy name: Akkadian Test accuracy value: 46.2 - type: accuracy name: Armenian Test accuracy value: 71.8 - type: accuracy name: Welsh Test accuracy value: 77.9 - type: accuracy name: Old East Slavic Test accuracy value: 67.3 - type: accuracy name: Albanian Test accuracy value: 79.8 - type: accuracy name: Slovenian Test accuracy value: 65.3 - type: accuracy name: Guajajara Test accuracy value: 46.4 - type: accuracy name: Kurmanji Test accuracy value: 74.9 - type: accuracy name: Turkish Test accuracy value: 73.7 - type: accuracy name: Finnish Test accuracy value: 73.8 - type: accuracy name: Indonesian Test accuracy value: 78.6 - type: accuracy name: Ukrainian Test accuracy value: 79.9 - type: accuracy name: Polish Test accuracy value: 82.5 - type: accuracy name: Portuguese Test accuracy value: 80.6 - type: accuracy name: Kazakh Test accuracy value: 75.6 - type: accuracy name: Latin Test accuracy value: 70.0 - type: accuracy name: Old French Test accuracy value: 49.1 - type: accuracy name: Buryat Test accuracy value: 60.3 - type: accuracy name: Kaapor Test accuracy value: 21.2 - type: accuracy name: Korean Test accuracy value: 60.5 - type: accuracy name: Estonian Test accuracy value: 75.7 - type: accuracy name: Croatian Test accuracy value: 77.3 - type: accuracy name: Gothic Test accuracy value: 29.1 - type: accuracy name: Swiss German Test accuracy value: 44.3 - type: accuracy name: Assyrian Test accuracy value: 16.3 - type: accuracy name: North Sami Test accuracy value: 45.0 - type: accuracy name: Naija Test accuracy value: 32.0 - type: accuracy name: Latvian Test accuracy value: 77.7 - type: accuracy name: Chinese Test accuracy value: 49.6 - type: accuracy name: Tagalog Test accuracy value: 71.1 - type: accuracy name: Bambara Test accuracy value: 29.1 - type: accuracy name: Lithuanian Test accuracy value: 76.4 - type: accuracy name: Galician Test accuracy value: 80.9 - type: accuracy name: Vietnamese Test accuracy value: 58.6 - type: accuracy name: Greek Test accuracy value: 77.5 - type: accuracy name: Catalan Test accuracy value: 79.7 - type: accuracy name: Czech Test accuracy value: 78.1 - type: accuracy name: Erzya Test accuracy value: 52.5 - type: accuracy name: Bhojpuri Test accuracy value: 59.2 - type: accuracy name: Thai Test accuracy value: 58.7 - type: accuracy name: Marathi Test accuracy value: 79.1 - type: accuracy name: Basque Test accuracy value: 68.1 - type: accuracy name: Slovak Test accuracy value: 80.0 - type: accuracy name: Kiche Test accuracy value: 46.4 - type: accuracy name: Yoruba Test accuracy value: 33.1 - type: accuracy name: Warlpiri Test accuracy value: 40.5 - type: accuracy name: Tamil Test accuracy value: 78.1 - type: accuracy name: Maltese Test accuracy value: 36.7 - type: accuracy name: Ancient Greek Test accuracy value: 58.5 - type: accuracy name: Icelandic Test accuracy value: 71.2 - type: accuracy name: Mbya Guarani Test accuracy value: 34.0 - type: accuracy name: Urdu Test accuracy value: 65.5 - type: accuracy name: Romanian Test accuracy value: 76.8 - type: accuracy name: Persian Test accuracy value: 79.7 - type: accuracy name: Apurina Test accuracy value: 51.8 - type: accuracy name: Japanese Test accuracy value: 36.1 - type: accuracy name: Hungarian Test accuracy value: 77.1 - type: accuracy name: Hindi Test accuracy value: 69.7 - type: accuracy name: Classical Chinese Test accuracy value: 32.1 - type: accuracy name: Komi Permyak Test accuracy value: 51.1 - type: accuracy name: Faroese Test accuracy value: 70.6 - type: accuracy name: Sanskrit Test accuracy value: 35.7 - type: accuracy name: Livvi Test accuracy value: 60.6 - type: accuracy name: Arabic Test accuracy value: 83.7 - type: accuracy name: Wolof Test accuracy value: 40.8 - type: accuracy name: Bulgarian Test accuracy value: 78.7 - type: accuracy name: Akuntsu Test accuracy value: 43.2 - type: accuracy name: Makurap Test accuracy value: 19.9 - type: accuracy name: Kangri Test accuracy value: 46.3 - type: accuracy name: Breton Test accuracy value: 61.7 - type: accuracy name: Telugu Test accuracy value: 76.8 - type: accuracy name: Cantonese Test accuracy value: 49.0 - type: accuracy name: Old Church Slavonic Test accuracy value: 43.9 - type: accuracy name: Karelian Test accuracy value: 64.1 - type: accuracy name: Upper Sorbian Test accuracy value: 69.3 - type: accuracy name: South Levantine Arabic Test accuracy value: 70.0 - type: accuracy name: Komi Zyrian Test accuracy value: 44.9 - type: accuracy name: Irish Test accuracy value: 86.0 - type: accuracy name: Nayini Test accuracy value: 46.2 - type: accuracy name: Munduruku Test accuracy value: 38.9 - type: accuracy name: Manx Test accuracy value: 57.2 - type: accuracy name: Skolt Sami Test accuracy value: 40.1 - type: accuracy name: Afrikaans Test accuracy value: 73.0 - type: accuracy name: Old Turkish Test accuracy value: 39.4 - type: accuracy name: Tupinamba Test accuracy value: 51.8 - type: accuracy name: Belarusian Test accuracy value: 79.1 - type: accuracy name: Serbian Test accuracy value: 78.5 - type: accuracy name: Moksha Test accuracy value: 49.9 - type: accuracy name: Western Armenian Test accuracy value: 68.2 - type: accuracy name: Scottish Gaelic Test accuracy value: 77.1 - type: accuracy name: Khunsari Test accuracy value: 50.0 - type: accuracy name: Hebrew Test accuracy value: 80.2 - type: accuracy name: Uyghur Test accuracy value: 70.2 - type: accuracy name: Chukchi Test accuracy value: 39.3 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Irish This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ga") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ga") ```
wietsedv/xlm-roberta-base-ft-udpos28-he	0b1c313b21ca58890f2e11837cee5fdfbabd39b2	2022-02-25T09:58:40.000Z	[ "pytorch", "xlm-roberta", "token-classification", "he", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	wietsedv	null	wietsedv/xlm-roberta-base-ft-udpos28-he	null	transformers	36,366	--- language: - he license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-he results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 76.6 - type: accuracy name: Dutch Test accuracy value: 73.7 - type: accuracy name: German Test accuracy value: 70.5 - type: accuracy name: Italian Test accuracy value: 75.1 - type: accuracy name: French Test accuracy value: 71.3 - type: accuracy name: Spanish Test accuracy value: 74.5 - type: accuracy name: Russian Test accuracy value: 80.3 - type: accuracy name: Swedish Test accuracy value: 79.3 - type: accuracy name: Norwegian Test accuracy value: 75.7 - type: accuracy name: Danish Test accuracy value: 80.4 - type: accuracy name: Low Saxon Test accuracy value: 42.6 - type: accuracy name: Akkadian Test accuracy value: 24.1 - type: accuracy name: Armenian Test accuracy value: 77.0 - type: accuracy name: Welsh Test accuracy value: 62.3 - type: accuracy name: Old East Slavic Test accuracy value: 66.2 - type: accuracy name: Albanian Test accuracy value: 73.9 - type: accuracy name: Slovenian Test accuracy value: 72.5 - type: accuracy name: Guajajara Test accuracy value: 21.4 - type: accuracy name: Kurmanji Test accuracy value: 74.2 - type: accuracy name: Turkish Test accuracy value: 71.8 - type: accuracy name: Finnish Test accuracy value: 80.5 - type: accuracy name: Indonesian Test accuracy value: 80.0 - type: accuracy name: Ukrainian Test accuracy value: 78.8 - type: accuracy name: Polish Test accuracy value: 78.9 - type: accuracy name: Portuguese Test accuracy value: 78.6 - type: accuracy name: Kazakh Test accuracy value: 77.2 - type: accuracy name: Latin Test accuracy value: 73.5 - type: accuracy name: Old French Test accuracy value: 50.6 - type: accuracy name: Buryat Test accuracy value: 45.0 - type: accuracy name: Kaapor Test accuracy value: 11.2 - type: accuracy name: Korean Test accuracy value: 60.2 - type: accuracy name: Estonian Test accuracy value: 81.4 - type: accuracy name: Croatian Test accuracy value: 77.9 - type: accuracy name: Gothic Test accuracy value: 13.7 - type: accuracy name: Swiss German Test accuracy value: 44.8 - type: accuracy name: Assyrian Test accuracy value: 17.0 - type: accuracy name: North Sami Test accuracy value: 24.8 - type: accuracy name: Naija Test accuracy value: 41.6 - type: accuracy name: Latvian Test accuracy value: 80.1 - type: accuracy name: Chinese Test accuracy value: 60.5 - type: accuracy name: Tagalog Test accuracy value: 79.2 - type: accuracy name: Bambara Test accuracy value: 21.1 - type: accuracy name: Lithuanian Test accuracy value: 81.0 - type: accuracy name: Galician Test accuracy value: 76.1 - type: accuracy name: Vietnamese Test accuracy value: 64.4 - type: accuracy name: Greek Test accuracy value: 67.4 - type: accuracy name: Catalan Test accuracy value: 71.5 - type: accuracy name: Czech Test accuracy value: 77.7 - type: accuracy name: Erzya Test accuracy value: 32.0 - type: accuracy name: Bhojpuri Test accuracy value: 50.7 - type: accuracy name: Thai Test accuracy value: 69.2 - type: accuracy name: Marathi Test accuracy value: 81.6 - type: accuracy name: Basque Test accuracy value: 76.2 - type: accuracy name: Slovak Test accuracy value: 78.0 - type: accuracy name: Kiche Test accuracy value: 23.6 - type: accuracy name: Yoruba Test accuracy value: 17.5 - type: accuracy name: Warlpiri Test accuracy value: 22.3 - type: accuracy name: Tamil Test accuracy value: 82.1 - type: accuracy name: Maltese Test accuracy value: 18.0 - type: accuracy name: Ancient Greek Test accuracy value: 45.4 - type: accuracy name: Icelandic Test accuracy value: 81.0 - type: accuracy name: Mbya Guarani Test accuracy value: 22.0 - type: accuracy name: Urdu Test accuracy value: 70.9 - type: accuracy name: Romanian Test accuracy value: 76.5 - type: accuracy name: Persian Test accuracy value: 75.4 - type: accuracy name: Apurina Test accuracy value: 22.2 - type: accuracy name: Japanese Test accuracy value: 39.4 - type: accuracy name: Hungarian Test accuracy value: 65.8 - type: accuracy name: Hindi Test accuracy value: 75.2 - type: accuracy name: Classical Chinese Test accuracy value: 44.3 - type: accuracy name: Komi Permyak Test accuracy value: 35.0 - type: accuracy name: Faroese Test accuracy value: 70.8 - type: accuracy name: Sanskrit Test accuracy value: 12.1 - type: accuracy name: Livvi Test accuracy value: 52.2 - type: accuracy name: Arabic Test accuracy value: 77.4 - type: accuracy name: Wolof Test accuracy value: 24.4 - type: accuracy name: Bulgarian Test accuracy value: 82.1 - type: accuracy name: Akuntsu Test accuracy value: 17.0 - type: accuracy name: Makurap Test accuracy value: 8.2 - type: accuracy name: Kangri Test accuracy value: 39.9 - type: accuracy name: Breton Test accuracy value: 56.7 - type: accuracy name: Telugu Test accuracy value: 81.4 - type: accuracy name: Cantonese Test accuracy value: 57.7 - type: accuracy name: Old Church Slavonic Test accuracy value: 40.3 - type: accuracy name: Karelian Test accuracy value: 60.0 - type: accuracy name: Upper Sorbian Test accuracy value: 61.2 - type: accuracy name: South Levantine Arabic Test accuracy value: 64.5 - type: accuracy name: Komi Zyrian Test accuracy value: 29.0 - type: accuracy name: Irish Test accuracy value: 58.7 - type: accuracy name: Nayini Test accuracy value: 41.0 - type: accuracy name: Munduruku Test accuracy value: 9.5 - type: accuracy name: Manx Test accuracy value: 21.8 - type: accuracy name: Skolt Sami Test accuracy value: 27.2 - type: accuracy name: Afrikaans Test accuracy value: 73.3 - type: accuracy name: Old Turkish Test accuracy value: 43.4 - type: accuracy name: Tupinamba Test accuracy value: 21.9 - type: accuracy name: Belarusian Test accuracy value: 78.5 - type: accuracy name: Serbian Test accuracy value: 78.9 - type: accuracy name: Moksha Test accuracy value: 29.7 - type: accuracy name: Western Armenian Test accuracy value: 69.6 - type: accuracy name: Scottish Gaelic Test accuracy value: 51.3 - type: accuracy name: Khunsari Test accuracy value: 36.5 - type: accuracy name: Hebrew Test accuracy value: 93.8 - type: accuracy name: Uyghur Test accuracy value: 70.2 - type: accuracy name: Chukchi Test accuracy value: 27.1 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Hebrew This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-he") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-he") ```
wietsedv/xlm-roberta-base-ft-udpos28-hr	8b72e6e02e4637a51fa8315b1c170d4c224457d8	2022-02-25T09:58:44.000Z	[ "pytorch", "xlm-roberta", "token-classification", "hr", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	wietsedv	null	wietsedv/xlm-roberta-base-ft-udpos28-hr	null	transformers	36,367	--- language: - hr license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-hr results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 83.7 - type: accuracy name: Dutch Test accuracy value: 83.7 - type: accuracy name: German Test accuracy value: 83.2 - type: accuracy name: Italian Test accuracy value: 83.2 - type: accuracy name: French Test accuracy value: 84.2 - type: accuracy name: Spanish Test accuracy value: 87.8 - type: accuracy name: Russian Test accuracy value: 91.4 - type: accuracy name: Swedish Test accuracy value: 85.4 - type: accuracy name: Norwegian Test accuracy value: 79.0 - type: accuracy name: Danish Test accuracy value: 83.8 - type: accuracy name: Low Saxon Test accuracy value: 43.5 - type: accuracy name: Akkadian Test accuracy value: 32.5 - type: accuracy name: Armenian Test accuracy value: 84.7 - type: accuracy name: Welsh Test accuracy value: 67.9 - type: accuracy name: Old East Slavic Test accuracy value: 76.8 - type: accuracy name: Albanian Test accuracy value: 75.2 - type: accuracy name: Slovenian Test accuracy value: 87.0 - type: accuracy name: Guajajara Test accuracy value: 28.3 - type: accuracy name: Kurmanji Test accuracy value: 78.5 - type: accuracy name: Turkish Test accuracy value: 75.9 - type: accuracy name: Finnish Test accuracy value: 83.2 - type: accuracy name: Indonesian Test accuracy value: 81.3 - type: accuracy name: Ukrainian Test accuracy value: 93.2 - type: accuracy name: Polish Test accuracy value: 92.3 - type: accuracy name: Portuguese Test accuracy value: 84.6 - type: accuracy name: Kazakh Test accuracy value: 79.4 - type: accuracy name: Latin Test accuracy value: 77.4 - type: accuracy name: Old French Test accuracy value: 54.3 - type: accuracy name: Buryat Test accuracy value: 61.1 - type: accuracy name: Kaapor Test accuracy value: 20.0 - type: accuracy name: Korean Test accuracy value: 60.7 - type: accuracy name: Estonian Test accuracy value: 85.7 - type: accuracy name: Croatian Test accuracy value: 98.3 - type: accuracy name: Gothic Test accuracy value: 16.5 - type: accuracy name: Swiss German Test accuracy value: 44.8 - type: accuracy name: Assyrian Test accuracy value: 15.9 - type: accuracy name: North Sami Test accuracy value: 35.3 - type: accuracy name: Naija Test accuracy value: 39.6 - type: accuracy name: Latvian Test accuracy value: 86.5 - type: accuracy name: Chinese Test accuracy value: 41.2 - type: accuracy name: Tagalog Test accuracy value: 70.9 - type: accuracy name: Bambara Test accuracy value: 28.2 - type: accuracy name: Lithuanian Test accuracy value: 86.1 - type: accuracy name: Galician Test accuracy value: 86.0 - type: accuracy name: Vietnamese Test accuracy value: 66.5 - type: accuracy name: Greek Test accuracy value: 85.8 - type: accuracy name: Catalan Test accuracy value: 85.5 - type: accuracy name: Czech Test accuracy value: 94.8 - type: accuracy name: Erzya Test accuracy value: 47.2 - type: accuracy name: Bhojpuri Test accuracy value: 49.2 - type: accuracy name: Thai Test accuracy value: 63.4 - type: accuracy name: Marathi Test accuracy value: 87.1 - type: accuracy name: Basque Test accuracy value: 75.0 - type: accuracy name: Slovak Test accuracy value: 95.0 - type: accuracy name: Kiche Test accuracy value: 35.8 - type: accuracy name: Yoruba Test accuracy value: 28.5 - type: accuracy name: Warlpiri Test accuracy value: 41.3 - type: accuracy name: Tamil Test accuracy value: 84.8 - type: accuracy name: Maltese Test accuracy value: 23.7 - type: accuracy name: Ancient Greek Test accuracy value: 62.1 - type: accuracy name: Icelandic Test accuracy value: 79.9 - type: accuracy name: Mbya Guarani Test accuracy value: 31.9 - type: accuracy name: Urdu Test accuracy value: 65.0 - type: accuracy name: Romanian Test accuracy value: 82.5 - type: accuracy name: Persian Test accuracy value: 79.4 - type: accuracy name: Apurina Test accuracy value: 38.4 - type: accuracy name: Japanese Test accuracy value: 30.1 - type: accuracy name: Hungarian Test accuracy value: 83.8 - type: accuracy name: Hindi Test accuracy value: 67.8 - type: accuracy name: Classical Chinese Test accuracy value: 27.0 - type: accuracy name: Komi Permyak Test accuracy value: 44.9 - type: accuracy name: Faroese Test accuracy value: 77.3 - type: accuracy name: Sanskrit Test accuracy value: 35.6 - type: accuracy name: Livvi Test accuracy value: 65.5 - type: accuracy name: Arabic Test accuracy value: 82.3 - type: accuracy name: Wolof Test accuracy value: 32.2 - type: accuracy name: Bulgarian Test accuracy value: 92.6 - type: accuracy name: Akuntsu Test accuracy value: 37.0 - type: accuracy name: Makurap Test accuracy value: 17.8 - type: accuracy name: Kangri Test accuracy value: 47.9 - type: accuracy name: Breton Test accuracy value: 62.2 - type: accuracy name: Telugu Test accuracy value: 82.4 - type: accuracy name: Cantonese Test accuracy value: 45.6 - type: accuracy name: Old Church Slavonic Test accuracy value: 48.9 - type: accuracy name: Karelian Test accuracy value: 71.7 - type: accuracy name: Upper Sorbian Test accuracy value: 79.4 - type: accuracy name: South Levantine Arabic Test accuracy value: 68.9 - type: accuracy name: Komi Zyrian Test accuracy value: 39.6 - type: accuracy name: Irish Test accuracy value: 65.4 - type: accuracy name: Nayini Test accuracy value: 42.3 - type: accuracy name: Munduruku Test accuracy value: 28.8 - type: accuracy name: Manx Test accuracy value: 35.7 - type: accuracy name: Skolt Sami Test accuracy value: 33.7 - type: accuracy name: Afrikaans Test accuracy value: 79.8 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 33.1 - type: accuracy name: Belarusian Test accuracy value: 91.6 - type: accuracy name: Serbian Test accuracy value: 97.5 - type: accuracy name: Moksha Test accuracy value: 45.7 - type: accuracy name: Western Armenian Test accuracy value: 77.7 - type: accuracy name: Scottish Gaelic Test accuracy value: 57.7 - type: accuracy name: Khunsari Test accuracy value: 36.5 - type: accuracy name: Hebrew Test accuracy value: 85.4 - type: accuracy name: Uyghur Test accuracy value: 72.2 - type: accuracy name: Chukchi Test accuracy value: 35.4 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Croatian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hr") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hr") ```
wietsedv/xlm-roberta-base-ft-udpos28-hyw	2d1fe3d93a920f84fabe65fba2549e7af7122c44	2022-02-25T09:58:48.000Z	[ "pytorch", "xlm-roberta", "token-classification", "hyw", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	wietsedv	null	wietsedv/xlm-roberta-base-ft-udpos28-hyw	null	transformers	36,368	--- language: - hyw license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-hyw results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 83.0 - type: accuracy name: Dutch Test accuracy value: 81.9 - type: accuracy name: German Test accuracy value: 83.9 - type: accuracy name: Italian Test accuracy value: 80.9 - type: accuracy name: French Test accuracy value: 79.2 - type: accuracy name: Spanish Test accuracy value: 80.9 - type: accuracy name: Russian Test accuracy value: 89.1 - type: accuracy name: Swedish Test accuracy value: 86.2 - type: accuracy name: Norwegian Test accuracy value: 80.6 - type: accuracy name: Danish Test accuracy value: 84.8 - type: accuracy name: Low Saxon Test accuracy value: 56.7 - type: accuracy name: Akkadian Test accuracy value: 29.3 - type: accuracy name: Armenian Test accuracy value: 90.2 - type: accuracy name: Welsh Test accuracy value: 63.8 - type: accuracy name: Old East Slavic Test accuracy value: 77.0 - type: accuracy name: Albanian Test accuracy value: 83.5 - type: accuracy name: Slovenian Test accuracy value: 78.0 - type: accuracy name: Guajajara Test accuracy value: 22.7 - type: accuracy name: Kurmanji Test accuracy value: 76.7 - type: accuracy name: Turkish Test accuracy value: 78.1 - type: accuracy name: Finnish Test accuracy value: 84.5 - type: accuracy name: Indonesian Test accuracy value: 80.7 - type: accuracy name: Ukrainian Test accuracy value: 88.4 - type: accuracy name: Polish Test accuracy value: 83.7 - type: accuracy name: Portuguese Test accuracy value: 83.1 - type: accuracy name: Kazakh Test accuracy value: 85.0 - type: accuracy name: Latin Test accuracy value: 79.0 - type: accuracy name: Old French Test accuracy value: 58.3 - type: accuracy name: Buryat Test accuracy value: 65.4 - type: accuracy name: Kaapor Test accuracy value: 16.2 - type: accuracy name: Korean Test accuracy value: 62.1 - type: accuracy name: Estonian Test accuracy value: 84.6 - type: accuracy name: Croatian Test accuracy value: 86.9 - type: accuracy name: Gothic Test accuracy value: 24.5 - type: accuracy name: Swiss German Test accuracy value: 57.3 - type: accuracy name: Assyrian Test accuracy value: 14.6 - type: accuracy name: North Sami Test accuracy value: 35.0 - type: accuracy name: Naija Test accuracy value: 43.0 - type: accuracy name: Latvian Test accuracy value: 87.5 - type: accuracy name: Chinese Test accuracy value: 41.7 - type: accuracy name: Tagalog Test accuracy value: 68.9 - type: accuracy name: Bambara Test accuracy value: 30.7 - type: accuracy name: Lithuanian Test accuracy value: 87.2 - type: accuracy name: Galician Test accuracy value: 80.9 - type: accuracy name: Vietnamese Test accuracy value: 65.0 - type: accuracy name: Greek Test accuracy value: 87.6 - type: accuracy name: Catalan Test accuracy value: 80.0 - type: accuracy name: Czech Test accuracy value: 86.0 - type: accuracy name: Erzya Test accuracy value: 47.6 - type: accuracy name: Bhojpuri Test accuracy value: 57.8 - type: accuracy name: Thai Test accuracy value: 59.9 - type: accuracy name: Marathi Test accuracy value: 84.7 - type: accuracy name: Basque Test accuracy value: 80.7 - type: accuracy name: Slovak Test accuracy value: 86.2 - type: accuracy name: Kiche Test accuracy value: 26.5 - type: accuracy name: Yoruba Test accuracy value: 24.8 - type: accuracy name: Warlpiri Test accuracy value: 38.5 - type: accuracy name: Tamil Test accuracy value: 84.2 - type: accuracy name: Maltese Test accuracy value: 28.2 - type: accuracy name: Ancient Greek Test accuracy value: 68.4 - type: accuracy name: Icelandic Test accuracy value: 79.5 - type: accuracy name: Mbya Guarani Test accuracy value: 28.7 - type: accuracy name: Urdu Test accuracy value: 68.1 - type: accuracy name: Romanian Test accuracy value: 82.1 - type: accuracy name: Persian Test accuracy value: 74.9 - type: accuracy name: Apurina Test accuracy value: 31.9 - type: accuracy name: Japanese Test accuracy value: 35.2 - type: accuracy name: Hungarian Test accuracy value: 83.7 - type: accuracy name: Hindi Test accuracy value: 74.9 - type: accuracy name: Classical Chinese Test accuracy value: 26.8 - type: accuracy name: Komi Permyak Test accuracy value: 51.5 - type: accuracy name: Faroese Test accuracy value: 77.9 - type: accuracy name: Sanskrit Test accuracy value: 39.4 - type: accuracy name: Livvi Test accuracy value: 67.5 - type: accuracy name: Arabic Test accuracy value: 77.6 - type: accuracy name: Wolof Test accuracy value: 31.3 - type: accuracy name: Bulgarian Test accuracy value: 86.3 - type: accuracy name: Akuntsu Test accuracy value: 21.3 - type: accuracy name: Makurap Test accuracy value: 11.6 - type: accuracy name: Kangri Test accuracy value: 57.8 - type: accuracy name: Breton Test accuracy value: 65.4 - type: accuracy name: Telugu Test accuracy value: 80.2 - type: accuracy name: Cantonese Test accuracy value: 48.5 - type: accuracy name: Old Church Slavonic Test accuracy value: 52.5 - type: accuracy name: Karelian Test accuracy value: 72.2 - type: accuracy name: Upper Sorbian Test accuracy value: 76.4 - type: accuracy name: South Levantine Arabic Test accuracy value: 69.8 - type: accuracy name: Komi Zyrian Test accuracy value: 44.2 - type: accuracy name: Irish Test accuracy value: 61.5 - type: accuracy name: Nayini Test accuracy value: 53.8 - type: accuracy name: Munduruku Test accuracy value: 12.5 - type: accuracy name: Manx Test accuracy value: 29.8 - type: accuracy name: Skolt Sami Test accuracy value: 34.2 - type: accuracy name: Afrikaans Test accuracy value: 81.7 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 30.8 - type: accuracy name: Belarusian Test accuracy value: 89.7 - type: accuracy name: Serbian Test accuracy value: 87.1 - type: accuracy name: Moksha Test accuracy value: 45.2 - type: accuracy name: Western Armenian Test accuracy value: 93.9 - type: accuracy name: Scottish Gaelic Test accuracy value: 56.8 - type: accuracy name: Khunsari Test accuracy value: 43.2 - type: accuracy name: Hebrew Test accuracy value: 85.4 - type: accuracy name: Uyghur Test accuracy value: 76.1 - type: accuracy name: Chukchi Test accuracy value: 38.1 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Western Armenian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hyw") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hyw") ```
wietsedv/xlm-roberta-base-ft-udpos28-lt	d608a2b1745c2d2d64e8676fe2c1e53e53da82a7	2022-02-25T09:58:59.000Z	[ "pytorch", "xlm-roberta", "token-classification", "lt", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	wietsedv	null	wietsedv/xlm-roberta-base-ft-udpos28-lt	null	transformers	36,369	--- language: - lt license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-lt results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 80.7 - type: accuracy name: Dutch Test accuracy value: 80.6 - type: accuracy name: German Test accuracy value: 76.0 - type: accuracy name: Italian Test accuracy value: 77.8 - type: accuracy name: French Test accuracy value: 75.5 - type: accuracy name: Spanish Test accuracy value: 79.6 - type: accuracy name: Russian Test accuracy value: 88.9 - type: accuracy name: Swedish Test accuracy value: 81.6 - type: accuracy name: Norwegian Test accuracy value: 76.3 - type: accuracy name: Danish Test accuracy value: 78.9 - type: accuracy name: Low Saxon Test accuracy value: 52.0 - type: accuracy name: Akkadian Test accuracy value: 31.6 - type: accuracy name: Armenian Test accuracy value: 84.1 - type: accuracy name: Welsh Test accuracy value: 63.8 - type: accuracy name: Old East Slavic Test accuracy value: 75.6 - type: accuracy name: Albanian Test accuracy value: 76.8 - type: accuracy name: Slovenian Test accuracy value: 81.4 - type: accuracy name: Guajajara Test accuracy value: 26.7 - type: accuracy name: Kurmanji Test accuracy value: 77.1 - type: accuracy name: Turkish Test accuracy value: 74.9 - type: accuracy name: Finnish Test accuracy value: 83.2 - type: accuracy name: Indonesian Test accuracy value: 78.0 - type: accuracy name: Ukrainian Test accuracy value: 88.1 - type: accuracy name: Polish Test accuracy value: 86.3 - type: accuracy name: Portuguese Test accuracy value: 81.6 - type: accuracy name: Kazakh Test accuracy value: 83.1 - type: accuracy name: Latin Test accuracy value: 78.7 - type: accuracy name: Old French Test accuracy value: 56.1 - type: accuracy name: Buryat Test accuracy value: 64.3 - type: accuracy name: Kaapor Test accuracy value: 22.5 - type: accuracy name: Korean Test accuracy value: 64.6 - type: accuracy name: Estonian Test accuracy value: 81.5 - type: accuracy name: Croatian Test accuracy value: 86.6 - type: accuracy name: Gothic Test accuracy value: 22.6 - type: accuracy name: Swiss German Test accuracy value: 48.1 - type: accuracy name: Assyrian Test accuracy value: 14.6 - type: accuracy name: North Sami Test accuracy value: 39.8 - type: accuracy name: Naija Test accuracy value: 41.4 - type: accuracy name: Latvian Test accuracy value: 89.0 - type: accuracy name: Chinese Test accuracy value: 34.4 - type: accuracy name: Tagalog Test accuracy value: 73.0 - type: accuracy name: Bambara Test accuracy value: 26.4 - type: accuracy name: Lithuanian Test accuracy value: 96.1 - type: accuracy name: Galician Test accuracy value: 81.1 - type: accuracy name: Vietnamese Test accuracy value: 65.3 - type: accuracy name: Greek Test accuracy value: 81.8 - type: accuracy name: Catalan Test accuracy value: 76.2 - type: accuracy name: Czech Test accuracy value: 86.5 - type: accuracy name: Erzya Test accuracy value: 48.7 - type: accuracy name: Bhojpuri Test accuracy value: 50.9 - type: accuracy name: Thai Test accuracy value: 54.5 - type: accuracy name: Marathi Test accuracy value: 82.8 - type: accuracy name: Basque Test accuracy value: 75.6 - type: accuracy name: Slovak Test accuracy value: 88.5 - type: accuracy name: Kiche Test accuracy value: 33.5 - type: accuracy name: Yoruba Test accuracy value: 24.6 - type: accuracy name: Warlpiri Test accuracy value: 44.1 - type: accuracy name: Tamil Test accuracy value: 79.1 - type: accuracy name: Maltese Test accuracy value: 25.5 - type: accuracy name: Ancient Greek Test accuracy value: 65.8 - type: accuracy name: Icelandic Test accuracy value: 80.7 - type: accuracy name: Mbya Guarani Test accuracy value: 32.2 - type: accuracy name: Urdu Test accuracy value: 59.1 - type: accuracy name: Romanian Test accuracy value: 78.6 - type: accuracy name: Persian Test accuracy value: 72.8 - type: accuracy name: Apurina Test accuracy value: 42.0 - type: accuracy name: Japanese Test accuracy value: 22.9 - type: accuracy name: Hungarian Test accuracy value: 76.9 - type: accuracy name: Hindi Test accuracy value: 62.2 - type: accuracy name: Classical Chinese Test accuracy value: 15.8 - type: accuracy name: Komi Permyak Test accuracy value: 48.3 - type: accuracy name: Faroese Test accuracy value: 77.3 - type: accuracy name: Sanskrit Test accuracy value: 41.0 - type: accuracy name: Livvi Test accuracy value: 67.2 - type: accuracy name: Arabic Test accuracy value: 73.9 - type: accuracy name: Wolof Test accuracy value: 28.0 - type: accuracy name: Bulgarian Test accuracy value: 85.9 - type: accuracy name: Akuntsu Test accuracy value: 26.0 - type: accuracy name: Makurap Test accuracy value: 17.8 - type: accuracy name: Kangri Test accuracy value: 50.6 - type: accuracy name: Breton Test accuracy value: 60.3 - type: accuracy name: Telugu Test accuracy value: 85.0 - type: accuracy name: Cantonese Test accuracy value: 39.1 - type: accuracy name: Old Church Slavonic Test accuracy value: 51.6 - type: accuracy name: Karelian Test accuracy value: 71.3 - type: accuracy name: Upper Sorbian Test accuracy value: 75.7 - type: accuracy name: South Levantine Arabic Test accuracy value: 67.0 - type: accuracy name: Komi Zyrian Test accuracy value: 43.0 - type: accuracy name: Irish Test accuracy value: 60.1 - type: accuracy name: Nayini Test accuracy value: 46.2 - type: accuracy name: Munduruku Test accuracy value: 18.8 - type: accuracy name: Manx Test accuracy value: 33.3 - type: accuracy name: Skolt Sami Test accuracy value: 37.3 - type: accuracy name: Afrikaans Test accuracy value: 76.4 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 34.1 - type: accuracy name: Belarusian Test accuracy value: 89.1 - type: accuracy name: Serbian Test accuracy value: 87.7 - type: accuracy name: Moksha Test accuracy value: 46.3 - type: accuracy name: Western Armenian Test accuracy value: 75.4 - type: accuracy name: Scottish Gaelic Test accuracy value: 56.2 - type: accuracy name: Khunsari Test accuracy value: 39.2 - type: accuracy name: Hebrew Test accuracy value: 83.3 - type: accuracy name: Uyghur Test accuracy value: 76.6 - type: accuracy name: Chukchi Test accuracy value: 35.4 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Lithuanian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lt") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lt") ```
wietsedv/xlm-roberta-base-ft-udpos28-lv	5b08566f22c46a1ebeca9961489d1da64a1dc88f	2022-02-25T09:59:00.000Z	[ "pytorch", "xlm-roberta", "token-classification", "lv", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	wietsedv	null	wietsedv/xlm-roberta-base-ft-udpos28-lv	null	transformers	36,370	--- language: - lv license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-lv results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 84.7 - type: accuracy name: Dutch Test accuracy value: 85.6 - type: accuracy name: German Test accuracy value: 82.5 - type: accuracy name: Italian Test accuracy value: 84.3 - type: accuracy name: French Test accuracy value: 84.1 - type: accuracy name: Spanish Test accuracy value: 84.7 - type: accuracy name: Russian Test accuracy value: 92.1 - type: accuracy name: Swedish Test accuracy value: 86.8 - type: accuracy name: Norwegian Test accuracy value: 81.3 - type: accuracy name: Danish Test accuracy value: 86.0 - type: accuracy name: Low Saxon Test accuracy value: 51.6 - type: accuracy name: Akkadian Test accuracy value: 32.4 - type: accuracy name: Armenian Test accuracy value: 87.5 - type: accuracy name: Welsh Test accuracy value: 65.4 - type: accuracy name: Old East Slavic Test accuracy value: 76.5 - type: accuracy name: Albanian Test accuracy value: 75.9 - type: accuracy name: Slovenian Test accuracy value: 82.0 - type: accuracy name: Guajajara Test accuracy value: 31.1 - type: accuracy name: Kurmanji Test accuracy value: 76.5 - type: accuracy name: Turkish Test accuracy value: 77.2 - type: accuracy name: Finnish Test accuracy value: 85.9 - type: accuracy name: Indonesian Test accuracy value: 79.3 - type: accuracy name: Ukrainian Test accuracy value: 91.1 - type: accuracy name: Polish Test accuracy value: 88.5 - type: accuracy name: Portuguese Test accuracy value: 84.9 - type: accuracy name: Kazakh Test accuracy value: 83.8 - type: accuracy name: Latin Test accuracy value: 81.0 - type: accuracy name: Old French Test accuracy value: 56.7 - type: accuracy name: Buryat Test accuracy value: 64.8 - type: accuracy name: Kaapor Test accuracy value: 25.0 - type: accuracy name: Korean Test accuracy value: 65.1 - type: accuracy name: Estonian Test accuracy value: 84.7 - type: accuracy name: Croatian Test accuracy value: 89.1 - type: accuracy name: Gothic Test accuracy value: 23.5 - type: accuracy name: Swiss German Test accuracy value: 45.2 - type: accuracy name: Assyrian Test accuracy value: 12.8 - type: accuracy name: North Sami Test accuracy value: 43.5 - type: accuracy name: Naija Test accuracy value: 36.1 - type: accuracy name: Latvian Test accuracy value: 96.9 - type: accuracy name: Chinese Test accuracy value: 53.1 - type: accuracy name: Tagalog Test accuracy value: 72.7 - type: accuracy name: Bambara Test accuracy value: 28.6 - type: accuracy name: Lithuanian Test accuracy value: 91.0 - type: accuracy name: Galician Test accuracy value: 84.2 - type: accuracy name: Vietnamese Test accuracy value: 65.7 - type: accuracy name: Greek Test accuracy value: 84.5 - type: accuracy name: Catalan Test accuracy value: 83.2 - type: accuracy name: Czech Test accuracy value: 88.0 - type: accuracy name: Erzya Test accuracy value: 52.5 - type: accuracy name: Bhojpuri Test accuracy value: 49.2 - type: accuracy name: Thai Test accuracy value: 63.3 - type: accuracy name: Marathi Test accuracy value: 85.3 - type: accuracy name: Basque Test accuracy value: 77.4 - type: accuracy name: Slovak Test accuracy value: 87.8 - type: accuracy name: Kiche Test accuracy value: 40.3 - type: accuracy name: Yoruba Test accuracy value: 28.4 - type: accuracy name: Warlpiri Test accuracy value: 44.9 - type: accuracy name: Tamil Test accuracy value: 86.4 - type: accuracy name: Maltese Test accuracy value: 25.9 - type: accuracy name: Ancient Greek Test accuracy value: 62.2 - type: accuracy name: Icelandic Test accuracy value: 81.7 - type: accuracy name: Mbya Guarani Test accuracy value: 35.3 - type: accuracy name: Urdu Test accuracy value: 61.9 - type: accuracy name: Romanian Test accuracy value: 82.2 - type: accuracy name: Persian Test accuracy value: 74.8 - type: accuracy name: Apurina Test accuracy value: 49.0 - type: accuracy name: Japanese Test accuracy value: 39.4 - type: accuracy name: Hungarian Test accuracy value: 79.9 - type: accuracy name: Hindi Test accuracy value: 64.1 - type: accuracy name: Classical Chinese Test accuracy value: 30.0 - type: accuracy name: Komi Permyak Test accuracy value: 51.7 - type: accuracy name: Faroese Test accuracy value: 76.2 - type: accuracy name: Sanskrit Test accuracy value: 39.7 - type: accuracy name: Livvi Test accuracy value: 67.7 - type: accuracy name: Arabic Test accuracy value: 79.4 - type: accuracy name: Wolof Test accuracy value: 31.7 - type: accuracy name: Bulgarian Test accuracy value: 89.0 - type: accuracy name: Akuntsu Test accuracy value: 35.5 - type: accuracy name: Makurap Test accuracy value: 20.5 - type: accuracy name: Kangri Test accuracy value: 50.6 - type: accuracy name: Breton Test accuracy value: 62.7 - type: accuracy name: Telugu Test accuracy value: 87.8 - type: accuracy name: Cantonese Test accuracy value: 50.7 - type: accuracy name: Old Church Slavonic Test accuracy value: 49.3 - type: accuracy name: Karelian Test accuracy value: 72.7 - type: accuracy name: Upper Sorbian Test accuracy value: 75.6 - type: accuracy name: South Levantine Arabic Test accuracy value: 68.7 - type: accuracy name: Komi Zyrian Test accuracy value: 44.5 - type: accuracy name: Irish Test accuracy value: 64.7 - type: accuracy name: Nayini Test accuracy value: 39.7 - type: accuracy name: Munduruku Test accuracy value: 26.0 - type: accuracy name: Manx Test accuracy value: 37.9 - type: accuracy name: Skolt Sami Test accuracy value: 34.7 - type: accuracy name: Afrikaans Test accuracy value: 81.6 - type: accuracy name: Old Turkish Test accuracy value: 22.6 - type: accuracy name: Tupinamba Test accuracy value: 40.6 - type: accuracy name: Belarusian Test accuracy value: 91.8 - type: accuracy name: Serbian Test accuracy value: 89.7 - type: accuracy name: Moksha Test accuracy value: 48.7 - type: accuracy name: Western Armenian Test accuracy value: 77.5 - type: accuracy name: Scottish Gaelic Test accuracy value: 58.1 - type: accuracy name: Khunsari Test accuracy value: 40.5 - type: accuracy name: Hebrew Test accuracy value: 85.4 - type: accuracy name: Uyghur Test accuracy value: 79.7 - type: accuracy name: Chukchi Test accuracy value: 37.0 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Latvian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lv") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lv") ```
wietsedv/xlm-roberta-base-ft-udpos28-ta	b52577ee75a2250d4135204b9ef9b42ff2862ac0	2022-02-25T09:59:28.000Z	[ "pytorch", "xlm-roberta", "token-classification", "ta", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]	token-classification	false	wietsedv	null	wietsedv/xlm-roberta-base-ft-udpos28-ta	null	transformers	36,371	--- language: - ta license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-ta results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 68.1 - type: accuracy name: Dutch Test accuracy value: 64.0 - type: accuracy name: German Test accuracy value: 65.8 - type: accuracy name: Italian Test accuracy value: 61.2 - type: accuracy name: French Test accuracy value: 56.9 - type: accuracy name: Spanish Test accuracy value: 59.5 - type: accuracy name: Russian Test accuracy value: 74.3 - type: accuracy name: Swedish Test accuracy value: 69.1 - type: accuracy name: Norwegian Test accuracy value: 64.8 - type: accuracy name: Danish Test accuracy value: 70.0 - type: accuracy name: Low Saxon Test accuracy value: 46.9 - type: accuracy name: Akkadian Test accuracy value: 28.4 - type: accuracy name: Armenian Test accuracy value: 76.5 - type: accuracy name: Welsh Test accuracy value: 54.2 - type: accuracy name: Old East Slavic Test accuracy value: 61.8 - type: accuracy name: Albanian Test accuracy value: 61.0 - type: accuracy name: Slovenian Test accuracy value: 59.8 - type: accuracy name: Guajajara Test accuracy value: 22.7 - type: accuracy name: Kurmanji Test accuracy value: 64.1 - type: accuracy name: Turkish Test accuracy value: 72.0 - type: accuracy name: Finnish Test accuracy value: 76.2 - type: accuracy name: Indonesian Test accuracy value: 70.3 - type: accuracy name: Ukrainian Test accuracy value: 75.5 - type: accuracy name: Polish Test accuracy value: 72.0 - type: accuracy name: Portuguese Test accuracy value: 65.9 - type: accuracy name: Kazakh Test accuracy value: 77.2 - type: accuracy name: Latin Test accuracy value: 67.8 - type: accuracy name: Old French Test accuracy value: 45.0 - type: accuracy name: Buryat Test accuracy value: 58.8 - type: accuracy name: Kaapor Test accuracy value: 21.2 - type: accuracy name: Korean Test accuracy value: 58.6 - type: accuracy name: Estonian Test accuracy value: 78.5 - type: accuracy name: Croatian Test accuracy value: 71.3 - type: accuracy name: Gothic Test accuracy value: 18.2 - type: accuracy name: Swiss German Test accuracy value: 44.1 - type: accuracy name: Assyrian Test accuracy value: 17.2 - type: accuracy name: North Sami Test accuracy value: 34.9 - type: accuracy name: Naija Test accuracy value: 37.5 - type: accuracy name: Latvian Test accuracy value: 79.2 - type: accuracy name: Chinese Test accuracy value: 47.9 - type: accuracy name: Tagalog Test accuracy value: 65.6 - type: accuracy name: Bambara Test accuracy value: 22.8 - type: accuracy name: Lithuanian Test accuracy value: 77.8 - type: accuracy name: Galician Test accuracy value: 61.9 - type: accuracy name: Vietnamese Test accuracy value: 56.1 - type: accuracy name: Greek Test accuracy value: 63.5 - type: accuracy name: Catalan Test accuracy value: 57.6 - type: accuracy name: Czech Test accuracy value: 71.7 - type: accuracy name: Erzya Test accuracy value: 43.5 - type: accuracy name: Bhojpuri Test accuracy value: 55.6 - type: accuracy name: Thai Test accuracy value: 56.7 - type: accuracy name: Marathi Test accuracy value: 79.1 - type: accuracy name: Basque Test accuracy value: 74.3 - type: accuracy name: Slovak Test accuracy value: 71.9 - type: accuracy name: Kiche Test accuracy value: 28.3 - type: accuracy name: Yoruba Test accuracy value: 22.3 - type: accuracy name: Warlpiri Test accuracy value: 32.4 - type: accuracy name: Tamil Test accuracy value: 85.6 - type: accuracy name: Maltese Test accuracy value: 23.1 - type: accuracy name: Ancient Greek Test accuracy value: 52.9 - type: accuracy name: Icelandic Test accuracy value: 67.9 - type: accuracy name: Mbya Guarani Test accuracy value: 28.5 - type: accuracy name: Urdu Test accuracy value: 69.0 - type: accuracy name: Romanian Test accuracy value: 65.5 - type: accuracy name: Persian Test accuracy value: 60.0 - type: accuracy name: Apurina Test accuracy value: 32.7 - type: accuracy name: Japanese Test accuracy value: 42.3 - type: accuracy name: Hungarian Test accuracy value: 69.8 - type: accuracy name: Hindi Test accuracy value: 73.6 - type: accuracy name: Classical Chinese Test accuracy value: 28.3 - type: accuracy name: Komi Permyak Test accuracy value: 40.2 - type: accuracy name: Faroese Test accuracy value: 59.9 - type: accuracy name: Sanskrit Test accuracy value: 36.9 - type: accuracy name: Livvi Test accuracy value: 61.4 - type: accuracy name: Arabic Test accuracy value: 62.9 - type: accuracy name: Wolof Test accuracy value: 28.3 - type: accuracy name: Bulgarian Test accuracy value: 71.6 - type: accuracy name: Akuntsu Test accuracy value: 19.3 - type: accuracy name: Makurap Test accuracy value: 12.3 - type: accuracy name: Kangri Test accuracy value: 51.6 - type: accuracy name: Breton Test accuracy value: 51.7 - type: accuracy name: Telugu Test accuracy value: 83.2 - type: accuracy name: Cantonese Test accuracy value: 50.3 - type: accuracy name: Old Church Slavonic Test accuracy value: 45.7 - type: accuracy name: Karelian Test accuracy value: 63.7 - type: accuracy name: Upper Sorbian Test accuracy value: 62.3 - type: accuracy name: South Levantine Arabic Test accuracy value: 57.5 - type: accuracy name: Komi Zyrian Test accuracy value: 35.3 - type: accuracy name: Irish Test accuracy value: 58.2 - type: accuracy name: Nayini Test accuracy value: 48.7 - type: accuracy name: Munduruku Test accuracy value: 15.9 - type: accuracy name: Manx Test accuracy value: 26.5 - type: accuracy name: Skolt Sami Test accuracy value: 32.7 - type: accuracy name: Afrikaans Test accuracy value: 66.5 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 27.8 - type: accuracy name: Belarusian Test accuracy value: 76.9 - type: accuracy name: Serbian Test accuracy value: 71.6 - type: accuracy name: Moksha Test accuracy value: 39.2 - type: accuracy name: Western Armenian Test accuracy value: 70.8 - type: accuracy name: Scottish Gaelic Test accuracy value: 50.2 - type: accuracy name: Khunsari Test accuracy value: 39.2 - type: accuracy name: Hebrew Test accuracy value: 81.2 - type: accuracy name: Uyghur Test accuracy value: 67.3 - type: accuracy name: Chukchi Test accuracy value: 33.6 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Tamil This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ta") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ta") ```
mohamed-illiyas/wav2vec-malayalam	1dcc0f9033e7dde9ef935bf3f5c8f24c45c19956	2022-02-28T16:07:13.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	mohamed-illiyas	null	mohamed-illiyas/wav2vec-malayalam	null	transformers	36,372	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec-malayalam 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. --> # wav2vec-malayalam This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0a0+3fd9dcf - Datasets 1.18.3 - Tokenizers 0.10.3
zfchen/codeparrot	54737857ba1f8cc3b41c545642fa9f6f93694f44	2022-02-24T14:52:15.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	zfchen	null	zfchen/codeparrot	null	transformers	36,373	Entry not found
vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k	153dc40a3ce127c6a0537940eda26d0dad5659f2	2022-02-24T19:08:20.000Z	[ "pytorch", "distilbert", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]	sentence-similarity	false	vocab-transformers	null	vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k	null	sentence-transformers	36,374	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # dense_encoder-msmarco-distilbert-word2vec256k This model is based on [msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec. It has been trained on MS MARCO using [MarginMSELoss](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py). See the train_script.py in this repository. Performance: - MS MARCO dev: - (MRR@10) - TREC-DL 2019: 65.53 (nDCG@10) - TREC-DL 2020: 67.42 (nDCG@10) - Avg. on 4 BEIR datasets: 38.97 The word embedding matrix has been frozen while training. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 7858 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MarginMSELoss.MarginMSELoss` Parameters of the fit()-Method: ``` { "epochs": 30, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 250, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
vesteinn/clip-nabirds	99805933b55c5ee1a384aefdfac664bc6a8ac150	2022-02-27T22:40:41.000Z	[ "pytorch", "clip", "feature-extraction", "transformers" ]	feature-extraction	false	vesteinn	null	vesteinn/clip-nabirds	null	transformers	36,375	Entry not found
huggingtweets/dril-nia_mp4	875388556d09a55ec7f566dc1a05afb713c356eb	2022-02-25T19:44:43.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/dril-nia_mp4	null	transformers	36,376	--- language: en thumbnail: http://www.huggingtweets.com/dril-nia_mp4/1645818279249/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1487740104340918272/7c9spp2E_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nia & wint</div> <div style="text-align: center; font-size: 14px;">@dril-nia_mp4</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nia & wint. \| Data \| Nia \| wint \| \| --- \| --- \| --- \| \| Tweets downloaded \| 278 \| 3229 \| \| Retweets \| 12 \| 473 \| \| Short tweets \| 13 \| 300 \| \| Tweets kept \| 253 \| 2456 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ybk5oh0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dril-nia_mp4's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ny6aucf) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ny6aucf/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/dril-nia_mp4') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
nadaAlnada/wav2vec2-base-timit-demo-colab	5f1daa05183ad42c85e378b91af603d602cbbd31	2022-02-27T13:55:32.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	nadaAlnada	null	nadaAlnada/wav2vec2-base-timit-demo-colab	null	transformers	36,377	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice 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 [anas/wav2vec2-large-xlsr-arabic](https://huggingface.co/anas/wav2vec2-large-xlsr-arabic) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
hcy11/distilbert-base-uncased-finetuned-squad	21d2fd9c156841c916a2bfd0b7e5fd9deefdadff	2022-03-02T20:32:33.000Z	[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	question-answering	false	hcy11	null	hcy11/distilbert-base-uncased-finetuned-squad	null	transformers	36,378	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2131 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2672 \| 1.0 \| 5533 \| 1.2131 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
zhoutong/best-t5	7debf4563bc6d9a9dff89ccf79bbe3510398bb97	2022-02-26T07:27:55.000Z	[ "pytorch" ]	null	false	zhoutong	null	zhoutong/best-t5	null	null	36,379	Entry not found
ianc89/hagrid	8589d4146189b95a75fb2c329cba312a492f21d6	2022-02-26T13:52:01.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	ianc89	null	ianc89/hagrid	null	transformers	36,380	--- tags: - conversational --- # My Awesome Model
nimrah/wav2vec2-large-xls-r-300m-my_hindi_home-colab	a5b877c919ab676748bb54a993f93d1d2455f28b	2022-02-26T17:11:23.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	nimrah	null	nimrah/wav2vec2-large-xls-r-300m-my_hindi_home-colab	null	transformers	36,381	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-my_hindi_home-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-my_hindi_home-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
huggingtweets/claresiobhan	23b5676b74c9dcb7c46edf5e8e6f38cc8eea61a7	2022-02-26T22:19:14.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/claresiobhan	null	transformers	36,382	--- language: en thumbnail: http://www.huggingtweets.com/claresiobhan/1645913945953/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1296785738978201600/J9LDndke_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">👰Clare Siobhán👰</div> <div style="text-align: center; font-size: 14px;">@claresiobhan</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 👰Clare Siobhán👰. \| Data \| 👰Clare Siobhán👰 \| \| --- \| --- \| \| Tweets downloaded \| 3249 \| \| Retweets \| 110 \| \| Short tweets \| 504 \| \| Tweets kept \| 2635 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vq9maap/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @claresiobhan's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/375bmhre) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/375bmhre/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/claresiobhan') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/zeebeecat01	150bdb14347a4bdbbbf8d1621cae6c5ed2d73260	2022-02-26T22:24:18.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/zeebeecat01	null	transformers	36,383	--- language: en thumbnail: http://www.huggingtweets.com/zeebeecat01/1645914254405/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1103665627183472642/OVXzwAk7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Shreya Mukherjee 💀🌻</div> <div style="text-align: center; font-size: 14px;">@zeebeecat01</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Shreya Mukherjee 💀🌻. \| Data \| Shreya Mukherjee 💀🌻 \| \| --- \| --- \| \| Tweets downloaded \| 731 \| \| Retweets \| 552 \| \| Short tweets \| 33 \| \| Tweets kept \| 146 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kz1pvshu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zeebeecat01's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3btkttwk) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3btkttwk/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/zeebeecat01') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
jiobiala24/wav2vec2-base-checkpoint-13	5c89362023b8b279eca9db1fe7ce75fde2cdae64	2022-02-27T12:36:13.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	jiobiala24	null	jiobiala24/wav2vec2-base-checkpoint-13	null	transformers	36,384	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-base-checkpoint-13 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-checkpoint-13 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-12](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-12) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.1804 - Wer: 0.3809 ## 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: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.2688 \| 1.92 \| 1000 \| 0.6518 \| 0.3692 \| \| 0.1944 \| 3.85 \| 2000 \| 0.7188 \| 0.3808 \| \| 0.1503 \| 5.77 \| 3000 \| 0.7552 \| 0.3853 \| \| 0.1218 \| 7.69 \| 4000 \| 0.8155 \| 0.3834 \| \| 0.1024 \| 9.62 \| 5000 \| 0.8867 \| 0.3779 \| \| 0.0874 \| 11.54 \| 6000 \| 0.8917 \| 0.3866 \| \| 0.0775 \| 13.46 \| 7000 \| 1.0320 \| 0.4019 \| \| 0.0712 \| 15.38 \| 8000 \| 1.0110 \| 0.3922 \| \| 0.0656 \| 17.31 \| 9000 \| 1.0494 \| 0.3885 \| \| 0.0578 \| 19.23 \| 10000 \| 1.1054 \| 0.3883 \| \| 0.053 \| 21.15 \| 11000 \| 1.1285 \| 0.3938 \| \| 0.0496 \| 23.08 \| 12000 \| 1.1358 \| 0.3884 \| \| 0.0459 \| 25.0 \| 13000 \| 1.2062 \| 0.3904 \| \| 0.0445 \| 26.92 \| 14000 \| 1.1811 \| 0.3830 \| \| 0.0414 \| 28.85 \| 15000 \| 1.1804 \| 0.3809 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
jt360/mt5-small-finetuned-amazon-en-es-video-games	c616e485a6c72203248922966d17c75ba839fdd7	2022-02-27T18:43:57.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]	text2text-generation	false	jt360	null	jt360/mt5-small-finetuned-amazon-en-es-video-games	null	transformers	36,385	--- license: afl-3.0 ---
flairbook2/flairmodel	dba1512be5619b7a11812b492b1a7d37f6639188	2022-04-09T16:58:21.000Z	[ "pytorch", "flair", "token-classification" ]	token-classification	false	flairbook2	null	flairbook2/flairmodel	null	flair	36,386	--- tags: - flair - token-classification widget: - text: "does this work" --- ## Test model README Some test README description
mipatov/rugpt3_nb_descr	268609ceb23151c73dbf07578fc23bbc51988240	2022-02-27T23:44:38.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	mipatov	null	mipatov/rugpt3_nb_descr	null	transformers	36,387	based on `sberbank-ai/rugpt3medium_based_on_gpt2` finetuned for generate text description for notebook-devices
facebook/wav2vec2-base-sv-voxpopuli-v2	36445212b2499f538da33aba9a1475981c82ed69	2022-02-27T13:13:27.000Z	[ "pytorch", "wav2vec2", "pretraining", "sv", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]	automatic-speech-recognition	false	facebook	null	facebook/wav2vec2-base-sv-voxpopuli-v2	null	transformers	36,388	--- language: sv tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-base-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in sv on 16.3k unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data in sv. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. Paper: [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390) Authors: Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux from Facebook AI. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-large-west_germanic-voxpopuli-v2	ba2575ca08f92658b827cb034da9ad4b5e3d56d9	2022-02-27T12:35:16.000Z	[ "pytorch", "wav2vec2", "pretraining", "west_germanic", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]	automatic-speech-recognition	false	facebook	null	facebook/wav2vec2-large-west_germanic-voxpopuli-v2	null	transformers	36,389	--- language: west_germanic tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in west_germanic on 66.3 unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data in west_germanic. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. Paper: [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390) Authors: Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux from Facebook AI. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-large-north_germanic-voxpopuli-v2	2f451c30b28238d13b1c54bbca1f4e6c241a5304	2022-02-27T12:37:56.000Z	[ "pytorch", "wav2vec2", "pretraining", "north_germanic", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]	automatic-speech-recognition	false	facebook	null	facebook/wav2vec2-large-north_germanic-voxpopuli-v2	null	transformers	36,390	--- language: north_germanic tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in north_germanic on 29.9 unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data in north_germanic. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. Paper: [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390) Authors: Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux from Facebook AI. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-large-slavic-voxpopuli-v2	fa95b126a53cb5b6803f6e7c3f77693525b97eff	2022-02-27T12:40:42.000Z	[ "pytorch", "wav2vec2", "pretraining", "slavic", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]	automatic-speech-recognition	false	facebook	null	facebook/wav2vec2-large-slavic-voxpopuli-v2	null	transformers	36,391	--- language: slavic tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in slavic on 88.99999999999999 unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data in slavic. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. Paper: [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390) Authors: Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux from Facebook AI. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-base-sl-voxpopuli-v2	0411d3dd04058c9d1547ece5f29e9f107e907930	2022-02-27T13:14:49.000Z	[ "pytorch", "wav2vec2", "pretraining", "sl", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]	automatic-speech-recognition	false	facebook	null	facebook/wav2vec2-base-sl-voxpopuli-v2	null	transformers	36,392	--- language: sl tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-base-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in sl on 11.3k unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data in sl. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. Paper: [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390) Authors: Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux from Facebook AI. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-large-mt-voxpopuli-v2	51ecaa56badc4d0aa1ad5d3d196e605e1369b31c	2022-02-27T12:51:06.000Z	[ "pytorch", "wav2vec2", "pretraining", "mt", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]	automatic-speech-recognition	false	facebook	null	facebook/wav2vec2-large-mt-voxpopuli-v2	null	transformers	36,393	--- language: mt tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in mt on 9.1 unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data in mt. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. Paper: [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390) Authors: Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux from Facebook AI. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-large-el-voxpopuli-v2	362fc887d5d4854e0299be872e30015396a38a2c	2022-02-27T12:48:30.000Z	[ "pytorch", "wav2vec2", "pretraining", "el", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]	automatic-speech-recognition	false	facebook	null	facebook/wav2vec2-large-el-voxpopuli-v2	null	transformers	36,394	--- language: el tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in el on 17.7 unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data in el. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. Paper: [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390) Authors: Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux from Facebook AI. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
spy24/autonlp-UK-to-US-600416931	eea0caf05b11c517c67f1ce46d7028cce22d3b17	2022-02-28T09:59:04.000Z	[ "pytorch", "t5", "text2text-generation", "unk", "dataset:spy24/autonlp-data-UK-to-US", "transformers", "autonlp", "co2_eq_emissions", "autotrain_compatible" ]	text2text-generation	false	spy24	null	spy24/autonlp-UK-to-US-600416931	1	transformers	36,395	--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - spy24/autonlp-data-UK-to-US co2_eq_emissions: 1.113131499202784 --- # Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 600416931 - CO2 Emissions (in grams): 1.113131499202784 ## Validation Metrics - Loss: 1.8278849124908447 - Rouge1: 45.7945 - Rouge2: 8.5245 - RougeL: 45.8031 - RougeLsum: 45.9067 - Gen Len: 3.0622 ## 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/spy24/autonlp-UK-to-US-600416931 ```
spy24/autonlp-AUS-to-US-601516964	58c67b6f95797e1ba26bd30dcb6d02dd133b04ef	2022-02-28T11:21:11.000Z	[ "pytorch", "t5", "text2text-generation", "unk", "dataset:spy24/autonlp-data-AUS-to-US", "transformers", "autonlp", "co2_eq_emissions", "autotrain_compatible" ]	text2text-generation	false	spy24	null	spy24/autonlp-AUS-to-US-601516964	null	transformers	36,396	--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - spy24/autonlp-data-AUS-to-US co2_eq_emissions: 3.3930796843275846 --- # Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 601516964 - CO2 Emissions (in grams): 3.3930796843275846 ## Validation Metrics - Loss: 1.9823806285858154 - Rouge1: 42.8783 - Rouge2: 7.4603 - RougeL: 42.8492 - RougeLsum: 43.0556 - Gen Len: 2.8952 ## 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/spy24/autonlp-AUS-to-US-601516964 ```
rockyend/distilbert-base-uncased-finetuned-ner	1a025a2170be4f9fd5e5cf7353729c0a1bb5b023	2022-02-28T15:45:56.000Z	[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	false	rockyend	null	rockyend/distilbert-base-uncased-finetuned-ner	null	transformers	36,397	Entry not found
peterhsu/test-bert-finetuned-squad-accelerate	bd5b6de35c21408d3ca5aa77302d7a5eaa419721	2022-02-28T18:47:40.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	peterhsu	null	peterhsu/test-bert-finetuned-squad-accelerate	null	transformers	36,398	Entry not found
nateraw/cryptopunks-gan	1eb7a477ae62ddf76aa01598347797aa2f3a248f	2022-03-01T01:59:49.000Z	[ "tensorboard", "pytorch", "dcgan" ]	null	false	nateraw	null	nateraw/cryptopunks-gan	2	pytorch	36,399	--- library_name: pytorch tags: - dcgan --- # cryptopunks-gan A DCGAN trained to generate novel Cryptopunks. Check out the code by Teddy Koker [here](https://github.com/teddykoker/cryptopunks-gan). ## Generated Punks Here are some punks generated by this model: ![](fake_samples_epoch_999.png) ## Usage You can try it out yourself, or you can play with the [demo](https://huggingface.co/spaces/nateraw/cryptopunks-generator). To use it yourself - make sure you have `torch`, `torchvision`, and `huggingface_hub` installed. Then, run the following to generate a grid of 64 random punks: ```python import torch from huggingface_hub import hf_hub_download from torch import nn from torchvision.utils import save_image class Generator(nn.Module): def __init__(self, nc=4, nz=100, ngf=64): super(Generator, self).__init__() self.network = nn.Sequential( nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False), nn.BatchNorm2d(ngf * 4), nn.ReLU(True), nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False), nn.BatchNorm2d(ngf * 2), nn.ReLU(True), nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False), nn.BatchNorm2d(ngf), nn.ReLU(True), nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False), nn.Tanh(), ) def forward(self, input): output = self.network(input) return output model = Generator() weights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth') model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))) out = model(torch.randn(64, 100, 1, 1)) save_image(out, "punks.png", normalize=True) ```

ydl233/t5_small_model

6aa550cd537ea06366ddabc0baa95e3c8d5c3cfc

2021-12-03T04:47:46.000Z

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

text2text-generation

false

ydl233

null

ydl233/t5_small_model

0

null

transformers

36,300

Entry not found

ying-tina/temp

d2a1b707767ccdf35d16c911c07009a574cc1eb0

2022-01-22T03:43:36.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

ying-tina

null

ying-tina/temp

0

null

transformers

36,301

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: temp results: [] ---  # temp 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: 0.4645 - Wer: 0.3527 ## 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: 100 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4324 | 0.4 | 50 | 0.5800 | 0.4458 | | 0.4027 | 0.8 | 100 | 0.5374 | 0.4109 | | 0.3163 | 1.2 | 150 | 0.5285 | 0.3881 | | 0.3064 | 1.6 | 200 | 0.5161 | 0.3815 | | 0.3235 | 2.0 | 250 | 0.4645 | 0.3527 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

ying-tina/wav2vec2-base-timit-demo-colab-32-epochs30

9913ce3470d5619481f2b662558f65dbea94ff4b

2022-01-09T09:21:52.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

ying-tina

null

ying-tina/wav2vec2-base-timit-demo-colab-32-epochs30

0

null

transformers

36,302

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab-32-epochs30 results: [] ---  # wav2vec2-base-timit-demo-colab-32-epochs30 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: 0.4615 - Wer: 0.3434 ## 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.5243 | 4.0 | 500 | 1.4532 | 0.9540 | | 0.6178 | 8.0 | 1000 | 0.5490 | 0.4627 | | 0.223 | 12.0 | 1500 | 0.4513 | 0.3881 | | 0.1299 | 16.0 | 2000 | 0.4573 | 0.3698 | | 0.0875 | 20.0 | 2500 | 0.4950 | 0.3637 | | 0.0613 | 24.0 | 3000 | 0.4327 | 0.3479 | | 0.0478 | 28.0 | 3500 | 0.4615 | 0.3434 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

ying-tina/wav2vec2-base-timit-demo-colab-32-epochs50-earlystop

cb6080ea36f69319bfbd2aecfe537e1cc29ae49d

2022-01-09T12:13:04.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

ying-tina

null

ying-tina/wav2vec2-base-timit-demo-colab-32-epochs50-earlystop

0

null

transformers

36,303

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab-32-epochs50-earlystop results: [] ---  # wav2vec2-base-timit-demo-colab-32-epochs50-earlystop 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: 0.5208 - Wer: 0.3561 ## 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: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4294 | 4.0 | 500 | 1.3397 | 0.8966 | | 0.5848 | 8.0 | 1000 | 0.4931 | 0.4585 | | 0.2323 | 12.0 | 1500 | 0.4781 | 0.4008 | | 0.14 | 16.0 | 2000 | 0.4294 | 0.3806 | | 0.1026 | 20.0 | 2500 | 0.5098 | 0.3663 | | 0.0725 | 24.0 | 3000 | 0.4527 | 0.3568 | | 0.058 | 28.0 | 3500 | 0.5208 | 0.3561 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

ying-tina/wav2vec2-base-timit-demo-colab-test

b690d97b77bf02b1c482d314e44602dcb62d696c

2021-12-05T14:55:36.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

ying-tina

null

ying-tina/wav2vec2-base-timit-demo-colab-test

0

null

transformers

36,304

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab-test results: [] ---  # wav2vec2-base-timit-demo-colab-test 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: 0.4283 - Wer: 0.3356 ## 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7386 | 4.0 | 500 | 2.2419 | 1.0 | | 0.9366 | 8.0 | 1000 | 0.4789 | 0.4807 | | 0.3118 | 12.0 | 1500 | 0.4197 | 0.3973 | | 0.1784 | 16.0 | 2000 | 0.4216 | 0.3614 | | 0.1297 | 20.0 | 2500 | 0.4298 | 0.3507 | | 0.1091 | 24.0 | 3000 | 0.4365 | 0.3437 | | 0.0819 | 28.0 | 3500 | 0.4283 | 0.3356 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

ykliu1892/opus-mt-zh-de-tuned-Tatoeba-small

a9a3017a8ba23bda34f6f62279377bee17542ef5

2022-01-02T04:09:53.000Z

[ "pytorch", "tensorboard", "marian", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

ykliu1892

null

ykliu1892/opus-mt-zh-de-tuned-Tatoeba-small

0

null

transformers

36,305

--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: opus-mt-zh-de-tuned-Tatoeba-small results: [] ---  # opus-mt-zh-de-tuned-Tatoeba-small This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-de](https://huggingface.co/Helsinki-NLP/opus-mt-zh-de) on a refined dataset of Tatoeba German - Chinese corpus https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/master/data/README.md. It achieves the following results on the evaluation set: - Loss: 2.2703 - Bleu: 16.504 - Gen Len: 16.6531 ## Model description More information needed ## Intended uses & limitations Prefix used during fine-tuning: "将中文翻译成德语". This prefix is also recommended in prediction. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:| | 2.7229 | 0.24 | 16000 | 2.5605 | 14.1956 | 16.2206 | | 2.5988 | 0.49 | 32000 | 2.4447 | 14.8619 | 16.2726 | | 2.515 | 0.73 | 48000 | 2.3817 | 15.3212 | 16.2823 | | 2.4683 | 0.97 | 64000 | 2.3367 | 15.9043 | 16.7138 | | 2.3873 | 1.22 | 80000 | 2.3115 | 16.1037 | 16.6369 | | 2.3792 | 1.46 | 96000 | 2.2919 | 16.2957 | 16.6304 | | 2.3626 | 1.7 | 112000 | 2.2790 | 16.2995 | 16.6235 | | 2.3353 | 1.95 | 128000 | 2.2703 | 16.504 | 16.6531 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles

56b97d94a094e8128c72c5abe93264ce2966dfb2

2021-11-30T13:22:24.000Z

[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]

text2text-generation

false

ykliu1892

null

ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles

0

null

transformers

36,306

--- tags: - generated_from_trainer model-index: - name: translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles results: [] ---  # translation-en-pt-t5-finetuned-Duolingo-Subtitles-finetuned-Duolingo-Subtitles 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles

e07a2cb8d52d60da480e037770f9a4290fe3f653

2021-12-13T17:37:09.000Z

[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]

text2text-generation

false

ykliu1892

null

ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles

0

1

transformers

36,307

--- tags: - generated_from_trainer model-index: - name: translation-en-pt-t5-finetuned-Duolingo-Subtitles results: [] ---  # translation-en-pt-t5-finetuned-Duolingo-Subtitles This model is a fine-tuned version of [ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles](https://huggingface.co/ykliu1892/translation-en-pt-t5-finetuned-Duolingo-Subtitles) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.0932 - eval_bleu: 28.4269 - eval_gen_len: 8.816 - eval_runtime: 1404.5946 - eval_samples_per_second: 106.792 - eval_steps_per_second: 3.338 - epoch: 0.52 - step: 28000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

ykliu1892/translation-en-pt-t5-finetuned-Duolingo

a97e566ab8248b8571308e19e176102c4d02a0a6

2021-12-01T04:58:54.000Z

[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]

text2text-generation

false

ykliu1892

null

ykliu1892/translation-en-pt-t5-finetuned-Duolingo

0

null

transformers

36,308

--- tags: - generated_from_trainer metrics: - bleu model-index: - name: translation-en-pt-t5-finetuned-Duolingo results: [] ---  # translation-en-pt-t5-finetuned-Duolingo This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7362 - Bleu: 39.4725 - Gen Len: 9.002 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.5429 | 0.24 | 9000 | 0.7461 | 39.4744 | 9.0 | | 0.5302 | 0.48 | 18000 | 0.7431 | 39.7559 | 8.97 | | 0.5309 | 0.72 | 27000 | 0.7388 | 39.6751 | 8.998 | | 0.5336 | 0.96 | 36000 | 0.7362 | 39.4725 | 9.002 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

ylh1013/ja_chatbot

8e19ffd505c79f8576618e27b3aeecdeb8997db6

2022-01-23T02:24:03.000Z

[ "pytorch", "gpt2", "text-generation", "finetuned_from", "transformers", "generated_from_trainer", "license:mit", "model-index" ]

text-generation

false

ylh1013

null

ylh1013/ja_chatbot

0

null

transformers

36,309

--- language: - finetuned_from license: mit tags: - generated_from_trainer model-index: - name: ja_chatbot results: [] ---  # ja_chatbot This model is a fine-tuned version of [rinna/japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2-medium) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 16 - 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: 1 ### Training results ### Framework versions - Transformers 4.12.3 - Pytorch 1.10.0+cu102 - Tokenizers 0.10.3

yliu337/t5_mask_cnn_dailymail

70be5e2acf7a0e2b95f3fd7fc20e188a3c19e7f0

2022-06-04T21:38:05.000Z

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

text2text-generation

false

yliu337

null

yliu337/t5_mask_cnn_dailymail

0

null

transformers

36,310

Entry not found

yliu337/t5_token_nonfilter_bothcontext

41feba322090a7451c4c7ec8e17f80d5717a53a0

2021-09-05T01:20:00.000Z

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

text2text-generation

false

yliu337

null

yliu337/t5_token_nonfilter_bothcontext

0

null

transformers

36,311

Entry not found

yliu337/t5_token_nonfilter_bothcontext_padded_ctx

6bcb300a8bf8f6e6378194909af1a5829510eb25

2021-09-15T01:14:36.000Z

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

text2text-generation

false

yliu337

null

yliu337/t5_token_nonfilter_bothcontext_padded_ctx

0

null

transformers

36,312

Entry not found

young/BertForFinance

c818d7abf7630585153f24ea946987f818ad1589

2021-03-17T05:13:04.000Z

[ "pytorch", "transformers" ]

null

false

young

null

young/BertForFinance

0

null

transformers

36,313

Entry not found

ytlin/1riatc43

f8c6535e20483763f2fc5d57cf6be48f14dd850b

2020-10-05T21:26:03.000Z

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

text2text-generation

false

ytlin

null

ytlin/1riatc43

0

null

transformers

36,314

Entry not found

ytlin/2jgyqp5g

e03e6637c42a8e88ac14155e4dd1b3feb94d7b64

2020-10-06T06:54:48.000Z

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

text2text-generation

false

ytlin

null

ytlin/2jgyqp5g

0

null

transformers

36,315

Entry not found

ytlin/46695u38_3

403eef05120ee96f0812eab7aa46363248aab25f

2021-05-23T13:51:39.000Z

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

text-generation

false

ytlin

null

ytlin/46695u38_3

0

null

transformers

36,316

Entry not found

yusufmorsi/georgebot

5ec19a640a9015980c3c5445f7e611e1df4ecb2c

2021-11-21T21:54:17.000Z

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

conversational

false

yusufmorsi

null

yusufmorsi/georgebot

0

null

transformers

36,317

--- tags: - conversational --- # George Costanza Model

yxchar/tlm-ag-large-scale

e85d8e664ab4dc59a7e288fd9e4fa6a8a983dfb8

2021-11-04T11:08:19.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-ag-large-scale

0

null

transformers

36,318

Entry not found

yxchar/tlm-ag-small-scale

d3aa55a8fe2f1e9c88777b89610eb2b58b4cec0f

2021-11-04T09:45:58.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-ag-small-scale

0

null

transformers

36,319

Entry not found

yxchar/tlm-amazon-large-scale

425213afb8e3cf6d980a4cc95796a650f14ad792

2021-11-04T13:45:00.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-amazon-large-scale

0

null

transformers

36,320

Entry not found

yxchar/tlm-amazon-small-scale

b5e9e950b40d0eeaf92715252d611f330d3fa921

2021-11-04T13:26:20.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-amazon-small-scale

0

null

transformers

36,321

Entry not found

yxchar/tlm-chemprot-large-scale

ec524900ada76dd3ce633bffc91d014cd64ca9b9

2021-11-04T14:25:13.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-chemprot-large-scale

0

null

transformers

36,322

Entry not found

yxchar/tlm-chemprot-small-scale

7f9915f7a0e3123bc99fd0202aa4ec9117dae60f

2021-11-04T14:09:54.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-chemprot-small-scale

0

null

transformers

36,323

Entry not found

yxchar/tlm-citation_intent-large-scale

63ec60d4b24f09930ec8ba0aceab23ef2666b349

2021-11-04T15:03:41.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-citation_intent-large-scale

0

null

transformers

36,324

Entry not found

yxchar/tlm-citation_intent-medium-scale

6502485b23dbc3f3fae9cd93afd8bbce46654bc6

2021-11-04T14:55:07.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-citation_intent-medium-scale

0

null

transformers

36,325

Entry not found

yxchar/tlm-citation_intent-small-scale

d3b9059002e08af3dc63ac9cbd72825b31cc5b49

2021-11-04T14:47:38.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-citation_intent-small-scale

0

null

transformers

36,326

Entry not found

yxchar/tlm-hyp-large-scale

5aed89ef25dc4b1034e4292ff553855d96973ea4

2021-11-04T15:42:49.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-hyp-large-scale

0

null

transformers

36,327

Entry not found

yxchar/tlm-imdb-medium-scale

87b82975e34b61010160008500b19731f073ef82

2021-11-04T09:43:24.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-imdb-medium-scale

0

null

transformers

36,328

Entry not found

yxchar/tlm-rct-20k-large-scale

22c465caf13bb019b7f347debae40385ef27b231

2021-11-04T16:02:51.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-rct-20k-large-scale

0

null

transformers

36,329

Entry not found

yxchar/tlm-rct-20k-medium-scale

baf67c6765879c4c324e56fbf4a5ab0a8f775050

2021-11-04T17:20:07.000Z

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

fill-mask

false

yxchar

null

yxchar/tlm-rct-20k-medium-scale

0

null

transformers

36,330

Entry not found

yysung53/dpr

d33c2c0b18ed241147daa8561f8f80f84d244fc9

2021-10-30T22:18:04.000Z

[ "pytorch", "text_similarity", "transformers" ]

null

false

yysung53

null

yysung53/dpr

0

null

transformers

36,331

Entry not found

yzhou992/NetMind-20211103-448

a78f2d6e0bacac3cc17a466744e2fce9b45282c9

2021-11-03T05:47:20.000Z

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

fill-mask

false

yzhou992

null

yzhou992/NetMind-20211103-448

0

null

transformers

36,332

Entry not found

yzhou992/test_model2

66febcd1d6734a22b868915c1d0bfce3c08c7d64

2021-11-03T05:50:54.000Z

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

fill-mask

false

yzhou992

null

yzhou992/test_model2

0

null

transformers

36,333

Entry not found

zachzhang/t5_hcm

1947584a85313c86463339539c45717a706b7470

2021-10-08T22:50:26.000Z

[ "pytorch" ]

null

false

zachzhang

null

zachzhang/t5_hcm

0

null

36,334

Entry not found

zari/my-awesome-model

c019cff5a02bc0a02780d74e0052a2d36ce17226

2021-06-22T21:29:07.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "license:apache-2.0" ]

text-generation

false

zari

null

zari/my-awesome-model

0

null

transformers

36,335

--- license: apache-2.0 datasets: - null model_index: - name: my-awesome-model results: - task: name: Causal Language Modeling type: text-generation ---  # my-awesome-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4356 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 91 | 3.4934 | | No log | 2.0 | 182 | 3.4451 | | No log | 3.0 | 273 | 3.4356 | ### Framework versions - Transformers 4.7.0 - Pytorch 1.9.0+cu102 - Datasets 1.8.0 - Tokenizers 0.10.3

zbmain/test

1ba8a708cacf37af6ac60a21a87986afe61bfa7f

2020-11-24T12:12:29.000Z

[ "pytorch" ]

null

false

zbmain

null

zbmain/test

0

null

36,336

123

zen-satvik/BotGPT-medium-HP

1a335d2e651151a6aebe745afac08dbf994adebc

2021-08-28T07:09:59.000Z

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

conversational

false

zen-satvik

null

zen-satvik/BotGPT-medium-HP

0

null

transformers

36,337

--- tags: conversational --- # Harry Potter Bot GPT Model

zeping/codeparrot

a094184fde7e3c78122fc8e16e31bd28436ec3fd

2022-01-19T10:03:09.000Z

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

text-generation

false

zeping

null

zeping/codeparrot

0

null

transformers

36,338

Entry not found

zgotter/gpt2-test

60a92ccd47c1c10a57fbde99442fc8db3cab0d39

2021-10-11T07:13:15.000Z

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

text-generation

false

zgotter

null

zgotter/gpt2-test

0

null

transformers

36,339

Entry not found

zhangxy-2019/cu_dstc9_dialoGPT

15b85cd201a34b6c52ac1cb1ed5a126cd84162a2

2021-05-23T14:05:15.000Z

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

text-generation

false

zhangxy-2019

null

zhangxy-2019/cu_dstc9_dialoGPT

0

null

transformers

36,340

Entry not found

zharry29/intent_fb-th_id

60e747d83d8c2a79a527965b835648eed92300dd

2020-09-16T20:16:29.000Z

[ "pytorch", "xlm-roberta", "multiple-choice", "transformers" ]

multiple-choice

false

zharry29

null

zharry29/intent_fb-th_id

0

null

transformers

36,341

Entry not found

zharry29/intent_fb-th_wh_id

55e35ceda0abe1a13031ccbe59f7a13a8b92c7fa

2020-09-16T20:17:00.000Z

[ "pytorch", "xlm-roberta", "multiple-choice", "transformers" ]

multiple-choice

false

zharry29

null

zharry29/intent_fb-th_wh_id

0

null

transformers

36,342

Entry not found

zharry29/intent_snips_id

4cefcb746824eb1b5ddb997878d2308e9a6cf371

2021-05-20T23:47:11.000Z

[ "pytorch", "jax", "roberta", "multiple-choice", "transformers" ]

multiple-choice

false

zharry29

null

zharry29/intent_snips_id

0

null

transformers

36,343

Entry not found

zhenghuabin/dummy_model

650ecd60a48463ff392cb24c61ba0f2c4d44d628

2021-11-06T09:59:44.000Z

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

fill-mask

false

zhenghuabin

null

zhenghuabin/dummy_model

0

null

transformers

36,344

Entry not found

zhuqing/bert-base-uncased-exp2-feminist

1f64c99dfb16602f3adbd32e97fe80ffe5d5879f

2021-08-28T13:07:52.000Z

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

fill-mask

false

zhuqing

null

zhuqing/bert-base-uncased-exp2-feminist

0

null

transformers

36,345

Entry not found

zhuqing/bert-base-uncased-mumsnet-all-0

eefd92d01121b055c77d4b3c7a2ca22f63d07218

2021-08-08T09:08:07.000Z

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

fill-mask

false

zhuqing

null

zhuqing/bert-base-uncased-mumsnet-all-0

0

null

transformers

36,346

Entry not found

zhuqing/bert-base-uncased-mumsnet-all-1

7a531cdfaa4f35f21e9891c3e728b7c745ad576c

2021-08-08T10:06:39.000Z

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

fill-mask

false

zhuqing

null

zhuqing/bert-base-uncased-mumsnet-all-1

0

null

transformers

36,347

Entry not found

zhuqing/bert-base-uncased-netmums-feminist-v2

a9936660af357cc407d1046278da8b8b8e2200b8

2021-08-15T11:41:05.000Z

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

fill-mask

false

zhuqing

null

zhuqing/bert-base-uncased-netmums-feminist-v2

0

null

transformers

36,348

Entry not found

zhuqing/bert-base-uncased-netmums-parent

5ff1779fe99f13bbd1593e7a929018756f5dbaf0

2021-08-14T07:43:23.000Z

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

fill-mask

false

zhuqing

null

zhuqing/bert-base-uncased-netmums-parent

0

null

transformers

36,349

Entry not found

zhuqing/bert-base-uncased-reddit-lib

92db2000a5c86c298d27ee07535a3626dd2c93bb

2021-08-01T16:27:15.000Z

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

fill-mask

false

zhuqing

null

zhuqing/bert-base-uncased-reddit-lib

0

null

transformers

36,350

Entry not found

zhuqing/bert-base-uncased-theme2

6a955641db575c9e832a497827b43c8e3361e641

2021-07-17T07:40:12.000Z

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

fill-mask

false

zhuqing

null

zhuqing/bert-base-uncased-theme2

0

null

transformers

36,351

Entry not found

zhuqing/comparison-bert-base-uncased-netmums-feminist

ce633f7fa72a837541d078ddd3286c7a370a1552

2021-08-19T19:32:49.000Z

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

fill-mask

false

zhuqing

null

zhuqing/comparison-bert-base-uncased-netmums-feminist

0

null

transformers

36,352

Entry not found

zhuqing/comparison-distilbert-base-uncased-netmums-feminist

f39c7ee5bcc20fea038095bdd51d0c155845e04e

2021-08-20T07:21:29.000Z

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

fill-mask

false

zhuqing

null

zhuqing/comparison-distilbert-base-uncased-netmums-feminist

0

null

transformers

36,353

Entry not found

zhuqing/distilroberta-base-theme2-6000

a53ffa9bb3f4531e1d519a67f321a135f6fffb84

2021-07-31T16:27:51.000Z

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

fill-mask

false

zhuqing

null

zhuqing/distilroberta-base-theme2-6000

0

null

transformers

36,354

Entry not found

zhuqing/roberta-base-uncased-netmums-all

179ff25e0099f2b454ec31f8cef1657c05e57bda

2021-08-20T09:23:54.000Z

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

fill-mask

false

zhuqing

null

zhuqing/roberta-base-uncased-netmums-all

0

null

transformers

36,355

Entry not found

zinary/DialoGPT-small-rick-new

f40df2511796a5d7e90e82a990acf856e4a15e4b

2021-09-19T07:26:27.000Z

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

conversational

false

zinary

null

zinary/DialoGPT-small-rick-new

0

null

transformers

36,356

--- tags: - conversational --- #Rick and Morty DialoGPT

zmingshi/roberta_L-12_H-768_A-12

a6f51347b5f5ed7f931c9b00059abc6bfc7f20ef

2021-11-29T06:47:26.000Z

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

fill-mask

false

zmingshi

null

zmingshi/roberta_L-12_H-768_A-12

0

null

transformers

36,357

Entry not found

zqf03118/bert_finetuning_test

88544fa69b07e75e6add24bb9c8d08a66b543448

2021-05-20T09:56:44.000Z

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

fill-mask

false

zqf03118

null

zqf03118/bert_finetuning_test

0

null

transformers

36,358

Entry not found

zuto37/DialoGPT-small-sadao

0221f7e03c60cb1daa16913449d731be90850c57

2021-09-23T09:07:55.000Z

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

conversational

false

zuto37

null

zuto37/DialoGPT-small-sadao

0

null

transformers

36,359

--- tags: - conversational --- # DialoGPT Model

zyayoung/cv-full-paper

b652f46baea9282cf60cca75dbf27a0413686f29

2021-11-23T06:27:33.000Z

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

text-generation

false

zyayoung

null

zyayoung/cv-full-paper

0

1

transformers

36,360

Entry not found

nielsr/enformer-preview-v2

03a454c666702bfdfbf37560bcbd9e4fca9deb9b

2022-02-24T07:09:45.000Z

[ "pytorch" ]

null

false

nielsr

null

nielsr/enformer-preview-v2

0

null

36,361

Entry not found

wietsedv/xlm-roberta-base-ft-udpos28-da

2c1c1ec9ac7b42bea7788a3896158d8a7b92d4cb

2022-02-25T09:58:14.000Z

[ "pytorch", "xlm-roberta", "token-classification", "da", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

wietsedv

null

wietsedv/xlm-roberta-base-ft-udpos28-da

0

null

transformers

36,362

--- language: - da license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-da results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 89.9 - type: accuracy name: Dutch Test accuracy value: 90.0 - type: accuracy name: German Test accuracy value: 88.8 - type: accuracy name: Italian Test accuracy value: 89.4 - type: accuracy name: French Test accuracy value: 89.0 - type: accuracy name: Spanish Test accuracy value: 91.6 - type: accuracy name: Russian Test accuracy value: 90.3 - type: accuracy name: Swedish Test accuracy value: 92.4 - type: accuracy name: Norwegian Test accuracy value: 87.3 - type: accuracy name: Danish Test accuracy value: 98.3 - type: accuracy name: Low Saxon Test accuracy value: 42.2 - type: accuracy name: Akkadian Test accuracy value: 24.0 - type: accuracy name: Armenian Test accuracy value: 89.1 - type: accuracy name: Welsh Test accuracy value: 69.2 - type: accuracy name: Old East Slavic Test accuracy value: 71.8 - type: accuracy name: Albanian Test accuracy value: 79.7 - type: accuracy name: Slovenian Test accuracy value: 78.9 - type: accuracy name: Guajajara Test accuracy value: 19.2 - type: accuracy name: Kurmanji Test accuracy value: 78.1 - type: accuracy name: Turkish Test accuracy value: 78.9 - type: accuracy name: Finnish Test accuracy value: 88.2 - type: accuracy name: Indonesian Test accuracy value: 84.8 - type: accuracy name: Ukrainian Test accuracy value: 88.6 - type: accuracy name: Polish Test accuracy value: 86.2 - type: accuracy name: Portuguese Test accuracy value: 91.0 - type: accuracy name: Kazakh Test accuracy value: 83.9 - type: accuracy name: Latin Test accuracy value: 79.8 - type: accuracy name: Old French Test accuracy value: 51.8 - type: accuracy name: Buryat Test accuracy value: 57.8 - type: accuracy name: Kaapor Test accuracy value: 12.5 - type: accuracy name: Korean Test accuracy value: 65.7 - type: accuracy name: Estonian Test accuracy value: 88.4 - type: accuracy name: Croatian Test accuracy value: 89.8 - type: accuracy name: Gothic Test accuracy value: 12.7 - type: accuracy name: Swiss German Test accuracy value: 44.8 - type: accuracy name: Assyrian Test accuracy value: 15.7 - type: accuracy name: North Sami Test accuracy value: 29.9 - type: accuracy name: Naija Test accuracy value: 38.0 - type: accuracy name: Latvian Test accuracy value: 88.4 - type: accuracy name: Chinese Test accuracy value: 43.2 - type: accuracy name: Tagalog Test accuracy value: 73.1 - type: accuracy name: Bambara Test accuracy value: 25.0 - type: accuracy name: Lithuanian Test accuracy value: 86.4 - type: accuracy name: Galician Test accuracy value: 88.1 - type: accuracy name: Vietnamese Test accuracy value: 65.2 - type: accuracy name: Greek Test accuracy value: 87.1 - type: accuracy name: Catalan Test accuracy value: 89.7 - type: accuracy name: Czech Test accuracy value: 89.0 - type: accuracy name: Erzya Test accuracy value: 40.8 - type: accuracy name: Bhojpuri Test accuracy value: 49.9 - type: accuracy name: Thai Test accuracy value: 59.9 - type: accuracy name: Marathi Test accuracy value: 85.9 - type: accuracy name: Basque Test accuracy value: 77.2 - type: accuracy name: Slovak Test accuracy value: 90.2 - type: accuracy name: Kiche Test accuracy value: 26.0 - type: accuracy name: Yoruba Test accuracy value: 18.1 - type: accuracy name: Warlpiri Test accuracy value: 38.5 - type: accuracy name: Tamil Test accuracy value: 84.0 - type: accuracy name: Maltese Test accuracy value: 17.5 - type: accuracy name: Ancient Greek Test accuracy value: 63.8 - type: accuracy name: Icelandic Test accuracy value: 85.0 - type: accuracy name: Mbya Guarani Test accuracy value: 23.4 - type: accuracy name: Urdu Test accuracy value: 70.1 - type: accuracy name: Romanian Test accuracy value: 85.4 - type: accuracy name: Persian Test accuracy value: 77.9 - type: accuracy name: Apurina Test accuracy value: 26.0 - type: accuracy name: Japanese Test accuracy value: 28.6 - type: accuracy name: Hungarian Test accuracy value: 85.1 - type: accuracy name: Hindi Test accuracy value: 74.6 - type: accuracy name: Classical Chinese Test accuracy value: 28.2 - type: accuracy name: Komi Permyak Test accuracy value: 39.0 - type: accuracy name: Faroese Test accuracy value: 79.3 - type: accuracy name: Sanskrit Test accuracy value: 26.8 - type: accuracy name: Livvi Test accuracy value: 62.8 - type: accuracy name: Arabic Test accuracy value: 80.8 - type: accuracy name: Wolof Test accuracy value: 24.3 - type: accuracy name: Bulgarian Test accuracy value: 91.0 - type: accuracy name: Akuntsu Test accuracy value: 18.5 - type: accuracy name: Makurap Test accuracy value: 10.3 - type: accuracy name: Kangri Test accuracy value: 44.7 - type: accuracy name: Breton Test accuracy value: 66.1 - type: accuracy name: Telugu Test accuracy value: 85.4 - type: accuracy name: Cantonese Test accuracy value: 45.0 - type: accuracy name: Old Church Slavonic Test accuracy value: 43.0 - type: accuracy name: Karelian Test accuracy value: 69.1 - type: accuracy name: Upper Sorbian Test accuracy value: 71.1 - type: accuracy name: South Levantine Arabic Test accuracy value: 66.5 - type: accuracy name: Komi Zyrian Test accuracy value: 33.2 - type: accuracy name: Irish Test accuracy value: 69.1 - type: accuracy name: Nayini Test accuracy value: 39.7 - type: accuracy name: Munduruku Test accuracy value: 11.6 - type: accuracy name: Manx Test accuracy value: 23.9 - type: accuracy name: Skolt Sami Test accuracy value: 27.0 - type: accuracy name: Afrikaans Test accuracy value: 90.0 - type: accuracy name: Old Turkish Test accuracy value: 38.5 - type: accuracy name: Tupinamba Test accuracy value: 24.0 - type: accuracy name: Belarusian Test accuracy value: 91.0 - type: accuracy name: Serbian Test accuracy value: 90.4 - type: accuracy name: Moksha Test accuracy value: 41.2 - type: accuracy name: Western Armenian Test accuracy value: 82.0 - type: accuracy name: Scottish Gaelic Test accuracy value: 60.3 - type: accuracy name: Khunsari Test accuracy value: 41.9 - type: accuracy name: Hebrew Test accuracy value: 94.8 - type: accuracy name: Uyghur Test accuracy value: 76.5 - type: accuracy name: Chukchi Test accuracy value: 33.2 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Danish This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-da") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-da") ```

wietsedv/xlm-roberta-base-ft-udpos28-el

7258c1aac060a6dfc235ddeec1b3e069a3a52461

2022-02-25T09:58:17.000Z

[ "pytorch", "xlm-roberta", "token-classification", "el", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

wietsedv

null

wietsedv/xlm-roberta-base-ft-udpos28-el

0

null

transformers

36,363

--- language: - el license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-el results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 83.6 - type: accuracy name: Dutch Test accuracy value: 82.2 - type: accuracy name: German Test accuracy value: 82.6 - type: accuracy name: Italian Test accuracy value: 82.0 - type: accuracy name: French Test accuracy value: 78.7 - type: accuracy name: Spanish Test accuracy value: 82.2 - type: accuracy name: Russian Test accuracy value: 88.4 - type: accuracy name: Swedish Test accuracy value: 87.4 - type: accuracy name: Norwegian Test accuracy value: 82.1 - type: accuracy name: Danish Test accuracy value: 85.9 - type: accuracy name: Low Saxon Test accuracy value: 49.8 - type: accuracy name: Akkadian Test accuracy value: 24.4 - type: accuracy name: Armenian Test accuracy value: 84.0 - type: accuracy name: Welsh Test accuracy value: 68.9 - type: accuracy name: Old East Slavic Test accuracy value: 75.0 - type: accuracy name: Albanian Test accuracy value: 87.7 - type: accuracy name: Slovenian Test accuracy value: 77.2 - type: accuracy name: Guajajara Test accuracy value: 25.8 - type: accuracy name: Kurmanji Test accuracy value: 74.3 - type: accuracy name: Turkish Test accuracy value: 75.3 - type: accuracy name: Finnish Test accuracy value: 83.4 - type: accuracy name: Indonesian Test accuracy value: 75.4 - type: accuracy name: Ukrainian Test accuracy value: 88.6 - type: accuracy name: Polish Test accuracy value: 84.0 - type: accuracy name: Portuguese Test accuracy value: 82.4 - type: accuracy name: Kazakh Test accuracy value: 80.5 - type: accuracy name: Latin Test accuracy value: 77.3 - type: accuracy name: Old French Test accuracy value: 52.5 - type: accuracy name: Buryat Test accuracy value: 56.0 - type: accuracy name: Kaapor Test accuracy value: 11.2 - type: accuracy name: Korean Test accuracy value: 59.9 - type: accuracy name: Estonian Test accuracy value: 83.6 - type: accuracy name: Croatian Test accuracy value: 84.9 - type: accuracy name: Gothic Test accuracy value: 20.2 - type: accuracy name: Swiss German Test accuracy value: 43.6 - type: accuracy name: Assyrian Test accuracy value: 14.6 - type: accuracy name: North Sami Test accuracy value: 33.5 - type: accuracy name: Naija Test accuracy value: 42.7 - type: accuracy name: Latvian Test accuracy value: 84.9 - type: accuracy name: Chinese Test accuracy value: 42.1 - type: accuracy name: Tagalog Test accuracy value: 66.7 - type: accuracy name: Bambara Test accuracy value: 28.2 - type: accuracy name: Lithuanian Test accuracy value: 85.3 - type: accuracy name: Galician Test accuracy value: 82.1 - type: accuracy name: Vietnamese Test accuracy value: 62.8 - type: accuracy name: Greek Test accuracy value: 98.0 - type: accuracy name: Catalan Test accuracy value: 80.4 - type: accuracy name: Czech Test accuracy value: 85.0 - type: accuracy name: Erzya Test accuracy value: 43.9 - type: accuracy name: Bhojpuri Test accuracy value: 45.0 - type: accuracy name: Thai Test accuracy value: 58.6 - type: accuracy name: Marathi Test accuracy value: 85.3 - type: accuracy name: Basque Test accuracy value: 72.4 - type: accuracy name: Slovak Test accuracy value: 82.8 - type: accuracy name: Kiche Test accuracy value: 36.2 - type: accuracy name: Yoruba Test accuracy value: 28.9 - type: accuracy name: Warlpiri Test accuracy value: 38.9 - type: accuracy name: Tamil Test accuracy value: 83.0 - type: accuracy name: Maltese Test accuracy value: 22.3 - type: accuracy name: Ancient Greek Test accuracy value: 64.2 - type: accuracy name: Icelandic Test accuracy value: 80.7 - type: accuracy name: Mbya Guarani Test accuracy value: 32.4 - type: accuracy name: Urdu Test accuracy value: 53.0 - type: accuracy name: Romanian Test accuracy value: 83.7 - type: accuracy name: Persian Test accuracy value: 74.4 - type: accuracy name: Apurina Test accuracy value: 41.3 - type: accuracy name: Japanese Test accuracy value: 30.0 - type: accuracy name: Hungarian Test accuracy value: 80.2 - type: accuracy name: Hindi Test accuracy value: 60.0 - type: accuracy name: Classical Chinese Test accuracy value: 30.1 - type: accuracy name: Komi Permyak Test accuracy value: 44.2 - type: accuracy name: Faroese Test accuracy value: 72.9 - type: accuracy name: Sanskrit Test accuracy value: 40.4 - type: accuracy name: Livvi Test accuracy value: 65.2 - type: accuracy name: Arabic Test accuracy value: 76.6 - type: accuracy name: Wolof Test accuracy value: 28.0 - type: accuracy name: Bulgarian Test accuracy value: 89.6 - type: accuracy name: Akuntsu Test accuracy value: 26.7 - type: accuracy name: Makurap Test accuracy value: 18.5 - type: accuracy name: Kangri Test accuracy value: 43.1 - type: accuracy name: Breton Test accuracy value: 63.5 - type: accuracy name: Telugu Test accuracy value: 85.3 - type: accuracy name: Cantonese Test accuracy value: 48.3 - type: accuracy name: Old Church Slavonic Test accuracy value: 51.6 - type: accuracy name: Karelian Test accuracy value: 71.0 - type: accuracy name: Upper Sorbian Test accuracy value: 69.5 - type: accuracy name: South Levantine Arabic Test accuracy value: 69.2 - type: accuracy name: Komi Zyrian Test accuracy value: 36.5 - type: accuracy name: Irish Test accuracy value: 61.3 - type: accuracy name: Nayini Test accuracy value: 43.6 - type: accuracy name: Munduruku Test accuracy value: 29.4 - type: accuracy name: Manx Test accuracy value: 33.8 - type: accuracy name: Skolt Sami Test accuracy value: 31.5 - type: accuracy name: Afrikaans Test accuracy value: 85.0 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 29.2 - type: accuracy name: Belarusian Test accuracy value: 89.1 - type: accuracy name: Serbian Test accuracy value: 85.2 - type: accuracy name: Moksha Test accuracy value: 43.8 - type: accuracy name: Western Armenian Test accuracy value: 76.9 - type: accuracy name: Scottish Gaelic Test accuracy value: 54.8 - type: accuracy name: Khunsari Test accuracy value: 45.9 - type: accuracy name: Hebrew Test accuracy value: 88.5 - type: accuracy name: Uyghur Test accuracy value: 75.7 - type: accuracy name: Chukchi Test accuracy value: 34.8 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Greek This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el") ```

wietsedv/xlm-roberta-base-ft-udpos28-et

dfc3d406501190ec0945726c67032a39e87fd0ed

2022-02-25T09:58:22.000Z

[ "pytorch", "xlm-roberta", "token-classification", "et", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

wietsedv

null

wietsedv/xlm-roberta-base-ft-udpos28-et

0

null

transformers

36,364

--- language: - et license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-et results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 82.3 - type: accuracy name: Dutch Test accuracy value: 80.9 - type: accuracy name: German Test accuracy value: 80.4 - type: accuracy name: Italian Test accuracy value: 78.0 - type: accuracy name: French Test accuracy value: 75.6 - type: accuracy name: Spanish Test accuracy value: 75.4 - type: accuracy name: Russian Test accuracy value: 88.2 - type: accuracy name: Swedish Test accuracy value: 89.1 - type: accuracy name: Norwegian Test accuracy value: 83.2 - type: accuracy name: Danish Test accuracy value: 87.0 - type: accuracy name: Low Saxon Test accuracy value: 52.2 - type: accuracy name: Akkadian Test accuracy value: 37.9 - type: accuracy name: Armenian Test accuracy value: 87.7 - type: accuracy name: Welsh Test accuracy value: 61.5 - type: accuracy name: Old East Slavic Test accuracy value: 74.6 - type: accuracy name: Albanian Test accuracy value: 74.0 - type: accuracy name: Slovenian Test accuracy value: 77.3 - type: accuracy name: Guajajara Test accuracy value: 30.7 - type: accuracy name: Kurmanji Test accuracy value: 76.7 - type: accuracy name: Turkish Test accuracy value: 79.3 - type: accuracy name: Finnish Test accuracy value: 90.5 - type: accuracy name: Indonesian Test accuracy value: 84.1 - type: accuracy name: Ukrainian Test accuracy value: 86.9 - type: accuracy name: Polish Test accuracy value: 84.4 - type: accuracy name: Portuguese Test accuracy value: 79.6 - type: accuracy name: Kazakh Test accuracy value: 83.0 - type: accuracy name: Latin Test accuracy value: 78.5 - type: accuracy name: Old French Test accuracy value: 50.0 - type: accuracy name: Buryat Test accuracy value: 64.6 - type: accuracy name: Kaapor Test accuracy value: 21.2 - type: accuracy name: Korean Test accuracy value: 62.9 - type: accuracy name: Estonian Test accuracy value: 96.8 - type: accuracy name: Croatian Test accuracy value: 87.0 - type: accuracy name: Gothic Test accuracy value: 24.7 - type: accuracy name: Swiss German Test accuracy value: 40.7 - type: accuracy name: Assyrian Test accuracy value: 20.1 - type: accuracy name: North Sami Test accuracy value: 46.7 - type: accuracy name: Naija Test accuracy value: 41.8 - type: accuracy name: Latvian Test accuracy value: 87.9 - type: accuracy name: Chinese Test accuracy value: 52.1 - type: accuracy name: Tagalog Test accuracy value: 65.9 - type: accuracy name: Bambara Test accuracy value: 27.9 - type: accuracy name: Lithuanian Test accuracy value: 86.0 - type: accuracy name: Galician Test accuracy value: 74.4 - type: accuracy name: Vietnamese Test accuracy value: 63.7 - type: accuracy name: Greek Test accuracy value: 77.4 - type: accuracy name: Catalan Test accuracy value: 73.4 - type: accuracy name: Czech Test accuracy value: 87.4 - type: accuracy name: Erzya Test accuracy value: 53.1 - type: accuracy name: Bhojpuri Test accuracy value: 52.4 - type: accuracy name: Thai Test accuracy value: 62.6 - type: accuracy name: Marathi Test accuracy value: 88.3 - type: accuracy name: Basque Test accuracy value: 77.1 - type: accuracy name: Slovak Test accuracy value: 87.0 - type: accuracy name: Kiche Test accuracy value: 37.8 - type: accuracy name: Yoruba Test accuracy value: 26.7 - type: accuracy name: Warlpiri Test accuracy value: 42.1 - type: accuracy name: Tamil Test accuracy value: 85.4 - type: accuracy name: Maltese Test accuracy value: 30.9 - type: accuracy name: Ancient Greek Test accuracy value: 65.9 - type: accuracy name: Icelandic Test accuracy value: 82.9 - type: accuracy name: Mbya Guarani Test accuracy value: 30.6 - type: accuracy name: Urdu Test accuracy value: 67.0 - type: accuracy name: Romanian Test accuracy value: 78.5 - type: accuracy name: Persian Test accuracy value: 73.9 - type: accuracy name: Apurina Test accuracy value: 47.9 - type: accuracy name: Japanese Test accuracy value: 38.9 - type: accuracy name: Hungarian Test accuracy value: 83.2 - type: accuracy name: Hindi Test accuracy value: 71.6 - type: accuracy name: Classical Chinese Test accuracy value: 35.4 - type: accuracy name: Komi Permyak Test accuracy value: 53.2 - type: accuracy name: Faroese Test accuracy value: 76.4 - type: accuracy name: Sanskrit Test accuracy value: 38.8 - type: accuracy name: Livvi Test accuracy value: 71.2 - type: accuracy name: Arabic Test accuracy value: 76.3 - type: accuracy name: Wolof Test accuracy value: 35.3 - type: accuracy name: Bulgarian Test accuracy value: 85.8 - type: accuracy name: Akuntsu Test accuracy value: 37.5 - type: accuracy name: Makurap Test accuracy value: 15.8 - type: accuracy name: Kangri Test accuracy value: 51.7 - type: accuracy name: Breton Test accuracy value: 60.1 - type: accuracy name: Telugu Test accuracy value: 84.2 - type: accuracy name: Cantonese Test accuracy value: 58.3 - type: accuracy name: Old Church Slavonic Test accuracy value: 51.8 - type: accuracy name: Karelian Test accuracy value: 75.7 - type: accuracy name: Upper Sorbian Test accuracy value: 77.3 - type: accuracy name: South Levantine Arabic Test accuracy value: 68.8 - type: accuracy name: Komi Zyrian Test accuracy value: 46.6 - type: accuracy name: Irish Test accuracy value: 60.5 - type: accuracy name: Nayini Test accuracy value: 42.3 - type: accuracy name: Munduruku Test accuracy value: 27.1 - type: accuracy name: Manx Test accuracy value: 35.3 - type: accuracy name: Skolt Sami Test accuracy value: 40.7 - type: accuracy name: Afrikaans Test accuracy value: 77.5 - type: accuracy name: Old Turkish Test accuracy value: 46.6 - type: accuracy name: Tupinamba Test accuracy value: 46.5 - type: accuracy name: Belarusian Test accuracy value: 87.1 - type: accuracy name: Serbian Test accuracy value: 86.9 - type: accuracy name: Moksha Test accuracy value: 48.3 - type: accuracy name: Western Armenian Test accuracy value: 80.6 - type: accuracy name: Scottish Gaelic Test accuracy value: 51.5 - type: accuracy name: Khunsari Test accuracy value: 40.5 - type: accuracy name: Hebrew Test accuracy value: 89.6 - type: accuracy name: Uyghur Test accuracy value: 77.1 - type: accuracy name: Chukchi Test accuracy value: 38.9 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Estonian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-et") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-et") ```

wietsedv/xlm-roberta-base-ft-udpos28-ga

b9d0c2a08fe20f325f8225321f0a39817a11c195

2022-02-25T09:58:33.000Z

[ "pytorch", "xlm-roberta", "token-classification", "ga", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

wietsedv

null

wietsedv/xlm-roberta-base-ft-udpos28-ga

0

null

transformers

36,365

--- language: - ga license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-ga results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 75.3 - type: accuracy name: Dutch Test accuracy value: 79.5 - type: accuracy name: German Test accuracy value: 76.2 - type: accuracy name: Italian Test accuracy value: 73.6 - type: accuracy name: French Test accuracy value: 76.4 - type: accuracy name: Spanish Test accuracy value: 82.4 - type: accuracy name: Russian Test accuracy value: 80.7 - type: accuracy name: Swedish Test accuracy value: 78.7 - type: accuracy name: Norwegian Test accuracy value: 74.5 - type: accuracy name: Danish Test accuracy value: 77.9 - type: accuracy name: Low Saxon Test accuracy value: 49.0 - type: accuracy name: Akkadian Test accuracy value: 46.2 - type: accuracy name: Armenian Test accuracy value: 71.8 - type: accuracy name: Welsh Test accuracy value: 77.9 - type: accuracy name: Old East Slavic Test accuracy value: 67.3 - type: accuracy name: Albanian Test accuracy value: 79.8 - type: accuracy name: Slovenian Test accuracy value: 65.3 - type: accuracy name: Guajajara Test accuracy value: 46.4 - type: accuracy name: Kurmanji Test accuracy value: 74.9 - type: accuracy name: Turkish Test accuracy value: 73.7 - type: accuracy name: Finnish Test accuracy value: 73.8 - type: accuracy name: Indonesian Test accuracy value: 78.6 - type: accuracy name: Ukrainian Test accuracy value: 79.9 - type: accuracy name: Polish Test accuracy value: 82.5 - type: accuracy name: Portuguese Test accuracy value: 80.6 - type: accuracy name: Kazakh Test accuracy value: 75.6 - type: accuracy name: Latin Test accuracy value: 70.0 - type: accuracy name: Old French Test accuracy value: 49.1 - type: accuracy name: Buryat Test accuracy value: 60.3 - type: accuracy name: Kaapor Test accuracy value: 21.2 - type: accuracy name: Korean Test accuracy value: 60.5 - type: accuracy name: Estonian Test accuracy value: 75.7 - type: accuracy name: Croatian Test accuracy value: 77.3 - type: accuracy name: Gothic Test accuracy value: 29.1 - type: accuracy name: Swiss German Test accuracy value: 44.3 - type: accuracy name: Assyrian Test accuracy value: 16.3 - type: accuracy name: North Sami Test accuracy value: 45.0 - type: accuracy name: Naija Test accuracy value: 32.0 - type: accuracy name: Latvian Test accuracy value: 77.7 - type: accuracy name: Chinese Test accuracy value: 49.6 - type: accuracy name: Tagalog Test accuracy value: 71.1 - type: accuracy name: Bambara Test accuracy value: 29.1 - type: accuracy name: Lithuanian Test accuracy value: 76.4 - type: accuracy name: Galician Test accuracy value: 80.9 - type: accuracy name: Vietnamese Test accuracy value: 58.6 - type: accuracy name: Greek Test accuracy value: 77.5 - type: accuracy name: Catalan Test accuracy value: 79.7 - type: accuracy name: Czech Test accuracy value: 78.1 - type: accuracy name: Erzya Test accuracy value: 52.5 - type: accuracy name: Bhojpuri Test accuracy value: 59.2 - type: accuracy name: Thai Test accuracy value: 58.7 - type: accuracy name: Marathi Test accuracy value: 79.1 - type: accuracy name: Basque Test accuracy value: 68.1 - type: accuracy name: Slovak Test accuracy value: 80.0 - type: accuracy name: Kiche Test accuracy value: 46.4 - type: accuracy name: Yoruba Test accuracy value: 33.1 - type: accuracy name: Warlpiri Test accuracy value: 40.5 - type: accuracy name: Tamil Test accuracy value: 78.1 - type: accuracy name: Maltese Test accuracy value: 36.7 - type: accuracy name: Ancient Greek Test accuracy value: 58.5 - type: accuracy name: Icelandic Test accuracy value: 71.2 - type: accuracy name: Mbya Guarani Test accuracy value: 34.0 - type: accuracy name: Urdu Test accuracy value: 65.5 - type: accuracy name: Romanian Test accuracy value: 76.8 - type: accuracy name: Persian Test accuracy value: 79.7 - type: accuracy name: Apurina Test accuracy value: 51.8 - type: accuracy name: Japanese Test accuracy value: 36.1 - type: accuracy name: Hungarian Test accuracy value: 77.1 - type: accuracy name: Hindi Test accuracy value: 69.7 - type: accuracy name: Classical Chinese Test accuracy value: 32.1 - type: accuracy name: Komi Permyak Test accuracy value: 51.1 - type: accuracy name: Faroese Test accuracy value: 70.6 - type: accuracy name: Sanskrit Test accuracy value: 35.7 - type: accuracy name: Livvi Test accuracy value: 60.6 - type: accuracy name: Arabic Test accuracy value: 83.7 - type: accuracy name: Wolof Test accuracy value: 40.8 - type: accuracy name: Bulgarian Test accuracy value: 78.7 - type: accuracy name: Akuntsu Test accuracy value: 43.2 - type: accuracy name: Makurap Test accuracy value: 19.9 - type: accuracy name: Kangri Test accuracy value: 46.3 - type: accuracy name: Breton Test accuracy value: 61.7 - type: accuracy name: Telugu Test accuracy value: 76.8 - type: accuracy name: Cantonese Test accuracy value: 49.0 - type: accuracy name: Old Church Slavonic Test accuracy value: 43.9 - type: accuracy name: Karelian Test accuracy value: 64.1 - type: accuracy name: Upper Sorbian Test accuracy value: 69.3 - type: accuracy name: South Levantine Arabic Test accuracy value: 70.0 - type: accuracy name: Komi Zyrian Test accuracy value: 44.9 - type: accuracy name: Irish Test accuracy value: 86.0 - type: accuracy name: Nayini Test accuracy value: 46.2 - type: accuracy name: Munduruku Test accuracy value: 38.9 - type: accuracy name: Manx Test accuracy value: 57.2 - type: accuracy name: Skolt Sami Test accuracy value: 40.1 - type: accuracy name: Afrikaans Test accuracy value: 73.0 - type: accuracy name: Old Turkish Test accuracy value: 39.4 - type: accuracy name: Tupinamba Test accuracy value: 51.8 - type: accuracy name: Belarusian Test accuracy value: 79.1 - type: accuracy name: Serbian Test accuracy value: 78.5 - type: accuracy name: Moksha Test accuracy value: 49.9 - type: accuracy name: Western Armenian Test accuracy value: 68.2 - type: accuracy name: Scottish Gaelic Test accuracy value: 77.1 - type: accuracy name: Khunsari Test accuracy value: 50.0 - type: accuracy name: Hebrew Test accuracy value: 80.2 - type: accuracy name: Uyghur Test accuracy value: 70.2 - type: accuracy name: Chukchi Test accuracy value: 39.3 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Irish This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ga") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ga") ```

wietsedv/xlm-roberta-base-ft-udpos28-he

0b1c313b21ca58890f2e11837cee5fdfbabd39b2

2022-02-25T09:58:40.000Z

[ "pytorch", "xlm-roberta", "token-classification", "he", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

wietsedv

null

wietsedv/xlm-roberta-base-ft-udpos28-he

0

null

transformers

36,366

--- language: - he license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-he results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 76.6 - type: accuracy name: Dutch Test accuracy value: 73.7 - type: accuracy name: German Test accuracy value: 70.5 - type: accuracy name: Italian Test accuracy value: 75.1 - type: accuracy name: French Test accuracy value: 71.3 - type: accuracy name: Spanish Test accuracy value: 74.5 - type: accuracy name: Russian Test accuracy value: 80.3 - type: accuracy name: Swedish Test accuracy value: 79.3 - type: accuracy name: Norwegian Test accuracy value: 75.7 - type: accuracy name: Danish Test accuracy value: 80.4 - type: accuracy name: Low Saxon Test accuracy value: 42.6 - type: accuracy name: Akkadian Test accuracy value: 24.1 - type: accuracy name: Armenian Test accuracy value: 77.0 - type: accuracy name: Welsh Test accuracy value: 62.3 - type: accuracy name: Old East Slavic Test accuracy value: 66.2 - type: accuracy name: Albanian Test accuracy value: 73.9 - type: accuracy name: Slovenian Test accuracy value: 72.5 - type: accuracy name: Guajajara Test accuracy value: 21.4 - type: accuracy name: Kurmanji Test accuracy value: 74.2 - type: accuracy name: Turkish Test accuracy value: 71.8 - type: accuracy name: Finnish Test accuracy value: 80.5 - type: accuracy name: Indonesian Test accuracy value: 80.0 - type: accuracy name: Ukrainian Test accuracy value: 78.8 - type: accuracy name: Polish Test accuracy value: 78.9 - type: accuracy name: Portuguese Test accuracy value: 78.6 - type: accuracy name: Kazakh Test accuracy value: 77.2 - type: accuracy name: Latin Test accuracy value: 73.5 - type: accuracy name: Old French Test accuracy value: 50.6 - type: accuracy name: Buryat Test accuracy value: 45.0 - type: accuracy name: Kaapor Test accuracy value: 11.2 - type: accuracy name: Korean Test accuracy value: 60.2 - type: accuracy name: Estonian Test accuracy value: 81.4 - type: accuracy name: Croatian Test accuracy value: 77.9 - type: accuracy name: Gothic Test accuracy value: 13.7 - type: accuracy name: Swiss German Test accuracy value: 44.8 - type: accuracy name: Assyrian Test accuracy value: 17.0 - type: accuracy name: North Sami Test accuracy value: 24.8 - type: accuracy name: Naija Test accuracy value: 41.6 - type: accuracy name: Latvian Test accuracy value: 80.1 - type: accuracy name: Chinese Test accuracy value: 60.5 - type: accuracy name: Tagalog Test accuracy value: 79.2 - type: accuracy name: Bambara Test accuracy value: 21.1 - type: accuracy name: Lithuanian Test accuracy value: 81.0 - type: accuracy name: Galician Test accuracy value: 76.1 - type: accuracy name: Vietnamese Test accuracy value: 64.4 - type: accuracy name: Greek Test accuracy value: 67.4 - type: accuracy name: Catalan Test accuracy value: 71.5 - type: accuracy name: Czech Test accuracy value: 77.7 - type: accuracy name: Erzya Test accuracy value: 32.0 - type: accuracy name: Bhojpuri Test accuracy value: 50.7 - type: accuracy name: Thai Test accuracy value: 69.2 - type: accuracy name: Marathi Test accuracy value: 81.6 - type: accuracy name: Basque Test accuracy value: 76.2 - type: accuracy name: Slovak Test accuracy value: 78.0 - type: accuracy name: Kiche Test accuracy value: 23.6 - type: accuracy name: Yoruba Test accuracy value: 17.5 - type: accuracy name: Warlpiri Test accuracy value: 22.3 - type: accuracy name: Tamil Test accuracy value: 82.1 - type: accuracy name: Maltese Test accuracy value: 18.0 - type: accuracy name: Ancient Greek Test accuracy value: 45.4 - type: accuracy name: Icelandic Test accuracy value: 81.0 - type: accuracy name: Mbya Guarani Test accuracy value: 22.0 - type: accuracy name: Urdu Test accuracy value: 70.9 - type: accuracy name: Romanian Test accuracy value: 76.5 - type: accuracy name: Persian Test accuracy value: 75.4 - type: accuracy name: Apurina Test accuracy value: 22.2 - type: accuracy name: Japanese Test accuracy value: 39.4 - type: accuracy name: Hungarian Test accuracy value: 65.8 - type: accuracy name: Hindi Test accuracy value: 75.2 - type: accuracy name: Classical Chinese Test accuracy value: 44.3 - type: accuracy name: Komi Permyak Test accuracy value: 35.0 - type: accuracy name: Faroese Test accuracy value: 70.8 - type: accuracy name: Sanskrit Test accuracy value: 12.1 - type: accuracy name: Livvi Test accuracy value: 52.2 - type: accuracy name: Arabic Test accuracy value: 77.4 - type: accuracy name: Wolof Test accuracy value: 24.4 - type: accuracy name: Bulgarian Test accuracy value: 82.1 - type: accuracy name: Akuntsu Test accuracy value: 17.0 - type: accuracy name: Makurap Test accuracy value: 8.2 - type: accuracy name: Kangri Test accuracy value: 39.9 - type: accuracy name: Breton Test accuracy value: 56.7 - type: accuracy name: Telugu Test accuracy value: 81.4 - type: accuracy name: Cantonese Test accuracy value: 57.7 - type: accuracy name: Old Church Slavonic Test accuracy value: 40.3 - type: accuracy name: Karelian Test accuracy value: 60.0 - type: accuracy name: Upper Sorbian Test accuracy value: 61.2 - type: accuracy name: South Levantine Arabic Test accuracy value: 64.5 - type: accuracy name: Komi Zyrian Test accuracy value: 29.0 - type: accuracy name: Irish Test accuracy value: 58.7 - type: accuracy name: Nayini Test accuracy value: 41.0 - type: accuracy name: Munduruku Test accuracy value: 9.5 - type: accuracy name: Manx Test accuracy value: 21.8 - type: accuracy name: Skolt Sami Test accuracy value: 27.2 - type: accuracy name: Afrikaans Test accuracy value: 73.3 - type: accuracy name: Old Turkish Test accuracy value: 43.4 - type: accuracy name: Tupinamba Test accuracy value: 21.9 - type: accuracy name: Belarusian Test accuracy value: 78.5 - type: accuracy name: Serbian Test accuracy value: 78.9 - type: accuracy name: Moksha Test accuracy value: 29.7 - type: accuracy name: Western Armenian Test accuracy value: 69.6 - type: accuracy name: Scottish Gaelic Test accuracy value: 51.3 - type: accuracy name: Khunsari Test accuracy value: 36.5 - type: accuracy name: Hebrew Test accuracy value: 93.8 - type: accuracy name: Uyghur Test accuracy value: 70.2 - type: accuracy name: Chukchi Test accuracy value: 27.1 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Hebrew This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-he") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-he") ```

wietsedv/xlm-roberta-base-ft-udpos28-hr

8b72e6e02e4637a51fa8315b1c170d4c224457d8

2022-02-25T09:58:44.000Z

[ "pytorch", "xlm-roberta", "token-classification", "hr", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

wietsedv

null

wietsedv/xlm-roberta-base-ft-udpos28-hr

0

null

transformers

36,367

--- language: - hr license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-hr results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 83.7 - type: accuracy name: Dutch Test accuracy value: 83.7 - type: accuracy name: German Test accuracy value: 83.2 - type: accuracy name: Italian Test accuracy value: 83.2 - type: accuracy name: French Test accuracy value: 84.2 - type: accuracy name: Spanish Test accuracy value: 87.8 - type: accuracy name: Russian Test accuracy value: 91.4 - type: accuracy name: Swedish Test accuracy value: 85.4 - type: accuracy name: Norwegian Test accuracy value: 79.0 - type: accuracy name: Danish Test accuracy value: 83.8 - type: accuracy name: Low Saxon Test accuracy value: 43.5 - type: accuracy name: Akkadian Test accuracy value: 32.5 - type: accuracy name: Armenian Test accuracy value: 84.7 - type: accuracy name: Welsh Test accuracy value: 67.9 - type: accuracy name: Old East Slavic Test accuracy value: 76.8 - type: accuracy name: Albanian Test accuracy value: 75.2 - type: accuracy name: Slovenian Test accuracy value: 87.0 - type: accuracy name: Guajajara Test accuracy value: 28.3 - type: accuracy name: Kurmanji Test accuracy value: 78.5 - type: accuracy name: Turkish Test accuracy value: 75.9 - type: accuracy name: Finnish Test accuracy value: 83.2 - type: accuracy name: Indonesian Test accuracy value: 81.3 - type: accuracy name: Ukrainian Test accuracy value: 93.2 - type: accuracy name: Polish Test accuracy value: 92.3 - type: accuracy name: Portuguese Test accuracy value: 84.6 - type: accuracy name: Kazakh Test accuracy value: 79.4 - type: accuracy name: Latin Test accuracy value: 77.4 - type: accuracy name: Old French Test accuracy value: 54.3 - type: accuracy name: Buryat Test accuracy value: 61.1 - type: accuracy name: Kaapor Test accuracy value: 20.0 - type: accuracy name: Korean Test accuracy value: 60.7 - type: accuracy name: Estonian Test accuracy value: 85.7 - type: accuracy name: Croatian Test accuracy value: 98.3 - type: accuracy name: Gothic Test accuracy value: 16.5 - type: accuracy name: Swiss German Test accuracy value: 44.8 - type: accuracy name: Assyrian Test accuracy value: 15.9 - type: accuracy name: North Sami Test accuracy value: 35.3 - type: accuracy name: Naija Test accuracy value: 39.6 - type: accuracy name: Latvian Test accuracy value: 86.5 - type: accuracy name: Chinese Test accuracy value: 41.2 - type: accuracy name: Tagalog Test accuracy value: 70.9 - type: accuracy name: Bambara Test accuracy value: 28.2 - type: accuracy name: Lithuanian Test accuracy value: 86.1 - type: accuracy name: Galician Test accuracy value: 86.0 - type: accuracy name: Vietnamese Test accuracy value: 66.5 - type: accuracy name: Greek Test accuracy value: 85.8 - type: accuracy name: Catalan Test accuracy value: 85.5 - type: accuracy name: Czech Test accuracy value: 94.8 - type: accuracy name: Erzya Test accuracy value: 47.2 - type: accuracy name: Bhojpuri Test accuracy value: 49.2 - type: accuracy name: Thai Test accuracy value: 63.4 - type: accuracy name: Marathi Test accuracy value: 87.1 - type: accuracy name: Basque Test accuracy value: 75.0 - type: accuracy name: Slovak Test accuracy value: 95.0 - type: accuracy name: Kiche Test accuracy value: 35.8 - type: accuracy name: Yoruba Test accuracy value: 28.5 - type: accuracy name: Warlpiri Test accuracy value: 41.3 - type: accuracy name: Tamil Test accuracy value: 84.8 - type: accuracy name: Maltese Test accuracy value: 23.7 - type: accuracy name: Ancient Greek Test accuracy value: 62.1 - type: accuracy name: Icelandic Test accuracy value: 79.9 - type: accuracy name: Mbya Guarani Test accuracy value: 31.9 - type: accuracy name: Urdu Test accuracy value: 65.0 - type: accuracy name: Romanian Test accuracy value: 82.5 - type: accuracy name: Persian Test accuracy value: 79.4 - type: accuracy name: Apurina Test accuracy value: 38.4 - type: accuracy name: Japanese Test accuracy value: 30.1 - type: accuracy name: Hungarian Test accuracy value: 83.8 - type: accuracy name: Hindi Test accuracy value: 67.8 - type: accuracy name: Classical Chinese Test accuracy value: 27.0 - type: accuracy name: Komi Permyak Test accuracy value: 44.9 - type: accuracy name: Faroese Test accuracy value: 77.3 - type: accuracy name: Sanskrit Test accuracy value: 35.6 - type: accuracy name: Livvi Test accuracy value: 65.5 - type: accuracy name: Arabic Test accuracy value: 82.3 - type: accuracy name: Wolof Test accuracy value: 32.2 - type: accuracy name: Bulgarian Test accuracy value: 92.6 - type: accuracy name: Akuntsu Test accuracy value: 37.0 - type: accuracy name: Makurap Test accuracy value: 17.8 - type: accuracy name: Kangri Test accuracy value: 47.9 - type: accuracy name: Breton Test accuracy value: 62.2 - type: accuracy name: Telugu Test accuracy value: 82.4 - type: accuracy name: Cantonese Test accuracy value: 45.6 - type: accuracy name: Old Church Slavonic Test accuracy value: 48.9 - type: accuracy name: Karelian Test accuracy value: 71.7 - type: accuracy name: Upper Sorbian Test accuracy value: 79.4 - type: accuracy name: South Levantine Arabic Test accuracy value: 68.9 - type: accuracy name: Komi Zyrian Test accuracy value: 39.6 - type: accuracy name: Irish Test accuracy value: 65.4 - type: accuracy name: Nayini Test accuracy value: 42.3 - type: accuracy name: Munduruku Test accuracy value: 28.8 - type: accuracy name: Manx Test accuracy value: 35.7 - type: accuracy name: Skolt Sami Test accuracy value: 33.7 - type: accuracy name: Afrikaans Test accuracy value: 79.8 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 33.1 - type: accuracy name: Belarusian Test accuracy value: 91.6 - type: accuracy name: Serbian Test accuracy value: 97.5 - type: accuracy name: Moksha Test accuracy value: 45.7 - type: accuracy name: Western Armenian Test accuracy value: 77.7 - type: accuracy name: Scottish Gaelic Test accuracy value: 57.7 - type: accuracy name: Khunsari Test accuracy value: 36.5 - type: accuracy name: Hebrew Test accuracy value: 85.4 - type: accuracy name: Uyghur Test accuracy value: 72.2 - type: accuracy name: Chukchi Test accuracy value: 35.4 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Croatian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hr") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hr") ```

wietsedv/xlm-roberta-base-ft-udpos28-hyw

2d1fe3d93a920f84fabe65fba2549e7af7122c44

2022-02-25T09:58:48.000Z

[ "pytorch", "xlm-roberta", "token-classification", "hyw", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

wietsedv

null

wietsedv/xlm-roberta-base-ft-udpos28-hyw

0

null

transformers

36,368

--- language: - hyw license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-hyw results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 83.0 - type: accuracy name: Dutch Test accuracy value: 81.9 - type: accuracy name: German Test accuracy value: 83.9 - type: accuracy name: Italian Test accuracy value: 80.9 - type: accuracy name: French Test accuracy value: 79.2 - type: accuracy name: Spanish Test accuracy value: 80.9 - type: accuracy name: Russian Test accuracy value: 89.1 - type: accuracy name: Swedish Test accuracy value: 86.2 - type: accuracy name: Norwegian Test accuracy value: 80.6 - type: accuracy name: Danish Test accuracy value: 84.8 - type: accuracy name: Low Saxon Test accuracy value: 56.7 - type: accuracy name: Akkadian Test accuracy value: 29.3 - type: accuracy name: Armenian Test accuracy value: 90.2 - type: accuracy name: Welsh Test accuracy value: 63.8 - type: accuracy name: Old East Slavic Test accuracy value: 77.0 - type: accuracy name: Albanian Test accuracy value: 83.5 - type: accuracy name: Slovenian Test accuracy value: 78.0 - type: accuracy name: Guajajara Test accuracy value: 22.7 - type: accuracy name: Kurmanji Test accuracy value: 76.7 - type: accuracy name: Turkish Test accuracy value: 78.1 - type: accuracy name: Finnish Test accuracy value: 84.5 - type: accuracy name: Indonesian Test accuracy value: 80.7 - type: accuracy name: Ukrainian Test accuracy value: 88.4 - type: accuracy name: Polish Test accuracy value: 83.7 - type: accuracy name: Portuguese Test accuracy value: 83.1 - type: accuracy name: Kazakh Test accuracy value: 85.0 - type: accuracy name: Latin Test accuracy value: 79.0 - type: accuracy name: Old French Test accuracy value: 58.3 - type: accuracy name: Buryat Test accuracy value: 65.4 - type: accuracy name: Kaapor Test accuracy value: 16.2 - type: accuracy name: Korean Test accuracy value: 62.1 - type: accuracy name: Estonian Test accuracy value: 84.6 - type: accuracy name: Croatian Test accuracy value: 86.9 - type: accuracy name: Gothic Test accuracy value: 24.5 - type: accuracy name: Swiss German Test accuracy value: 57.3 - type: accuracy name: Assyrian Test accuracy value: 14.6 - type: accuracy name: North Sami Test accuracy value: 35.0 - type: accuracy name: Naija Test accuracy value: 43.0 - type: accuracy name: Latvian Test accuracy value: 87.5 - type: accuracy name: Chinese Test accuracy value: 41.7 - type: accuracy name: Tagalog Test accuracy value: 68.9 - type: accuracy name: Bambara Test accuracy value: 30.7 - type: accuracy name: Lithuanian Test accuracy value: 87.2 - type: accuracy name: Galician Test accuracy value: 80.9 - type: accuracy name: Vietnamese Test accuracy value: 65.0 - type: accuracy name: Greek Test accuracy value: 87.6 - type: accuracy name: Catalan Test accuracy value: 80.0 - type: accuracy name: Czech Test accuracy value: 86.0 - type: accuracy name: Erzya Test accuracy value: 47.6 - type: accuracy name: Bhojpuri Test accuracy value: 57.8 - type: accuracy name: Thai Test accuracy value: 59.9 - type: accuracy name: Marathi Test accuracy value: 84.7 - type: accuracy name: Basque Test accuracy value: 80.7 - type: accuracy name: Slovak Test accuracy value: 86.2 - type: accuracy name: Kiche Test accuracy value: 26.5 - type: accuracy name: Yoruba Test accuracy value: 24.8 - type: accuracy name: Warlpiri Test accuracy value: 38.5 - type: accuracy name: Tamil Test accuracy value: 84.2 - type: accuracy name: Maltese Test accuracy value: 28.2 - type: accuracy name: Ancient Greek Test accuracy value: 68.4 - type: accuracy name: Icelandic Test accuracy value: 79.5 - type: accuracy name: Mbya Guarani Test accuracy value: 28.7 - type: accuracy name: Urdu Test accuracy value: 68.1 - type: accuracy name: Romanian Test accuracy value: 82.1 - type: accuracy name: Persian Test accuracy value: 74.9 - type: accuracy name: Apurina Test accuracy value: 31.9 - type: accuracy name: Japanese Test accuracy value: 35.2 - type: accuracy name: Hungarian Test accuracy value: 83.7 - type: accuracy name: Hindi Test accuracy value: 74.9 - type: accuracy name: Classical Chinese Test accuracy value: 26.8 - type: accuracy name: Komi Permyak Test accuracy value: 51.5 - type: accuracy name: Faroese Test accuracy value: 77.9 - type: accuracy name: Sanskrit Test accuracy value: 39.4 - type: accuracy name: Livvi Test accuracy value: 67.5 - type: accuracy name: Arabic Test accuracy value: 77.6 - type: accuracy name: Wolof Test accuracy value: 31.3 - type: accuracy name: Bulgarian Test accuracy value: 86.3 - type: accuracy name: Akuntsu Test accuracy value: 21.3 - type: accuracy name: Makurap Test accuracy value: 11.6 - type: accuracy name: Kangri Test accuracy value: 57.8 - type: accuracy name: Breton Test accuracy value: 65.4 - type: accuracy name: Telugu Test accuracy value: 80.2 - type: accuracy name: Cantonese Test accuracy value: 48.5 - type: accuracy name: Old Church Slavonic Test accuracy value: 52.5 - type: accuracy name: Karelian Test accuracy value: 72.2 - type: accuracy name: Upper Sorbian Test accuracy value: 76.4 - type: accuracy name: South Levantine Arabic Test accuracy value: 69.8 - type: accuracy name: Komi Zyrian Test accuracy value: 44.2 - type: accuracy name: Irish Test accuracy value: 61.5 - type: accuracy name: Nayini Test accuracy value: 53.8 - type: accuracy name: Munduruku Test accuracy value: 12.5 - type: accuracy name: Manx Test accuracy value: 29.8 - type: accuracy name: Skolt Sami Test accuracy value: 34.2 - type: accuracy name: Afrikaans Test accuracy value: 81.7 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 30.8 - type: accuracy name: Belarusian Test accuracy value: 89.7 - type: accuracy name: Serbian Test accuracy value: 87.1 - type: accuracy name: Moksha Test accuracy value: 45.2 - type: accuracy name: Western Armenian Test accuracy value: 93.9 - type: accuracy name: Scottish Gaelic Test accuracy value: 56.8 - type: accuracy name: Khunsari Test accuracy value: 43.2 - type: accuracy name: Hebrew Test accuracy value: 85.4 - type: accuracy name: Uyghur Test accuracy value: 76.1 - type: accuracy name: Chukchi Test accuracy value: 38.1 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Western Armenian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hyw") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hyw") ```

wietsedv/xlm-roberta-base-ft-udpos28-lt

d608a2b1745c2d2d64e8676fe2c1e53e53da82a7

2022-02-25T09:58:59.000Z

[ "pytorch", "xlm-roberta", "token-classification", "lt", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

wietsedv

null

wietsedv/xlm-roberta-base-ft-udpos28-lt

0

null

transformers

36,369

--- language: - lt license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-lt results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 80.7 - type: accuracy name: Dutch Test accuracy value: 80.6 - type: accuracy name: German Test accuracy value: 76.0 - type: accuracy name: Italian Test accuracy value: 77.8 - type: accuracy name: French Test accuracy value: 75.5 - type: accuracy name: Spanish Test accuracy value: 79.6 - type: accuracy name: Russian Test accuracy value: 88.9 - type: accuracy name: Swedish Test accuracy value: 81.6 - type: accuracy name: Norwegian Test accuracy value: 76.3 - type: accuracy name: Danish Test accuracy value: 78.9 - type: accuracy name: Low Saxon Test accuracy value: 52.0 - type: accuracy name: Akkadian Test accuracy value: 31.6 - type: accuracy name: Armenian Test accuracy value: 84.1 - type: accuracy name: Welsh Test accuracy value: 63.8 - type: accuracy name: Old East Slavic Test accuracy value: 75.6 - type: accuracy name: Albanian Test accuracy value: 76.8 - type: accuracy name: Slovenian Test accuracy value: 81.4 - type: accuracy name: Guajajara Test accuracy value: 26.7 - type: accuracy name: Kurmanji Test accuracy value: 77.1 - type: accuracy name: Turkish Test accuracy value: 74.9 - type: accuracy name: Finnish Test accuracy value: 83.2 - type: accuracy name: Indonesian Test accuracy value: 78.0 - type: accuracy name: Ukrainian Test accuracy value: 88.1 - type: accuracy name: Polish Test accuracy value: 86.3 - type: accuracy name: Portuguese Test accuracy value: 81.6 - type: accuracy name: Kazakh Test accuracy value: 83.1 - type: accuracy name: Latin Test accuracy value: 78.7 - type: accuracy name: Old French Test accuracy value: 56.1 - type: accuracy name: Buryat Test accuracy value: 64.3 - type: accuracy name: Kaapor Test accuracy value: 22.5 - type: accuracy name: Korean Test accuracy value: 64.6 - type: accuracy name: Estonian Test accuracy value: 81.5 - type: accuracy name: Croatian Test accuracy value: 86.6 - type: accuracy name: Gothic Test accuracy value: 22.6 - type: accuracy name: Swiss German Test accuracy value: 48.1 - type: accuracy name: Assyrian Test accuracy value: 14.6 - type: accuracy name: North Sami Test accuracy value: 39.8 - type: accuracy name: Naija Test accuracy value: 41.4 - type: accuracy name: Latvian Test accuracy value: 89.0 - type: accuracy name: Chinese Test accuracy value: 34.4 - type: accuracy name: Tagalog Test accuracy value: 73.0 - type: accuracy name: Bambara Test accuracy value: 26.4 - type: accuracy name: Lithuanian Test accuracy value: 96.1 - type: accuracy name: Galician Test accuracy value: 81.1 - type: accuracy name: Vietnamese Test accuracy value: 65.3 - type: accuracy name: Greek Test accuracy value: 81.8 - type: accuracy name: Catalan Test accuracy value: 76.2 - type: accuracy name: Czech Test accuracy value: 86.5 - type: accuracy name: Erzya Test accuracy value: 48.7 - type: accuracy name: Bhojpuri Test accuracy value: 50.9 - type: accuracy name: Thai Test accuracy value: 54.5 - type: accuracy name: Marathi Test accuracy value: 82.8 - type: accuracy name: Basque Test accuracy value: 75.6 - type: accuracy name: Slovak Test accuracy value: 88.5 - type: accuracy name: Kiche Test accuracy value: 33.5 - type: accuracy name: Yoruba Test accuracy value: 24.6 - type: accuracy name: Warlpiri Test accuracy value: 44.1 - type: accuracy name: Tamil Test accuracy value: 79.1 - type: accuracy name: Maltese Test accuracy value: 25.5 - type: accuracy name: Ancient Greek Test accuracy value: 65.8 - type: accuracy name: Icelandic Test accuracy value: 80.7 - type: accuracy name: Mbya Guarani Test accuracy value: 32.2 - type: accuracy name: Urdu Test accuracy value: 59.1 - type: accuracy name: Romanian Test accuracy value: 78.6 - type: accuracy name: Persian Test accuracy value: 72.8 - type: accuracy name: Apurina Test accuracy value: 42.0 - type: accuracy name: Japanese Test accuracy value: 22.9 - type: accuracy name: Hungarian Test accuracy value: 76.9 - type: accuracy name: Hindi Test accuracy value: 62.2 - type: accuracy name: Classical Chinese Test accuracy value: 15.8 - type: accuracy name: Komi Permyak Test accuracy value: 48.3 - type: accuracy name: Faroese Test accuracy value: 77.3 - type: accuracy name: Sanskrit Test accuracy value: 41.0 - type: accuracy name: Livvi Test accuracy value: 67.2 - type: accuracy name: Arabic Test accuracy value: 73.9 - type: accuracy name: Wolof Test accuracy value: 28.0 - type: accuracy name: Bulgarian Test accuracy value: 85.9 - type: accuracy name: Akuntsu Test accuracy value: 26.0 - type: accuracy name: Makurap Test accuracy value: 17.8 - type: accuracy name: Kangri Test accuracy value: 50.6 - type: accuracy name: Breton Test accuracy value: 60.3 - type: accuracy name: Telugu Test accuracy value: 85.0 - type: accuracy name: Cantonese Test accuracy value: 39.1 - type: accuracy name: Old Church Slavonic Test accuracy value: 51.6 - type: accuracy name: Karelian Test accuracy value: 71.3 - type: accuracy name: Upper Sorbian Test accuracy value: 75.7 - type: accuracy name: South Levantine Arabic Test accuracy value: 67.0 - type: accuracy name: Komi Zyrian Test accuracy value: 43.0 - type: accuracy name: Irish Test accuracy value: 60.1 - type: accuracy name: Nayini Test accuracy value: 46.2 - type: accuracy name: Munduruku Test accuracy value: 18.8 - type: accuracy name: Manx Test accuracy value: 33.3 - type: accuracy name: Skolt Sami Test accuracy value: 37.3 - type: accuracy name: Afrikaans Test accuracy value: 76.4 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 34.1 - type: accuracy name: Belarusian Test accuracy value: 89.1 - type: accuracy name: Serbian Test accuracy value: 87.7 - type: accuracy name: Moksha Test accuracy value: 46.3 - type: accuracy name: Western Armenian Test accuracy value: 75.4 - type: accuracy name: Scottish Gaelic Test accuracy value: 56.2 - type: accuracy name: Khunsari Test accuracy value: 39.2 - type: accuracy name: Hebrew Test accuracy value: 83.3 - type: accuracy name: Uyghur Test accuracy value: 76.6 - type: accuracy name: Chukchi Test accuracy value: 35.4 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Lithuanian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lt") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lt") ```

wietsedv/xlm-roberta-base-ft-udpos28-lv

5b08566f22c46a1ebeca9961489d1da64a1dc88f

2022-02-25T09:59:00.000Z

[ "pytorch", "xlm-roberta", "token-classification", "lv", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

wietsedv

null

wietsedv/xlm-roberta-base-ft-udpos28-lv

0

null

transformers

36,370

--- language: - lv license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-lv results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 84.7 - type: accuracy name: Dutch Test accuracy value: 85.6 - type: accuracy name: German Test accuracy value: 82.5 - type: accuracy name: Italian Test accuracy value: 84.3 - type: accuracy name: French Test accuracy value: 84.1 - type: accuracy name: Spanish Test accuracy value: 84.7 - type: accuracy name: Russian Test accuracy value: 92.1 - type: accuracy name: Swedish Test accuracy value: 86.8 - type: accuracy name: Norwegian Test accuracy value: 81.3 - type: accuracy name: Danish Test accuracy value: 86.0 - type: accuracy name: Low Saxon Test accuracy value: 51.6 - type: accuracy name: Akkadian Test accuracy value: 32.4 - type: accuracy name: Armenian Test accuracy value: 87.5 - type: accuracy name: Welsh Test accuracy value: 65.4 - type: accuracy name: Old East Slavic Test accuracy value: 76.5 - type: accuracy name: Albanian Test accuracy value: 75.9 - type: accuracy name: Slovenian Test accuracy value: 82.0 - type: accuracy name: Guajajara Test accuracy value: 31.1 - type: accuracy name: Kurmanji Test accuracy value: 76.5 - type: accuracy name: Turkish Test accuracy value: 77.2 - type: accuracy name: Finnish Test accuracy value: 85.9 - type: accuracy name: Indonesian Test accuracy value: 79.3 - type: accuracy name: Ukrainian Test accuracy value: 91.1 - type: accuracy name: Polish Test accuracy value: 88.5 - type: accuracy name: Portuguese Test accuracy value: 84.9 - type: accuracy name: Kazakh Test accuracy value: 83.8 - type: accuracy name: Latin Test accuracy value: 81.0 - type: accuracy name: Old French Test accuracy value: 56.7 - type: accuracy name: Buryat Test accuracy value: 64.8 - type: accuracy name: Kaapor Test accuracy value: 25.0 - type: accuracy name: Korean Test accuracy value: 65.1 - type: accuracy name: Estonian Test accuracy value: 84.7 - type: accuracy name: Croatian Test accuracy value: 89.1 - type: accuracy name: Gothic Test accuracy value: 23.5 - type: accuracy name: Swiss German Test accuracy value: 45.2 - type: accuracy name: Assyrian Test accuracy value: 12.8 - type: accuracy name: North Sami Test accuracy value: 43.5 - type: accuracy name: Naija Test accuracy value: 36.1 - type: accuracy name: Latvian Test accuracy value: 96.9 - type: accuracy name: Chinese Test accuracy value: 53.1 - type: accuracy name: Tagalog Test accuracy value: 72.7 - type: accuracy name: Bambara Test accuracy value: 28.6 - type: accuracy name: Lithuanian Test accuracy value: 91.0 - type: accuracy name: Galician Test accuracy value: 84.2 - type: accuracy name: Vietnamese Test accuracy value: 65.7 - type: accuracy name: Greek Test accuracy value: 84.5 - type: accuracy name: Catalan Test accuracy value: 83.2 - type: accuracy name: Czech Test accuracy value: 88.0 - type: accuracy name: Erzya Test accuracy value: 52.5 - type: accuracy name: Bhojpuri Test accuracy value: 49.2 - type: accuracy name: Thai Test accuracy value: 63.3 - type: accuracy name: Marathi Test accuracy value: 85.3 - type: accuracy name: Basque Test accuracy value: 77.4 - type: accuracy name: Slovak Test accuracy value: 87.8 - type: accuracy name: Kiche Test accuracy value: 40.3 - type: accuracy name: Yoruba Test accuracy value: 28.4 - type: accuracy name: Warlpiri Test accuracy value: 44.9 - type: accuracy name: Tamil Test accuracy value: 86.4 - type: accuracy name: Maltese Test accuracy value: 25.9 - type: accuracy name: Ancient Greek Test accuracy value: 62.2 - type: accuracy name: Icelandic Test accuracy value: 81.7 - type: accuracy name: Mbya Guarani Test accuracy value: 35.3 - type: accuracy name: Urdu Test accuracy value: 61.9 - type: accuracy name: Romanian Test accuracy value: 82.2 - type: accuracy name: Persian Test accuracy value: 74.8 - type: accuracy name: Apurina Test accuracy value: 49.0 - type: accuracy name: Japanese Test accuracy value: 39.4 - type: accuracy name: Hungarian Test accuracy value: 79.9 - type: accuracy name: Hindi Test accuracy value: 64.1 - type: accuracy name: Classical Chinese Test accuracy value: 30.0 - type: accuracy name: Komi Permyak Test accuracy value: 51.7 - type: accuracy name: Faroese Test accuracy value: 76.2 - type: accuracy name: Sanskrit Test accuracy value: 39.7 - type: accuracy name: Livvi Test accuracy value: 67.7 - type: accuracy name: Arabic Test accuracy value: 79.4 - type: accuracy name: Wolof Test accuracy value: 31.7 - type: accuracy name: Bulgarian Test accuracy value: 89.0 - type: accuracy name: Akuntsu Test accuracy value: 35.5 - type: accuracy name: Makurap Test accuracy value: 20.5 - type: accuracy name: Kangri Test accuracy value: 50.6 - type: accuracy name: Breton Test accuracy value: 62.7 - type: accuracy name: Telugu Test accuracy value: 87.8 - type: accuracy name: Cantonese Test accuracy value: 50.7 - type: accuracy name: Old Church Slavonic Test accuracy value: 49.3 - type: accuracy name: Karelian Test accuracy value: 72.7 - type: accuracy name: Upper Sorbian Test accuracy value: 75.6 - type: accuracy name: South Levantine Arabic Test accuracy value: 68.7 - type: accuracy name: Komi Zyrian Test accuracy value: 44.5 - type: accuracy name: Irish Test accuracy value: 64.7 - type: accuracy name: Nayini Test accuracy value: 39.7 - type: accuracy name: Munduruku Test accuracy value: 26.0 - type: accuracy name: Manx Test accuracy value: 37.9 - type: accuracy name: Skolt Sami Test accuracy value: 34.7 - type: accuracy name: Afrikaans Test accuracy value: 81.6 - type: accuracy name: Old Turkish Test accuracy value: 22.6 - type: accuracy name: Tupinamba Test accuracy value: 40.6 - type: accuracy name: Belarusian Test accuracy value: 91.8 - type: accuracy name: Serbian Test accuracy value: 89.7 - type: accuracy name: Moksha Test accuracy value: 48.7 - type: accuracy name: Western Armenian Test accuracy value: 77.5 - type: accuracy name: Scottish Gaelic Test accuracy value: 58.1 - type: accuracy name: Khunsari Test accuracy value: 40.5 - type: accuracy name: Hebrew Test accuracy value: 85.4 - type: accuracy name: Uyghur Test accuracy value: 79.7 - type: accuracy name: Chukchi Test accuracy value: 37.0 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Latvian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lv") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lv") ```

wietsedv/xlm-roberta-base-ft-udpos28-ta

b52577ee75a2250d4135204b9ef9b42ff2862ac0

2022-02-25T09:59:28.000Z

[ "pytorch", "xlm-roberta", "token-classification", "ta", "dataset:universal_dependencies", "transformers", "part-of-speech", "license:apache-2.0", "model-index", "autotrain_compatible" ]

token-classification

false

wietsedv

null

wietsedv/xlm-roberta-base-ft-udpos28-ta

0

null

transformers

36,371

--- language: - ta license: apache-2.0 library_name: transformers tags: - part-of-speech - token-classification datasets: - universal_dependencies metrics: - accuracy model-index: - name: xlm-roberta-base-ft-udpos28-ta results: - task: type: token-classification name: Part-of-Speech Tagging dataset: type: universal_dependencies name: Universal Dependencies v2.8 metrics: - type: accuracy name: English Test accuracy value: 68.1 - type: accuracy name: Dutch Test accuracy value: 64.0 - type: accuracy name: German Test accuracy value: 65.8 - type: accuracy name: Italian Test accuracy value: 61.2 - type: accuracy name: French Test accuracy value: 56.9 - type: accuracy name: Spanish Test accuracy value: 59.5 - type: accuracy name: Russian Test accuracy value: 74.3 - type: accuracy name: Swedish Test accuracy value: 69.1 - type: accuracy name: Norwegian Test accuracy value: 64.8 - type: accuracy name: Danish Test accuracy value: 70.0 - type: accuracy name: Low Saxon Test accuracy value: 46.9 - type: accuracy name: Akkadian Test accuracy value: 28.4 - type: accuracy name: Armenian Test accuracy value: 76.5 - type: accuracy name: Welsh Test accuracy value: 54.2 - type: accuracy name: Old East Slavic Test accuracy value: 61.8 - type: accuracy name: Albanian Test accuracy value: 61.0 - type: accuracy name: Slovenian Test accuracy value: 59.8 - type: accuracy name: Guajajara Test accuracy value: 22.7 - type: accuracy name: Kurmanji Test accuracy value: 64.1 - type: accuracy name: Turkish Test accuracy value: 72.0 - type: accuracy name: Finnish Test accuracy value: 76.2 - type: accuracy name: Indonesian Test accuracy value: 70.3 - type: accuracy name: Ukrainian Test accuracy value: 75.5 - type: accuracy name: Polish Test accuracy value: 72.0 - type: accuracy name: Portuguese Test accuracy value: 65.9 - type: accuracy name: Kazakh Test accuracy value: 77.2 - type: accuracy name: Latin Test accuracy value: 67.8 - type: accuracy name: Old French Test accuracy value: 45.0 - type: accuracy name: Buryat Test accuracy value: 58.8 - type: accuracy name: Kaapor Test accuracy value: 21.2 - type: accuracy name: Korean Test accuracy value: 58.6 - type: accuracy name: Estonian Test accuracy value: 78.5 - type: accuracy name: Croatian Test accuracy value: 71.3 - type: accuracy name: Gothic Test accuracy value: 18.2 - type: accuracy name: Swiss German Test accuracy value: 44.1 - type: accuracy name: Assyrian Test accuracy value: 17.2 - type: accuracy name: North Sami Test accuracy value: 34.9 - type: accuracy name: Naija Test accuracy value: 37.5 - type: accuracy name: Latvian Test accuracy value: 79.2 - type: accuracy name: Chinese Test accuracy value: 47.9 - type: accuracy name: Tagalog Test accuracy value: 65.6 - type: accuracy name: Bambara Test accuracy value: 22.8 - type: accuracy name: Lithuanian Test accuracy value: 77.8 - type: accuracy name: Galician Test accuracy value: 61.9 - type: accuracy name: Vietnamese Test accuracy value: 56.1 - type: accuracy name: Greek Test accuracy value: 63.5 - type: accuracy name: Catalan Test accuracy value: 57.6 - type: accuracy name: Czech Test accuracy value: 71.7 - type: accuracy name: Erzya Test accuracy value: 43.5 - type: accuracy name: Bhojpuri Test accuracy value: 55.6 - type: accuracy name: Thai Test accuracy value: 56.7 - type: accuracy name: Marathi Test accuracy value: 79.1 - type: accuracy name: Basque Test accuracy value: 74.3 - type: accuracy name: Slovak Test accuracy value: 71.9 - type: accuracy name: Kiche Test accuracy value: 28.3 - type: accuracy name: Yoruba Test accuracy value: 22.3 - type: accuracy name: Warlpiri Test accuracy value: 32.4 - type: accuracy name: Tamil Test accuracy value: 85.6 - type: accuracy name: Maltese Test accuracy value: 23.1 - type: accuracy name: Ancient Greek Test accuracy value: 52.9 - type: accuracy name: Icelandic Test accuracy value: 67.9 - type: accuracy name: Mbya Guarani Test accuracy value: 28.5 - type: accuracy name: Urdu Test accuracy value: 69.0 - type: accuracy name: Romanian Test accuracy value: 65.5 - type: accuracy name: Persian Test accuracy value: 60.0 - type: accuracy name: Apurina Test accuracy value: 32.7 - type: accuracy name: Japanese Test accuracy value: 42.3 - type: accuracy name: Hungarian Test accuracy value: 69.8 - type: accuracy name: Hindi Test accuracy value: 73.6 - type: accuracy name: Classical Chinese Test accuracy value: 28.3 - type: accuracy name: Komi Permyak Test accuracy value: 40.2 - type: accuracy name: Faroese Test accuracy value: 59.9 - type: accuracy name: Sanskrit Test accuracy value: 36.9 - type: accuracy name: Livvi Test accuracy value: 61.4 - type: accuracy name: Arabic Test accuracy value: 62.9 - type: accuracy name: Wolof Test accuracy value: 28.3 - type: accuracy name: Bulgarian Test accuracy value: 71.6 - type: accuracy name: Akuntsu Test accuracy value: 19.3 - type: accuracy name: Makurap Test accuracy value: 12.3 - type: accuracy name: Kangri Test accuracy value: 51.6 - type: accuracy name: Breton Test accuracy value: 51.7 - type: accuracy name: Telugu Test accuracy value: 83.2 - type: accuracy name: Cantonese Test accuracy value: 50.3 - type: accuracy name: Old Church Slavonic Test accuracy value: 45.7 - type: accuracy name: Karelian Test accuracy value: 63.7 - type: accuracy name: Upper Sorbian Test accuracy value: 62.3 - type: accuracy name: South Levantine Arabic Test accuracy value: 57.5 - type: accuracy name: Komi Zyrian Test accuracy value: 35.3 - type: accuracy name: Irish Test accuracy value: 58.2 - type: accuracy name: Nayini Test accuracy value: 48.7 - type: accuracy name: Munduruku Test accuracy value: 15.9 - type: accuracy name: Manx Test accuracy value: 26.5 - type: accuracy name: Skolt Sami Test accuracy value: 32.7 - type: accuracy name: Afrikaans Test accuracy value: 66.5 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 27.8 - type: accuracy name: Belarusian Test accuracy value: 76.9 - type: accuracy name: Serbian Test accuracy value: 71.6 - type: accuracy name: Moksha Test accuracy value: 39.2 - type: accuracy name: Western Armenian Test accuracy value: 70.8 - type: accuracy name: Scottish Gaelic Test accuracy value: 50.2 - type: accuracy name: Khunsari Test accuracy value: 39.2 - type: accuracy name: Hebrew Test accuracy value: 81.2 - type: accuracy name: Uyghur Test accuracy value: 67.3 - type: accuracy name: Chukchi Test accuracy value: 33.6 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Tamil This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ta") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ta") ```

mohamed-illiyas/wav2vec-malayalam

1dcc0f9033e7dde9ef935bf3f5c8f24c45c19956

2022-02-28T16:07:13.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

mohamed-illiyas

null

mohamed-illiyas/wav2vec-malayalam

0

null

transformers

36,372

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec-malayalam results: [] ---  # wav2vec-malayalam This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0a0+3fd9dcf - Datasets 1.18.3 - Tokenizers 0.10.3

zfchen/codeparrot

54737857ba1f8cc3b41c545642fa9f6f93694f44

2022-02-24T14:52:15.000Z

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

text-generation

false

zfchen

null

zfchen/codeparrot

0

null

transformers

36,373

Entry not found

vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k

153dc40a3ce127c6a0537940eda26d0dad5659f2

2022-02-24T19:08:20.000Z

[ "pytorch", "distilbert", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]

sentence-similarity

false

vocab-transformers

null

vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k

0

null

sentence-transformers

36,374

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # dense_encoder-msmarco-distilbert-word2vec256k This model is based on [msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec. It has been trained on MS MARCO using [MarginMSELoss](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py). See the train_script.py in this repository. Performance: - MS MARCO dev: - (MRR@10) - TREC-DL 2019: 65.53 (nDCG@10) - TREC-DL 2020: 67.42 (nDCG@10) - Avg. on 4 BEIR datasets: 38.97 The word embedding matrix has been frozen while training. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 7858 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MarginMSELoss.MarginMSELoss` Parameters of the fit()-Method: ``` { "epochs": 30, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 250, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

vesteinn/clip-nabirds

99805933b55c5ee1a384aefdfac664bc6a8ac150

2022-02-27T22:40:41.000Z

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

feature-extraction

false

vesteinn

null

vesteinn/clip-nabirds

0

null

transformers

36,375

Entry not found

huggingtweets/dril-nia_mp4

875388556d09a55ec7f566dc1a05afb713c356eb

2022-02-25T19:44:43.000Z

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

text-generation

false

huggingtweets

null

huggingtweets/dril-nia_mp4

0

null

transformers

36,376

--- language: en thumbnail: http://www.huggingtweets.com/dril-nia_mp4/1645818279249/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1487740104340918272/7c9spp2E_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nia & wint</div> <div style="text-align: center; font-size: 14px;">@dril-nia_mp4</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nia & wint. | Data | Nia | wint | | --- | --- | --- | | Tweets downloaded | 278 | 3229 | | Retweets | 12 | 473 | | Short tweets | 13 | 300 | | Tweets kept | 253 | 2456 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ybk5oh0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dril-nia_mp4's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ny6aucf) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ny6aucf/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/dril-nia_mp4') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

nadaAlnada/wav2vec2-base-timit-demo-colab

5f1daa05183ad42c85e378b91af603d602cbbd31

2022-02-27T13:55:32.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

nadaAlnada

null

nadaAlnada/wav2vec2-base-timit-demo-colab

0

null

transformers

36,377

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-base-timit-demo-colab results: [] ---  # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [anas/wav2vec2-large-xlsr-arabic](https://huggingface.co/anas/wav2vec2-large-xlsr-arabic) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

hcy11/distilbert-base-uncased-finetuned-squad

21d2fd9c156841c916a2bfd0b7e5fd9deefdadff

2022-03-02T20:32:33.000Z

[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

question-answering

false

hcy11

null

hcy11/distilbert-base-uncased-finetuned-squad

0

null

transformers

36,378

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2131 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2672 | 1.0 | 5533 | 1.2131 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6

zhoutong/best-t5

7debf4563bc6d9a9dff89ccf79bbe3510398bb97

2022-02-26T07:27:55.000Z

[ "pytorch" ]

null

false

zhoutong

null

zhoutong/best-t5

0

null

36,379

Entry not found

ianc89/hagrid

8589d4146189b95a75fb2c329cba312a492f21d6

2022-02-26T13:52:01.000Z

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

conversational

false

ianc89

null

ianc89/hagrid

0

null

transformers

36,380

--- tags: - conversational --- # My Awesome Model

nimrah/wav2vec2-large-xls-r-300m-my_hindi_home-colab

a5b877c919ab676748bb54a993f93d1d2455f28b

2022-02-26T17:11:23.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

nimrah

null

nimrah/wav2vec2-large-xls-r-300m-my_hindi_home-colab

0

null

transformers

36,381

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-my_hindi_home-colab results: [] ---  # wav2vec2-large-xls-r-300m-my_hindi_home-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

huggingtweets/claresiobhan

23b5676b74c9dcb7c46edf5e8e6f38cc8eea61a7

2022-02-26T22:19:14.000Z

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

text-generation

false

huggingtweets

null

huggingtweets/claresiobhan

0

null

transformers

36,382

--- language: en thumbnail: http://www.huggingtweets.com/claresiobhan/1645913945953/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1296785738978201600/J9LDndke_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">👰Clare Siobhán👰</div> <div style="text-align: center; font-size: 14px;">@claresiobhan</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 👰Clare Siobhán👰. | Data | 👰Clare Siobhán👰 | | --- | --- | | Tweets downloaded | 3249 | | Retweets | 110 | | Short tweets | 504 | | Tweets kept | 2635 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vq9maap/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @claresiobhan's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/375bmhre) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/375bmhre/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/claresiobhan') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/zeebeecat01

150bdb14347a4bdbbbf8d1621cae6c5ed2d73260

2022-02-26T22:24:18.000Z

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

text-generation

false

huggingtweets

null

huggingtweets/zeebeecat01

0

null

transformers

36,383

--- language: en thumbnail: http://www.huggingtweets.com/zeebeecat01/1645914254405/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1103665627183472642/OVXzwAk7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Shreya Mukherjee 💀🌻</div> <div style="text-align: center; font-size: 14px;">@zeebeecat01</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Shreya Mukherjee 💀🌻. | Data | Shreya Mukherjee 💀🌻 | | --- | --- | | Tweets downloaded | 731 | | Retweets | 552 | | Short tweets | 33 | | Tweets kept | 146 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kz1pvshu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zeebeecat01's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3btkttwk) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3btkttwk/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/zeebeecat01') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

jiobiala24/wav2vec2-base-checkpoint-13

5c89362023b8b279eca9db1fe7ce75fde2cdae64

2022-02-27T12:36:13.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

jiobiala24

null

jiobiala24/wav2vec2-base-checkpoint-13

0

null

transformers

36,384

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-base-checkpoint-13 results: [] ---  # wav2vec2-base-checkpoint-13 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-12](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-12) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.1804 - Wer: 0.3809 ## 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: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.2688 | 1.92 | 1000 | 0.6518 | 0.3692 | | 0.1944 | 3.85 | 2000 | 0.7188 | 0.3808 | | 0.1503 | 5.77 | 3000 | 0.7552 | 0.3853 | | 0.1218 | 7.69 | 4000 | 0.8155 | 0.3834 | | 0.1024 | 9.62 | 5000 | 0.8867 | 0.3779 | | 0.0874 | 11.54 | 6000 | 0.8917 | 0.3866 | | 0.0775 | 13.46 | 7000 | 1.0320 | 0.4019 | | 0.0712 | 15.38 | 8000 | 1.0110 | 0.3922 | | 0.0656 | 17.31 | 9000 | 1.0494 | 0.3885 | | 0.0578 | 19.23 | 10000 | 1.1054 | 0.3883 | | 0.053 | 21.15 | 11000 | 1.1285 | 0.3938 | | 0.0496 | 23.08 | 12000 | 1.1358 | 0.3884 | | 0.0459 | 25.0 | 13000 | 1.2062 | 0.3904 | | 0.0445 | 26.92 | 14000 | 1.1811 | 0.3830 | | 0.0414 | 28.85 | 15000 | 1.1804 | 0.3809 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

jt360/mt5-small-finetuned-amazon-en-es-video-games

c616e485a6c72203248922966d17c75ba839fdd7

2022-02-27T18:43:57.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "license:afl-3.0", "autotrain_compatible" ]

text2text-generation

false

jt360

null

jt360/mt5-small-finetuned-amazon-en-es-video-games

0

null

transformers

36,385

--- license: afl-3.0 ---

flairbook2/flairmodel

dba1512be5619b7a11812b492b1a7d37f6639188

2022-04-09T16:58:21.000Z

[ "pytorch", "flair", "token-classification" ]

token-classification

false

flairbook2

null

flairbook2/flairmodel

0

null

flair

36,386

--- tags: - flair - token-classification widget: - text: "does this work" --- ## Test model README Some test README description

mipatov/rugpt3_nb_descr

268609ceb23151c73dbf07578fc23bbc51988240

2022-02-27T23:44:38.000Z

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

text-generation

false

mipatov

null

mipatov/rugpt3_nb_descr

0

null

transformers

36,387

based on `sberbank-ai/rugpt3medium_based_on_gpt2` finetuned for generate text description for notebook-devices

facebook/wav2vec2-base-sv-voxpopuli-v2

36445212b2499f538da33aba9a1475981c82ed69

2022-02-27T13:13:27.000Z

[ "pytorch", "wav2vec2", "pretraining", "sv", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]

automatic-speech-recognition

false

facebook

null

facebook/wav2vec2-base-sv-voxpopuli-v2

0

null

transformers

36,388

--- language: sv tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-base-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **sv** on **16.3k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **sv**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).

facebook/wav2vec2-large-west_germanic-voxpopuli-v2

ba2575ca08f92658b827cb034da9ad4b5e3d56d9

2022-02-27T12:35:16.000Z

[ "pytorch", "wav2vec2", "pretraining", "west_germanic", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]

automatic-speech-recognition

false

facebook

null

facebook/wav2vec2-large-west_germanic-voxpopuli-v2

0

null

transformers

36,389

--- language: west_germanic tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **west_germanic** on **66.3** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **west_germanic**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).

facebook/wav2vec2-large-north_germanic-voxpopuli-v2

2f451c30b28238d13b1c54bbca1f4e6c241a5304

2022-02-27T12:37:56.000Z

[ "pytorch", "wav2vec2", "pretraining", "north_germanic", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]

automatic-speech-recognition

false

facebook

null

facebook/wav2vec2-large-north_germanic-voxpopuli-v2

0

null

transformers

36,390

--- language: north_germanic tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **north_germanic** on **29.9** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **north_germanic**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).

facebook/wav2vec2-large-slavic-voxpopuli-v2

fa95b126a53cb5b6803f6e7c3f77693525b97eff

2022-02-27T12:40:42.000Z

[ "pytorch", "wav2vec2", "pretraining", "slavic", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]

automatic-speech-recognition

false

facebook

null

facebook/wav2vec2-large-slavic-voxpopuli-v2

0

null

transformers

36,391

--- language: slavic tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **slavic** on **88.99999999999999** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **slavic**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).

facebook/wav2vec2-base-sl-voxpopuli-v2

0411d3dd04058c9d1547ece5f29e9f107e907930

2022-02-27T13:14:49.000Z

[ "pytorch", "wav2vec2", "pretraining", "sl", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]

automatic-speech-recognition

false

facebook

null

facebook/wav2vec2-base-sl-voxpopuli-v2

0

null

transformers

36,392

--- language: sl tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-base-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **sl** on **11.3k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **sl**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).

facebook/wav2vec2-large-mt-voxpopuli-v2

51ecaa56badc4d0aa1ad5d3d196e605e1369b31c

2022-02-27T12:51:06.000Z

[ "pytorch", "wav2vec2", "pretraining", "mt", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]

automatic-speech-recognition

false

facebook

null

facebook/wav2vec2-large-mt-voxpopuli-v2

0

null

transformers

36,393

--- language: mt tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **mt** on **9.1** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **mt**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).

facebook/wav2vec2-large-el-voxpopuli-v2

362fc887d5d4854e0299be872e30015396a38a2c

2022-02-27T12:48:30.000Z

[ "pytorch", "wav2vec2", "pretraining", "el", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]

automatic-speech-recognition

false

facebook

null

facebook/wav2vec2-large-el-voxpopuli-v2

0

null

transformers

36,394

--- language: el tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **el** on **17.7** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **el**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).

spy24/autonlp-UK-to-US-600416931

eea0caf05b11c517c67f1ce46d7028cce22d3b17

2022-02-28T09:59:04.000Z

[ "pytorch", "t5", "text2text-generation", "unk", "dataset:spy24/autonlp-data-UK-to-US", "transformers", "autonlp", "co2_eq_emissions", "autotrain_compatible" ]

text2text-generation

false

spy24

null

spy24/autonlp-UK-to-US-600416931

0

1

transformers

36,395

--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - spy24/autonlp-data-UK-to-US co2_eq_emissions: 1.113131499202784 --- # Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 600416931 - CO2 Emissions (in grams): 1.113131499202784 ## Validation Metrics - Loss: 1.8278849124908447 - Rouge1: 45.7945 - Rouge2: 8.5245 - RougeL: 45.8031 - RougeLsum: 45.9067 - Gen Len: 3.0622 ## 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/spy24/autonlp-UK-to-US-600416931 ```

spy24/autonlp-AUS-to-US-601516964

58c67b6f95797e1ba26bd30dcb6d02dd133b04ef

2022-02-28T11:21:11.000Z

[ "pytorch", "t5", "text2text-generation", "unk", "dataset:spy24/autonlp-data-AUS-to-US", "transformers", "autonlp", "co2_eq_emissions", "autotrain_compatible" ]

text2text-generation

false

spy24

null

spy24/autonlp-AUS-to-US-601516964

0

null

transformers

36,396

--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - spy24/autonlp-data-AUS-to-US co2_eq_emissions: 3.3930796843275846 --- # Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 601516964 - CO2 Emissions (in grams): 3.3930796843275846 ## Validation Metrics - Loss: 1.9823806285858154 - Rouge1: 42.8783 - Rouge2: 7.4603 - RougeL: 42.8492 - RougeLsum: 43.0556 - Gen Len: 2.8952 ## 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/spy24/autonlp-AUS-to-US-601516964 ```

rockyend/distilbert-base-uncased-finetuned-ner

1a025a2170be4f9fd5e5cf7353729c0a1bb5b023

2022-02-28T15:45:56.000Z

[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

false

rockyend

null

rockyend/distilbert-base-uncased-finetuned-ner

0

null

transformers

36,397

Entry not found

peterhsu/test-bert-finetuned-squad-accelerate

bd5b6de35c21408d3ca5aa77302d7a5eaa419721

2022-02-28T18:47:40.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

peterhsu

null

peterhsu/test-bert-finetuned-squad-accelerate

0

null

transformers

36,398

Entry not found

nateraw/cryptopunks-gan

1eb7a477ae62ddf76aa01598347797aa2f3a248f

2022-03-01T01:59:49.000Z

[ "tensorboard", "pytorch", "dcgan" ]

null

false

nateraw

null

nateraw/cryptopunks-gan

0

2

pytorch

36,399

--- library_name: pytorch tags: - dcgan --- # cryptopunks-gan A DCGAN trained to generate novel Cryptopunks. Check out the code by Teddy Koker [here](https://github.com/teddykoker/cryptopunks-gan). ## Generated Punks Here are some punks generated by this model: ![](fake_samples_epoch_999.png) ## Usage You can try it out yourself, or you can play with the [demo](https://huggingface.co/spaces/nateraw/cryptopunks-generator). To use it yourself - make sure you have `torch`, `torchvision`, and `huggingface_hub` installed. Then, run the following to generate a grid of 64 random punks: ```python import torch from huggingface_hub import hf_hub_download from torch import nn from torchvision.utils import save_image class Generator(nn.Module): def __init__(self, nc=4, nz=100, ngf=64): super(Generator, self).__init__() self.network = nn.Sequential( nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False), nn.BatchNorm2d(ngf * 4), nn.ReLU(True), nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False), nn.BatchNorm2d(ngf * 2), nn.ReLU(True), nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False), nn.BatchNorm2d(ngf), nn.ReLU(True), nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False), nn.Tanh(), ) def forward(self, input): output = self.network(input) return output model = Generator() weights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth') model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))) out = model(torch.randn(64, 100, 1, 1)) save_image(out, "punks.png", normalize=True) ```