Datasets:
davanstrien
/
pytorchmodelmetadata

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xkang/distilbert-base-uncased-finetuned-imdb
93dc187c4e9322cbe239b3501f30d57997a34474
2021-12-27T07:30:09.000Z
[ "pytorch", "tensorboard", "distilbert", "fill-mask", "dataset:imdb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
fill-mask
false
xkang
null
xkang/distilbert-base-uncased-finetuned-imdb
1
null
transformers
30,500
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4717 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7096 | 1.0 | 157 | 2.4920 | | 2.5741 | 2.0 | 314 | 2.4237 | | 2.5386 | 3.0 | 471 | 2.4355 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0 - Datasets 1.17.1.dev0 - Tokenizers 0.10.3
xkang/dummy-model
7b760ad72655b5822aade87e3a07d47d303fd052
2021-12-03T01:22:28.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
xkang
null
xkang/dummy-model
1
null
transformers
30,501
Entry not found
xxr/bert-base-uncased-issues-128
fe3aa1bf0ebaccde9490ece01c93d317114fc27a
2022-02-15T14:09:11.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
xxr
null
xxr/bert-base-uncased-issues-128
1
null
transformers
30,502
--- license: apache-2.0 tags: - generated_from_trainer datasets: - null model_index: - name: bert-base-uncased-issues-128 results: - task: name: Masked Language Modeling type: fill-mask --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2109 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.9845 | 1.0 | 1163 | 1.6403 | | 1.5695 | 2.0 | 2326 | 1.4212 | | 1.4221 | 3.0 | 3489 | 1.3714 | | 1.3302 | 4.0 | 4652 | 1.3592 | | 1.2734 | 5.0 | 5815 | 1.2781 | | 1.2143 | 6.0 | 6978 | 1.2286 | | 1.1704 | 7.0 | 8141 | 1.2492 | | 1.1261 | 8.0 | 9304 | 1.2044 | | 1.0812 | 9.0 | 10467 | 1.1878 | | 1.0657 | 10.0 | 11630 | 1.2177 | | 1.0319 | 11.0 | 12793 | 1.1428 | | 1.0063 | 12.0 | 13956 | 1.0910 | | 0.9731 | 13.0 | 15119 | 1.1111 | | 0.9674 | 14.0 | 16282 | 1.1699 | | 0.9391 | 15.0 | 17445 | 1.0805 | | 0.9381 | 16.0 | 18608 | 1.2109 | ### Framework versions - Transformers 4.8.0 - Pytorch 1.9.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
xysmalobia/t5-finetuned-amazon-en
406e92567971566dc823a255beb3ceb2190e0284
2021-11-14T17:41:19.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
xysmalobia
null
xysmalobia/t5-finetuned-amazon-en
1
null
transformers
30,503
Entry not found
yahya1994/DialoGPT-small-AOT-Eren
a65dc5e23a8a7f352542efc6adb45df678a551ab
2021-09-08T19:49:47.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
yahya1994
null
yahya1994/DialoGPT-small-AOT-Eren
1
null
transformers
30,504
--- tags: - conversational --- # Eren dialog
yahya1994/DialoGPT-small-Parasyte-Migi
3dd670c3a04a795e82b0a01bfc04f785d50956a6
2021-09-04T18:09:34.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
yahya1994
null
yahya1994/DialoGPT-small-Parasyte-Migi
1
null
transformers
30,505
--- tags: - conversational --- # Migi dialog
yahya1994/DialoGPT-small-ReZero-Rem
ec939b9b956866015ee78e7a265d3cd1ca8f97bc
2021-09-09T00:23:38.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
yahya1994
null
yahya1994/DialoGPT-small-ReZero-Rem
1
null
transformers
30,506
--- tags: - conversational --- # Rem dialog
yancong/dummy-model
e27b408374aeebe976b463ba68c2689bea7d785b
2021-07-24T23:56:58.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
yancong
null
yancong/dummy-model
1
null
transformers
30,507
Entry not found
yarik921/Teflon_0.2
9014569f78c704993511546da7ba118fa1c2666d
2022-02-18T12:44:03.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
yarik921
null
yarik921/Teflon_0.2
1
null
transformers
30,508
Entry not found
yazdipour/sparql-qald9-t5-small-2021-10-19_00-01
c891ac2dcab7315223ca8c9d817b61c3508b7cc1
2021-10-19T00:13:21.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
yazdipour
null
yazdipour/sparql-qald9-t5-small-2021-10-19_00-01
1
null
transformers
30,509
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: sparql-qald9-t5-small-2021-10-19_00-01 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. --> # sparql-qald9-t5-small-2021-10-19_00-01 This model is a fine-tuned version of [yazdipour/text-to-sparql-t5-small-2021-10-18_23-00](https://huggingface.co/yazdipour/text-to-sparql-t5-small-2021-10-18_23-00) 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 - 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 | Gen Len | P | R | F1 | Bleu-score | Bleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:----------:|:-------------------------------------------------------------------------------:|:-------:| | No log | 1.0 | 51 | 2.4058 | 19.0 | 0.3946 | 0.0660 | 0.2253 | 9.8438 | [72.36042012161415, 47.920433996383366, 33.929754804506295, 26.416482707873435] | 0.2344 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
yazdipour/sparql-qald9-t5-small-2021-10-19_07-12_RAW
b0f36a4222512ac18988d33ec78822f80900d8e5
2021-10-19T07:25:13.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
yazdipour
null
yazdipour/sparql-qald9-t5-small-2021-10-19_07-12_RAW
1
null
transformers
30,510
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: sparql-qald9-t5-small-2021-10-19_07-12_RAW 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. --> # sparql-qald9-t5-small-2021-10-19_07-12_RAW This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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 - 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 | Gen Len | P | R | F1 | Bleu-score | Bleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:----------:|:----------------------------------------------------------------------------:|:-------:| | No log | 1.0 | 51 | 2.8581 | 19.0 | 0.3301 | 0.0433 | 0.1830 | 7.5917 | [69.82603479304139, 45.68226763348714, 32.33357717629846, 24.56861133935908] | 0.1903 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
yazdipour/text-to-sparql-t5-base-2021-10-18_16-15
29e577318f9cddf8dd0d199df6da1b8502f1d6b8
2021-10-18T18:58:01.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
yazdipour
null
yazdipour/text-to-sparql-t5-base-2021-10-18_16-15
1
null
transformers
30,511
--- license: apache-2.0 tags: - generated_from_trainer datasets: - null model-index: - name: text-to-sparql-t5-base-2021-10-18_16-15 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-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. --> # text-to-sparql-t5-base-2021-10-18_16-15 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1294 - Gen Len: 19.0 - Bertscorer-p: 0.5827 - Bertscorer-r: 0.0812 - Bertscorer-f1: 0.3202 - Sacrebleu-score: 5.9410 - Sacrebleu-precisions: [92.24641734333713, 84.24354361048307, 78.78523204758982, 75.43428275229601] - Bleu-bp: 0.0721 ## 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 - 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 | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:----------------------------------------------------------------------------:|:-------:| | nan | 1.0 | 4772 | 0.1294 | 19.0 | 0.5827 | 0.0812 | 0.3202 | 5.9410 | [92.24641734333713, 84.24354361048307, 78.78523204758982, 75.43428275229601] | 0.0721 | ### Framework versions - Transformers 4.10.0 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
yazdipour/text-to-sparql-t5-small-2021-10-15_01-00
371c071cdbf1c4230db732832950c8f70b9a6a05
2021-10-15T15:19:59.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
yazdipour
null
yazdipour/text-to-sparql-t5-small-2021-10-15_01-00
1
null
transformers
30,512
--- tags: - generated_from_trainer model-index: - name: text-to-sparql-t5-small-2021-10-15_01-00 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. --> # text-to-sparql-t5-small-2021-10-15_01-00 This model was trained from scratch 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: 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Bleu-score | Bleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:------:|:----------:|:-----------------------------------------------------------------:|:-------:| | No log | 1.0 | 26 | 4.1488 | 19.0 | 0.2368 | -0.0304 | 0.1003 | 0.8868 | [56.84848484848485, 25.0, 8.88888888888889, 0.041666666666666664] | 0.1851 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.13.2 - Tokenizers 0.10.3
yazdipour/text-to-sparql-t5-small-2021-10-18_12-12
e0ae3b1802c899d3ab41b2376a4e902abc776e54
2021-10-18T13:14:26.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
yazdipour
null
yazdipour/text-to-sparql-t5-small-2021-10-18_12-12
1
null
transformers
30,513
--- license: apache-2.0 tags: - generated_from_trainer datasets: - null model-index: - name: text-to-sparql-t5-small-2021-10-18_12-12 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-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. --> # text-to-sparql-t5-small-2021-10-18_12-12 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3284 - Gen Len: 19.0 - Bertscorer-p: 0.5420 - Bertscorer-r: 0.0732 - Bertscorer-f1: 0.2972 - Sacrebleu-score: 4.8763 - Sacrebleu-precisions: [87.2581084764241, 73.48869132519009, 64.19139944127409, 58.342420937840785] - Bleu-bp: 0.0697 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:----------------------------------------------------------------------------:|:-------:| | 0.4209 | 1.0 | 4772 | 0.3284 | 19.0 | 0.5420 | 0.0732 | 0.2972 | 4.8763 | [87.2581084764241, 73.48869132519009, 64.19139944127409, 58.342420937840785] | 0.0697 | ### Framework versions - Transformers 4.10.0 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
yazdipour/text-to-sparql-t5-small-2021-10-18_23-00
bf722627d1fb7036e35cc07ad951d8156b44614d
2021-10-19T00:01:17.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
yazdipour
null
yazdipour/text-to-sparql-t5-small-2021-10-18_23-00
1
null
transformers
30,514
--- license: apache-2.0 tags: - generated_from_trainer datasets: - null model-index: - name: text-to-sparql-t5-small-2021-10-18_23-00 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-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. --> # text-to-sparql-t5-small-2021-10-18_23-00 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2284 - Gen Len: 19.0 - Bertscorer-p: 0.5644 - Bertscorer-r: 0.0815 - Bertscorer-f1: 0.3120 - Sacrebleu-score: 5.5690 - Sacrebleu-precisions: [89.6746395837541, 79.06489438259324, 71.93407601726916, 67.21220306665607] - Bleu-bp: 0.0728 ## 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 - 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 | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:---------------------------------------------------------------------------:|:-------:| | 0.2808 | 1.0 | 4772 | 0.2284 | 19.0 | 0.5644 | 0.0815 | 0.3120 | 5.5690 | [89.6746395837541, 79.06489438259324, 71.93407601726916, 67.21220306665607] | 0.0728 | ### Framework versions - Transformers 4.10.0 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
ydl233/bart_model
f32b20a9e81f7abb89bffffc772489fc49c0d87c
2021-09-08T06:40:12.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
ydl233
null
ydl233/bart_model
1
null
transformers
30,515
Entry not found
yfyang/wav2vec2-base-timit-fine-tuned
83276625a54f7a4bafbb3345550037ca4bfd0f42
2021-11-04T08:21:31.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
yfyang
null
yfyang/wav2vec2-base-timit-fine-tuned
1
null
transformers
30,516
Entry not found
yhk04150/yhkBERT
e1189d5bd65da8f738d7452f0504781aeacf07fa
2021-05-20T09:28:34.000Z
[ "pytorch", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
yhk04150
null
yhk04150/yhkBERT
1
null
transformers
30,517
Entry not found
ying-tina/wav2vec2-base-timit-demo-colab-32
454b4455e24da308a0c2d7b57cc4c1c7b4378339
2021-12-01T10:54:26.000Z
[ "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-32
1
null
transformers
30,518
--- license: apache-2.0 tags: - generated_from_trainer model-index: name: wav2vec2-base-timit-demo-colab-32 --- <!-- 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 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4488 - Wer: 0.3149 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6155 | 4.0 | 500 | 2.2647 | 0.9992 | | 0.9037 | 8.0 | 1000 | 0.4701 | 0.4336 | | 0.3159 | 12.0 | 1500 | 0.4247 | 0.3575 | | 0.1877 | 16.0 | 2000 | 0.4477 | 0.3442 | | 0.1368 | 20.0 | 2500 | 0.4932 | 0.3384 | | 0.1062 | 24.0 | 3000 | 0.4758 | 0.3202 | | 0.0928 | 28.0 | 3500 | 0.4488 | 0.3149 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Tokenizers 0.10.3
ying-tina/wav2vec2-base-timit-demo-colab
3127ccf8f9908770dc926aa2ef2ffe0616c6ac6b
2021-11-30T10:52:25.000Z
[ "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
1
null
transformers
30,519
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5127 - Wer: 0.3082 ## 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: 16 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7645 | 2.01 | 500 | 2.5179 | 0.9999 | | 1.1873 | 4.02 | 1000 | 0.5464 | 0.4798 | | 0.46 | 6.02 | 1500 | 0.4625 | 0.4025 | | 0.2869 | 8.03 | 2000 | 0.4252 | 0.3650 | | 0.2213 | 10.04 | 2500 | 0.4340 | 0.3585 | | 0.1905 | 12.05 | 3000 | 0.4310 | 0.3404 | | 0.1545 | 14.06 | 3500 | 0.4547 | 0.3381 | | 0.1206 | 16.06 | 4000 | 0.4902 | 0.3384 | | 0.1116 | 18.07 | 4500 | 0.4767 | 0.3253 | | 0.0925 | 20.08 | 5000 | 0.5248 | 0.3160 | | 0.0897 | 22.09 | 5500 | 0.4960 | 0.3126 | | 0.0687 | 24.1 | 6000 | 0.4876 | 0.3086 | | 0.063 | 26.1 | 6500 | 0.4895 | 0.3065 | | 0.0558 | 28.11 | 7000 | 0.5127 | 0.3082 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
yliu337/filter_maskQA
46029c057dbd7233dfad4457387a54cf45392c8a
2021-08-10T16:48:55.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
yliu337
null
yliu337/filter_maskQA
1
null
transformers
30,520
Entry not found
yliu337/mt5_sliding_window_en
c2075c80301ec8551eeed5f3d4ad24adfcce5402
2021-11-14T21:19:16.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
yliu337
null
yliu337/mt5_sliding_window_en
1
null
transformers
30,521
Entry not found
yliu337/t5_fillmask_src_hyp_format
9e83a102b47eca1222ab9d9085558b4c787595b2
2021-10-13T02:48:33.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
yliu337
null
yliu337/t5_fillmask_src_hyp_format
1
null
transformers
30,522
Entry not found
yliu337/t5_neg_nonfilter_bothcontext
5a97e6560532f81db3faadf5dd6dee61beb0472c
2021-08-23T21:15:56.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
yliu337
null
yliu337/t5_neg_nonfilter_bothcontext
1
null
transformers
30,523
Entry not found
yoonseob/yaiBERT-v2
ce46bb76a306ab28d380284e846b14c4ac976999
2020-12-04T00:40:42.000Z
[ "pytorch", "transformers" ]
null
false
yoonseob
null
yoonseob/yaiBERT-v2
1
null
transformers
30,524
Entry not found
yoonseob/yaiBERT
e1355900109029f96a14d49200d8e9f3b4a88cbf
2020-12-03T17:23:58.000Z
[ "pytorch", "transformers" ]
null
false
yoonseob
null
yoonseob/yaiBERT
1
null
transformers
30,525
Entry not found
yoonseob/ysBERT
dcc45f6203426c0b990a6c410789195776cff950
2021-05-20T09:31:54.000Z
[ "pytorch", "bert", "transformers" ]
null
false
yoonseob
null
yoonseob/ysBERT
1
null
transformers
30,526
Entry not found
youngjae/bert-finetuned-squad-accelerate
dd318c1c48258c9d40d282c8e05ee7a24f56c248
2021-12-30T05:20:14.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
youngjae
null
youngjae/bert-finetuned-squad-accelerate
1
null
transformers
30,527
Entry not found
youngjae/bert-finetuned-squad
a3fdd2131d607621fd606c3002093b2d21625248
2021-12-30T04:13:47.000Z
[ "pytorch", "tensorboard", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
youngjae
null
youngjae/bert-finetuned-squad
1
null
transformers
30,528
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-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. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.14.1 - Pytorch 1.9.0.dev20210415+cu101 - Datasets 1.16.1 - Tokenizers 0.10.3
ysharma/dummy-model-2
6fa13bfd2b75228aca61f1889896ef297e9b6bb3
2021-07-12T06:25:53.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
ysharma
null
ysharma/dummy-model-2
1
null
transformers
30,529
Entry not found
ytlin/19rdmhqc
83ea27cff123c650a4455eab1962f56d78ae16b7
2020-10-06T06:39:21.000Z
[ "pytorch", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
ytlin
null
ytlin/19rdmhqc
1
null
transformers
30,530
Entry not found
ytlin/1pm2c7qw_5
4a4164efe8478672b58652e1b8757979efd6b20a
2021-05-23T13:49:02.000Z
[ "pytorch", "gpt2", "transformers" ]
null
false
ytlin
null
ytlin/1pm2c7qw_5
1
null
transformers
30,531
Entry not found
ytlin/1pm2c7qw_6
88cb5d98ef1786ef1c7bc636b51d984d690d26a7
2021-05-23T13:49:27.000Z
[ "pytorch", "gpt2", "transformers" ]
null
false
ytlin
null
ytlin/1pm2c7qw_6
1
null
transformers
30,532
Entry not found
ytlin/329vcm1b_4
65f8904790198679fd41cdb4217eb4695c460a9b
2020-10-05T06:03:46.000Z
[ "pytorch", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
ytlin
null
ytlin/329vcm1b_4
1
null
transformers
30,533
Entry not found
ytlin/35oote4t_52
85cfbf115a8c8b5c7219a21f740aba06c5e89455
2021-05-23T13:50:14.000Z
[ "pytorch", "gpt2", "transformers" ]
null
false
ytlin
null
ytlin/35oote4t_52
1
null
transformers
30,534
Entry not found
ytlin/38hbj3w7_10
60ada85699b05a096f5c6918a0699efed17d891f
2021-05-23T13:50:35.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
ytlin
null
ytlin/38hbj3w7_10
1
null
transformers
30,535
Entry not found
ytlin/38hbj3w7_13
b068e56fd7fe244231ec2607a3959a73554fa8bc
2021-05-23T13:50:57.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
ytlin
null
ytlin/38hbj3w7_13
1
null
transformers
30,536
Entry not found
ytlin/q4b4siil
3e7ecdd7fc47f22e0db5175145cad0ed63500ad3
2021-05-23T13:52:22.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
ytlin
null
ytlin/q4b4siil
1
null
transformers
30,537
Entry not found
yuchenlin/BART0_CSR
d6dcfeac15e382e9ec7748557abefca9b118b0f9
2022-02-02T22:11:06.000Z
[ "pytorch", "bart", "text2text-generation", "en", "dataset:bigscience/P3", "transformers", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
yuchenlin
null
yuchenlin/BART0_CSR
1
null
transformers
30,538
--- datasets: - bigscience/P3 language: en license: apache-2.0 widget: - text: "A is the son's of B's uncle. What is the family relationship between A and B?" - text: "Reorder the words in this sentence: justin and name bieber years is my am I 27 old." - text: "Task: copy but say the opposite.\n PSG won its match against Barca." - text: "Is this review positive or negative? Review: Best cast iron skillet you will every buy." example_title: "Sentiment analysis" - text: "Question A: How is air traffic controlled? \nQuestion B: How do you become an air traffic controller?\nPick one: these questions are duplicates or not duplicates." - text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had foreign affairs experience as a former First Lady. \nIn the previous sentence, decide who 'her' is referring to." example_title: "Coreference resolution" - text: "Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.\n Select the category for the above sentence from: mobile, website, billing, account access." - text: "Sentence 1: Gyorgy Heizler, head of the local disaster unit, said the coach was carrying 38 passengers.\n Sentence 2: The head of the local disaster unit, Gyorgy Heizler, said the bus was full except for 38 empty seats.\n\n Do sentences 1 and 2 have the same meaning?" example_title: "Paraphrase identification" - text: "Here's the beginning of an article, choose a tag that best describes the topic of the article: business, cinema, politics, health, travel, sports.\n\n The best and worst fo 007 as 'No time to die' marks Daniel Craig's exit.\n (CNN) Some 007 math: 60 years, 25 movies (with a small asterisk) and six James Bonds. For a Cold War creation, Ian Fleming's suave spy has certainly gotten around, but despite different guises in the tuxedo and occasional scuba gear, when it comes to Bond ratings, there really shouldn't be much argument about who wore it best." - text: "Max: Know any good websites to buy clothes from?\n Payton: Sure :) LINK 1, LINK 2, LINK 3\n Max: That's a lot of them!\n Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.\n Max: I'll check them out. Thanks.\n\n Who or what are Payton and Max referring to when they say 'them'?" - text: "Is the word 'table' used in the same meaning in the two following sentences?\n\n Sentence A: you can leave the books on the table over there.\n Sentence B: the tables in this book are very hard to read." - text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.\n The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.\n\n Which book is the leftmost book?" example_title: "Logic puzzles" - text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night.\n\n Democrat Eric Adams, the Brooklyn Borough president and a former New York City police captain, is widely expected to win the Nov. 2 election against Republican Curtis Sliwa, the founder of the 1970s-era Guardian Angels anti-crime patril.\n\n Who are the men running for mayor?" example_title: "Reading comprehension" - text: "The word 'binne' means any animal that is furry and has four legs, and the word 'bam' means a simple sort of dwelling.\n\n Which of the following best characterizes binne bams?\n - Sentence 1: Binne bams are for pets.\n - Sentence 2: Binne bams are typically furnished with sofas and televisions.\n - Sentence 3: Binne bams are luxurious apartments.\n - Sentence 4: Binne bams are places where people live." --- TBA
yunpeng/bert_cn_finetuning
609ca07a5d75152c2fe7b9b1e451ba31b284ce8c
2021-11-02T14:50:06.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
yunpeng
null
yunpeng/bert_cn_finetuning
1
null
transformers
30,539
Entry not found
yxchar/tlm-ag-medium-scale
7dfb0969a055e4d937d8aa984e48174846dc19af
2021-11-04T10:54:14.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
yxchar
null
yxchar/tlm-ag-medium-scale
1
null
transformers
30,540
Entry not found
yxchar/tlm-amazon-medium-scale
0009bf0481abe57ad7cf7443de5c382f613d662b
2021-11-04T13:29:16.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
yxchar
null
yxchar/tlm-amazon-medium-scale
1
null
transformers
30,541
Entry not found
yxchar/tlm-chemprot-medium-scale
dd1be1d09f1be4f13cde92aa70cf3cd122d89978
2021-11-04T14:17:06.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
yxchar
null
yxchar/tlm-chemprot-medium-scale
1
null
transformers
30,542
Entry not found
yxchar/tlm-hyp-medium-scale
81178f9c020cccb1200f83f788ffc1477ce5f7cb
2021-11-04T15:30:55.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
yxchar
null
yxchar/tlm-hyp-medium-scale
1
null
transformers
30,543
Entry not found
yxchar/tlm-imdb-small-scale
049daff62c091f89a078d9cdc21a9db9346a25f1
2021-11-04T09:34:38.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
yxchar
null
yxchar/tlm-imdb-small-scale
1
null
transformers
30,544
Entry not found
yxchar/tlm-rct-20k-small-scale
98185626a895a2ba88528db1bea4c649978fb9d6
2021-11-04T17:13:06.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
yxchar
null
yxchar/tlm-rct-20k-small-scale
1
null
transformers
30,545
Entry not found
yxchar/tlm-sciie-large-scale
7ccffcf6cab1d841085e557fd28743f1921dc828
2021-11-04T16:27:51.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
yxchar
null
yxchar/tlm-sciie-large-scale
1
null
transformers
30,546
Entry not found
yzhou992/NetMind-20211104-781
978e11f554ed79c1a32c0e51493eed683a635c49
2021-11-04T08:38:46.000Z
[ "pytorch", "albert", "pretraining", "transformers" ]
null
false
yzhou992
null
yzhou992/NetMind-20211104-781
1
null
transformers
30,547
Entry not found
yzhou992/test_model
d1edfd479df2c603796eb99d3d24283af613271d
2021-11-02T08:45:27.000Z
[ "pytorch", "albert", "pretraining", "transformers" ]
null
false
yzhou992
null
yzhou992/test_model
1
null
transformers
30,548
Entry not found
zgotter/test
9b1166130e23653ba0650e10a3bf97913d284e01
2021-09-28T06:48:42.000Z
[ "pytorch", "bert", "transformers" ]
null
false
zgotter
null
zgotter/test
1
null
transformers
30,549
Entry not found
zhaoyang/BertFinetuning
20d1671bc6b0e1f82b0f5d2b97623fbcc933ebaa
2021-12-06T08:23:02.000Z
[ "pytorch", "tensorboard", "en", "dataset:glue", "generated_from_trainer", "license:apache-2.0", "model-index" ]
null
false
zhaoyang
null
zhaoyang/BertFinetuning
1
null
null
30,550
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: bert_finetunning results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8259803921568627 - name: F1 type: f1 value: 0.8786324786324787 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_finetunning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.4018 - Accuracy: 0.8260 - F1: 0.8786 - Combined Score: 0.8523 ## 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: 1.0 ### Training results ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.9.0 - Datasets 1.16.1 - Tokenizers 0.10.3
zharry29/goal_benchmark_roberta
9f5836a352ba46649e4ef56c7b2903242a1974a0
2021-05-20T23:25:11.000Z
[ "pytorch", "jax", "roberta", "multiple-choice", "transformers" ]
multiple-choice
false
zharry29
null
zharry29/goal_benchmark_roberta
1
null
transformers
30,551
Entry not found
zharry29/intent_fb-en_id_xlmr
eac20f22c77590d63f14b9550ab1c7714c3bbaf0
2021-05-20T23:30:29.000Z
[ "pytorch", "jax", "roberta", "multiple-choice", "transformers" ]
multiple-choice
false
zharry29
null
zharry29/intent_fb-en_id_xlmr
1
null
transformers
30,552
Entry not found
zharry29/intent_fb-es_id
9e0edf6da5bbb1b1cd54ffc14f04f5915ad968a3
2020-09-16T20:14:32.000Z
[ "pytorch", "xlm-roberta", "multiple-choice", "transformers" ]
multiple-choice
false
zharry29
null
zharry29/intent_fb-es_id
1
null
transformers
30,553
Entry not found
zharry29/intent_sgd_wh_id
e567c90d01f1a71f1e327bc87145917c5de18d6b
2021-05-20T23:38:40.000Z
[ "pytorch", "jax", "roberta", "multiple-choice", "transformers" ]
multiple-choice
false
zharry29
null
zharry29/intent_sgd_wh_id
1
null
transformers
30,554
Entry not found
zharry29/intent_thwh
385d612d9d712d369c7fbc53f0631a1b74e5a995
2020-09-16T20:44:55.000Z
[ "pytorch", "xlm-roberta", "multiple-choice", "transformers" ]
multiple-choice
false
zharry29
null
zharry29/intent_thwh
1
null
transformers
30,555
Entry not found
zharry29/order_benchmark_gpt
a2fbc6b71e5a8aaa14f8ec2e29ed0f6ad75ba73c
2021-05-23T14:09:14.000Z
[ "pytorch", "gpt2", "transformers" ]
null
false
zharry29
null
zharry29/order_benchmark_gpt
1
null
transformers
30,556
Entry not found
zharry29/order_benchmark_roberta
bebaea5c5ba2784ccc6127f99c199288a3fcc5ea
2021-05-20T23:51:12.000Z
[ "pytorch", "jax", "roberta", "multiple-choice", "transformers" ]
multiple-choice
false
zharry29
null
zharry29/order_benchmark_roberta
1
null
transformers
30,557
Entry not found
zhichao158/wav2vec2-xls-r-common_voice-tr-ft
3056c66e43b286734b51ea5a74980cc23daf9a4e
2022-01-14T07:03:32.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "tr", "dataset:common_voice", "transformers", "common_voice", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
zhichao158
null
zhichao158/wav2vec2-xls-r-common_voice-tr-ft
1
null
transformers
30,558
--- language: - tr license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-xls-r-common_voice-tr-ft 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-xls-r-common_voice-tr-ft This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.3736 - Wer: 0.2930 - Cer: 0.0708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 96 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 0.5462 | 13.51 | 500 | 0.4423 | 0.4807 | 0.1188 | | 0.342 | 27.03 | 1000 | 0.3781 | 0.3954 | 0.0967 | | 0.2272 | 40.54 | 1500 | 0.3816 | 0.3595 | 0.0893 | | 0.1805 | 54.05 | 2000 | 0.3943 | 0.3487 | 0.0854 | | 0.1318 | 67.57 | 2500 | 0.3818 | 0.3262 | 0.0801 | | 0.1213 | 81.08 | 3000 | 0.3777 | 0.3113 | 0.0758 | | 0.0639 | 94.59 | 3500 | 0.3788 | 0.2953 | 0.0716 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.8.0 - Datasets 1.17.0 - Tokenizers 0.10.3
zhizihuabai/ai12nlp
2db9febf0fe86e77e25dfaaf6fe1a0bbf58a3554
2022-02-11T03:13:31.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhizihuabai
null
zhizihuabai/ai12nlp
1
null
transformers
30,559
Entry not found
zhizihuabai/ai12one
00bcca8573fbea10203537874672fa64b5eb56fa
2022-02-11T10:18:28.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhizihuabai
null
zhizihuabai/ai12one
1
null
transformers
30,560
Entry not found
zhizihuabai/ai12two
99ba69ce3e306f0cbc4a405a3788081a75ba1c75
2022-02-12T03:04:37.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhizihuabai
null
zhizihuabai/ai12two
1
null
transformers
30,561
Entry not found
zhuqing/RoBERTa-large-uncased-exp2-parent
902edf36b08019260eab8c6ee6265b27ae153b9e
2021-08-28T16:28:58.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhuqing
null
zhuqing/RoBERTa-large-uncased-exp2-parent
1
null
transformers
30,562
Entry not found
zhuqing/bert-base-uncased-mumsnet-first-no859-1
2d5e43d4c01f60307af2bb9509089629e7a99c0f
2021-08-10T03:19:32.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhuqing
null
zhuqing/bert-base-uncased-mumsnet-first-no859-1
1
null
transformers
30,563
Entry not found
zhuqing/bert-base-uncased-mumsnet-first-no859-2
4a287f0d5fedbf9a2df42036957bcdb74ea3909d
2021-08-10T03:27:05.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhuqing
null
zhuqing/bert-base-uncased-mumsnet-first-no859-2
1
null
transformers
30,564
Entry not found
zhuqing/bert-base-uncased-netmums-parent-v2
fa00a29f754f6b0b2a243efe19e31407cf52d71c
2021-08-15T04:45:13.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhuqing
null
zhuqing/bert-base-uncased-netmums-parent-v2
1
null
transformers
30,565
Entry not found
zhuqing/bert-base-uncased-reddit-lib-v2
0856105f6bc2ecb47354d9a106427aa39e659170
2021-08-03T06:36:15.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhuqing
null
zhuqing/bert-base-uncased-reddit-lib-v2
1
null
transformers
30,566
Entry not found
zhuqing/bert-base-uncased-theme1-6000
edb3a6ac6107896e73378dff7976e8ba441d69ed
2021-07-31T17:19:02.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhuqing
null
zhuqing/bert-base-uncased-theme1-6000
1
null
transformers
30,567
Entry not found
zhuqing/bert-base-uncased-theme1
0f603f88e5521690daf65a6650fbe746a76fc332
2021-07-17T08:56:55.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhuqing
null
zhuqing/bert-base-uncased-theme1
1
null
transformers
30,568
Entry not found
zhuqing/bert-base-uncased-theme2-6000
f65b4066c66d2e3ef92c39bfc33d0fbc6f309e3f
2021-07-31T17:25:18.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhuqing
null
zhuqing/bert-base-uncased-theme2-6000
1
null
transformers
30,569
Entry not found
zhuqing/distilbert-uncased-exp2-parent
9dd0bebcd5b416bbed37a081c3ed2c5ffa37ec75
2021-08-29T07:07:38.000Z
[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhuqing
null
zhuqing/distilbert-uncased-exp2-parent
1
null
transformers
30,570
Entry not found
zhuqing/distilroberta-base-theme1-6000
acc101e842bee9fe3627fefb072b1d0f01aa8c55
2021-07-31T16:21:20.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhuqing
null
zhuqing/distilroberta-base-theme1-6000
1
null
transformers
30,571
Entry not found
zhuqing/roberta-base-uncased-all-intersection
d7bd2f81d2972a7ad50fa9f8895908758b447b56
2021-08-23T13:10:19.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zhuqing
null
zhuqing/roberta-base-uncased-all-intersection
1
null
transformers
30,572
Entry not found
ziqingyang/XLMRobertaBaseForXNLI-en
b7436f5e3b36095e1b6e2259c58203ebfd2996e6
2022-01-26T02:03:42.000Z
[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
ziqingyang
null
ziqingyang/XLMRobertaBaseForXNLI-en
1
null
transformers
30,573
--- license: apache-2.0 ---
zzecf/AI12
584eb753003bfc64fd9457315e7760cf196bb898
2022-02-10T12:39:01.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
zzecf
null
zzecf/AI12
1
null
transformers
30,574
Entry not found
wietsedv/xlm-roberta-base-ft-udpos28-eu
b937fd1ee2d9470b5475882bc4ee982c32178b7c
2022-02-25T09:58:23.000Z
[ "pytorch", "xlm-roberta", "token-classification", "eu", "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-eu
1
null
transformers
30,575
--- language: - eu 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-eu 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: 65.8 - type: accuracy name: Dutch Test accuracy value: 63.5 - type: accuracy name: German Test accuracy value: 66.3 - type: accuracy name: Italian Test accuracy value: 65.5 - type: accuracy name: French Test accuracy value: 61.2 - type: accuracy name: Spanish Test accuracy value: 62.0 - type: accuracy name: Russian Test accuracy value: 74.9 - type: accuracy name: Swedish Test accuracy value: 66.6 - type: accuracy name: Norwegian Test accuracy value: 61.8 - type: accuracy name: Danish Test accuracy value: 66.5 - type: accuracy name: Low Saxon Test accuracy value: 48.3 - type: accuracy name: Akkadian Test accuracy value: 40.9 - type: accuracy name: Armenian Test accuracy value: 80.8 - type: accuracy name: Welsh Test accuracy value: 53.5 - type: accuracy name: Old East Slavic Test accuracy value: 65.1 - type: accuracy name: Albanian Test accuracy value: 66.9 - type: accuracy name: Slovenian Test accuracy value: 67.3 - type: accuracy name: Guajajara Test accuracy value: 32.0 - type: accuracy name: Kurmanji Test accuracy value: 66.2 - type: accuracy name: Turkish Test accuracy value: 75.7 - type: accuracy name: Finnish Test accuracy value: 79.2 - type: accuracy name: Indonesian Test accuracy value: 71.5 - type: accuracy name: Ukrainian Test accuracy value: 74.6 - type: accuracy name: Polish Test accuracy value: 73.8 - type: accuracy name: Portuguese Test accuracy value: 69.5 - type: accuracy name: Kazakh Test accuracy value: 84.0 - type: accuracy name: Latin Test accuracy value: 68.1 - type: accuracy name: Old French Test accuracy value: 45.0 - type: accuracy name: Buryat Test accuracy value: 66.6 - type: accuracy name: Kaapor Test accuracy value: 27.9 - type: accuracy name: Korean Test accuracy value: 65.4 - type: accuracy name: Estonian Test accuracy value: 79.4 - type: accuracy name: Croatian Test accuracy value: 74.6 - type: accuracy name: Gothic Test accuracy value: 30.8 - type: accuracy name: Swiss German Test accuracy value: 41.3 - type: accuracy name: Assyrian Test accuracy value: 15.9 - type: accuracy name: North Sami Test accuracy value: 41.9 - type: accuracy name: Naija Test accuracy value: 37.4 - type: accuracy name: Latvian Test accuracy value: 79.8 - type: accuracy name: Chinese Test accuracy value: 46.9 - type: accuracy name: Tagalog Test accuracy value: 56.6 - type: accuracy name: Bambara Test accuracy value: 29.8 - type: accuracy name: Lithuanian Test accuracy value: 80.9 - type: accuracy name: Galician Test accuracy value: 68.7 - type: accuracy name: Vietnamese Test accuracy value: 63.8 - type: accuracy name: Greek Test accuracy value: 65.3 - type: accuracy name: Catalan Test accuracy value: 58.0 - type: accuracy name: Czech Test accuracy value: 74.0 - type: accuracy name: Erzya Test accuracy value: 49.4 - type: accuracy name: Bhojpuri Test accuracy value: 53.4 - type: accuracy name: Thai Test accuracy value: 53.1 - type: accuracy name: Marathi Test accuracy value: 78.5 - type: accuracy name: Basque Test accuracy value: 95.7 - type: accuracy name: Slovak Test accuracy value: 75.9 - type: accuracy name: Kiche Test accuracy value: 35.3 - type: accuracy name: Yoruba Test accuracy value: 28.4 - type: accuracy name: Warlpiri Test accuracy value: 43.3 - type: accuracy name: Tamil Test accuracy value: 86.5 - type: accuracy name: Maltese Test accuracy value: 35.5 - type: accuracy name: Ancient Greek Test accuracy value: 59.2 - type: accuracy name: Icelandic Test accuracy value: 65.2 - type: accuracy name: Mbya Guarani Test accuracy value: 35.4 - type: accuracy name: Urdu Test accuracy value: 64.4 - type: accuracy name: Romanian Test accuracy value: 68.9 - type: accuracy name: Persian Test accuracy value: 63.9 - type: accuracy name: Apurina Test accuracy value: 39.4 - type: accuracy name: Japanese Test accuracy value: 39.2 - type: accuracy name: Hungarian Test accuracy value: 69.6 - type: accuracy name: Hindi Test accuracy value: 68.7 - type: accuracy name: Classical Chinese Test accuracy value: 27.9 - type: accuracy name: Komi Permyak Test accuracy value: 52.0 - type: accuracy name: Faroese Test accuracy value: 62.5 - type: accuracy name: Sanskrit Test accuracy value: 40.8 - type: accuracy name: Livvi Test accuracy value: 65.8 - type: accuracy name: Arabic Test accuracy value: 63.5 - type: accuracy name: Wolof Test accuracy value: 37.6 - type: accuracy name: Bulgarian Test accuracy value: 68.8 - type: accuracy name: Akuntsu Test accuracy value: 41.1 - type: accuracy name: Makurap Test accuracy value: 24.0 - type: accuracy name: Kangri Test accuracy value: 54.3 - type: accuracy name: Breton Test accuracy value: 52.9 - type: accuracy name: Telugu Test accuracy value: 82.4 - type: accuracy name: Cantonese Test accuracy value: 49.0 - type: accuracy name: Old Church Slavonic Test accuracy value: 46.7 - type: accuracy name: Karelian Test accuracy value: 71.1 - type: accuracy name: Upper Sorbian Test accuracy value: 65.9 - type: accuracy name: South Levantine Arabic Test accuracy value: 61.3 - type: accuracy name: Komi Zyrian Test accuracy value: 47.2 - type: accuracy name: Irish Test accuracy value: 53.7 - type: accuracy name: Nayini Test accuracy value: 41.0 - type: accuracy name: Munduruku Test accuracy value: 26.4 - type: accuracy name: Manx Test accuracy value: 33.3 - type: accuracy name: Skolt Sami Test accuracy value: 45.5 - type: accuracy name: Afrikaans Test accuracy value: 61.2 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 44.8 - type: accuracy name: Belarusian Test accuracy value: 74.6 - type: accuracy name: Serbian Test accuracy value: 74.5 - type: accuracy name: Moksha Test accuracy value: 46.1 - type: accuracy name: Western Armenian Test accuracy value: 77.4 - type: accuracy name: Scottish Gaelic Test accuracy value: 48.8 - type: accuracy name: Khunsari Test accuracy value: 39.2 - type: accuracy name: Hebrew Test accuracy value: 80.2 - type: accuracy name: Uyghur Test accuracy value: 75.3 - type: accuracy name: Chukchi Test accuracy value: 41.2 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Basque 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-eu") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-eu") ```
wietsedv/xlm-roberta-base-ft-udpos28-fo
bf32dcefc6c49a30d6b9b14f4d0ca2f3cadbfb88
2022-02-25T09:58:28.000Z
[ "pytorch", "xlm-roberta", "token-classification", "fo", "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-fo
1
null
transformers
30,576
--- language: - fo 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-fo 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: 86.4 - type: accuracy name: Dutch Test accuracy value: 83.2 - 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: 80.6 - type: accuracy name: Spanish Test accuracy value: 83.4 - type: accuracy name: Russian Test accuracy value: 83.6 - type: accuracy name: Swedish Test accuracy value: 87.3 - type: accuracy name: Norwegian Test accuracy value: 83.9 - type: accuracy name: Danish Test accuracy value: 87.5 - type: accuracy name: Low Saxon Test accuracy value: 58.9 - type: accuracy name: Akkadian Test accuracy value: 32.9 - type: accuracy name: Armenian Test accuracy value: 81.2 - type: accuracy name: Welsh Test accuracy value: 66.8 - type: accuracy name: Old East Slavic Test accuracy value: 75.4 - type: accuracy name: Albanian Test accuracy value: 72.5 - type: accuracy name: Slovenian Test accuracy value: 74.9 - type: accuracy name: Guajajara Test accuracy value: 34.2 - type: accuracy name: Kurmanji Test accuracy value: 72.8 - type: accuracy name: Turkish Test accuracy value: 74.0 - type: accuracy name: Finnish Test accuracy value: 81.9 - type: accuracy name: Indonesian Test accuracy value: 79.8 - type: accuracy name: Ukrainian Test accuracy value: 82.0 - type: accuracy name: Polish Test accuracy value: 82.1 - type: accuracy name: Portuguese Test accuracy value: 84.3 - type: accuracy name: Kazakh Test accuracy value: 78.3 - type: accuracy name: Latin Test accuracy value: 75.4 - type: accuracy name: Old French Test accuracy value: 63.5 - type: accuracy name: Buryat Test accuracy value: 60.8 - type: accuracy name: Kaapor Test accuracy value: 28.8 - type: accuracy name: Korean Test accuracy value: 61.5 - type: accuracy name: Estonian Test accuracy value: 83.9 - type: accuracy name: Croatian Test accuracy value: 82.2 - type: accuracy name: Gothic Test accuracy value: 34.2 - type: accuracy name: Swiss German Test accuracy value: 51.9 - type: accuracy name: Assyrian Test accuracy value: 21.6 - type: accuracy name: North Sami Test accuracy value: 46.5 - type: accuracy name: Naija Test accuracy value: 44.0 - type: accuracy name: Latvian Test accuracy value: 83.2 - type: accuracy name: Chinese Test accuracy value: 44.9 - type: accuracy name: Tagalog Test accuracy value: 76.1 - type: accuracy name: Bambara Test accuracy value: 30.5 - type: accuracy name: Lithuanian Test accuracy value: 83.2 - type: accuracy name: Galician Test accuracy value: 79.1 - type: accuracy name: Vietnamese Test accuracy value: 63.0 - type: accuracy name: Greek Test accuracy value: 77.4 - type: accuracy name: Catalan Test accuracy value: 81.4 - type: accuracy name: Czech Test accuracy value: 81.0 - type: accuracy name: Erzya Test accuracy value: 50.8 - type: accuracy name: Bhojpuri Test accuracy value: 54.9 - type: accuracy name: Thai Test accuracy value: 60.7 - type: accuracy name: Marathi Test accuracy value: 81.0 - type: accuracy name: Basque Test accuracy value: 75.4 - type: accuracy name: Slovak Test accuracy value: 81.3 - type: accuracy name: Kiche Test accuracy value: 37.5 - type: accuracy name: Yoruba Test accuracy value: 33.7 - type: accuracy name: Warlpiri Test accuracy value: 41.3 - type: accuracy name: Tamil Test accuracy value: 75.2 - type: accuracy name: Maltese Test accuracy value: 32.9 - type: accuracy name: Ancient Greek Test accuracy value: 64.4 - type: accuracy name: Icelandic Test accuracy value: 86.5 - type: accuracy name: Mbya Guarani Test accuracy value: 32.7 - type: accuracy name: Urdu Test accuracy value: 69.2 - type: accuracy name: Romanian Test accuracy value: 80.3 - type: accuracy name: Persian Test accuracy value: 75.2 - type: accuracy name: Apurina Test accuracy value: 47.1 - type: accuracy name: Japanese Test accuracy value: 37.5 - type: accuracy name: Hungarian Test accuracy value: 73.6 - type: accuracy name: Hindi Test accuracy value: 70.7 - type: accuracy name: Classical Chinese Test accuracy value: 29.1 - type: accuracy name: Komi Permyak Test accuracy value: 54.2 - type: accuracy name: Faroese Test accuracy value: 91.4 - type: accuracy name: Sanskrit Test accuracy value: 35.1 - type: accuracy name: Livvi Test accuracy value: 65.6 - type: accuracy name: Arabic Test accuracy value: 73.9 - type: accuracy name: Wolof Test accuracy value: 36.7 - type: accuracy name: Bulgarian Test accuracy value: 85.2 - type: accuracy name: Akuntsu Test accuracy value: 24.9 - type: accuracy name: Makurap Test accuracy value: 20.5 - type: accuracy name: Kangri Test accuracy value: 50.0 - type: accuracy name: Breton Test accuracy value: 64.4 - type: accuracy name: Telugu Test accuracy value: 82.8 - type: accuracy name: Cantonese Test accuracy value: 50.7 - type: accuracy name: Old Church Slavonic Test accuracy value: 56.5 - type: accuracy name: Karelian Test accuracy value: 70.2 - type: accuracy name: Upper Sorbian Test accuracy value: 72.9 - type: accuracy name: South Levantine Arabic Test accuracy value: 69.3 - type: accuracy name: Komi Zyrian Test accuracy value: 46.2 - type: accuracy name: Irish Test accuracy value: 63.1 - type: accuracy name: Nayini Test accuracy value: 47.4 - type: accuracy name: Munduruku Test accuracy value: 20.9 - type: accuracy name: Manx Test accuracy value: 40.1 - type: accuracy name: Skolt Sami Test accuracy value: 42.6 - type: accuracy name: Afrikaans Test accuracy value: 84.3 - type: accuracy name: Old Turkish Test accuracy value: 38.0 - type: accuracy name: Tupinamba Test accuracy value: 40.9 - type: accuracy name: Belarusian Test accuracy value: 82.1 - type: accuracy name: Serbian Test accuracy value: 82.3 - type: accuracy name: Moksha Test accuracy value: 48.5 - type: accuracy name: Western Armenian Test accuracy value: 80.0 - type: accuracy name: Scottish Gaelic Test accuracy value: 59.4 - type: accuracy name: Khunsari Test accuracy value: 44.6 - type: accuracy name: Hebrew Test accuracy value: 80.2 - type: accuracy name: Uyghur Test accuracy value: 72.8 - type: accuracy name: Chukchi Test accuracy value: 41.0 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Faroese 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-fo") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fo") ```
wietsedv/xlm-roberta-base-ft-udpos28-gd
dc305bc9674ce76729e04825fa76475785c38082
2022-02-25T09:58:34.000Z
[ "pytorch", "xlm-roberta", "token-classification", "gd", "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-gd
1
null
transformers
30,577
--- language: - gd 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-gd 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: 75.0 - type: accuracy name: Dutch Test accuracy value: 77.8 - type: accuracy name: German Test accuracy value: 76.5 - type: accuracy name: Italian Test accuracy value: 70.8 - type: accuracy name: French Test accuracy value: 74.6 - type: accuracy name: Spanish Test accuracy value: 78.7 - type: accuracy name: Russian Test accuracy value: 79.2 - type: accuracy name: Swedish Test accuracy value: 78.9 - type: accuracy name: Norwegian Test accuracy value: 72.7 - type: accuracy name: Danish Test accuracy value: 78.0 - type: accuracy name: Low Saxon Test accuracy value: 51.0 - type: accuracy name: Akkadian Test accuracy value: 47.0 - type: accuracy name: Armenian Test accuracy value: 69.2 - type: accuracy name: Welsh Test accuracy value: 77.0 - type: accuracy name: Old East Slavic Test accuracy value: 70.1 - type: accuracy name: Albanian Test accuracy value: 76.1 - type: accuracy name: Slovenian Test accuracy value: 64.3 - type: accuracy name: Guajajara Test accuracy value: 42.6 - type: accuracy name: Kurmanji Test accuracy value: 73.6 - type: accuracy name: Turkish Test accuracy value: 71.7 - type: accuracy name: Finnish Test accuracy value: 74.4 - type: accuracy name: Indonesian Test accuracy value: 74.2 - type: accuracy name: Ukrainian Test accuracy value: 78.7 - type: accuracy name: Polish Test accuracy value: 81.4 - type: accuracy name: Portuguese Test accuracy value: 77.9 - type: accuracy name: Kazakh Test accuracy value: 73.3 - type: accuracy name: Latin Test accuracy value: 68.8 - type: accuracy name: Old French Test accuracy value: 48.7 - type: accuracy name: Buryat Test accuracy value: 58.4 - type: accuracy name: Kaapor Test accuracy value: 24.6 - type: accuracy name: Korean Test accuracy value: 58.9 - type: accuracy name: Estonian Test accuracy value: 76.8 - type: accuracy name: Croatian Test accuracy value: 74.0 - type: accuracy name: Gothic Test accuracy value: 29.4 - type: accuracy name: Swiss German Test accuracy value: 48.3 - type: accuracy name: Assyrian Test accuracy value: 20.1 - type: accuracy name: North Sami Test accuracy value: 44.3 - type: accuracy name: Naija Test accuracy value: 40.4 - type: accuracy name: Latvian Test accuracy value: 76.7 - type: accuracy name: Chinese Test accuracy value: 51.6 - type: accuracy name: Tagalog Test accuracy value: 68.3 - type: accuracy name: Bambara Test accuracy value: 30.3 - type: accuracy name: Lithuanian Test accuracy value: 77.2 - type: accuracy name: Galician Test accuracy value: 77.6 - type: accuracy name: Vietnamese Test accuracy value: 56.5 - type: accuracy name: Greek Test accuracy value: 79.1 - type: accuracy name: Catalan Test accuracy value: 74.5 - type: accuracy name: Czech Test accuracy value: 78.7 - type: accuracy name: Erzya Test accuracy value: 51.6 - type: accuracy name: Bhojpuri Test accuracy value: 49.4 - type: accuracy name: Thai Test accuracy value: 57.1 - type: accuracy name: Marathi Test accuracy value: 72.4 - type: accuracy name: Basque Test accuracy value: 65.9 - type: accuracy name: Slovak Test accuracy value: 80.3 - type: accuracy name: Kiche Test accuracy value: 45.0 - type: accuracy name: Yoruba Test accuracy value: 32.5 - type: accuracy name: Warlpiri Test accuracy value: 43.7 - type: accuracy name: Tamil Test accuracy value: 76.7 - type: accuracy name: Maltese Test accuracy value: 34.9 - type: accuracy name: Ancient Greek Test accuracy value: 59.3 - type: accuracy name: Icelandic Test accuracy value: 73.1 - type: accuracy name: Mbya Guarani Test accuracy value: 34.5 - type: accuracy name: Urdu Test accuracy value: 56.0 - type: accuracy name: Romanian Test accuracy value: 74.4 - type: accuracy name: Persian Test accuracy value: 77.3 - type: accuracy name: Apurina Test accuracy value: 48.4 - type: accuracy name: Japanese Test accuracy value: 38.6 - type: accuracy name: Hungarian Test accuracy value: 78.5 - type: accuracy name: Hindi Test accuracy value: 60.5 - type: accuracy name: Classical Chinese Test accuracy value: 31.6 - type: accuracy name: Komi Permyak Test accuracy value: 50.4 - type: accuracy name: Faroese Test accuracy value: 71.2 - type: accuracy name: Sanskrit Test accuracy value: 33.5 - type: accuracy name: Livvi Test accuracy value: 61.6 - type: accuracy name: Arabic Test accuracy value: 81.6 - type: accuracy name: Wolof Test accuracy value: 38.1 - type: accuracy name: Bulgarian Test accuracy value: 76.6 - type: accuracy name: Akuntsu Test accuracy value: 39.8 - type: accuracy name: Makurap Test accuracy value: 23.3 - type: accuracy name: Kangri Test accuracy value: 44.0 - type: accuracy name: Breton Test accuracy value: 60.9 - type: accuracy name: Telugu Test accuracy value: 74.5 - type: accuracy name: Cantonese Test accuracy value: 48.9 - type: accuracy name: Old Church Slavonic Test accuracy value: 47.7 - type: accuracy name: Karelian Test accuracy value: 65.4 - type: accuracy name: Upper Sorbian Test accuracy value: 70.9 - type: accuracy name: South Levantine Arabic Test accuracy value: 68.4 - type: accuracy name: Komi Zyrian Test accuracy value: 45.0 - type: accuracy name: Irish Test accuracy value: 76.6 - type: accuracy name: Nayini Test accuracy value: 44.9 - type: accuracy name: Munduruku Test accuracy value: 34.0 - type: accuracy name: Manx Test accuracy value: 52.0 - type: accuracy name: Skolt Sami Test accuracy value: 39.7 - type: accuracy name: Afrikaans Test accuracy value: 74.0 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 48.1 - type: accuracy name: Belarusian Test accuracy value: 79.7 - type: accuracy name: Serbian Test accuracy value: 72.7 - type: accuracy name: Moksha Test accuracy value: 49.3 - type: accuracy name: Western Armenian Test accuracy value: 68.1 - type: accuracy name: Scottish Gaelic Test accuracy value: 93.3 - type: accuracy name: Khunsari Test accuracy value: 44.6 - type: accuracy name: Hebrew Test accuracy value: 86.5 - type: accuracy name: Uyghur Test accuracy value: 67.5 - type: accuracy name: Chukchi Test accuracy value: 38.8 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Scottish Gaelic 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-gd") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-gd") ```
wietsedv/xlm-roberta-base-ft-udpos28-got
b16275a5c0341834f0748a0b6f4703dd9127c6f9
2022-02-25T09:58:37.000Z
[ "pytorch", "xlm-roberta", "token-classification", "got", "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-got
1
null
transformers
30,578
--- language: - got 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-got 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: 47.9 - type: accuracy name: Dutch Test accuracy value: 50.2 - type: accuracy name: German Test accuracy value: 38.9 - type: accuracy name: Italian Test accuracy value: 46.8 - type: accuracy name: French Test accuracy value: 50.2 - type: accuracy name: Spanish Test accuracy value: 51.3 - type: accuracy name: Russian Test accuracy value: 52.4 - type: accuracy name: Swedish Test accuracy value: 51.5 - type: accuracy name: Norwegian Test accuracy value: 49.1 - type: accuracy name: Danish Test accuracy value: 50.8 - type: accuracy name: Low Saxon Test accuracy value: 32.8 - type: accuracy name: Akkadian Test accuracy value: 43.8 - type: accuracy name: Armenian Test accuracy value: 50.4 - type: accuracy name: Welsh Test accuracy value: 41.1 - type: accuracy name: Old East Slavic Test accuracy value: 53.9 - type: accuracy name: Albanian Test accuracy value: 49.0 - type: accuracy name: Slovenian Test accuracy value: 45.3 - type: accuracy name: Guajajara Test accuracy value: 23.8 - type: accuracy name: Kurmanji Test accuracy value: 49.3 - type: accuracy name: Turkish Test accuracy value: 46.6 - type: accuracy name: Finnish Test accuracy value: 51.2 - type: accuracy name: Indonesian Test accuracy value: 55.4 - type: accuracy name: Ukrainian Test accuracy value: 50.0 - type: accuracy name: Polish Test accuracy value: 52.4 - type: accuracy name: Portuguese Test accuracy value: 50.4 - type: accuracy name: Kazakh Test accuracy value: 46.5 - type: accuracy name: Latin Test accuracy value: 49.1 - type: accuracy name: Old French Test accuracy value: 47.6 - type: accuracy name: Buryat Test accuracy value: 37.4 - type: accuracy name: Kaapor Test accuracy value: 33.8 - type: accuracy name: Korean Test accuracy value: 41.5 - type: accuracy name: Estonian Test accuracy value: 49.5 - type: accuracy name: Croatian Test accuracy value: 57.2 - type: accuracy name: Gothic Test accuracy value: 93.6 - type: accuracy name: Swiss German Test accuracy value: 25.1 - type: accuracy name: Assyrian Test accuracy value: 4.0 - type: accuracy name: North Sami Test accuracy value: 27.9 - type: accuracy name: Naija Test accuracy value: 29.2 - type: accuracy name: Latvian Test accuracy value: 51.5 - type: accuracy name: Chinese Test accuracy value: 16.4 - type: accuracy name: Tagalog Test accuracy value: 42.0 - type: accuracy name: Bambara Test accuracy value: 13.1 - type: accuracy name: Lithuanian Test accuracy value: 50.5 - type: accuracy name: Galician Test accuracy value: 49.2 - type: accuracy name: Vietnamese Test accuracy value: 47.1 - type: accuracy name: Greek Test accuracy value: 42.0 - type: accuracy name: Catalan Test accuracy value: 50.1 - type: accuracy name: Czech Test accuracy value: 54.3 - type: accuracy name: Erzya Test accuracy value: 22.1 - type: accuracy name: Bhojpuri Test accuracy value: 38.8 - type: accuracy name: Thai Test accuracy value: 34.7 - type: accuracy name: Marathi Test accuracy value: 35.0 - type: accuracy name: Basque Test accuracy value: 45.9 - type: accuracy name: Slovak Test accuracy value: 55.3 - type: accuracy name: Kiche Test accuracy value: 23.3 - type: accuracy name: Yoruba Test accuracy value: 15.0 - type: accuracy name: Warlpiri Test accuracy value: 23.5 - type: accuracy name: Tamil Test accuracy value: 41.1 - type: accuracy name: Maltese Test accuracy value: 21.4 - type: accuracy name: Ancient Greek Test accuracy value: 50.9 - type: accuracy name: Icelandic Test accuracy value: 50.3 - type: accuracy name: Mbya Guarani Test accuracy value: 14.8 - type: accuracy name: Urdu Test accuracy value: 41.4 - type: accuracy name: Romanian Test accuracy value: 50.1 - type: accuracy name: Persian Test accuracy value: 53.1 - type: accuracy name: Apurina Test accuracy value: 20.8 - type: accuracy name: Japanese Test accuracy value: 16.3 - type: accuracy name: Hungarian Test accuracy value: 42.3 - type: accuracy name: Hindi Test accuracy value: 45.2 - type: accuracy name: Classical Chinese Test accuracy value: 19.6 - type: accuracy name: Komi Permyak Test accuracy value: 23.4 - type: accuracy name: Faroese Test accuracy value: 48.9 - type: accuracy name: Sanskrit Test accuracy value: 32.4 - type: accuracy name: Livvi Test accuracy value: 38.5 - type: accuracy name: Arabic Test accuracy value: 49.6 - type: accuracy name: Wolof Test accuracy value: 28.4 - type: accuracy name: Bulgarian Test accuracy value: 55.6 - type: accuracy name: Akuntsu Test accuracy value: 25.2 - type: accuracy name: Makurap Test accuracy value: 18.5 - type: accuracy name: Kangri Test accuracy value: 34.2 - type: accuracy name: Breton Test accuracy value: 36.7 - type: accuracy name: Telugu Test accuracy value: 38.8 - type: accuracy name: Cantonese Test accuracy value: 17.1 - type: accuracy name: Old Church Slavonic Test accuracy value: 50.2 - type: accuracy name: Karelian Test accuracy value: 41.7 - type: accuracy name: Upper Sorbian Test accuracy value: 42.7 - type: accuracy name: South Levantine Arabic Test accuracy value: 38.9 - type: accuracy name: Komi Zyrian Test accuracy value: 21.1 - type: accuracy name: Irish Test accuracy value: 37.2 - type: accuracy name: Nayini Test accuracy value: 33.3 - type: accuracy name: Munduruku Test accuracy value: 26.6 - type: accuracy name: Manx Test accuracy value: 17.6 - type: accuracy name: Skolt Sami Test accuracy value: 19.9 - type: accuracy name: Afrikaans Test accuracy value: 45.9 - type: accuracy name: Old Turkish Test accuracy value: 2.7 - type: accuracy name: Tupinamba Test accuracy value: 23.4 - type: accuracy name: Belarusian Test accuracy value: 53.0 - type: accuracy name: Serbian Test accuracy value: 57.4 - type: accuracy name: Moksha Test accuracy value: 24.5 - type: accuracy name: Western Armenian Test accuracy value: 47.2 - type: accuracy name: Scottish Gaelic Test accuracy value: 36.7 - type: accuracy name: Khunsari Test accuracy value: 28.4 - type: accuracy name: Hebrew Test accuracy value: 44.8 - type: accuracy name: Uyghur Test accuracy value: 48.6 - type: accuracy name: Chukchi Test accuracy value: 21.0 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Gothic 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-got") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-got") ```
wietsedv/xlm-roberta-base-ft-udpos28-grc
4ab6d1a5a67008b56dcf449292544f39682cbaea
2022-02-25T09:58:39.000Z
[ "pytorch", "xlm-roberta", "token-classification", "grc", "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-grc
1
null
transformers
30,579
--- language: - grc 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-grc 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: 58.3 - type: accuracy name: Dutch Test accuracy value: 57.1 - type: accuracy name: German Test accuracy value: 61.3 - type: accuracy name: Italian Test accuracy value: 56.6 - type: accuracy name: French Test accuracy value: 57.3 - type: accuracy name: Spanish Test accuracy value: 54.5 - type: accuracy name: Russian Test accuracy value: 71.1 - type: accuracy name: Swedish Test accuracy value: 62.9 - type: accuracy name: Norwegian Test accuracy value: 59.9 - type: accuracy name: Danish Test accuracy value: 61.6 - type: accuracy name: Low Saxon Test accuracy value: 45.3 - type: accuracy name: Akkadian Test accuracy value: 38.9 - type: accuracy name: Armenian Test accuracy value: 69.4 - type: accuracy name: Welsh Test accuracy value: 57.9 - type: accuracy name: Old East Slavic Test accuracy value: 68.0 - type: accuracy name: Albanian Test accuracy value: 63.3 - type: accuracy name: Slovenian Test accuracy value: 58.2 - type: accuracy name: Guajajara Test accuracy value: 26.5 - type: accuracy name: Kurmanji Test accuracy value: 62.0 - type: accuracy name: Turkish Test accuracy value: 66.5 - type: accuracy name: Finnish Test accuracy value: 70.3 - type: accuracy name: Indonesian Test accuracy value: 59.7 - type: accuracy name: Ukrainian Test accuracy value: 72.6 - type: accuracy name: Polish Test accuracy value: 70.3 - type: accuracy name: Portuguese Test accuracy value: 59.7 - type: accuracy name: Kazakh Test accuracy value: 71.0 - type: accuracy name: Latin Test accuracy value: 68.8 - type: accuracy name: Old French Test accuracy value: 49.4 - type: accuracy name: Buryat Test accuracy value: 56.4 - type: accuracy name: Kaapor Test accuracy value: 27.9 - type: accuracy name: Korean Test accuracy value: 55.5 - type: accuracy name: Estonian Test accuracy value: 70.0 - type: accuracy name: Croatian Test accuracy value: 64.8 - type: accuracy name: Gothic Test accuracy value: 33.9 - type: accuracy name: Swiss German Test accuracy value: 47.2 - type: accuracy name: Assyrian Test accuracy value: 29.1 - type: accuracy name: North Sami Test accuracy value: 37.4 - type: accuracy name: Naija Test accuracy value: 37.2 - type: accuracy name: Latvian Test accuracy value: 74.5 - type: accuracy name: Chinese Test accuracy value: 56.6 - type: accuracy name: Tagalog Test accuracy value: 57.6 - type: accuracy name: Bambara Test accuracy value: 28.6 - type: accuracy name: Lithuanian Test accuracy value: 77.4 - type: accuracy name: Galician Test accuracy value: 61.6 - type: accuracy name: Vietnamese Test accuracy value: 63.7 - type: accuracy name: Greek Test accuracy value: 63.3 - type: accuracy name: Catalan Test accuracy value: 54.2 - type: accuracy name: Czech Test accuracy value: 70.1 - type: accuracy name: Erzya Test accuracy value: 46.7 - type: accuracy name: Bhojpuri Test accuracy value: 43.7 - type: accuracy name: Thai Test accuracy value: 61.1 - type: accuracy name: Marathi Test accuracy value: 75.5 - type: accuracy name: Basque Test accuracy value: 63.3 - type: accuracy name: Slovak Test accuracy value: 67.3 - type: accuracy name: Kiche Test accuracy value: 29.7 - type: accuracy name: Yoruba Test accuracy value: 30.4 - type: accuracy name: Warlpiri Test accuracy value: 49.4 - type: accuracy name: Tamil Test accuracy value: 68.7 - type: accuracy name: Maltese Test accuracy value: 29.6 - type: accuracy name: Ancient Greek Test accuracy value: 89.6 - type: accuracy name: Icelandic Test accuracy value: 63.6 - type: accuracy name: Mbya Guarani Test accuracy value: 36.4 - type: accuracy name: Urdu Test accuracy value: 44.8 - type: accuracy name: Romanian Test accuracy value: 66.3 - type: accuracy name: Persian Test accuracy value: 64.4 - type: accuracy name: Apurina Test accuracy value: 41.7 - type: accuracy name: Japanese Test accuracy value: 44.3 - type: accuracy name: Hungarian Test accuracy value: 61.4 - type: accuracy name: Hindi Test accuracy value: 47.8 - type: accuracy name: Classical Chinese Test accuracy value: 48.0 - type: accuracy name: Komi Permyak Test accuracy value: 45.9 - type: accuracy name: Faroese Test accuracy value: 59.2 - type: accuracy name: Sanskrit Test accuracy value: 42.9 - type: accuracy name: Livvi Test accuracy value: 61.8 - type: accuracy name: Arabic Test accuracy value: 65.3 - type: accuracy name: Wolof Test accuracy value: 27.8 - type: accuracy name: Bulgarian Test accuracy value: 64.9 - type: accuracy name: Akuntsu Test accuracy value: 30.8 - type: accuracy name: Makurap Test accuracy value: 18.5 - type: accuracy name: Kangri Test accuracy value: 45.9 - type: accuracy name: Breton Test accuracy value: 47.1 - type: accuracy name: Telugu Test accuracy value: 75.3 - type: accuracy name: Cantonese Test accuracy value: 60.2 - type: accuracy name: Old Church Slavonic Test accuracy value: 58.8 - type: accuracy name: Karelian Test accuracy value: 64.5 - type: accuracy name: Upper Sorbian Test accuracy value: 62.9 - type: accuracy name: South Levantine Arabic Test accuracy value: 61.7 - type: accuracy name: Komi Zyrian Test accuracy value: 45.4 - type: accuracy name: Irish Test accuracy value: 52.4 - type: accuracy name: Nayini Test accuracy value: 51.3 - type: accuracy name: Munduruku Test accuracy value: 21.6 - type: accuracy name: Manx Test accuracy value: 27.1 - type: accuracy name: Skolt Sami Test accuracy value: 44.7 - type: accuracy name: Afrikaans Test accuracy value: 58.4 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 44.4 - type: accuracy name: Belarusian Test accuracy value: 75.3 - type: accuracy name: Serbian Test accuracy value: 63.3 - type: accuracy name: Moksha Test accuracy value: 46.1 - type: accuracy name: Western Armenian Test accuracy value: 67.1 - type: accuracy name: Scottish Gaelic Test accuracy value: 49.2 - type: accuracy name: Khunsari Test accuracy value: 45.9 - type: accuracy name: Hebrew Test accuracy value: 72.9 - type: accuracy name: Uyghur Test accuracy value: 72.7 - type: accuracy name: Chukchi Test accuracy value: 40.2 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Ancient 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-grc") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-grc") ```
wietsedv/xlm-roberta-base-ft-udpos28-hu
77d78b6bda952043e844e8db14d2a5b1f491a21f
2022-02-25T09:58:45.000Z
[ "pytorch", "xlm-roberta", "token-classification", "hu", "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-hu
1
null
transformers
30,580
--- language: - hu 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-hu 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: 77.0 - type: accuracy name: Dutch Test accuracy value: 77.0 - type: accuracy name: German Test accuracy value: 77.0 - type: accuracy name: Italian Test accuracy value: 77.6 - type: accuracy name: French Test accuracy value: 75.9 - type: accuracy name: Spanish Test accuracy value: 76.1 - type: accuracy name: Russian Test accuracy value: 78.7 - type: accuracy name: Swedish Test accuracy value: 78.9 - type: accuracy name: Norwegian Test accuracy value: 74.6 - type: accuracy name: Danish Test accuracy value: 77.7 - type: accuracy name: Low Saxon Test accuracy value: 55.5 - type: accuracy name: Akkadian Test accuracy value: 31.1 - type: accuracy name: Armenian Test accuracy value: 85.7 - type: accuracy name: Welsh Test accuracy value: 54.9 - type: accuracy name: Old East Slavic Test accuracy value: 65.6 - type: accuracy name: Albanian Test accuracy value: 80.0 - type: accuracy name: Slovenian Test accuracy value: 71.9 - type: accuracy name: Guajajara Test accuracy value: 23.6 - type: accuracy name: Kurmanji Test accuracy value: 70.0 - type: accuracy name: Turkish Test accuracy value: 80.4 - type: accuracy name: Finnish Test accuracy value: 85.1 - type: accuracy name: Indonesian Test accuracy value: 76.6 - type: accuracy name: Ukrainian Test accuracy value: 78.5 - type: accuracy name: Polish Test accuracy value: 77.9 - type: accuracy name: Portuguese Test accuracy value: 79.1 - type: accuracy name: Kazakh Test accuracy value: 80.9 - type: accuracy name: Latin Test accuracy value: 71.3 - type: accuracy name: Old French Test accuracy value: 55.1 - type: accuracy name: Buryat Test accuracy value: 62.2 - type: accuracy name: Kaapor Test accuracy value: 22.1 - type: accuracy name: Korean Test accuracy value: 59.1 - type: accuracy name: Estonian Test accuracy value: 87.6 - type: accuracy name: Croatian Test accuracy value: 78.9 - type: accuracy name: Gothic Test accuracy value: 25.6 - type: accuracy name: Swiss German Test accuracy value: 45.7 - type: accuracy name: Assyrian Test accuracy value: 16.3 - type: accuracy name: North Sami Test accuracy value: 44.7 - type: accuracy name: Naija Test accuracy value: 39.3 - type: accuracy name: Latvian Test accuracy value: 81.8 - type: accuracy name: Chinese Test accuracy value: 40.9 - type: accuracy name: Tagalog Test accuracy value: 63.9 - type: accuracy name: Bambara Test accuracy value: 27.0 - type: accuracy name: Lithuanian Test accuracy value: 79.7 - type: accuracy name: Galician Test accuracy value: 77.4 - type: accuracy name: Vietnamese Test accuracy value: 59.9 - type: accuracy name: Greek Test accuracy value: 79.2 - type: accuracy name: Catalan Test accuracy value: 76.1 - type: accuracy name: Czech Test accuracy value: 79.0 - type: accuracy name: Erzya Test accuracy value: 50.9 - type: accuracy name: Bhojpuri Test accuracy value: 53.1 - type: accuracy name: Thai Test accuracy value: 45.2 - type: accuracy name: Marathi Test accuracy value: 87.1 - type: accuracy name: Basque Test accuracy value: 73.7 - type: accuracy name: Slovak Test accuracy value: 78.7 - type: accuracy name: Kiche Test accuracy value: 33.5 - type: accuracy name: Yoruba Test accuracy value: 28.0 - type: accuracy name: Warlpiri Test accuracy value: 33.2 - type: accuracy name: Tamil Test accuracy value: 82.7 - type: accuracy name: Maltese Test accuracy value: 29.6 - type: accuracy name: Ancient Greek Test accuracy value: 55.9 - type: accuracy name: Icelandic Test accuracy value: 73.5 - type: accuracy name: Mbya Guarani Test accuracy value: 33.3 - type: accuracy name: Urdu Test accuracy value: 69.4 - type: accuracy name: Romanian Test accuracy value: 72.4 - type: accuracy name: Persian Test accuracy value: 69.2 - type: accuracy name: Apurina Test accuracy value: 38.4 - type: accuracy name: Japanese Test accuracy value: 30.2 - type: accuracy name: Hungarian Test accuracy value: 97.3 - type: accuracy name: Hindi Test accuracy value: 73.9 - type: accuracy name: Classical Chinese Test accuracy value: 32.8 - type: accuracy name: Komi Permyak Test accuracy value: 53.6 - type: accuracy name: Faroese Test accuracy value: 67.4 - type: accuracy name: Sanskrit Test accuracy value: 40.9 - type: accuracy name: Livvi Test accuracy value: 69.7 - type: accuracy name: Arabic Test accuracy value: 69.2 - type: accuracy name: Wolof Test accuracy value: 34.7 - type: accuracy name: Bulgarian Test accuracy value: 74.3 - type: accuracy name: Akuntsu Test accuracy value: 29.6 - type: accuracy name: Makurap Test accuracy value: 18.5 - type: accuracy name: Kangri Test accuracy value: 51.8 - type: accuracy name: Breton Test accuracy value: 59.7 - type: accuracy name: Telugu Test accuracy value: 82.1 - type: accuracy name: Cantonese Test accuracy value: 48.3 - type: accuracy name: Old Church Slavonic Test accuracy value: 48.9 - type: accuracy name: Karelian Test accuracy value: 74.4 - type: accuracy name: Upper Sorbian Test accuracy value: 69.7 - type: accuracy name: South Levantine Arabic Test accuracy value: 61.7 - type: accuracy name: Komi Zyrian Test accuracy value: 44.1 - type: accuracy name: Irish Test accuracy value: 59.8 - type: accuracy name: Nayini Test accuracy value: 44.9 - type: accuracy name: Munduruku Test accuracy value: 23.0 - type: accuracy name: Manx Test accuracy value: 33.5 - type: accuracy name: Skolt Sami Test accuracy value: 50.0 - type: accuracy name: Afrikaans Test accuracy value: 73.4 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 36.6 - type: accuracy name: Belarusian Test accuracy value: 77.3 - type: accuracy name: Serbian Test accuracy value: 80.1 - type: accuracy name: Moksha Test accuracy value: 47.6 - type: accuracy name: Western Armenian Test accuracy value: 75.9 - type: accuracy name: Scottish Gaelic Test accuracy value: 54.4 - type: accuracy name: Khunsari Test accuracy value: 37.8 - type: accuracy name: Hebrew Test accuracy value: 85.4 - type: accuracy name: Uyghur Test accuracy value: 71.3 - type: accuracy name: Chukchi Test accuracy value: 40.5 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Hungarian 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-hu") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hu") ```
wietsedv/xlm-roberta-base-ft-udpos28-is
48dad98f1cbe6cadec41782455abd2b481d9e2f9
2022-02-25T09:58:51.000Z
[ "pytorch", "xlm-roberta", "token-classification", "is", "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-is
1
null
transformers
30,581
--- language: - is 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-is 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: 88.4 - type: accuracy name: Dutch Test accuracy value: 86.9 - type: accuracy name: German Test accuracy value: 82.7 - type: accuracy name: Italian Test accuracy value: 84.6 - type: accuracy name: French Test accuracy value: 83.6 - type: accuracy name: Spanish Test accuracy value: 83.6 - type: accuracy name: Russian Test accuracy value: 87.6 - type: accuracy name: Swedish Test accuracy value: 89.9 - type: accuracy name: Norwegian Test accuracy value: 86.4 - type: accuracy name: Danish Test accuracy value: 89.6 - type: accuracy name: Low Saxon Test accuracy value: 57.6 - type: accuracy name: Akkadian Test accuracy value: 30.5 - type: accuracy name: Armenian Test accuracy value: 86.6 - type: accuracy name: Welsh Test accuracy value: 66.9 - type: accuracy name: Old East Slavic Test accuracy value: 76.3 - type: accuracy name: Albanian Test accuracy value: 80.8 - type: accuracy name: Slovenian Test accuracy value: 76.8 - type: accuracy name: Guajajara Test accuracy value: 31.8 - type: accuracy name: Kurmanji Test accuracy value: 78.6 - type: accuracy name: Turkish Test accuracy value: 77.3 - type: accuracy name: Finnish Test accuracy value: 84.8 - type: accuracy name: Indonesian Test accuracy value: 84.4 - type: accuracy name: Ukrainian Test accuracy value: 85.9 - type: accuracy name: Polish Test accuracy value: 84.2 - type: accuracy name: Portuguese Test accuracy value: 86.6 - type: accuracy name: Kazakh Test accuracy value: 81.8 - type: accuracy name: Latin Test accuracy value: 75.8 - type: accuracy name: Old French Test accuracy value: 58.6 - type: accuracy name: Buryat Test accuracy value: 63.1 - type: accuracy name: Kaapor Test accuracy value: 18.3 - type: accuracy name: Korean Test accuracy value: 64.3 - type: accuracy name: Estonian Test accuracy value: 86.7 - type: accuracy name: Croatian Test accuracy value: 86.0 - type: accuracy name: Gothic Test accuracy value: 26.6 - type: accuracy name: Swiss German Test accuracy value: 45.6 - type: accuracy name: Assyrian Test accuracy value: 15.5 - type: accuracy name: North Sami Test accuracy value: 43.9 - type: accuracy name: Naija Test accuracy value: 46.6 - type: accuracy name: Latvian Test accuracy value: 85.3 - type: accuracy name: Chinese Test accuracy value: 60.4 - type: accuracy name: Tagalog Test accuracy value: 80.0 - type: accuracy name: Bambara Test accuracy value: 32.5 - type: accuracy name: Lithuanian Test accuracy value: 85.9 - type: accuracy name: Galician Test accuracy value: 80.7 - type: accuracy name: Vietnamese Test accuracy value: 64.1 - type: accuracy name: Greek Test accuracy value: 80.5 - type: accuracy name: Catalan Test accuracy value: 82.7 - type: accuracy name: Czech Test accuracy value: 84.6 - type: accuracy name: Erzya Test accuracy value: 52.8 - type: accuracy name: Bhojpuri Test accuracy value: 59.0 - type: accuracy name: Thai Test accuracy value: 68.2 - type: accuracy name: Marathi Test accuracy value: 87.1 - type: accuracy name: Basque Test accuracy value: 79.5 - type: accuracy name: Slovak Test accuracy value: 86.0 - type: accuracy name: Kiche Test accuracy value: 42.2 - type: accuracy name: Yoruba Test accuracy value: 34.3 - type: accuracy name: Warlpiri Test accuracy value: 43.7 - type: accuracy name: Tamil Test accuracy value: 83.9 - type: accuracy name: Maltese Test accuracy value: 27.5 - type: accuracy name: Ancient Greek Test accuracy value: 64.0 - type: accuracy name: Icelandic Test accuracy value: 95.6 - type: accuracy name: Mbya Guarani Test accuracy value: 31.9 - type: accuracy name: Urdu Test accuracy value: 72.7 - type: accuracy name: Romanian Test accuracy value: 82.0 - type: accuracy name: Persian Test accuracy value: 78.3 - type: accuracy name: Apurina Test accuracy value: 47.9 - type: accuracy name: Japanese Test accuracy value: 44.0 - type: accuracy name: Hungarian Test accuracy value: 77.2 - type: accuracy name: Hindi Test accuracy value: 77.4 - type: accuracy name: Classical Chinese Test accuracy value: 46.0 - type: accuracy name: Komi Permyak Test accuracy value: 52.7 - type: accuracy name: Faroese Test accuracy value: 83.9 - type: accuracy name: Sanskrit Test accuracy value: 37.4 - type: accuracy name: Livvi Test accuracy value: 66.8 - type: accuracy name: Arabic Test accuracy value: 79.2 - type: accuracy name: Wolof Test accuracy value: 39.9 - type: accuracy name: Bulgarian Test accuracy value: 87.7 - type: accuracy name: Akuntsu Test accuracy value: 37.0 - type: accuracy name: Makurap Test accuracy value: 24.7 - type: accuracy name: Kangri Test accuracy value: 50.2 - type: accuracy name: Breton Test accuracy value: 61.8 - type: accuracy name: Telugu Test accuracy value: 84.5 - type: accuracy name: Cantonese Test accuracy value: 60.6 - type: accuracy name: Old Church Slavonic Test accuracy value: 53.9 - type: accuracy name: Karelian Test accuracy value: 74.0 - type: accuracy name: Upper Sorbian Test accuracy value: 75.5 - type: accuracy name: South Levantine Arabic Test accuracy value: 70.8 - type: accuracy name: Komi Zyrian Test accuracy value: 47.1 - type: accuracy name: Irish Test accuracy value: 66.8 - type: accuracy name: Nayini Test accuracy value: 43.6 - type: accuracy name: Munduruku Test accuracy value: 28.3 - type: accuracy name: Manx Test accuracy value: 48.6 - type: accuracy name: Skolt Sami Test accuracy value: 39.6 - type: accuracy name: Afrikaans Test accuracy value: 87.4 - type: accuracy name: Old Turkish Test accuracy value: 38.9 - type: accuracy name: Tupinamba Test accuracy value: 37.6 - type: accuracy name: Belarusian Test accuracy value: 86.8 - type: accuracy name: Serbian Test accuracy value: 87.2 - type: accuracy name: Moksha Test accuracy value: 49.8 - type: accuracy name: Western Armenian Test accuracy value: 79.9 - type: accuracy name: Scottish Gaelic Test accuracy value: 56.8 - type: accuracy name: Khunsari Test accuracy value: 52.7 - type: accuracy name: Hebrew Test accuracy value: 85.4 - type: accuracy name: Uyghur Test accuracy value: 76.9 - type: accuracy name: Chukchi Test accuracy value: 37.7 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Icelandic 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-is") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-is") ```
wietsedv/xlm-roberta-base-ft-udpos28-lzh
3747fe9c0019f2104de495030664f2f59debd43b
2022-02-25T09:59:02.000Z
[ "pytorch", "xlm-roberta", "token-classification", "lzh", "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-lzh
1
null
transformers
30,582
--- language: - lzh 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-lzh 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: 33.6 - type: accuracy name: Dutch Test accuracy value: 30.9 - type: accuracy name: German Test accuracy value: 31.1 - type: accuracy name: Italian Test accuracy value: 31.1 - type: accuracy name: French Test accuracy value: 30.3 - type: accuracy name: Spanish Test accuracy value: 30.6 - type: accuracy name: Russian Test accuracy value: 37.1 - type: accuracy name: Swedish Test accuracy value: 35.6 - type: accuracy name: Norwegian Test accuracy value: 32.7 - type: accuracy name: Danish Test accuracy value: 35.0 - type: accuracy name: Low Saxon Test accuracy value: 19.0 - type: accuracy name: Akkadian Test accuracy value: 25.9 - type: accuracy name: Armenian Test accuracy value: 40.9 - type: accuracy name: Welsh Test accuracy value: 27.3 - type: accuracy name: Old East Slavic Test accuracy value: 36.4 - type: accuracy name: Albanian Test accuracy value: 31.6 - type: accuracy name: Slovenian Test accuracy value: 31.1 - type: accuracy name: Guajajara Test accuracy value: 13.9 - type: accuracy name: Kurmanji Test accuracy value: 36.5 - type: accuracy name: Turkish Test accuracy value: 42.7 - type: accuracy name: Finnish Test accuracy value: 45.0 - type: accuracy name: Indonesian Test accuracy value: 40.6 - type: accuracy name: Ukrainian Test accuracy value: 36.0 - type: accuracy name: Polish Test accuracy value: 35.3 - type: accuracy name: Portuguese Test accuracy value: 34.8 - type: accuracy name: Kazakh Test accuracy value: 45.4 - type: accuracy name: Latin Test accuracy value: 37.9 - type: accuracy name: Old French Test accuracy value: 33.4 - type: accuracy name: Buryat Test accuracy value: 27.2 - type: accuracy name: Kaapor Test accuracy value: 19.6 - type: accuracy name: Korean Test accuracy value: 44.8 - type: accuracy name: Estonian Test accuracy value: 41.4 - type: accuracy name: Croatian Test accuracy value: 34.2 - type: accuracy name: Gothic Test accuracy value: 12.3 - type: accuracy name: Swiss German Test accuracy value: 18.1 - type: accuracy name: Assyrian Test accuracy value: 3.5 - type: accuracy name: North Sami Test accuracy value: 8.9 - type: accuracy name: Naija Test accuracy value: 25.4 - type: accuracy name: Latvian Test accuracy value: 45.0 - type: accuracy name: Chinese Test accuracy value: 53.2 - type: accuracy name: Tagalog Test accuracy value: 34.0 - type: accuracy name: Bambara Test accuracy value: 13.9 - type: accuracy name: Lithuanian Test accuracy value: 44.0 - type: accuracy name: Galician Test accuracy value: 29.0 - type: accuracy name: Vietnamese Test accuracy value: 40.9 - type: accuracy name: Greek Test accuracy value: 31.3 - type: accuracy name: Catalan Test accuracy value: 29.6 - type: accuracy name: Czech Test accuracy value: 35.4 - type: accuracy name: Erzya Test accuracy value: 9.6 - type: accuracy name: Bhojpuri Test accuracy value: 22.9 - type: accuracy name: Thai Test accuracy value: 51.6 - type: accuracy name: Marathi Test accuracy value: 36.8 - type: accuracy name: Basque Test accuracy value: 42.1 - type: accuracy name: Slovak Test accuracy value: 36.3 - type: accuracy name: Kiche Test accuracy value: 11.9 - type: accuracy name: Yoruba Test accuracy value: 10.9 - type: accuracy name: Warlpiri Test accuracy value: 15.0 - type: accuracy name: Tamil Test accuracy value: 53.4 - type: accuracy name: Maltese Test accuracy value: 9.4 - type: accuracy name: Ancient Greek Test accuracy value: 31.9 - type: accuracy name: Icelandic Test accuracy value: 38.4 - type: accuracy name: Mbya Guarani Test accuracy value: 7.1 - type: accuracy name: Urdu Test accuracy value: 33.4 - type: accuracy name: Romanian Test accuracy value: 33.5 - type: accuracy name: Persian Test accuracy value: 35.2 - type: accuracy name: Apurina Test accuracy value: 11.9 - type: accuracy name: Japanese Test accuracy value: 39.6 - type: accuracy name: Hungarian Test accuracy value: 37.2 - type: accuracy name: Hindi Test accuracy value: 33.0 - type: accuracy name: Classical Chinese Test accuracy value: 88.0 - type: accuracy name: Komi Permyak Test accuracy value: 11.3 - type: accuracy name: Faroese Test accuracy value: 30.3 - type: accuracy name: Sanskrit Test accuracy value: 20.6 - type: accuracy name: Livvi Test accuracy value: 29.1 - type: accuracy name: Arabic Test accuracy value: 34.9 - type: accuracy name: Wolof Test accuracy value: 17.0 - type: accuracy name: Bulgarian Test accuracy value: 34.3 - type: accuracy name: Akuntsu Test accuracy value: 19.3 - type: accuracy name: Makurap Test accuracy value: 21.2 - type: accuracy name: Kangri Test accuracy value: 19.8 - type: accuracy name: Breton Test accuracy value: 27.4 - type: accuracy name: Telugu Test accuracy value: 49.4 - type: accuracy name: Cantonese Test accuracy value: 53.7 - type: accuracy name: Old Church Slavonic Test accuracy value: 27.9 - type: accuracy name: Karelian Test accuracy value: 32.8 - type: accuracy name: Upper Sorbian Test accuracy value: 22.1 - type: accuracy name: South Levantine Arabic Test accuracy value: 29.8 - type: accuracy name: Komi Zyrian Test accuracy value: 9.7 - type: accuracy name: Irish Test accuracy value: 29.5 - type: accuracy name: Nayini Test accuracy value: 32.1 - type: accuracy name: Munduruku Test accuracy value: 14.4 - type: accuracy name: Manx Test accuracy value: 16.8 - type: accuracy name: Skolt Sami Test accuracy value: 5.3 - type: accuracy name: Afrikaans Test accuracy value: 31.8 - type: accuracy name: Old Turkish Test accuracy value: 13.6 - type: accuracy name: Tupinamba Test accuracy value: 9.4 - type: accuracy name: Belarusian Test accuracy value: 36.7 - type: accuracy name: Serbian Test accuracy value: 33.9 - type: accuracy name: Moksha Test accuracy value: 10.4 - type: accuracy name: Western Armenian Test accuracy value: 34.8 - type: accuracy name: Scottish Gaelic Test accuracy value: 29.2 - type: accuracy name: Khunsari Test accuracy value: 23.0 - type: accuracy name: Hebrew Test accuracy value: 44.8 - type: accuracy name: Uyghur Test accuracy value: 44.6 - type: accuracy name: Chukchi Test accuracy value: 7.0 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Classical Chinese 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-lzh") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lzh") ```
wietsedv/xlm-roberta-base-ft-udpos28-mr
7c1da08f23db1e666ede432aea8bae7befc7bb06
2022-02-25T09:59:04.000Z
[ "pytorch", "xlm-roberta", "token-classification", "mr", "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-mr
1
null
transformers
30,583
--- language: - mr 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-mr 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: 67.4 - type: accuracy name: Dutch Test accuracy value: 61.5 - type: accuracy name: German Test accuracy value: 66.9 - type: accuracy name: Italian Test accuracy value: 64.8 - type: accuracy name: French Test accuracy value: 61.7 - type: accuracy name: Spanish Test accuracy value: 60.1 - type: accuracy name: Russian Test accuracy value: 68.1 - type: accuracy name: Swedish Test accuracy value: 68.4 - type: accuracy name: Norwegian Test accuracy value: 64.1 - type: accuracy name: Danish Test accuracy value: 66.4 - type: accuracy name: Low Saxon Test accuracy value: 51.7 - type: accuracy name: Akkadian Test accuracy value: 23.7 - type: accuracy name: Armenian Test accuracy value: 74.4 - type: accuracy name: Welsh Test accuracy value: 50.1 - type: accuracy name: Old East Slavic Test accuracy value: 57.8 - type: accuracy name: Albanian Test accuracy value: 61.9 - type: accuracy name: Slovenian Test accuracy value: 60.1 - type: accuracy name: Guajajara Test accuracy value: 20.5 - type: accuracy name: Kurmanji Test accuracy value: 60.0 - type: accuracy name: Turkish Test accuracy value: 71.8 - type: accuracy name: Finnish Test accuracy value: 74.5 - type: accuracy name: Indonesian Test accuracy value: 59.0 - type: accuracy name: Ukrainian Test accuracy value: 67.1 - type: accuracy name: Polish Test accuracy value: 65.0 - type: accuracy name: Portuguese Test accuracy value: 66.7 - type: accuracy name: Kazakh Test accuracy value: 73.8 - type: accuracy name: Latin Test accuracy value: 66.2 - type: accuracy name: Old French Test accuracy value: 48.6 - type: accuracy name: Buryat Test accuracy value: 57.0 - type: accuracy name: Kaapor Test accuracy value: 19.2 - type: accuracy name: Korean Test accuracy value: 59.7 - type: accuracy name: Estonian Test accuracy value: 75.4 - type: accuracy name: Croatian Test accuracy value: 63.8 - type: accuracy name: Gothic Test accuracy value: 20.0 - type: accuracy name: Swiss German Test accuracy value: 46.8 - type: accuracy name: Assyrian Test accuracy value: 16.1 - type: accuracy name: North Sami Test accuracy value: 37.1 - type: accuracy name: Naija Test accuracy value: 37.9 - type: accuracy name: Latvian Test accuracy value: 75.6 - type: accuracy name: Chinese Test accuracy value: 49.7 - type: accuracy name: Tagalog Test accuracy value: 55.1 - type: accuracy name: Bambara Test accuracy value: 28.9 - type: accuracy name: Lithuanian Test accuracy value: 75.9 - type: accuracy name: Galician Test accuracy value: 65.5 - type: accuracy name: Vietnamese Test accuracy value: 61.0 - type: accuracy name: Greek Test accuracy value: 70.4 - type: accuracy name: Catalan Test accuracy value: 57.9 - type: accuracy name: Czech Test accuracy value: 64.9 - type: accuracy name: Erzya Test accuracy value: 47.7 - type: accuracy name: Bhojpuri Test accuracy value: 41.9 - type: accuracy name: Thai Test accuracy value: 44.1 - type: accuracy name: Marathi Test accuracy value: 89.0 - type: accuracy name: Basque Test accuracy value: 71.8 - type: accuracy name: Slovak Test accuracy value: 61.3 - type: accuracy name: Kiche Test accuracy value: 25.7 - type: accuracy name: Yoruba Test accuracy value: 22.8 - type: accuracy name: Warlpiri Test accuracy value: 42.9 - type: accuracy name: Tamil Test accuracy value: 73.5 - type: accuracy name: Maltese Test accuracy value: 26.7 - type: accuracy name: Ancient Greek Test accuracy value: 63.5 - type: accuracy name: Icelandic Test accuracy value: 64.0 - type: accuracy name: Mbya Guarani Test accuracy value: 29.7 - type: accuracy name: Urdu Test accuracy value: 50.3 - type: accuracy name: Romanian Test accuracy value: 63.3 - type: accuracy name: Persian Test accuracy value: 61.0 - type: accuracy name: Apurina Test accuracy value: 38.4 - type: accuracy name: Japanese Test accuracy value: 40.5 - type: accuracy name: Hungarian Test accuracy value: 69.4 - type: accuracy name: Hindi Test accuracy value: 52.7 - type: accuracy name: Classical Chinese Test accuracy value: 32.4 - type: accuracy name: Komi Permyak Test accuracy value: 50.1 - type: accuracy name: Faroese Test accuracy value: 58.0 - type: accuracy name: Sanskrit Test accuracy value: 34.1 - type: accuracy name: Livvi Test accuracy value: 65.3 - type: accuracy name: Arabic Test accuracy value: 55.9 - type: accuracy name: Wolof Test accuracy value: 27.8 - type: accuracy name: Bulgarian Test accuracy value: 63.2 - type: accuracy name: Akuntsu Test accuracy value: 23.1 - type: accuracy name: Makurap Test accuracy value: 17.1 - type: accuracy name: Kangri Test accuracy value: 48.8 - type: accuracy name: Breton Test accuracy value: 50.8 - type: accuracy name: Telugu Test accuracy value: 82.0 - type: accuracy name: Cantonese Test accuracy value: 52.5 - type: accuracy name: Old Church Slavonic Test accuracy value: 42.8 - type: accuracy name: Karelian Test accuracy value: 61.8 - type: accuracy name: Upper Sorbian Test accuracy value: 54.1 - type: accuracy name: South Levantine Arabic Test accuracy value: 55.8 - type: accuracy name: Komi Zyrian Test accuracy value: 47.0 - type: accuracy name: Irish Test accuracy value: 50.1 - type: accuracy name: Nayini Test accuracy value: 48.7 - type: accuracy name: Munduruku Test accuracy value: 18.6 - type: accuracy name: Manx Test accuracy value: 31.1 - type: accuracy name: Skolt Sami Test accuracy value: 40.8 - type: accuracy name: Afrikaans Test accuracy value: 66.4 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 29.9 - type: accuracy name: Belarusian Test accuracy value: 65.4 - type: accuracy name: Serbian Test accuracy value: 62.6 - type: accuracy name: Moksha Test accuracy value: 46.8 - type: accuracy name: Western Armenian Test accuracy value: 70.6 - type: accuracy name: Scottish Gaelic Test accuracy value: 47.4 - type: accuracy name: Khunsari Test accuracy value: 45.9 - type: accuracy name: Hebrew Test accuracy value: 77.1 - type: accuracy name: Uyghur Test accuracy value: 73.2 - type: accuracy name: Chukchi Test accuracy value: 33.5 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Marathi 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-mr") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-mr") ```
wietsedv/xlm-roberta-base-ft-udpos28-mt
5f992379118e3aa5a7077081a4782c5e03481366
2022-02-25T09:59:05.000Z
[ "pytorch", "xlm-roberta", "token-classification", "mt", "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-mt
1
null
transformers
30,584
--- language: - mt 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-mt 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: 37.5 - type: accuracy name: Dutch Test accuracy value: 52.3 - type: accuracy name: German Test accuracy value: 51.7 - type: accuracy name: Italian Test accuracy value: 54.7 - type: accuracy name: French Test accuracy value: 49.1 - type: accuracy name: Spanish Test accuracy value: 49.5 - type: accuracy name: Russian Test accuracy value: 64.7 - type: accuracy name: Swedish Test accuracy value: 52.0 - type: accuracy name: Norwegian Test accuracy value: 48.7 - type: accuracy name: Danish Test accuracy value: 52.3 - type: accuracy name: Low Saxon Test accuracy value: 41.7 - type: accuracy name: Akkadian Test accuracy value: 27.7 - type: accuracy name: Armenian Test accuracy value: 65.4 - type: accuracy name: Welsh Test accuracy value: 50.5 - type: accuracy name: Old East Slavic Test accuracy value: 58.1 - type: accuracy name: Albanian Test accuracy value: 55.2 - type: accuracy name: Slovenian Test accuracy value: 52.3 - type: accuracy name: Guajajara Test accuracy value: 30.7 - type: accuracy name: Kurmanji Test accuracy value: 53.3 - type: accuracy name: Turkish Test accuracy value: 61.0 - type: accuracy name: Finnish Test accuracy value: 62.4 - type: accuracy name: Indonesian Test accuracy value: 59.4 - type: accuracy name: Ukrainian Test accuracy value: 66.6 - type: accuracy name: Polish Test accuracy value: 62.6 - type: accuracy name: Portuguese Test accuracy value: 54.2 - type: accuracy name: Kazakh Test accuracy value: 68.7 - type: accuracy name: Latin Test accuracy value: 54.5 - type: accuracy name: Old French Test accuracy value: 33.8 - type: accuracy name: Buryat Test accuracy value: 51.2 - type: accuracy name: Kaapor Test accuracy value: 22.9 - type: accuracy name: Korean Test accuracy value: 51.7 - type: accuracy name: Estonian Test accuracy value: 62.3 - type: accuracy name: Croatian Test accuracy value: 61.4 - type: accuracy name: Gothic Test accuracy value: 26.8 - type: accuracy name: Swiss German Test accuracy value: 43.6 - type: accuracy name: Assyrian Test accuracy value: 26.0 - type: accuracy name: North Sami Test accuracy value: 40.4 - type: accuracy name: Naija Test accuracy value: 10.9 - type: accuracy name: Latvian Test accuracy value: 65.5 - type: accuracy name: Chinese Test accuracy value: 47.3 - type: accuracy name: Tagalog Test accuracy value: 56.3 - type: accuracy name: Bambara Test accuracy value: 28.1 - type: accuracy name: Lithuanian Test accuracy value: 67.2 - type: accuracy name: Galician Test accuracy value: 54.3 - type: accuracy name: Vietnamese Test accuracy value: 55.0 - type: accuracy name: Greek Test accuracy value: 52.4 - type: accuracy name: Catalan Test accuracy value: 51.2 - type: accuracy name: Czech Test accuracy value: 64.6 - type: accuracy name: Erzya Test accuracy value: 46.6 - type: accuracy name: Bhojpuri Test accuracy value: 39.6 - type: accuracy name: Thai Test accuracy value: 44.9 - type: accuracy name: Marathi Test accuracy value: 70.6 - type: accuracy name: Basque Test accuracy value: 63.4 - type: accuracy name: Slovak Test accuracy value: 68.4 - type: accuracy name: Kiche Test accuracy value: 33.0 - type: accuracy name: Yoruba Test accuracy value: 31.1 - type: accuracy name: Warlpiri Test accuracy value: 32.0 - type: accuracy name: Tamil Test accuracy value: 73.8 - type: accuracy name: Maltese Test accuracy value: 94.4 - type: accuracy name: Ancient Greek Test accuracy value: 47.8 - type: accuracy name: Icelandic Test accuracy value: 51.3 - type: accuracy name: Mbya Guarani Test accuracy value: 34.7 - type: accuracy name: Urdu Test accuracy value: 45.9 - type: accuracy name: Romanian Test accuracy value: 57.9 - type: accuracy name: Persian Test accuracy value: 52.9 - type: accuracy name: Apurina Test accuracy value: 38.2 - type: accuracy name: Japanese Test accuracy value: 37.8 - type: accuracy name: Hungarian Test accuracy value: 61.1 - type: accuracy name: Hindi Test accuracy value: 45.0 - type: accuracy name: Classical Chinese Test accuracy value: 34.5 - type: accuracy name: Komi Permyak Test accuracy value: 48.7 - type: accuracy name: Faroese Test accuracy value: 55.1 - type: accuracy name: Sanskrit Test accuracy value: 28.3 - type: accuracy name: Livvi Test accuracy value: 52.1 - type: accuracy name: Arabic Test accuracy value: 63.9 - type: accuracy name: Wolof Test accuracy value: 36.6 - type: accuracy name: Bulgarian Test accuracy value: 59.0 - type: accuracy name: Akuntsu Test accuracy value: 29.6 - type: accuracy name: Makurap Test accuracy value: 29.5 - type: accuracy name: Kangri Test accuracy value: 39.2 - type: accuracy name: Breton Test accuracy value: 49.8 - type: accuracy name: Telugu Test accuracy value: 64.6 - type: accuracy name: Cantonese Test accuracy value: 46.0 - type: accuracy name: Old Church Slavonic Test accuracy value: 38.1 - type: accuracy name: Karelian Test accuracy value: 57.4 - type: accuracy name: Upper Sorbian Test accuracy value: 62.4 - type: accuracy name: South Levantine Arabic Test accuracy value: 61.1 - type: accuracy name: Komi Zyrian Test accuracy value: 43.0 - type: accuracy name: Irish Test accuracy value: 46.8 - type: accuracy name: Nayini Test accuracy value: 48.7 - type: accuracy name: Munduruku Test accuracy value: 21.6 - type: accuracy name: Manx Test accuracy value: 42.0 - type: accuracy name: Skolt Sami Test accuracy value: 41.4 - type: accuracy name: Afrikaans Test accuracy value: 49.8 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 32.9 - type: accuracy name: Belarusian Test accuracy value: 68.2 - type: accuracy name: Serbian Test accuracy value: 60.7 - type: accuracy name: Moksha Test accuracy value: 43.5 - type: accuracy name: Western Armenian Test accuracy value: 60.2 - type: accuracy name: Scottish Gaelic Test accuracy value: 41.5 - type: accuracy name: Khunsari Test accuracy value: 43.2 - type: accuracy name: Hebrew Test accuracy value: 74.0 - type: accuracy name: Uyghur Test accuracy value: 61.9 - type: accuracy name: Chukchi Test accuracy value: 48.1 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Maltese 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-mt") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-mt") ```
wietsedv/xlm-roberta-base-ft-udpos28-orv
0d528e05ed934343892ac7101775c463e4794d33
2022-02-25T09:59:10.000Z
[ "pytorch", "xlm-roberta", "token-classification", "orv", "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-orv
1
null
transformers
30,585
--- language: - orv 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-orv 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: 79.4 - type: accuracy name: Dutch Test accuracy value: 77.8 - type: accuracy name: German Test accuracy value: 79.3 - type: accuracy name: Italian Test accuracy value: 77.5 - type: accuracy name: French Test accuracy value: 75.2 - type: accuracy name: Spanish Test accuracy value: 77.2 - type: accuracy name: Russian Test accuracy value: 87.9 - type: accuracy name: Swedish Test accuracy value: 83.0 - type: accuracy name: Norwegian Test accuracy value: 78.6 - type: accuracy name: Danish Test accuracy value: 82.9 - type: accuracy name: Low Saxon Test accuracy value: 58.9 - type: accuracy name: Akkadian Test accuracy value: 41.8 - type: accuracy name: Armenian Test accuracy value: 82.7 - type: accuracy name: Welsh Test accuracy value: 64.3 - type: accuracy name: Old East Slavic Test accuracy value: 91.0 - type: accuracy name: Albanian Test accuracy value: 73.4 - type: accuracy name: Slovenian Test accuracy value: 73.8 - type: accuracy name: Guajajara Test accuracy value: 41.7 - type: accuracy name: Kurmanji Test accuracy value: 76.7 - type: accuracy name: Turkish Test accuracy value: 73.5 - type: accuracy name: Finnish Test accuracy value: 83.0 - type: accuracy name: Indonesian Test accuracy value: 78.9 - type: accuracy name: Ukrainian Test accuracy value: 86.7 - type: accuracy name: Polish Test accuracy value: 85.5 - type: accuracy name: Portuguese Test accuracy value: 79.5 - type: accuracy name: Kazakh Test accuracy value: 79.7 - type: accuracy name: Latin Test accuracy value: 80.9 - type: accuracy name: Old French Test accuracy value: 60.5 - type: accuracy name: Buryat Test accuracy value: 59.8 - type: accuracy name: Kaapor Test accuracy value: 27.1 - type: accuracy name: Korean Test accuracy value: 61.0 - type: accuracy name: Estonian Test accuracy value: 83.9 - type: accuracy name: Croatian Test accuracy value: 84.7 - type: accuracy name: Gothic Test accuracy value: 33.1 - type: accuracy name: Swiss German Test accuracy value: 53.5 - type: accuracy name: Assyrian Test accuracy value: 15.7 - type: accuracy name: North Sami Test accuracy value: 39.9 - type: accuracy name: Naija Test accuracy value: 41.9 - type: accuracy name: Latvian Test accuracy value: 85.7 - type: accuracy name: Chinese Test accuracy value: 42.7 - type: accuracy name: Tagalog Test accuracy value: 73.5 - type: accuracy name: Bambara Test accuracy value: 29.5 - type: accuracy name: Lithuanian Test accuracy value: 86.1 - type: accuracy name: Galician Test accuracy value: 77.7 - type: accuracy name: Vietnamese Test accuracy value: 64.8 - type: accuracy name: Greek Test accuracy value: 73.8 - type: accuracy name: Catalan Test accuracy value: 74.2 - type: accuracy name: Czech Test accuracy value: 85.0 - type: accuracy name: Erzya Test accuracy value: 46.1 - type: accuracy name: Bhojpuri Test accuracy value: 56.8 - type: accuracy name: Thai Test accuracy value: 60.6 - type: accuracy name: Marathi Test accuracy value: 84.0 - type: accuracy name: Basque Test accuracy value: 77.2 - type: accuracy name: Slovak Test accuracy value: 84.3 - type: accuracy name: Kiche Test accuracy value: 35.3 - type: accuracy name: Yoruba Test accuracy value: 29.9 - type: accuracy name: Warlpiri Test accuracy value: 33.6 - type: accuracy name: Tamil Test accuracy value: 84.3 - type: accuracy name: Maltese Test accuracy value: 32.0 - type: accuracy name: Ancient Greek Test accuracy value: 65.7 - type: accuracy name: Icelandic Test accuracy value: 81.6 - type: accuracy name: Mbya Guarani Test accuracy value: 33.2 - type: accuracy name: Urdu Test accuracy value: 66.2 - type: accuracy name: Romanian Test accuracy value: 80.9 - type: accuracy name: Persian Test accuracy value: 74.6 - type: accuracy name: Apurina Test accuracy value: 44.6 - type: accuracy name: Japanese Test accuracy value: 35.7 - type: accuracy name: Hungarian Test accuracy value: 73.3 - type: accuracy name: Hindi Test accuracy value: 75.3 - type: accuracy name: Classical Chinese Test accuracy value: 41.5 - type: accuracy name: Komi Permyak Test accuracy value: 49.0 - type: accuracy name: Faroese Test accuracy value: 78.3 - type: accuracy name: Sanskrit Test accuracy value: 43.3 - type: accuracy name: Livvi Test accuracy value: 70.2 - type: accuracy name: Arabic Test accuracy value: 79.8 - type: accuracy name: Wolof Test accuracy value: 39.8 - type: accuracy name: Bulgarian Test accuracy value: 85.8 - type: accuracy name: Akuntsu Test accuracy value: 36.5 - type: accuracy name: Makurap Test accuracy value: 14.4 - type: accuracy name: Kangri Test accuracy value: 52.0 - type: accuracy name: Breton Test accuracy value: 58.1 - type: accuracy name: Telugu Test accuracy value: 79.9 - type: accuracy name: Cantonese Test accuracy value: 50.8 - type: accuracy name: Old Church Slavonic Test accuracy value: 78.2 - type: accuracy name: Karelian Test accuracy value: 73.5 - type: accuracy name: Upper Sorbian Test accuracy value: 76.0 - type: accuracy name: South Levantine Arabic Test accuracy value: 70.0 - type: accuracy name: Komi Zyrian Test accuracy value: 43.1 - type: accuracy name: Irish Test accuracy value: 61.1 - type: accuracy name: Nayini Test accuracy value: 53.8 - type: accuracy name: Munduruku Test accuracy value: 26.4 - type: accuracy name: Manx Test accuracy value: 44.6 - type: accuracy name: Skolt Sami Test accuracy value: 45.2 - type: accuracy name: Afrikaans Test accuracy value: 76.9 - type: accuracy name: Old Turkish Test accuracy value: 2.7 - type: accuracy name: Tupinamba Test accuracy value: 39.0 - type: accuracy name: Belarusian Test accuracy value: 89.5 - type: accuracy name: Serbian Test accuracy value: 85.1 - type: accuracy name: Moksha Test accuracy value: 42.8 - type: accuracy name: Western Armenian Test accuracy value: 77.0 - type: accuracy name: Scottish Gaelic Test accuracy value: 51.6 - type: accuracy name: Khunsari Test accuracy value: 54.1 - type: accuracy name: Hebrew Test accuracy value: 85.4 - type: accuracy name: Uyghur Test accuracy value: 74.4 - type: accuracy name: Chukchi Test accuracy value: 34.5 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Old East Slavic 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-orv") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-orv") ```
wietsedv/xlm-roberta-base-ft-udpos28-pcm
a0e4eddb78b41c3b5c5b8616a7aeb926c5f89b96
2022-02-25T09:59:11.000Z
[ "pytorch", "xlm-roberta", "token-classification", "pcm", "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-pcm
1
null
transformers
30,586
--- language: - pcm 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-pcm 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: 77.2 - type: accuracy name: Dutch Test accuracy value: 75.2 - type: accuracy name: German Test accuracy value: 73.2 - type: accuracy name: Italian Test accuracy value: 68.9 - type: accuracy name: French Test accuracy value: 74.0 - type: accuracy name: Spanish Test accuracy value: 75.1 - type: accuracy name: Russian Test accuracy value: 70.3 - type: accuracy name: Swedish Test accuracy value: 78.9 - type: accuracy name: Norwegian Test accuracy value: 74.3 - type: accuracy name: Danish Test accuracy value: 73.4 - type: accuracy name: Low Saxon Test accuracy value: 37.9 - type: accuracy name: Akkadian Test accuracy value: 28.0 - type: accuracy name: Armenian Test accuracy value: 65.4 - type: accuracy name: Welsh Test accuracy value: 59.7 - type: accuracy name: Old East Slavic Test accuracy value: 61.0 - type: accuracy name: Albanian Test accuracy value: 66.1 - type: accuracy name: Slovenian Test accuracy value: 67.6 - type: accuracy name: Guajajara Test accuracy value: 16.1 - type: accuracy name: Kurmanji Test accuracy value: 54.8 - type: accuracy name: Turkish Test accuracy value: 58.2 - type: accuracy name: Finnish Test accuracy value: 67.4 - type: accuracy name: Indonesian Test accuracy value: 68.5 - type: accuracy name: Ukrainian Test accuracy value: 68.1 - type: accuracy name: Polish Test accuracy value: 68.8 - type: accuracy name: Portuguese Test accuracy value: 72.9 - type: accuracy name: Kazakh Test accuracy value: 60.1 - type: accuracy name: Latin Test accuracy value: 64.3 - type: accuracy name: Old French Test accuracy value: 51.1 - type: accuracy name: Buryat Test accuracy value: 38.9 - type: accuracy name: Kaapor Test accuracy value: 16.7 - type: accuracy name: Korean Test accuracy value: 52.4 - type: accuracy name: Estonian Test accuracy value: 68.3 - type: accuracy name: Croatian Test accuracy value: 73.0 - type: accuracy name: Gothic Test accuracy value: 21.4 - type: accuracy name: Swiss German Test accuracy value: 33.4 - type: accuracy name: Assyrian Test accuracy value: 0.0 - type: accuracy name: North Sami Test accuracy value: 24.3 - type: accuracy name: Naija Test accuracy value: 97.9 - type: accuracy name: Latvian Test accuracy value: 66.3 - type: accuracy name: Chinese Test accuracy value: 34.3 - type: accuracy name: Tagalog Test accuracy value: 49.9 - type: accuracy name: Bambara Test accuracy value: 16.7 - type: accuracy name: Lithuanian Test accuracy value: 65.7 - type: accuracy name: Galician Test accuracy value: 72.4 - type: accuracy name: Vietnamese Test accuracy value: 54.3 - type: accuracy name: Greek Test accuracy value: 73.3 - type: accuracy name: Catalan Test accuracy value: 73.6 - type: accuracy name: Czech Test accuracy value: 69.5 - type: accuracy name: Erzya Test accuracy value: 22.1 - type: accuracy name: Bhojpuri Test accuracy value: 36.6 - type: accuracy name: Thai Test accuracy value: 65.4 - type: accuracy name: Marathi Test accuracy value: 50.3 - type: accuracy name: Basque Test accuracy value: 58.5 - type: accuracy name: Slovak Test accuracy value: 70.4 - type: accuracy name: Kiche Test accuracy value: 8.0 - type: accuracy name: Yoruba Test accuracy value: 6.1 - type: accuracy name: Warlpiri Test accuracy value: 15.4 - type: accuracy name: Tamil Test accuracy value: 60.1 - type: accuracy name: Maltese Test accuracy value: 12.2 - type: accuracy name: Ancient Greek Test accuracy value: 45.8 - type: accuracy name: Icelandic Test accuracy value: 72.5 - type: accuracy name: Mbya Guarani Test accuracy value: 11.4 - type: accuracy name: Urdu Test accuracy value: 59.1 - type: accuracy name: Romanian Test accuracy value: 64.8 - type: accuracy name: Persian Test accuracy value: 67.2 - type: accuracy name: Apurina Test accuracy value: 15.5 - type: accuracy name: Japanese Test accuracy value: 26.1 - type: accuracy name: Hungarian Test accuracy value: 68.6 - type: accuracy name: Hindi Test accuracy value: 65.0 - type: accuracy name: Classical Chinese Test accuracy value: 30.4 - type: accuracy name: Komi Permyak Test accuracy value: 21.2 - type: accuracy name: Faroese Test accuracy value: 61.6 - type: accuracy name: Sanskrit Test accuracy value: 25.6 - type: accuracy name: Livvi Test accuracy value: 39.7 - type: accuracy name: Arabic Test accuracy value: 63.5 - type: accuracy name: Wolof Test accuracy value: 15.9 - type: accuracy name: Bulgarian Test accuracy value: 74.6 - type: accuracy name: Akuntsu Test accuracy value: 26.5 - type: accuracy name: Makurap Test accuracy value: 11.6 - type: accuracy name: Kangri Test accuracy value: 27.8 - type: accuracy name: Breton Test accuracy value: 46.6 - type: accuracy name: Telugu Test accuracy value: 59.4 - type: accuracy name: Cantonese Test accuracy value: 30.7 - type: accuracy name: Old Church Slavonic Test accuracy value: 36.7 - type: accuracy name: Karelian Test accuracy value: 45.9 - type: accuracy name: Upper Sorbian Test accuracy value: 49.3 - type: accuracy name: South Levantine Arabic Test accuracy value: 42.5 - type: accuracy name: Komi Zyrian Test accuracy value: 18.4 - type: accuracy name: Irish Test accuracy value: 48.3 - type: accuracy name: Nayini Test accuracy value: 24.4 - type: accuracy name: Munduruku Test accuracy value: 16.1 - type: accuracy name: Manx Test accuracy value: 14.7 - type: accuracy name: Skolt Sami Test accuracy value: 5.4 - type: accuracy name: Afrikaans Test accuracy value: 76.5 - type: accuracy name: Old Turkish Test accuracy value: 0.0 - type: accuracy name: Tupinamba Test accuracy value: 16.3 - type: accuracy name: Belarusian Test accuracy value: 70.7 - type: accuracy name: Serbian Test accuracy value: 74.8 - type: accuracy name: Moksha Test accuracy value: 24.1 - type: accuracy name: Western Armenian Test accuracy value: 59.8 - type: accuracy name: Scottish Gaelic Test accuracy value: 45.4 - type: accuracy name: Khunsari Test accuracy value: 21.6 - type: accuracy name: Hebrew Test accuracy value: 65.6 - type: accuracy name: Uyghur Test accuracy value: 55.0 - type: accuracy name: Chukchi Test accuracy value: 12.6 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Naija 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-pcm") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-pcm") ```
wietsedv/xlm-roberta-base-ft-udpos28-sl
1393c2c2e13e15b8d2feb942f8eb828450e5162f
2022-02-25T09:59:22.000Z
[ "pytorch", "xlm-roberta", "token-classification", "sl", "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-sl
1
null
transformers
30,587
--- language: - sl 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-sl 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: 81.7 - type: accuracy name: Dutch Test accuracy value: 83.1 - type: accuracy name: German Test accuracy value: 81.2 - type: accuracy name: Italian Test accuracy value: 81.3 - type: accuracy name: French Test accuracy value: 79.9 - type: accuracy name: Spanish Test accuracy value: 84.9 - type: accuracy name: Russian Test accuracy value: 91.5 - type: accuracy name: Swedish Test accuracy value: 86.0 - type: accuracy name: Norwegian Test accuracy value: 78.4 - type: accuracy name: Danish Test accuracy value: 83.7 - type: accuracy name: Low Saxon Test accuracy value: 41.9 - type: accuracy name: Akkadian Test accuracy value: 17.3 - type: accuracy name: Armenian Test accuracy value: 84.3 - type: accuracy name: Welsh Test accuracy value: 65.5 - type: accuracy name: Old East Slavic Test accuracy value: 74.1 - type: accuracy name: Albanian Test accuracy value: 76.6 - type: accuracy name: Slovenian Test accuracy value: 97.6 - type: accuracy name: Guajajara Test accuracy value: 22.5 - type: accuracy name: Kurmanji Test accuracy value: 75.7 - type: accuracy name: Turkish Test accuracy value: 75.4 - type: accuracy name: Finnish Test accuracy value: 81.2 - type: accuracy name: Indonesian Test accuracy value: 81.8 - type: accuracy name: Ukrainian Test accuracy value: 92.6 - type: accuracy name: Polish Test accuracy value: 93.2 - type: accuracy name: Portuguese Test accuracy value: 84.0 - type: accuracy name: Kazakh Test accuracy value: 79.4 - type: accuracy name: Latin Test accuracy value: 76.7 - type: accuracy name: Old French Test accuracy value: 40.3 - type: accuracy name: Buryat Test accuracy value: 53.1 - type: accuracy name: Kaapor Test accuracy value: 11.2 - type: accuracy name: Korean Test accuracy value: 61.9 - type: accuracy name: Estonian Test accuracy value: 82.2 - type: accuracy name: Croatian Test accuracy value: 93.1 - type: accuracy name: Gothic Test accuracy value: 6.2 - type: accuracy name: Swiss German Test accuracy value: 40.7 - type: accuracy name: Assyrian Test accuracy value: 14.6 - type: accuracy name: North Sami Test accuracy value: 22.5 - type: accuracy name: Naija Test accuracy value: 33.9 - type: accuracy name: Latvian Test accuracy value: 86.0 - type: accuracy name: Chinese Test accuracy value: 39.7 - type: accuracy name: Tagalog Test accuracy value: 72.0 - type: accuracy name: Bambara Test accuracy value: 23.5 - type: accuracy name: Lithuanian Test accuracy value: 87.3 - type: accuracy name: Galician Test accuracy value: 82.5 - type: accuracy name: Vietnamese Test accuracy value: 67.3 - type: accuracy name: Greek Test accuracy value: 79.7 - type: accuracy name: Catalan Test accuracy value: 79.0 - type: accuracy name: Czech Test accuracy value: 94.1 - type: accuracy name: Erzya Test accuracy value: 40.1 - type: accuracy name: Bhojpuri Test accuracy value: 46.5 - type: accuracy name: Thai Test accuracy value: 53.2 - type: accuracy name: Marathi Test accuracy value: 87.7 - type: accuracy name: Basque Test accuracy value: 74.6 - type: accuracy name: Slovak Test accuracy value: 95.5 - type: accuracy name: Kiche Test accuracy value: 24.7 - type: accuracy name: Yoruba Test accuracy value: 17.1 - type: accuracy name: Warlpiri Test accuracy value: 27.5 - type: accuracy name: Tamil Test accuracy value: 83.4 - type: accuracy name: Maltese Test accuracy value: 18.4 - type: accuracy name: Ancient Greek Test accuracy value: 60.8 - type: accuracy name: Icelandic Test accuracy value: 80.0 - type: accuracy name: Mbya Guarani Test accuracy value: 23.7 - type: accuracy name: Urdu Test accuracy value: 61.6 - type: accuracy name: Romanian Test accuracy value: 82.4 - type: accuracy name: Persian Test accuracy value: 78.6 - type: accuracy name: Apurina Test accuracy value: 29.2 - type: accuracy name: Japanese Test accuracy value: 25.5 - type: accuracy name: Hungarian Test accuracy value: 74.6 - type: accuracy name: Hindi Test accuracy value: 67.4 - type: accuracy name: Classical Chinese Test accuracy value: 14.8 - type: accuracy name: Komi Permyak Test accuracy value: 40.3 - type: accuracy name: Faroese Test accuracy value: 75.0 - type: accuracy name: Sanskrit Test accuracy value: 14.3 - type: accuracy name: Livvi Test accuracy value: 58.2 - type: accuracy name: Arabic Test accuracy value: 79.8 - type: accuracy name: Wolof Test accuracy value: 24.7 - type: accuracy name: Bulgarian Test accuracy value: 90.4 - type: accuracy name: Akuntsu Test accuracy value: 20.6 - type: accuracy name: Makurap Test accuracy value: 6.2 - type: accuracy name: Kangri Test accuracy value: 44.2 - type: accuracy name: Breton Test accuracy value: 53.2 - type: accuracy name: Telugu Test accuracy value: 83.4 - type: accuracy name: Cantonese Test accuracy value: 48.9 - type: accuracy name: Old Church Slavonic Test accuracy value: 41.9 - type: accuracy name: Karelian Test accuracy value: 64.7 - type: accuracy name: Upper Sorbian Test accuracy value: 79.9 - type: accuracy name: South Levantine Arabic Test accuracy value: 67.2 - type: accuracy name: Komi Zyrian Test accuracy value: 33.3 - type: accuracy name: Irish Test accuracy value: 63.0 - type: accuracy name: Nayini Test accuracy value: 32.1 - type: accuracy name: Munduruku Test accuracy value: 10.1 - type: accuracy name: Manx Test accuracy value: 22.0 - type: accuracy name: Skolt Sami Test accuracy value: 27.4 - type: accuracy name: Afrikaans Test accuracy value: 74.0 - type: accuracy name: Old Turkish Test accuracy value: 37.1 - type: accuracy name: Tupinamba Test accuracy value: 22.5 - type: accuracy name: Belarusian Test accuracy value: 90.2 - type: accuracy name: Serbian Test accuracy value: 94.4 - type: accuracy name: Moksha Test accuracy value: 37.6 - type: accuracy name: Western Armenian Test accuracy value: 73.8 - type: accuracy name: Scottish Gaelic Test accuracy value: 55.0 - type: accuracy name: Khunsari Test accuracy value: 32.4 - type: accuracy name: Hebrew Test accuracy value: 81.2 - type: accuracy name: Uyghur Test accuracy value: 72.1 - type: accuracy name: Chukchi Test accuracy value: 30.2 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Slovenian 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-sl") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sl") ```
wietsedv/xlm-roberta-base-ft-udpos28-sme
a115d0fea7ebf875e7b2d3d7537b58bfd2a71e43
2022-02-25T09:59:24.000Z
[ "pytorch", "xlm-roberta", "token-classification", "sme", "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-sme
1
null
transformers
30,588
--- language: - sme 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-sme 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: 48.1 - type: accuracy name: Dutch Test accuracy value: 49.5 - type: accuracy name: German Test accuracy value: 40.4 - type: accuracy name: Italian Test accuracy value: 48.9 - type: accuracy name: French Test accuracy value: 43.9 - type: accuracy name: Spanish Test accuracy value: 47.1 - type: accuracy name: Russian Test accuracy value: 57.3 - type: accuracy name: Swedish Test accuracy value: 47.9 - type: accuracy name: Norwegian Test accuracy value: 45.5 - type: accuracy name: Danish Test accuracy value: 50.7 - type: accuracy name: Low Saxon Test accuracy value: 38.7 - type: accuracy name: Akkadian Test accuracy value: 29.6 - type: accuracy name: Armenian Test accuracy value: 63.0 - type: accuracy name: Welsh Test accuracy value: 36.9 - type: accuracy name: Old East Slavic Test accuracy value: 46.0 - type: accuracy name: Albanian Test accuracy value: 47.8 - type: accuracy name: Slovenian Test accuracy value: 45.5 - type: accuracy name: Guajajara Test accuracy value: 31.8 - type: accuracy name: Kurmanji Test accuracy value: 42.5 - type: accuracy name: Turkish Test accuracy value: 56.3 - type: accuracy name: Finnish Test accuracy value: 64.7 - type: accuracy name: Indonesian Test accuracy value: 59.3 - type: accuracy name: Ukrainian Test accuracy value: 56.6 - type: accuracy name: Polish Test accuracy value: 55.0 - type: accuracy name: Portuguese Test accuracy value: 52.0 - type: accuracy name: Kazakh Test accuracy value: 62.2 - type: accuracy name: Latin Test accuracy value: 50.3 - type: accuracy name: Old French Test accuracy value: 30.8 - type: accuracy name: Buryat Test accuracy value: 50.6 - type: accuracy name: Kaapor Test accuracy value: 18.3 - type: accuracy name: Korean Test accuracy value: 51.7 - type: accuracy name: Estonian Test accuracy value: 65.2 - type: accuracy name: Croatian Test accuracy value: 55.9 - type: accuracy name: Gothic Test accuracy value: 31.1 - type: accuracy name: Swiss German Test accuracy value: 37.1 - type: accuracy name: Assyrian Test accuracy value: 24.1 - type: accuracy name: North Sami Test accuracy value: 87.7 - type: accuracy name: Naija Test accuracy value: 19.8 - type: accuracy name: Latvian Test accuracy value: 64.2 - type: accuracy name: Chinese Test accuracy value: 33.9 - type: accuracy name: Tagalog Test accuracy value: 46.3 - type: accuracy name: Bambara Test accuracy value: 30.2 - type: accuracy name: Lithuanian Test accuracy value: 63.5 - type: accuracy name: Galician Test accuracy value: 48.5 - type: accuracy name: Vietnamese Test accuracy value: 46.0 - type: accuracy name: Greek Test accuracy value: 45.6 - type: accuracy name: Catalan Test accuracy value: 45.8 - type: accuracy name: Czech Test accuracy value: 54.5 - type: accuracy name: Erzya Test accuracy value: 45.8 - type: accuracy name: Bhojpuri Test accuracy value: 34.3 - type: accuracy name: Thai Test accuracy value: 23.9 - type: accuracy name: Marathi Test accuracy value: 67.5 - type: accuracy name: Basque Test accuracy value: 59.6 - type: accuracy name: Slovak Test accuracy value: 57.7 - type: accuracy name: Kiche Test accuracy value: 35.6 - type: accuracy name: Yoruba Test accuracy value: 31.0 - type: accuracy name: Warlpiri Test accuracy value: 43.3 - type: accuracy name: Tamil Test accuracy value: 60.4 - type: accuracy name: Maltese Test accuracy value: 34.1 - type: accuracy name: Ancient Greek Test accuracy value: 41.8 - type: accuracy name: Icelandic Test accuracy value: 47.2 - type: accuracy name: Mbya Guarani Test accuracy value: 36.0 - type: accuracy name: Urdu Test accuracy value: 36.8 - type: accuracy name: Romanian Test accuracy value: 50.1 - type: accuracy name: Persian Test accuracy value: 45.8 - type: accuracy name: Apurina Test accuracy value: 48.4 - type: accuracy name: Japanese Test accuracy value: 30.6 - type: accuracy name: Hungarian Test accuracy value: 54.7 - type: accuracy name: Hindi Test accuracy value: 39.5 - type: accuracy name: Classical Chinese Test accuracy value: 18.3 - type: accuracy name: Komi Permyak Test accuracy value: 51.1 - type: accuracy name: Faroese Test accuracy value: 52.2 - type: accuracy name: Sanskrit Test accuracy value: 28.4 - type: accuracy name: Livvi Test accuracy value: 57.7 - type: accuracy name: Arabic Test accuracy value: 40.5 - type: accuracy name: Wolof Test accuracy value: 36.2 - type: accuracy name: Bulgarian Test accuracy value: 54.1 - type: accuracy name: Akuntsu Test accuracy value: 31.6 - type: accuracy name: Makurap Test accuracy value: 17.8 - type: accuracy name: Kangri Test accuracy value: 33.8 - type: accuracy name: Breton Test accuracy value: 47.0 - type: accuracy name: Telugu Test accuracy value: 58.7 - type: accuracy name: Cantonese Test accuracy value: 36.0 - type: accuracy name: Old Church Slavonic Test accuracy value: 35.1 - type: accuracy name: Karelian Test accuracy value: 57.5 - type: accuracy name: Upper Sorbian Test accuracy value: 51.1 - type: accuracy name: South Levantine Arabic Test accuracy value: 44.5 - type: accuracy name: Komi Zyrian Test accuracy value: 42.2 - type: accuracy name: Irish Test accuracy value: 34.8 - type: accuracy name: Nayini Test accuracy value: 41.0 - type: accuracy name: Munduruku Test accuracy value: 21.6 - type: accuracy name: Manx Test accuracy value: 28.0 - type: accuracy name: Skolt Sami Test accuracy value: 49.2 - type: accuracy name: Afrikaans Test accuracy value: 43.2 - type: accuracy name: Old Turkish Test accuracy value: 38.9 - type: accuracy name: Tupinamba Test accuracy value: 44.2 - type: accuracy name: Belarusian Test accuracy value: 58.7 - type: accuracy name: Serbian Test accuracy value: 55.9 - type: accuracy name: Moksha Test accuracy value: 45.0 - type: accuracy name: Western Armenian Test accuracy value: 56.1 - type: accuracy name: Scottish Gaelic Test accuracy value: 31.0 - type: accuracy name: Khunsari Test accuracy value: 27.0 - type: accuracy name: Hebrew Test accuracy value: 61.5 - type: accuracy name: Uyghur Test accuracy value: 61.4 - type: accuracy name: Chukchi Test accuracy value: 41.5 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: North Sami 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-sme") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sme") ```
wietsedv/xlm-roberta-base-ft-udpos28-sv
2f9a7219927445d3fb837455a2d9a593fa8d9201
2022-02-25T09:59:27.000Z
[ "pytorch", "xlm-roberta", "token-classification", "sv", "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-sv
1
null
transformers
30,589
--- language: - sv 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-sv 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: 92.3 - type: accuracy name: Dutch Test accuracy value: 90.0 - type: accuracy name: German Test accuracy value: 91.1 - type: accuracy name: Italian Test accuracy value: 88.0 - type: accuracy name: French Test accuracy value: 88.2 - type: accuracy name: Spanish Test accuracy value: 91.1 - type: accuracy name: Russian Test accuracy value: 91.4 - type: accuracy name: Swedish Test accuracy value: 97.9 - type: accuracy name: Norwegian Test accuracy value: 89.7 - type: accuracy name: Danish Test accuracy value: 92.9 - type: accuracy name: Low Saxon Test accuracy value: 57.4 - type: accuracy name: Akkadian Test accuracy value: 40.4 - type: accuracy name: Armenian Test accuracy value: 87.5 - type: accuracy name: Welsh Test accuracy value: 69.6 - type: accuracy name: Old East Slavic Test accuracy value: 76.2 - type: accuracy name: Albanian Test accuracy value: 80.3 - type: accuracy name: Slovenian Test accuracy value: 81.0 - type: accuracy name: Guajajara Test accuracy value: 35.1 - type: accuracy name: Kurmanji Test accuracy value: 77.3 - type: accuracy name: Turkish Test accuracy value: 79.2 - type: accuracy name: Finnish Test accuracy value: 87.0 - type: accuracy name: Indonesian Test accuracy value: 84.2 - type: accuracy name: Ukrainian Test accuracy value: 90.4 - type: accuracy name: Polish Test accuracy value: 88.9 - type: accuracy name: Portuguese Test accuracy value: 90.1 - type: accuracy name: Kazakh Test accuracy value: 83.4 - type: accuracy name: Latin Test accuracy value: 79.1 - type: accuracy name: Old French Test accuracy value: 62.6 - type: accuracy name: Buryat Test accuracy value: 63.0 - type: accuracy name: Kaapor Test accuracy value: 20.8 - type: accuracy name: Korean Test accuracy value: 64.3 - type: accuracy name: Estonian Test accuracy value: 89.6 - type: accuracy name: Croatian Test accuracy value: 90.8 - type: accuracy name: Gothic Test accuracy value: 26.0 - type: accuracy name: Swiss German Test accuracy value: 51.8 - type: accuracy name: Assyrian Test accuracy value: 17.2 - type: accuracy name: North Sami Test accuracy value: 45.4 - type: accuracy name: Naija Test accuracy value: 48.1 - type: accuracy name: Latvian Test accuracy value: 87.1 - type: accuracy name: Chinese Test accuracy value: 48.5 - type: accuracy name: Tagalog Test accuracy value: 72.3 - type: accuracy name: Bambara Test accuracy value: 31.8 - type: accuracy name: Lithuanian Test accuracy value: 86.2 - type: accuracy name: Galician Test accuracy value: 88.1 - type: accuracy name: Vietnamese Test accuracy value: 66.3 - type: accuracy name: Greek Test accuracy value: 88.1 - type: accuracy name: Catalan Test accuracy value: 90.1 - type: accuracy name: Czech Test accuracy value: 90.1 - type: accuracy name: Erzya Test accuracy value: 50.8 - type: accuracy name: Bhojpuri Test accuracy value: 51.7 - type: accuracy name: Thai Test accuracy value: 66.4 - type: accuracy name: Marathi Test accuracy value: 86.5 - type: accuracy name: Basque Test accuracy value: 76.4 - type: accuracy name: Slovak Test accuracy value: 90.5 - type: accuracy name: Kiche Test accuracy value: 42.4 - type: accuracy name: Yoruba Test accuracy value: 31.2 - type: accuracy name: Warlpiri Test accuracy value: 42.5 - type: accuracy name: Tamil Test accuracy value: 85.3 - type: accuracy name: Maltese Test accuracy value: 30.6 - type: accuracy name: Ancient Greek Test accuracy value: 63.0 - type: accuracy name: Icelandic Test accuracy value: 85.3 - type: accuracy name: Mbya Guarani Test accuracy value: 32.3 - type: accuracy name: Urdu Test accuracy value: 67.6 - type: accuracy name: Romanian Test accuracy value: 85.5 - type: accuracy name: Persian Test accuracy value: 77.4 - type: accuracy name: Apurina Test accuracy value: 47.4 - type: accuracy name: Japanese Test accuracy value: 35.5 - type: accuracy name: Hungarian Test accuracy value: 87.1 - type: accuracy name: Hindi Test accuracy value: 75.1 - type: accuracy name: Classical Chinese Test accuracy value: 30.8 - type: accuracy name: Komi Permyak Test accuracy value: 52.4 - type: accuracy name: Faroese Test accuracy value: 80.3 - type: accuracy name: Sanskrit Test accuracy value: 40.7 - type: accuracy name: Livvi Test accuracy value: 68.5 - type: accuracy name: Arabic Test accuracy value: 82.0 - type: accuracy name: Wolof Test accuracy value: 37.4 - type: accuracy name: Bulgarian Test accuracy value: 92.9 - type: accuracy name: Akuntsu Test accuracy value: 41.1 - type: accuracy name: Makurap Test accuracy value: 22.6 - type: accuracy name: Kangri Test accuracy value: 47.1 - type: accuracy name: Breton Test accuracy value: 64.3 - type: accuracy name: Telugu Test accuracy value: 84.9 - type: accuracy name: Cantonese Test accuracy value: 48.8 - type: accuracy name: Old Church Slavonic Test accuracy value: 51.1 - type: accuracy name: Karelian Test accuracy value: 74.1 - type: accuracy name: Upper Sorbian Test accuracy value: 77.5 - type: accuracy name: South Levantine Arabic Test accuracy value: 69.6 - type: accuracy name: Komi Zyrian Test accuracy value: 44.5 - type: accuracy name: Irish Test accuracy value: 70.5 - type: accuracy name: Nayini Test accuracy value: 44.9 - type: accuracy name: Munduruku Test accuracy value: 24.3 - type: accuracy name: Manx Test accuracy value: 34.1 - type: accuracy name: Skolt Sami Test accuracy value: 42.0 - type: accuracy name: Afrikaans Test accuracy value: 92.1 - type: accuracy name: Old Turkish Test accuracy value: 40.3 - type: accuracy name: Tupinamba Test accuracy value: 41.4 - type: accuracy name: Belarusian Test accuracy value: 89.8 - type: accuracy name: Serbian Test accuracy value: 91.5 - type: accuracy name: Moksha Test accuracy value: 46.7 - type: accuracy name: Western Armenian Test accuracy value: 80.3 - type: accuracy name: Scottish Gaelic Test accuracy value: 60.4 - type: accuracy name: Khunsari Test accuracy value: 45.9 - type: accuracy name: Hebrew Test accuracy value: 87.5 - type: accuracy name: Uyghur Test accuracy value: 76.9 - type: accuracy name: Chukchi Test accuracy value: 35.9 --- # XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Swedish 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-sv") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sv") ```
cammy/t5-base-finetuned-weaksup-1000
87d55b016c71958c857870d31ffd7f5fddc3ba95
2022-02-24T10:26:36.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
cammy
null
cammy/t5-base-finetuned-weaksup-1000
1
null
transformers
30,590
--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-finetuned-weaksup-1000 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. --> # t5-base-finetuned-weaksup-1000 This model is a fine-tuned version of [cammy/t5-base-finetuned-weaksup-1000](https://huggingface.co/cammy/t5-base-finetuned-weaksup-1000) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6699 - Rouge1: 22.2079 - Rouge2: 9.54 - Rougel: 19.9593 - Rougelsum: 20.2524 - Gen Len: 18.17 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 1.6257 | 1.0 | 1000 | 1.6699 | 22.2079 | 9.54 | 19.9593 | 20.2524 | 18.17 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0
lhbit20010120/distilgpt2-finetuned-wikitext2
6571f4ca3ee7f706c407895af5ecb8bef3f63c17
2022-02-24T10:45:51.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-generation
false
lhbit20010120
null
lhbit20010120/distilgpt2-finetuned-wikitext2
1
null
transformers
30,591
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6423 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7602 | 1.0 | 2334 | 3.6669 | | 3.633 | 2.0 | 4668 | 3.6455 | | 3.6078 | 3.0 | 7002 | 3.6423 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
izzy-lazerson/wav2vec2-base-timit-demo-colab
24a34e63191a7fefcaf4db767ffc3daa60d95dc9
2022-02-24T13:44:39.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
izzy-lazerson
null
izzy-lazerson/wav2vec2-base-timit-demo-colab
1
null
transformers
30,592
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4545 - Wer: 0.3450 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3801 | 4.0 | 500 | 1.1501 | 0.8820 | | 0.561 | 8.0 | 1000 | 0.4583 | 0.4211 | | 0.2198 | 12.0 | 1500 | 0.4467 | 0.3997 | | 0.1255 | 16.0 | 2000 | 0.4390 | 0.3677 | | 0.0862 | 20.0 | 2500 | 0.4934 | 0.3603 | | 0.0617 | 24.0 | 3000 | 0.4641 | 0.3549 | | 0.0465 | 28.0 | 3500 | 0.4545 | 0.3450 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
andresestevez/bert-finetuned-squad-accelerate
7338aab55a84d553b9a4a41f9b46f9e20b577333
2022-03-02T03:12:15.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
andresestevez
null
andresestevez/bert-finetuned-squad-accelerate
1
null
transformers
30,593
Entry not found
Francesco/resnet50
60efd8ae5dc9bf423ec7b8e62c61b1b8284536a2
2022-03-01T15:04:37.000Z
[ "pytorch", "resnet", "image-classification", "transformers" ]
image-classification
false
Francesco
null
Francesco/resnet50
1
null
transformers
30,594
Entry not found
Francesco/resnet101
d5b89071222d9a3dccfb174fa5c83dad26c82e7d
2022-03-01T15:06:55.000Z
[ "pytorch", "resnet", "image-classification", "transformers" ]
image-classification
false
Francesco
null
Francesco/resnet101
1
null
transformers
30,595
Entry not found
mrm8488/biomedtra-base-es
e33448a03f869e96e836aa23d55b8d85b984b1c5
2022-03-25T16:58:53.000Z
[ "pytorch", "tensorboard", "electra", "pretraining", "transformers" ]
null
false
mrm8488
null
mrm8488/biomedtra-base-es
1
null
transformers
30,596
Entry not found
Shakaw/DialoGPT-small-spongebot
939412916bdb9d179228ee9237b54ee51b611c7a
2022-02-24T13:34:09.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Shakaw
null
Shakaw/DialoGPT-small-spongebot
1
null
transformers
30,597
--- tags: - conversational --- # Spongebob DialoGPT model
anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6
f418599aa60108afe24eb219cb6567e0d7193c2b
2022-02-24T21:09:03.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6
1
null
transformers
30,598
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-8
99169292576e90b4b3ff8904c0dfd7aae1835b5a
2022-02-24T21:24:10.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-8
1
null
transformers
30,599
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-base-uncased-few-shot-k-16-finetuned-squad-seed-8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-few-shot-k-16-finetuned-squad-seed-8 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3