pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
token-classification | transformers |
<!-- 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. -->
# shopee-ner
This model is a fine-tuned version of [cahya/xlm-roberta-base-indonesian-NER](https://huggingface.co/cahya/xlm-robert... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "shopee-ner", "results": []}]} | vkhangpham/shopee-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| shopee-ner
==========
This model is a fine-tuned version of cahya/xlm-roberta-base-indonesian-NER on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2046
* Precision: 0.7666
* Recall: 0.8666
* F1: 0.8135
* Accuracy: 0.9320
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* ... |
text-classification | transformers |
<!-- 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. -->
# xlnet-base-cased-finetuned-qqp
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased)... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "xlnet-base-cased-finetuned-qqp", "results": []}]} | vkk1710/xlnet-base-cased-finetuned-qqp | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# xlnet-base-cased-finetuned-qqp
This model is a fine-tuned version of xlnet-base-cased on the qqp dataset (part of glue dataset).
It achieves the following results on the evaluation set:
- eval_loss: 0.27
- eval_accuracy: 0.9084
- eval_f1: 0.8775
- epoch: 3
## Model description
More information needed
## Intend... | [
"# xlnet-base-cased-finetuned-qqp\n\nThis model is a fine-tuned version of xlnet-base-cased on the qqp dataset (part of glue dataset).\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.27\n- eval_accuracy: 0.9084\n- eval_f1: 0.8775\n- epoch: 3",
"## Model description\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlnet-base-cased-finetuned-qqp\n\nThis model is a fine-tuned version of xlnet-base-cased on the qqp dataset (part of glue dataset... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | vkmr/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4488
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text-generation | transformers | # NLboto_o aki | {"tags": ["conversational"]} | vlco-o/NLboto_o-aki-dialogpt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # NLboto_o aki | [
"# NLboto_o aki"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NLboto_o aki"
] |
text-generation | transformers |
# NLboto_o model | {"tags": ["conversational"]} | vlco-o/NLboto_o-small-dialogpt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# NLboto_o model | [
"# NLboto_o model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NLboto_o model"
] |
text-generation | transformers | GPT model developed in [Language Models are Few-Shot Butlers](https://arxiv.org/abs/2104.07972). | {} | vmicheli/lm-butlers-gpt | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"arxiv:2104.07972",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.07972"
] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #arxiv-2104.07972 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT model developed in Language Models are Few-Shot Butlers. | [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #arxiv-2104.07972 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-XLSR-53
[Facebook's XLSR-Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned o... | {"language": "multilingual", "license": "apache-2.0", "tags": ["speech", "automatic-speech-recognition"], "datasets": ["common_voice"]} | vneralla/xlrs-53-finnish | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"speech",
"automatic-speech-recognition",
"multilingual",
"dataset:common_voice",
"arxiv:2006.13979",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.13979"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #pretraining #speech #automatic-speech-recognition #multilingual #dataset-common_voice #arxiv-2006.13979 #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-XLSR-53
Facebook's XLSR-Wav2Vec2
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check out this blog for more informa... | [
"# Wav2Vec2-XLSR-53 \n\nFacebook's XLSR-Wav2Vec2\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check out this blog for more... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #pretraining #speech #automatic-speech-recognition #multilingual #dataset-common_voice #arxiv-2006.13979 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-XLSR-53 \n\nFacebook's XLSR-Wav2Vec2\n\nThe base model pretrained on 16kHz sampled speech audi... |
text-classification | transformers | #cross_encoder-msmarco-distilbert-word2vec256k-MLM_400k
This CrossEncoder was trained with MarginMSE loss from the [vocab-transformers/msmarco-distilbert-word2vec256k-MLM_400k](https://hf.co/vocab-transformers/msmarco-distilbert-word2vec256k-MLM_400k) checkpoint. **Word embedding matrix has been frozen during traini... | {} | vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k-MLM_400k | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| #cross_encoder-msmarco-distilbert-word2vec256k-MLM_400k
This CrossEncoder was trained with MarginMSE loss from the vocab-transformers/msmarco-distilbert-word2vec256k-MLM_400k checkpoint. Word embedding matrix has been frozen during training.
You can load the model with sentence-transformers:
Performance on T... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | #cross_encoder-msmarco-distilbert-word2vec256k-MLM_785k_emb_updated
This CrossEncoder was trained with MarginMSE loss from the [vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated](https://hf.co/vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated) checkpoint. **Word embedding ... | {} | vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k-MLM_785k_emb_updated | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| #cross_encoder-msmarco-distilbert-word2vec256k-MLM_785k_emb_updated
This CrossEncoder was trained with MarginMSE loss from the vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated checkpoint. Word embedding matrix has been updated during training.
You can load the model with sentence-transforme... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | #cross_encoder-msmarco-word2vec256k
This CrossEncoder was trained with MarginMSE loss from the [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://hf.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) checkpoint. **Word embedding matrix has been frozen during training**.
You can load ... | {} | vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| #cross_encoder-msmarco-word2vec256k
This CrossEncoder was trained with MarginMSE loss from the nicoladecao/msmarco-word2vec256000-distilbert-base-uncased checkpoint. Word embedding matrix has been frozen during training.
You can load the model with sentence-transformers:
Performance on TREC Deep Learning (nD... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# dense_encoder-msmarco-bert-base-word2vec256k
**Note: Token embeddings where updated!**
This model is based on [msmarco-word2vec256000-bert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-bert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
It has been trained on MS M... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | vocab-transformers/dense_encoder-msmarco-bert-base-word2vec256k_emb_updated | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# dense_encoder-msmarco-bert-base-word2vec256k
Note: Token embeddings where updated!
This model is based on msmarco-word2vec256000-bert-base-uncased with a 256k sized vocabulary initialized with word2vec.
It has been trained on MS MARCO using MarginMSELoss. See the train_script.py in this repository.
Performance... | [
"# dense_encoder-msmarco-bert-base-word2vec256k\n\nNote: Token embeddings where updated!\n\nThis model is based on msmarco-word2vec256000-bert-base-uncased with a 256k sized vocabulary initialized with word2vec.\n\nIt has been trained on MS MARCO using MarginMSELoss. See the train_script.py in this repository.\n\n\... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# dense_encoder-msmarco-bert-base-word2vec256k\n\nNote: Token embeddings where updated!\n\nThis model is based on msmarco-word2vec256000-bert-base-uncased with a 256k sized vo... |
sentence-similarity | sentence-transformers |
# dense_encoder-msmarco-distilbert-word2vec256k-MLM_210k
**Note: Token embeddings where updated!**
This model is based on [vocab-transformers/msmarco-distilbert-word2vec256k-MLM_210k](https://huggingface.co/vocab-transformers/msmarco-distilbert-word2vec256k-MLM_210k) with a 256k sized vocabulary initialized with wor... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k-MLM_210k_emb_updated | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# dense_encoder-msmarco-distilbert-word2vec256k-MLM_210k
Note: Token embeddings where updated!
This model is based on vocab-transformers/msmarco-distilbert-word2vec256k-MLM_210k with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 210k.
It has been trained on MS MARCO using Marg... | [
"# dense_encoder-msmarco-distilbert-word2vec256k-MLM_210k\n\nNote: Token embeddings where updated!\n\nThis model is based on vocab-transformers/msmarco-distilbert-word2vec256k-MLM_210k with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 210k.\n\nIt has been trained on MS MARCO ... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# dense_encoder-msmarco-distilbert-word2vec256k-MLM_210k\n\nNote: Token embeddings where updated!\n\nThis model is based on vocab-transformers/msmarco-distilbert-word2ve... |
sentence-similarity | sentence-transformers |
# dense_encoder-msmarco-distilbert-word2vec256k-MLM_445k
This model is based on [vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k](https://huggingface.co/vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k) with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 44... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k-MLM_445k_emb_updated | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# dense_encoder-msmarco-distilbert-word2vec256k-MLM_445k
This model is based on vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 445k steps. Note: Token embeddings where updated!
It has been trained on MS MARCO using... | [
"# dense_encoder-msmarco-distilbert-word2vec256k-MLM_445k\n\nThis model is based on vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 445k steps. Note: Token embeddings where updated!\n\nIt has been trained on MS MAR... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# dense_encoder-msmarco-distilbert-word2vec256k-MLM_445k\n\nThis model is based on vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k with a 256k sized vocabula... |
sentence-similarity | sentence-transformers |
# dense_encoder-msmarco-distilbert-word2vec256k-MLM_785k_emb_updated
**Note: Token embeddings where updated!**
This model is based on [vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated](https://huggingface.co/vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated) with a 256k s... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k-MLM_785k_emb_updated | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# dense_encoder-msmarco-distilbert-word2vec256k-MLM_785k_emb_updated
Note: Token embeddings where updated!
This model is based on vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 785k.
It has been traine... | [
"# dense_encoder-msmarco-distilbert-word2vec256k-MLM_785k_emb_updated\n\nNote: Token embeddings where updated!\n\nThis model is based on vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 785k.\n\nIt has b... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# dense_encoder-msmarco-distilbert-word2vec256k-MLM_785k_emb_updated\n\nNote: Token embeddings where updated!\n\nThis model is based on vocab-transformers/msmarco-distil... |
sentence-similarity | sentence-transformers |
# dense_encoder-msmarco-distilbert-word2vec256k
This model is based on [msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
It has been trained on MS MARCO using [MarginMSELoss](http... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# dense_encoder-msmarco-distilbert-word2vec256k
This model is based on msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.
It has been trained on MS MARCO using MarginMSELoss. See the train_script.py in this repository.
Performance:
- MS MARCO dev: - (MRR@10)
- TREC... | [
"# dense_encoder-msmarco-distilbert-word2vec256k\n\nThis model is based on msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\n\nIt has been trained on MS MARCO using MarginMSELoss. See the train_script.py in this repository.\nPerformance:\n- MS MARCO dev: - (MRR@... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# dense_encoder-msmarco-distilbert-word2vec256k\n\nThis model is based on msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with wo... |
sentence-similarity | sentence-transformers |
# dense_encoder-msmarco-distilbert-word2vec256k
**Note: Token embeddings where updated!**
This model is based on [msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
It has been tra... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | vocab-transformers/dense_encoder-msmarco-distilbert-word2vec256k_emb_updated | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# dense_encoder-msmarco-distilbert-word2vec256k
Note: Token embeddings where updated!
This model is based on msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.
It has been trained on MS MARCO using MarginMSELoss. See the train_script.py in this repository.
Perfo... | [
"# dense_encoder-msmarco-distilbert-word2vec256k\n\nNote: Token embeddings where updated!\n\nThis model is based on msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\n\nIt has been trained on MS MARCO using MarginMSELoss. See the train_script.py in this repositor... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# dense_encoder-msmarco-distilbert-word2vec256k\n\nNote: Token embeddings where updated!\n\nThis model is based on msmarco-word2vec256000-distilbert-base-uncased with a ... |
fill-mask | transformers | # Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 210k steps... | {} | vocab-transformers/msmarco-distilbert-word2vec256k-MLM_210k_emb_updated | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Model
This model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 210k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs.
Note: Token e... | [
"# Model\r\nThis model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\r\n\r\nThis model has been trained with MLM on the MS MARCO corpus collection for 210k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs.\r\n\r... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model\r\nThis model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\r\n\r\nThis model has been trained with MLM on the ... |
fill-mask | transformers | # Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 230k steps... | {} | vocab-transformers/msmarco-distilbert-word2vec256k-MLM_230k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Model
This model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 230k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs. The word embeddi... | [
"# Model\r\nThis model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\r\n\r\nThis model has been trained with MLM on the MS MARCO corpus collection for 230k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs. The w... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model\r\nThis model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\r\n\r\nThis model has been trained with MLM on the ... |
fill-mask | transformers | # Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 400k steps... | {} | vocab-transformers/msmarco-distilbert-word2vec256k-MLM_400k | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Model
This model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 400k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs. The word embeddi... | [
"# Model\r\nThis model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\r\n\r\nThis model has been trained with MLM on the MS MARCO corpus collection for 400k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs. The w... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model\r\nThis model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\r\n\r\nThis model has been trained with MLM on the ... |
fill-mask | transformers | # Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 445k steps... | {} | vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k_emb_updated | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Model
This model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 445k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs.
Note: Token e... | [
"# Model\r\nThis model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\r\n\r\nThis model has been trained with MLM on the MS MARCO corpus collection for 445k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs.\r\n\r... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model\r\nThis model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\r\n\r\nThis model has been trained with MLM on the ... |
fill-mask | transformers | # Model
This model is based on [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://huggingface.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 785k steps... | {} | vocab-transformers/msmarco-distilbert-word2vec256k-MLM_785k_emb_updated | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Model
This model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.
This model has been trained with MLM on the MS MARCO corpus collection for 785k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs.
Note: Token e... | [
"# Model\r\nThis model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\r\n\r\nThis model has been trained with MLM on the MS MARCO corpus collection for 785k steps. See train_mlm.py for the train script. It was run on 2x V100 GPUs.\r\n\r... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model\r\nThis model is based on nicoladecao/msmarco-word2vec256000-distilbert-base-uncased with a 256k sized vocabulary initialized with word2vec.\r\n\r\nThis model has been trained with MLM on the ... |
fill-mask | transformers |
# albert_chinese_base
This a albert_chinese_base model from [Google's github](https://github.com/google-research/ALBERT)
converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
## Notice
*Support AutoTokenize... | {"language": "zh", "pipeline_tag": "fill-mask", "widget": [{"text": "\u4eca\u5929[MASK]\u60c5\u5f88\u597d"}]} | voidful/albert_chinese_base | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"fill-mask",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us
|
# albert_chinese_base
This a albert_chinese_base model from Google's github
converted by huggingface's script
## Notice
*Support AutoTokenizer*
Since sentencepiece is not used in albert_chinese_base model
you have to call BertTokenizer instead of AlbertTokenizer !!!
we can eval it using an example on Mask... | [
"# albert_chinese_base\n\nThis a albert_chinese_base model from Google's github \nconverted by huggingface's script",
"## Notice\n*Support AutoTokenizer*\n\nSince sentencepiece is not used in albert_chinese_base model \nyou have to call BertTokenizer instead of AlbertTokenizer !!! \nwe can eval it using an ... | [
"TAGS\n#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"# albert_chinese_base\n\nThis a albert_chinese_base model from Google's github \nconverted by huggingface's script",
"## Notice\n*Support AutoTokenizer*\n\nSince sentencepiece is not u... |
fill-mask | transformers |
# albert_chinese_large
This a albert_chinese_large model from [Google's github](https://github.com/google-research/ALBERT)
converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
## Notice
*Support AutoTokeniz... | {"language": "zh", "pipeline_tag": "fill-mask", "widget": [{"text": "\u4eca\u5929[MASK]\u60c5\u5f88\u597d"}]} | voidful/albert_chinese_large | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"fill-mask",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us
|
# albert_chinese_large
This a albert_chinese_large model from Google's github
converted by huggingface's script
## Notice
*Support AutoTokenizer*
Since sentencepiece is not used in albert_chinese_base model
you have to call BertTokenizer instead of AlbertTokenizer !!!
we can eval it using an example on Mas... | [
"# albert_chinese_large\n\nThis a albert_chinese_large model from Google's github \nconverted by huggingface's script",
"## Notice\n*Support AutoTokenizer*\n\nSince sentencepiece is not used in albert_chinese_base model \nyou have to call BertTokenizer instead of AlbertTokenizer !!! \nwe can eval it using a... | [
"TAGS\n#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"# albert_chinese_large\n\nThis a albert_chinese_large model from Google's github \nconverted by huggingface's script",
"## Notice\n*Support AutoTokenizer*\n\nSince sentencepiece is not... |
fill-mask | transformers |
# albert_chinese_small
This a albert_chinese_small model from [brightmart/albert_zh project](https://github.com/brightmart/albert_zh), albert_small_google_zh model
converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_... | {"language": "zh", "pipeline_tag": "fill-mask", "widget": [{"text": "\u4eca\u5929[MASK]\u60c5\u5f88\u597d"}]} | voidful/albert_chinese_small | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"fill-mask",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us
|
# albert_chinese_small
This a albert_chinese_small model from brightmart/albert_zh project, albert_small_google_zh model
converted by huggingface's script
## Notice
*Support AutoTokenizer*
Since sentencepiece is not used in albert_chinese_base model
you have to call BertTokenizer instead of AlbertTokenizer !... | [
"# albert_chinese_small\n\nThis a albert_chinese_small model from brightmart/albert_zh project, albert_small_google_zh model \nconverted by huggingface's script",
"## Notice\n*Support AutoTokenizer*\n\nSince sentencepiece is not used in albert_chinese_base model \nyou have to call BertTokenizer instead of Al... | [
"TAGS\n#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"# albert_chinese_small\n\nThis a albert_chinese_small model from brightmart/albert_zh project, albert_small_google_zh model \nconverted by huggingface's script",
"## Notice\n*Support... |
fill-mask | transformers |
# albert_chinese_tiny
This a albert_chinese_tiny model from [brightmart/albert_zh project](https://github.com/brightmart/albert_zh), albert_tiny_google_zh model
converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pyt... | {"language": "zh", "pipeline_tag": "fill-mask", "widget": [{"text": "\u4eca\u5929[MASK]\u60c5\u5f88\u597d"}]} | voidful/albert_chinese_tiny | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"fill-mask",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us
|
# albert_chinese_tiny
This a albert_chinese_tiny model from brightmart/albert_zh project, albert_tiny_google_zh model
converted by huggingface's script
## Notice
*Support AutoTokenizer*
Since sentencepiece is not used in albert_chinese_base model
you have to call BertTokenizer instead of AlbertTokenizer !!! ... | [
"# albert_chinese_tiny\n\nThis a albert_chinese_tiny model from brightmart/albert_zh project, albert_tiny_google_zh model \nconverted by huggingface's script",
"## Notice\n*Support AutoTokenizer*\n\nSince sentencepiece is not used in albert_chinese_base model \nyou have to call BertTokenizer instead of Alber... | [
"TAGS\n#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"# albert_chinese_tiny\n\nThis a albert_chinese_tiny model from brightmart/albert_zh project, albert_tiny_google_zh model \nconverted by huggingface's script",
"## Notice\n*Support Au... |
fill-mask | transformers |
# albert_chinese_xlarge
This a albert_chinese_xlarge model from [Google's github](https://github.com/google-research/ALBERT)
converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
## Notice
*Support AutoToke... | {"language": "zh", "pipeline_tag": "fill-mask", "widget": [{"text": "\u4eca\u5929[MASK]\u60c5\u5f88\u597d"}]} | voidful/albert_chinese_xlarge | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"fill-mask",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us
|
# albert_chinese_xlarge
This a albert_chinese_xlarge model from Google's github
converted by huggingface's script
## Notice
*Support AutoTokenizer*
Since sentencepiece is not used in albert_chinese_base model
you have to call BertTokenizer instead of AlbertTokenizer !!!
we can eval it using an example on ... | [
"# albert_chinese_xlarge\n\nThis a albert_chinese_xlarge model from Google's github \nconverted by huggingface's script",
"## Notice\n*Support AutoTokenizer*\n\nSince sentencepiece is not used in albert_chinese_base model \nyou have to call BertTokenizer instead of AlbertTokenizer !!! \nwe can eval it using... | [
"TAGS\n#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"# albert_chinese_xlarge\n\nThis a albert_chinese_xlarge model from Google's github \nconverted by huggingface's script",
"## Notice\n*Support AutoTokenizer*\n\nSince sentencepiece is n... |
fill-mask | transformers |
# albert_chinese_xxlarge
This a albert_chinese_xxlarge model from [Google's github](https://github.com/google-research/ALBERT)
converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
## Notice
*Support AutoTok... | {"language": "zh", "pipeline_tag": "fill-mask", "widget": [{"text": "\u4eca\u5929[MASK]\u60c5\u5f88\u597d"}]} | voidful/albert_chinese_xxlarge | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"fill-mask",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us
|
# albert_chinese_xxlarge
This a albert_chinese_xxlarge model from Google's github
converted by huggingface's script
## Notice
*Support AutoTokenizer*
Since sentencepiece is not used in albert_chinese_base model
you have to call BertTokenizer instead of AlbertTokenizer !!!
we can eval it using an example on... | [
"# albert_chinese_xxlarge\n\nThis a albert_chinese_xxlarge model from Google's github \nconverted by huggingface's script",
"## Notice\n*Support AutoTokenizer*\n\nSince sentencepiece is not used in albert_chinese_base model \nyou have to call BertTokenizer instead of AlbertTokenizer !!! \nwe can eval it usi... | [
"TAGS\n#transformers #pytorch #safetensors #albert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"# albert_chinese_xxlarge\n\nThis a albert_chinese_xxlarge model from Google's github \nconverted by huggingface's script",
"## Notice\n*Support AutoTokenizer*\n\nSince sentencepiece is... |
automatic-speech-recognition | transformers |
# voidful/asr_hubert_cluster_bart_base
## Usage
download file
```shell
wget https://raw.githubusercontent.com/voidful/hubert-cluster-code/main/km_feat_100_layer_20
wget https://cdn-media.huggingface.co/speech_samples/sample1.flac
```
Hubert kmeans code
```python
import joblib
import torch
from transformers import W... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "asr", "hubert"], "datasets": ["librispeech"], "metrics": ["wer", "cer"]} | voidful/asr_hubert_cluster_bart_base | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"audio",
"automatic-speech-recognition",
"speech",
"asr",
"hubert",
"en",
"dataset:librispeech",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #audio #automatic-speech-recognition #speech #asr #hubert #en #dataset-librispeech #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# voidful/asr_hubert_cluster_bart_base
## Usage
download file
Hubert kmeans code
input
bart model
'
generate output
## Result
'going along slushy country roads and speaking to damp audience in drifty school rooms day after day for a fortnight he'll have to put in an appearance at some place of worship on sunda... | [
"# voidful/asr_hubert_cluster_bart_base",
"## Usage\ndownload file\n\n\nHubert kmeans code\n\ninput\n\nbart model\n'\ngenerate output",
"## Result\n'going along slushy country roads and speaking to damp audience in drifty school rooms day after day for a fortnight he'll have to put in an appearance at some plac... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #audio #automatic-speech-recognition #speech #asr #hubert #en #dataset-librispeech #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# voidful/asr_hubert_cluster_bart_base",
"## Usage\ndownload file\n\n\nHubert... |
text2text-generation | transformers | # bart-distractor-generation-both
## Model description
This model is a sequence-to-sequence distractor generator which takes an answer, question and context as an input, and generates a distractor as an output. It is based on a pretrained `bart-base` model.
This model trained with Parallel MLM & Answer Negative Re... | {"language": "en", "tags": ["bart", "distractor", "generation", "seq2seq"], "datasets": ["race"], "metrics": ["bleu", "rouge"], "pipeline_tag": "text2text-generation", "widget": [{"text": "When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes t... | voidful/bart-distractor-generation-both | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"distractor",
"generation",
"seq2seq",
"en",
"dataset:race",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #distractor #generation #seq2seq #en #dataset-race #autotrain_compatible #endpoints_compatible #region-us
| # bart-distractor-generation-both
## Model description
This model is a sequence-to-sequence distractor generator which takes an answer, question and context as an input, and generates a distractor as an output. It is based on a pretrained 'bart-base' model.
This model trained with Parallel MLM & Answer Negative Re... | [
"# bart-distractor-generation-both",
"## Model description\n\nThis model is a sequence-to-sequence distractor generator which takes an answer, question and context as an input, and generates a distractor as an output. It is based on a pretrained 'bart-base' model. \nThis model trained with Parallel MLM & Answer... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #distractor #generation #seq2seq #en #dataset-race #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-distractor-generation-both",
"## Model description\n\nThis model is a sequence-to-sequence distractor generator which take... |
text2text-generation | transformers | # bart-distractor-generation-pm
## Model description
This model is a sequence-to-sequence distractor generator which takes an answer, question and context as an input, and generates a distractor as an output. It is based on a pretrained `bart-base` model.
This model trained with Parallel MLM refer to the [Paper](... | {"language": "en", "tags": ["bart", "distractor", "generation", "seq2seq"], "datasets": ["race"], "metrics": ["bleu", "rouge"], "pipeline_tag": "text2text-generation", "widget": [{"text": "When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes t... | voidful/bart-distractor-generation-pm | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"distractor",
"generation",
"seq2seq",
"en",
"dataset:race",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #distractor #generation #seq2seq #en #dataset-race #autotrain_compatible #endpoints_compatible #region-us
| # bart-distractor-generation-pm
## Model description
This model is a sequence-to-sequence distractor generator which takes an answer, question and context as an input, and generates a distractor as an output. It is based on a pretrained 'bart-base' model.
This model trained with Parallel MLM refer to the Paper. ... | [
"# bart-distractor-generation-pm",
"## Model description\n\nThis model is a sequence-to-sequence distractor generator which takes an answer, question and context as an input, and generates a distractor as an output. It is based on a pretrained 'bart-base' model. \nThis model trained with Parallel MLM refer to ... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #distractor #generation #seq2seq #en #dataset-race #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-distractor-generation-pm",
"## Model description\n\nThis model is a sequence-to-sequence distractor generator which takes ... |
text2text-generation | transformers | # bart-distractor-generation
## Model description
This model is a sequence-to-sequence distractor generator which takes an answer, question and context as an input, and generates a distractor as an output. It is based on a pretrained `bart-base` model.
For details, please see https://github.com/voidful/BDG. ... | {"language": "en", "tags": ["bart", "distractor", "generation", "seq2seq"], "datasets": ["race"], "metrics": ["bleu", "rouge"], "pipeline_tag": "text2text-generation", "widget": [{"text": "When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes t... | voidful/bart-distractor-generation | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"distractor",
"generation",
"seq2seq",
"en",
"dataset:race",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #distractor #generation #seq2seq #en #dataset-race #autotrain_compatible #endpoints_compatible #region-us
| # bart-distractor-generation
## Model description
This model is a sequence-to-sequence distractor generator which takes an answer, question and context as an input, and generates a distractor as an output. It is based on a pretrained 'bart-base' model.
For details, please see URL
## Intended uses & limita... | [
"# bart-distractor-generation",
"## Model description\n\nThis model is a sequence-to-sequence distractor generator which takes an answer, question and context as an input, and generates a distractor as an output. It is based on a pretrained 'bart-base' model. \nFor details, please see URL",
"## Intended ... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #distractor #generation #seq2seq #en #dataset-race #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-distractor-generation",
"## Model description\n\nThis model is a sequence-to-sequence distractor generator which takes an ... |
text2text-generation | transformers | # voidful/bart-eqg-question-generator
## Model description
This model is a sequence-to-sequence question generator with only the context as an input, and generates a question as an output.
It is based on a pretrained `bart-base` model, and trained on [EQG-RACE](https://github.com/jemmryx/EQG-RACE) corpus. ... | {"language": "en", "tags": ["bart", "question", "generation", "seq2seq"], "datasets": ["eqg-race"], "metrics": ["bleu", "rouge"], "pipeline_tag": "text2text-generation", "widget": [{"text": "When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes... | voidful/bart-eqg-question-generator | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"question",
"generation",
"seq2seq",
"en",
"dataset:eqg-race",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #question #generation #seq2seq #en #dataset-eqg-race #autotrain_compatible #endpoints_compatible #has_space #region-us
| # voidful/bart-eqg-question-generator
## Model description
This model is a sequence-to-sequence question generator with only the context as an input, and generates a question as an output.
It is based on a pretrained 'bart-base' model, and trained on EQG-RACE corpus.
## Intended uses & limitations
The ... | [
"# voidful/bart-eqg-question-generator",
"## Model description\n\nThis model is a sequence-to-sequence question generator with only the context as an input, and generates a question as an output. \nIt is based on a pretrained 'bart-base' model, and trained on EQG-RACE corpus.",
"## Intended uses & limitat... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #question #generation #seq2seq #en #dataset-eqg-race #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# voidful/bart-eqg-question-generator",
"## Model description\n\nThis model is a sequence-to-sequence question gener... |
text2text-generation | transformers | # context-only-question-generator
## Model description
This model is a sequence-to-sequence question generator which takes context as an input, and generates a question as an output.
It is based on a pretrained `bart-base` model.
#### How to use
Inputs should be organised into the following format:
```
c... | {"language": "en", "tags": ["bart", "question", "generation", "seq2seq"], "datasets": ["unifiedQA"], "metrics": ["bleu", "rouge"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Harry Potter is a series of seven fantasy novels written by British author J. K. Rowling. The novels chronicle the lives of a yo... | voidful/context-only-question-generator | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"question",
"generation",
"seq2seq",
"en",
"dataset:unifiedQA",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #question #generation #seq2seq #en #dataset-unifiedQA #autotrain_compatible #endpoints_compatible #has_space #region-us
| # context-only-question-generator
## Model description
This model is a sequence-to-sequence question generator which takes context as an input, and generates a question as an output.
It is based on a pretrained 'bart-base' model.
#### How to use
Inputs should be organised into the following format:
The ... | [
"# context-only-question-generator",
"## Model description\n\nThis model is a sequence-to-sequence question generator which takes context as an input, and generates a question as an output. \nIt is based on a pretrained 'bart-base' model.",
"#### How to use\n\nInputs should be organised into the following for... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #question #generation #seq2seq #en #dataset-unifiedQA #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# context-only-question-generator",
"## Model description\n\nThis model is a sequence-to-sequence question generato... |
null | transformers |
# dpr-ctx_encoder-bert-base-multilingual
## Description
Multilingual DPR Model base on bert-base-multilingual-cased.
[DPR model](https://arxiv.org/abs/2004.04906)
[DPR repo](https://github.com/facebookresearch/DPR)
## Data
1. [NQ](https://github.com/facebookresearch/DPR/blob/master/data/download_data.py)
2. [Trivi... | {"language": "multilingual", "datasets": ["NQ", "Trivia", "SQuAD", "MLQA", "DRCD"]} | voidful/dpr-ctx_encoder-bert-base-multilingual | null | [
"transformers",
"pytorch",
"dpr",
"multilingual",
"dataset:NQ",
"dataset:Trivia",
"dataset:SQuAD",
"dataset:MLQA",
"dataset:DRCD",
"arxiv:2004.04906",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #dpr #multilingual #dataset-NQ #dataset-Trivia #dataset-SQuAD #dataset-MLQA #dataset-DRCD #arxiv-2004.04906 #endpoints_compatible #region-us
|
# dpr-ctx_encoder-bert-base-multilingual
## Description
Multilingual DPR Model base on bert-base-multilingual-cased.
DPR model
DPR repo
## Data
1. NQ
2. Trivia
3. SQuAD
4. DRCD*
5. MLQA*
'question pairs for train'： 644,217
'question pairs for dev'： 73,710
*DRCD and MLQA are converted using script from haystack... | [
"# dpr-ctx_encoder-bert-base-multilingual",
"## Description\n\nMultilingual DPR Model base on bert-base-multilingual-cased. \nDPR model\nDPR repo",
"## Data\n1. NQ\n2. Trivia\n3. SQuAD\n4. DRCD*\n5. MLQA*\n\n'question pairs for train'： 644,217 \n'question pairs for dev'： 73,710\n\n*DRCD and MLQA are converted ... | [
"TAGS\n#transformers #pytorch #dpr #multilingual #dataset-NQ #dataset-Trivia #dataset-SQuAD #dataset-MLQA #dataset-DRCD #arxiv-2004.04906 #endpoints_compatible #region-us \n",
"# dpr-ctx_encoder-bert-base-multilingual",
"## Description\n\nMultilingual DPR Model base on bert-base-multilingual-cased. \nDPR model\... |
feature-extraction | transformers |
# dpr-ctx_encoder-bert-base-multilingual
## Description
Multilingual DPR Model base on bert-base-multilingual-cased.
[DPR model](https://arxiv.org/abs/2004.04906)
[DPR repo](https://github.com/facebookresearch/DPR)
## Data
1. [NQ](https://github.com/facebookresearch/DPR/blob/master/data/download_data.py)
2. [Trivi... | {"language": "multilingual", "datasets": ["NQ", "Trivia", "SQuAD", "MLQA", "DRCD"]} | voidful/dpr-question_encoder-bert-base-multilingual | null | [
"transformers",
"pytorch",
"safetensors",
"dpr",
"feature-extraction",
"multilingual",
"dataset:NQ",
"dataset:Trivia",
"dataset:SQuAD",
"dataset:MLQA",
"dataset:DRCD",
"arxiv:2004.04906",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #dpr #feature-extraction #multilingual #dataset-NQ #dataset-Trivia #dataset-SQuAD #dataset-MLQA #dataset-DRCD #arxiv-2004.04906 #endpoints_compatible #has_space #region-us
|
# dpr-ctx_encoder-bert-base-multilingual
## Description
Multilingual DPR Model base on bert-base-multilingual-cased.
DPR model
DPR repo
## Data
1. NQ
2. Trivia
3. SQuAD
4. DRCD*
5. MLQA*
'question pairs for train'： 644,217
'question pairs for dev'： 73,710
*DRCD and MLQA are converted using script from haystack... | [
"# dpr-ctx_encoder-bert-base-multilingual",
"## Description\n\nMultilingual DPR Model base on bert-base-multilingual-cased. \nDPR model\nDPR repo",
"## Data\n1. NQ\n2. Trivia\n3. SQuAD\n4. DRCD*\n5. MLQA*\n\n'question pairs for train'： 644,217 \n'question pairs for dev'： 73,710\n\n*DRCD and MLQA are converted ... | [
"TAGS\n#transformers #pytorch #safetensors #dpr #feature-extraction #multilingual #dataset-NQ #dataset-Trivia #dataset-SQuAD #dataset-MLQA #dataset-DRCD #arxiv-2004.04906 #endpoints_compatible #has_space #region-us \n",
"# dpr-ctx_encoder-bert-base-multilingual",
"## Description\n\nMultilingual DPR Model base o... |
automatic-speech-recognition | transformers |
# voidful/tts_hubert_cluster_bart_base
## Usage
````python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("voidful/tts_hubert_cluster_bart_base")
model = AutoModelForSeq2SeqLM.from_pretrained("voidful/tts_hubert_cluster_bart_base")
````
generate output
```pyth... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "asr", "hubert"], "datasets": ["librispeech"], "metrics": ["wer", "cer"]} | voidful/tts_hubert_cluster_bart_base | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"audio",
"automatic-speech-recognition",
"speech",
"asr",
"hubert",
"en",
"dataset:librispeech",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #audio #automatic-speech-recognition #speech #asr #hubert #en #dataset-librispeech #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# voidful/tts_hubert_cluster_bart_base
## Usage
'
generate output
## Result
':vtok402::vtok329::vtok329::vtok75::vtok75::vtok75::vtok44::vtok150::vtok150::vtok222::vtok280::vtok280::vtok138::vtok409::vtok409::vtok409::vtok46::vtok441:' | [
"# voidful/tts_hubert_cluster_bart_base",
"## Usage\n'\ngenerate output",
"## Result\n':vtok402::vtok329::vtok329::vtok75::vtok75::vtok75::vtok44::vtok150::vtok150::vtok222::vtok280::vtok280::vtok138::vtok409::vtok409::vtok409::vtok46::vtok441:'"
] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #audio #automatic-speech-recognition #speech #asr #hubert #en #dataset-librispeech #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# voidful/tts_hubert_cluster_bart_base",
"## Usage\n'\ngenerate output",
"#... |
text2text-generation | transformers | # unifiedqg-bart-base
## Model description
This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output.
It is based on a pretrained `bart-base` model.
#### How to use
The model takes concatenated context and answers as an input... | {"language": "en", "tags": ["bart", "question", "generation", "seq2seq"], "datasets": ["unifiedQA"], "metrics": ["bleu", "rouge"], "pipeline_tag": "text2text-generation", "widget": [{"text": "treehouses in france. \n When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose... | voidful/unifiedqg-bart-base | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"question",
"generation",
"seq2seq",
"en",
"dataset:unifiedQA",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #question #generation #seq2seq #en #dataset-unifiedQA #autotrain_compatible #endpoints_compatible #region-us
| # unifiedqg-bart-base
## Model description
This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output.
It is based on a pretrained 'bart-base' model.
#### How to use
The model takes concatenated context and answers as an input... | [
"# unifiedqg-bart-base",
"## Model description\n\nThis model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. \nIt is based on a pretrained 'bart-base' model.",
"#### How to use\n\nThe model takes concatenated context and answer... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #question #generation #seq2seq #en #dataset-unifiedQA #autotrain_compatible #endpoints_compatible #region-us \n",
"# unifiedqg-bart-base",
"## Model description\n\nThis model is a sequence-to-sequence question generator which takes an answer... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-hk
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Cantonese using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
[Colab trial](ht... | {"language": "zh-HK", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Cantonese (Hong Kong) by Voidful", "results": [{"task": {"type": "automa... | voidful/wav2vec2-large-xlsr-53-hk | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh-HK"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #robust-speech-event #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-hk
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Cantonese using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
Colab trial
Predict
## Evaluation
The model can be evaluated as follows on the Cantonese (Hong Kong) test data of Com... | [
"# Wav2Vec2-Large-XLSR-53-hk\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Cantonese using the Common Voice. \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nColab trial\n\n\n\nPredict",
"## Evaluation\nThe model can be evaluated as follows on the Cantonese (Hong Kon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #robust-speech-event #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-hk\nFine-tuned facebook/wav2vec2-large-xlsr-53... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-tw-gpt
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on zh-tw using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
[Colab trial](h... | {"language": "zh-TW", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Taiwanese Mandarin(zh-tw) by Voidful", "results": [{"task": {"type": "au... | voidful/wav2vec2-large-xlsr-53-tw-gpt | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh-TW"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #robust-speech-event #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-tw-gpt
Fine-tuned facebook/wav2vec2-large-xlsr-53 on zh-tw using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
Colab trial
Predict
## Evaluation
The model can be evaluated as follows on the zh-tw test data of Common Voice.
C... | [
"# Wav2Vec2-Large-XLSR-53-tw-gpt\nFine-tuned facebook/wav2vec2-large-xlsr-53 on zh-tw using the Common Voice. \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nColab trial\n\n\n\nPredict",
"## Evaluation\nThe model can be evaluated as follows on the zh-tw test data of... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #robust-speech-event #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-tw-gpt\nFine-tuned facebook/wav2vec2-large-xls... |
automatic-speech-recognition | transformers |
# Model Card for wav2vec2-xlsr-multilingual-56
# Model Details
## Model Description
- **Developed by:** voidful
- **Shared by [Optional]:** Hugging Face
- **Model type:** automatic-speech-recognition
- **Language(s) (NLP):** multilingual (*56 language, 1 model Multilingual ASR*)
- **License:** Apache-2.0
- **R... | {"language": ["multilingual", "ar", "as", "br", "ca", "cnh", "cs", "cv", "cy", "de", "dv", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "hi", "hsb", "hu", "ia", "id", "ja", "ka", "ky", "lg", "lt", "ly", "mn", "mt", "nl", "or", "pl", "pt", "ro", "ru", "sah", "sl", "ta", "th", "tr", "tt", "uk", "vi"], "license":... | voidful/wav2vec2-xlsr-multilingual-56 | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"multilingual",
"ar",
"as",
"br",
"ca",
"cnh",
"cs",
"cv",
"cy",
"de",
"dv",
"el",
"en",
"eo",
... | null | 2022-03-02T23:29:05+00:00 | [
"1910.09700"
] | [
"multilingual",
"ar",
"as",
"br",
"ca",
"cnh",
"cs",
"cv",
"cy",
"de",
"dv",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"hi",
"hsb",
"hu",
"ia",
"id",
"ja",
"ka",
"ky",
"lg",
"lt",
"ly",
"mn",
"mt",
"nl",
"or",
"pl",
"pt",
"ro",
... | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #robust-speech-event #speech #xlsr-fine-tuning-week #multilingual #ar #as #br #ca #cnh #cs #cv #cy #de #dv #el #en #eo #es #et #eu #fa #fi #fr #hi #hsb #hu #ia #id #ja #ka #ky #lg #lt #ly #mn #mt #nl #or #pl #pt ... | Model Card for wav2vec2-xlsr-multilingual-56
============================================
Model Details
=============
Model Description
-----------------
* Developed by: voidful
* Shared by [Optional]: Hugging Face
* Model type: automatic-speech-recognition
* Language(s) (NLP): multilingual (*56 language, 1 model... | [
"### Preprocessing\n\n\nMore information needed",
"### Speeds, Sizes, Times\n\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\n\nEvaluation\n==========\n\n\nTesting Data, Factors & Metrics\n-------------------------------",
"### Testing Data\n\n\nMore information needed",
"##... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #robust-speech-event #speech #xlsr-fine-tuning-week #multilingual #ar #as #br #ca #cnh #cs #cv #cy #de #dv #el #en #eo #es #et #eu #fa #fi #fr #hi #hsb #hu #ia #id #ja #ka #ky #lg #lt #ly #mn #mt #nl #or #p... |
fill-mask | transformers |
<!-- 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. -->
# BiblItBERT-1
This model is a fine-tuned version of [vppvgit/BiblItBERT](https://huggingface.co/vppvgit/BiblItBERT) on the None d... | {"tags": ["generated_from_trainer"], "datasets": []} | vppvgit/BiblItBERT-1 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BiblItBERT-1
============
This model is a fine-tuned version of vppvgit/BiblItBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7775
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed:... |
fill-mask | transformers |
<!-- 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. -->
# BibliBERT
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-itali... | {"tags": ["generated_from_trainer"], "datasets": []} | vppvgit/Finetuned | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BibliBERT
=========
This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7784
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed:... |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `fr_ner_ingredients` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
|... | {"language": ["fr"], "tags": ["spacy", "token-classification"]} | vsalamand/fr_ner_ingredients | null | [
"spacy",
"token-classification",
"fr",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#spacy #token-classification #fr #model-index #region-us
|
### Label Scheme
View label scheme (5 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (5 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fr #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (5 labels for 1 components)",
"### Accuracy"
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `fr_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Auth... | {"language": ["fr"], "tags": ["spacy", "token-classification"]} | vsalamand/fr_pipeline | null | [
"spacy",
"token-classification",
"fr",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#spacy #token-classification #fr #model-index #region-us
|
### Label Scheme
View label scheme (4 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fr #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] |
null | transformers | This model is a downstream optimization of [```vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt```](https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt) using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
1. magnitude sparsification at 50%... | {} | vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-50.0sparse-qat-lt | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #bert #endpoints_compatible #region-us
| This model is a downstream optimization of [](URL using OpenVINO/NNCF. Applied optimization includes:
1. magnitude sparsification at 50% upon initialization. Parameters are ranked globally via thier absolute norm. Only linear layers of self-attention and ffnn are targeted.
2. NNCF Quantize-Aware Training - Symmetric 8-... | [
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally."
] | [
"TAGS\n#transformers #pytorch #onnx #bert #endpoints_compatible #region-us \n",
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally."
] |
null | transformers | This model is a downstream optimization of [```vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt```](https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt) using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
1. magnitude sparsification at 57.... | {} | vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-lt | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #bert #endpoints_compatible #region-us
| This model is a downstream optimization of [](URL using OpenVINO/NNCF. Applied optimization includes:
1. magnitude sparsification at 57.92% upon initialization so that sparsity over all linear layers of bert-base is at 90%. Parameters are ranked globally via thier absolute norm. Only linear layers of self-attention and... | [
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally."
] | [
"TAGS\n#transformers #pytorch #onnx #bert #endpoints_compatible #region-us \n",
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally."
] |
null | transformers | This model is a downstream optimization of [```vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt```](https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt) using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
1. magnitude sparsification at 57.... | {} | vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-57.92sparse-qat-lt | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #bert #endpoints_compatible #region-us
| This model is a downstream optimization of [](URL using OpenVINO/NNCF. Applied optimization includes:
1. magnitude sparsification at 57.92% upon initialization so that sparsity over all linear layers of bert-base is at 90%. Parameters are ranked globally via thier absolute norm. Only linear layers of self-attention and... | [
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally.",
"### tile-alignment\nto evaluate tile-alignment checkpoint, add and point to checkpoint with 'tilealigned' postfix. Use branch with commit id"
] | [
"TAGS\n#transformers #pytorch #onnx #bert #endpoints_compatible #region-us \n",
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally.",
"### tile-alignment\nto evaluate tile-alignment checkpoint, add and point to checkpoint with 'tilealigned' postfix. Use branch with commit id"
] |
null | transformers | This model is a downstream optimization of [```vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt```](https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt) using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
1. magnitude sparsification at 60%... | {} | vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-nncf-60.0sparse-qat-lt | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #bert #endpoints_compatible #region-us
| This model is a downstream optimization of [](URL using OpenVINO/NNCF. Applied optimization includes:
1. magnitude sparsification at 60% upon initialization. Parameters are ranked globally via thier absolute norm. Only linear layers of self-attention and ffnn are targeted.
2. NNCF Quantize-Aware Training - Symmetric 8-... | [
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally."
] | [
"TAGS\n#transformers #pytorch #onnx #bert #endpoints_compatible #region-us \n",
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally."
] |
null | transformers | This model is a downstream optimization of [```vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt```](https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt) using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
1. NNCF Quantize-Aware Training -... | {} | vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt-qat-lt | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #bert #endpoints_compatible #region-us
| This model is a downstream optimization of [](URL using OpenVINO/NNCF. Applied optimization includes:
1. NNCF Quantize-Aware Training - Symmetric 8-bit for both weight and activation on all learnable layers.
2. Custom distillation with large model
# Setup
# Train
# Eval
This repo must be cloned locally.
#... | [
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally.",
"### tile-alignment\nto evaluate tile-alignment checkpoint, add and point to checkpoint with 'tilealigned' postfix. Use branch with commit id"
] | [
"TAGS\n#transformers #pytorch #onnx #bert #endpoints_compatible #region-us \n",
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally.",
"### tile-alignment\nto evaluate tile-alignment checkpoint, add and point to checkpoint with 'tilealigned' postfix. Use branch with commit id"
] |
question-answering | transformers | This model is a downstream fine-tuning of [```vuiseng9/bert-base-squadv1-block-pruning-hybrid```](https://huggingface.co/vuiseng9/bert-base-squadv1-block-pruning-hybrid). "filled" means unstructured fine-grained sparsified parameters are allowed to learn during fine-tuning. "lt" means distillation of larger model as te... | {} | vuiseng9/bert-base-squadv1-block-pruning-hybrid-filled-lt | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2109.04838",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.04838"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2109.04838 #endpoints_compatible #region-us
| This model is a downstream fine-tuning of [](URL "filled" means unstructured fine-grained sparsified parameters are allowed to learn during fine-tuning. "lt" means distillation of larger model as teacher, i.e.
This model is a replication of block pruning paper with its open-sourced codebase (forked and modified).... | [
"# Eval\nThe model cannot be evaluated with HF QA example out-of-the-box as the final dimension of the model architecture has been realized. Follow the custom setup below.\n\n\nThis repo must be cloned locally.\n\nAdd and during evaluation."
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2109.04838 #endpoints_compatible #region-us \n",
"# Eval\nThe model cannot be evaluated with HF QA example out-of-the-box as the final dimension of the model architecture has been realized. Follow the custom setup below.\n\n\nThis repo must be cloned ... |
question-answering | transformers | BERT-base tuned for Squadv1.1 is pruned with movement pruning algorithm in hybrid fashion, i.e. 32x32 block for self-attention layers, per-dimension grain size for ffn layers.
```
eval_exact_match = 78.5241
eval_f1 = 86.4138
eval_samples = 10784
```
This model is a replication of [block pruning pa... | {} | vuiseng9/bert-base-squadv1-block-pruning-hybrid | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2109.04838",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.04838"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2109.04838 #endpoints_compatible #region-us
| BERT-base tuned for Squadv1.1 is pruned with movement pruning algorithm in hybrid fashion, i.e. 32x32 block for self-attention layers, per-dimension grain size for ffn layers.
This model is a replication of block pruning paper with its open-sourced codebase (forked and modified).
To reproduce this model, pls follow ... | [
"# Eval\nThe model can be evaluated out-of-the-box with HF QA example. Note that only pruned self-attention heads are discarded where pruned ffn dimension are sparsified instead of removal. Verified in v4.13.0, v4.9.1.\n\n\nIf the intent is to observe inference acceleration, the pruned structure in the model must b... | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2109.04838 #endpoints_compatible #region-us \n",
"# Eval\nThe model can be evaluated out-of-the-box with HF QA example. Note that only pruned self-attention heads are discarded where pruned ffn dimension are sparsified instead of removal. Verified in ... |
null | transformers | This model is a downstream optimization of [```vuiseng9/bert-base-squadv1-pruneofa-90pc-bt```](https://huggingface.co/vuiseng9/bert-base-squadv1-pruneofa-90pc-bt) using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
1. magnitude sparsification at 0% upon initialization. Custom ... | {} | vuiseng9/bert-base-squadv1-pruneofa-90pc-bt-qat-lt | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #bert #endpoints_compatible #region-us
| This model is a downstream optimization of [](URL using OpenVINO/NNCF. Applied optimization includes:
1. magnitude sparsification at 0% upon initialization. Custom reverse masking and sparsity freezing are applied.
2. NNCF Quantize-Aware Training - Symmetric 8-bit for both weight and activation on all learnable layers.... | [
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally."
] | [
"TAGS\n#transformers #pytorch #onnx #bert #endpoints_compatible #region-us \n",
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally."
] |
question-answering | transformers | This model is transfer-learning of [bert-base pruneofa 90% sparse](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) on Squadv1 dataset.
```
eval_exact_match = 80.2933
eval_f1 = 87.6788
eval_samples = 10784
```
# Train
use https://github.com/IntelLabs/Model-Compres... | {} | vuiseng9/bert-base-squadv1-pruneofa-90pc-bt | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #bert #question-answering #endpoints_compatible #region-us
| This model is transfer-learning of bert-base pruneofa 90% sparse on Squadv1 dataset.
# Train
use URL
see
# Eval
| [
"# Train\nuse URL\nsee",
"# Eval"
] | [
"TAGS\n#transformers #pytorch #onnx #bert #question-answering #endpoints_compatible #region-us \n",
"# Train\nuse URL\nsee",
"# Eval"
] |
null | transformers | This model is a quantized-aware transfer learning of bert-base-uncased on Squadv1 using [OpenVINO/NNCF](https://github.com/openvinotoolkit/nncf). Applied optimization includes:
1. NNCF Quantize-Aware Training - Symmetric 8-bit for both weight and activation on all learnable layers.
2. Custom distillation with fine-tune... | {} | vuiseng9/bert-base-squadv1-qat-bt | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #bert #endpoints_compatible #region-us
| This model is a quantized-aware transfer learning of bert-base-uncased on Squadv1 using OpenVINO/NNCF. Applied optimization includes:
1. NNCF Quantize-Aware Training - Symmetric 8-bit for both weight and activation on all learnable layers.
2. Custom distillation with fine-tuned model [](URL
# Setup
# Train
#... | [
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally."
] | [
"TAGS\n#transformers #pytorch #onnx #bert #endpoints_compatible #region-us \n",
"# Setup",
"# Train",
"# Eval\nThis repo must be cloned locally."
] |
question-answering | transformers | This model is a fork of [```csarron/bert-base-uncased-squad-v1```](https://huggingface.co/csarron/bert-base-uncased-squad-v1).
```
eval_exact_match = 80.9082
eval_f1 = 88.2275
eval_samples = 10784
```
# Eval
```bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=eval-bert-base-squadv1
WORKDIR=transformer... | {} | vuiseng9/bert-base-squadv1 | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #bert #question-answering #endpoints_compatible #region-us
| This model is a fork of [](URL
# Eval
| [
"# Eval"
] | [
"TAGS\n#transformers #pytorch #onnx #bert #question-answering #endpoints_compatible #region-us \n",
"# Eval"
] |
text-classification | transformers | This model is developed with transformers v4.10.3.
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=bert-based-uncased-mnli
WORKDIR=transformers/examples/pytorch/text-classification
cd $WORKDIR
nohup python run_glue.py \
--model_name_or_path bert-base-uncased \
--task_name mnli \
... | {} | vuiseng9/bert-base-uncased-mnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is developed with transformers v4.10.3.
# Train
# Eval
| [
"# Train",
"# Eval"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Train",
"# Eval"
] |
question-answering | transformers | This model is developed with transformers v4.10.3.
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=bert-base-uncased-squad
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR
nohup python run_qa.py \
--model_name_or_path bert-base-uncased \
--dataset_name squad \
... | {} | vuiseng9/bert-base-uncased-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #endpoints_compatible #region-us
| This model is developed with transformers v4.10.3.
# Train
# Eval
| [
"# Train",
"# Eval"
] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #endpoints_compatible #region-us \n",
"# Train",
"# Eval"
] |
null | transformers | * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using ```TFAutoModelForQuestionAnswering.from_pretrained(..., from_pt=True)``` and ```model.save_pretrained(tf_pth)```.
* Observed issue - loss in model translation, discrepancy observed in evaluation between... | {} | vuiseng9/bert-base-uncased-squadv1-52.0-sparse | null | [
"transformers",
"pytorch",
"tf",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #endpoints_compatible #region-us
| * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using and .
* Observed issue - loss in model translation, discrepancy observed in evaluation between pytorch and tensorflow models.
* Table below is evaluated in HF's transformers v4.9.2. Sparsity is normaliz... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #endpoints_compatible #region-us \n"
] |
null | transformers | * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using ```TFAutoModelForQuestionAnswering.from_pretrained(..., from_pt=True)``` and ```model.save_pretrained(tf_pth)```.
* Observed issue - loss in model translation, discrepancy observed in evaluation between... | {} | vuiseng9/bert-base-uncased-squadv1-59.6-sparse | null | [
"transformers",
"pytorch",
"tf",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #endpoints_compatible #region-us
| * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using and .
* Observed issue - loss in model translation, discrepancy observed in evaluation between pytorch and tensorflow models.
* Table below is evaluated in HF's transformers v4.9.2. Sparsity is normaliz... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #endpoints_compatible #region-us \n"
] |
null | transformers | * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using ```TFAutoModelForQuestionAnswering.from_pretrained(..., from_pt=True)``` and ```model.save_pretrained(tf_pth)```.
* Observed issue - loss in model translation, discrepancy observed in evaluation between... | {} | vuiseng9/bert-base-uncased-squadv1-65.1-sparse | null | [
"transformers",
"pytorch",
"tf",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #endpoints_compatible #region-us
| * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using and .
* Observed issue - loss in model translation, discrepancy observed in evaluation between pytorch and tensorflow models.
* Table below is evaluated in HF's transformers v4.9.2. Sparsity is normaliz... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #endpoints_compatible #region-us \n"
] |
null | transformers | * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using ```TFAutoModelForQuestionAnswering.from_pretrained(..., from_pt=True)``` and ```model.save_pretrained(tf_pth)```.
* Observed issue - loss in model translation, discrepancy observed in evaluation between... | {} | vuiseng9/bert-base-uncased-squadv1-72.9-sparse | null | [
"transformers",
"pytorch",
"tf",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #endpoints_compatible #region-us
| * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using and .
* Observed issue - loss in model translation, discrepancy observed in evaluation between pytorch and tensorflow models.
* Table below is evaluated in HF's transformers v4.9.2. Sparsity is normaliz... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #endpoints_compatible #region-us \n"
] |
null | transformers | * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using ```TFAutoModelForQuestionAnswering.from_pretrained(..., from_pt=True)``` and ```model.save_pretrained(tf_pth)```.
* Observed issue - loss in model translation, discrepancy observed in evaluation between... | {} | vuiseng9/bert-base-uncased-squadv1-85.4-sparse | null | [
"transformers",
"pytorch",
"tf",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #endpoints_compatible #region-us
| * A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
* Tensorflow models are created using and .
* Observed issue - loss in model translation, discrepancy observed in evaluation between pytorch and tensorflow models.
* Table below is evaluated in HF's transformers v4.9.2. Sparsity is normaliz... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #endpoints_compatible #region-us \n"
] |
null | null | ### Reproducibility
```bash
# 1. install nncf
# checkout nncf 9c2845eeb38b4ab1b6d4ca19e31a1886e5bdf17c
# patch b/nncf/torch/sparsity/magnitude/algo.py
def sparsify_params(self):
from collections import OrderedDict
sparse_sd = OrderedDict()
with torch.no_grad():
for sparse_i... | {} | vuiseng9/bert-large-hybrid-sparse-onnx-ir | null | [
"onnx",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#onnx #region-us
| ### Reproducibility
### Key Content
| [
"### Reproducibility",
"### Key Content"
] | [
"TAGS\n#onnx #region-us \n",
"### Reproducibility",
"### Key Content"
] |
text-classification | transformers | This model is developed with transformers v4.9.1.
```
m = 0.8444
eval_samples = 9815
mm = 0.8495
eval_samples = 9832
```
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=bert-mnli
NEPOCH=3
WORKDIR=transformers/examples/pytorch/text-classification
cd $W... | {} | vuiseng9/bert-mnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is developed with transformers v4.9.1.
# Train
# Eval
| [
"# Train",
"# Eval"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Train",
"# Eval"
] |
text2text-generation | transformers | This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13).
# Setup
```bash
git clone https://github.com/vuiseng9/transformers
cd transformers
git checkout pegasus-v4p13 && git reset --hard 41eeb07
# installation, set summarization depen... | {} | vuiseng9/pegasus-arxiv | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| This model is developed with transformers v4.13 with minor patch in this fork.
# Setup
# Train
# Eval
Although fine-tuning is carried out for 5 epochs, this model is the checkpoint @150000 steps, 5.91 epoch, 34hrs) with lowest eval loss during training. Test/predict with this checkpoint should give results below... | [
"# Setup",
"# Train",
"# Eval\n\n\nAlthough fine-tuning is carried out for 5 epochs, this model is the checkpoint @150000 steps, 5.91 epoch, 34hrs) with lowest eval loss during training. Test/predict with this checkpoint should give results below. Note that we observe model at 80000 steps is closed to published... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# Setup",
"# Train",
"# Eval\n\n\nAlthough fine-tuning is carried out for 5 epochs, this model is the checkpoint @150000 steps, 5.91 epoch, 34hrs) with lowest eval loss during training. Te... |
text2text-generation | transformers | This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13).
# Setup
```bash
git clone https://github.com/vuiseng9/transformers
cd transformers
git checkout pegasus-v4p13 && git reset --hard 41eeb07
# installation, set summarization depen... | {} | vuiseng9/pegasus-billsum | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| This model is developed with transformers v4.13 with minor patch in this fork.
# Setup
# Train
# Eval
Although fine-tuning is carried out for 10 epochs, this model is the checkpoint (@12000 steps, 6.6epoch, 210mins) with lowest eval loss during training. Test/predict with this checkpoint should give results belo... | [
"# Setup",
"# Train",
"# Eval\n\n\nAlthough fine-tuning is carried out for 10 epochs, this model is the checkpoint (@12000 steps, 6.6epoch, 210mins) with lowest eval loss during training. Test/predict with this checkpoint should give results below."
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# Setup",
"# Train",
"# Eval\n\n\nAlthough fine-tuning is carried out for 10 epochs, this model is the checkpoint (@12000 steps, 6.6epoch, 210mins) with lowest eval loss during training. T... |
text2text-generation | transformers | This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13).
# Setup
```bash
git clone https://github.com/vuiseng9/transformers
cd transformers
git checkout pegasus-v4p13 && git reset --hard 3db4b452
# installation, set summarization depe... | {} | vuiseng9/pegasus-xsum | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| This model is developed with transformers v4.13 with minor patch in this fork.
# Setup
# Train
# Eval
Although fine-tuning is carried out for 10 epochs, this model is the checkpoint (@62000 steps, 4.9epoch, 20hrs) with lower loss during training. Test/predict with this checkpoint should give results below.
| [
"# Setup",
"# Train",
"# Eval\n\n\nAlthough fine-tuning is carried out for 10 epochs, this model is the checkpoint (@62000 steps, 4.9epoch, 20hrs) with lower loss during training. Test/predict with this checkpoint should give results below."
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# Setup",
"# Train",
"# Eval\n\n\nAlthough fine-tuning is carried out for 10 epochs, this model is the checkpoint (@62000 steps, 4.9epoch, 20hrs) with lower loss during training. Test/pred... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-100h
This is a fork of [```facebook/wav2vec2-base-100h```](https://huggingface.co/facebook/wav2vec2-base-100h)
### Changes & Notes
1. Document reproducible evaluation (below) to new transformer and datasets version.
2. Use batch size of 1 to reproduce results.
3. Validated with ```transformers v4.15... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"]} | vuiseng9/wav2vec2-base-100h | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2-Base-100h
==================
This is a fork of [](URL
### Changes & Notes
1. Document reproducible evaluation (below) to new transformer and datasets version.
2. Use batch size of 1 to reproduce results.
3. Validated with ,
4. You may need to manually install pypkg ,
Evaluation
----------
This code s... | [
"### Changes & Notes\n\n\n1. Document reproducible evaluation (below) to new transformer and datasets version.\n2. Use batch size of 1 to reproduce results.\n3. Validated with ,\n4. You may need to manually install pypkg ,\n\n\nEvaluation\n----------\n\n\nThis code snippet shows how to evaluate facebook/wav2vec2-ba... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Changes & Notes\n\n\n1. Document reproducible evaluation (below) to new transformer and datasets version.\n2. Use batch size of 1 to reproduce re... |
null | transformers | ## TrOCR (small-sized model, fine-tuned on Synthetic Math Expression Dataset)
TrOCR model fine-tuned on the Synthetic Math Expression Dataset. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released... | {} | vukpetar/trocr-small-photomath | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"arxiv:2109.10282",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.10282"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #arxiv-2109.10282 #endpoints_compatible #has_space #region-us
| ## TrOCR (small-sized model, fine-tuned on Synthetic Math Expression Dataset)
TrOCR model fine-tuned on the Synthetic Math Expression Dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.
Disclaimer: Th... | [
"## TrOCR (small-sized model, fine-tuned on Synthetic Math Expression Dataset)\nTrOCR model fine-tuned on the Synthetic Math Expression Dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository.\n\nDiscla... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #arxiv-2109.10282 #endpoints_compatible #has_space #region-us \n",
"## TrOCR (small-sized model, fine-tuned on Synthetic Math Expression Dataset)\nTrOCR model fine-tuned on the Synthetic Math Expression Dataset. It was introduced in the paper TrOCR: Transforme... |
automatic-speech-recognition | transformers | # Wav2Vec2 Accent Japanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese accent dataset
When using this model, make sure that your speech input is sampled at 16kHz.
## Test Result
WER: 15.82% | {"language": ["ja"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["Japanese accent datasets"], "metrics": ["wer"], "model-index": [{"name": "Wav2vec2 Accent Japanese", "results": [{"task": {"type": "Speech Recognition", "name": "automatic-speech-recognition"}, "data... | vumichien/wav2vec2-large-pitch-recognition | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"ja",
"doi:10.57967/hf/0343",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #ja #doi-10.57967/hf/0343 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2 Accent Japanese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese accent dataset
When using this model, make sure that your speech input is sampled at 16kHz.
## Test Result
WER: 15.82% | [
"# Wav2Vec2 Accent Japanese\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese accent dataset \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Test Result\nWER: 15.82%"
] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #ja #doi-10.57967/hf/0343 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2 Accent Japanese\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese accent dataset \nWhen using this mo... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Japanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the [Common Voice](https://huggingface.co/datasets/common_voice) and Japanese speech corpus of Saruwatari-lab, University of Tokyo [JSUT](https://sites.google.com/site... | {"language": "ja", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Japanese Hiragana by Chien Vu", "results": [{"task": {"type": "automatic-speech-recognition", "name"... | vumichien/wav2vec2-large-xlsr-japanese-hiragana | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ja",
"dataset:common_voice",
"doi:10.57967/hf/0344",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ja #dataset-common_voice #doi-10.57967/hf/0344 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| # Wav2Vec2-Large-XLSR-53-Japanese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the Common Voice and Japanese speech corpus of Saruwatari-lab, University of Tokyo JSUT.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language... | [
"# Wav2Vec2-Large-XLSR-53-Japanese\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the Common Voice and Japanese speech corpus of Saruwatari-lab, University of Tokyo JSUT.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (witho... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ja #dataset-common_voice #doi-10.57967/hf/0344 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Japanese\nFine-tuned facebook/wav2vec... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Japanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the [Common Voice](https://huggingface.co/datasets/common_voice) and Japanese speech corpus of Saruwatari-lab, University of Tokyo [JSUT](https://sites.google.com/site... | {"language": "ja", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "widget": [{"example_title": "Japanese speech corpus sample 1", "src": "https://u.pcloud.link/publink/show?code=XZwhAlXZFOtXiqKHMzmYS9wXrCP8... | vumichien/wav2vec2-large-xlsr-japanese | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ja",
"dataset:common_voice",
"doi:10.57967/hf/0337",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ja #dataset-common_voice #doi-10.57967/hf/0337 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Japanese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the Common Voice and Japanese speech corpus of Saruwatari-lab, University of Tokyo JSUT.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language... | [
"# Wav2Vec2-Large-XLSR-53-Japanese\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the Common Voice and Japanese speech corpus of Saruwatari-lab, University of Tokyo JSUT.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (witho... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ja #dataset-common_voice #doi-10.57967/hf/0337 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Japanese\nFine-tuned facebook/wav2vec2-large-xls... |
automatic-speech-recognition | transformers | ## Model description
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on my collection of Public Japanese Voice datasets for research [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0), [JUST](https://sites.google... | {"language": ["ja"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common-voice", "hf-asr-leaderboard", "ja", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-xls-r-1b", "results": [{"task": {"type": "automatic-speech-recognition", "nam... | vumichien/wav2vec2-xls-r-1b-japanese | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"common-voice",
"hf-asr-leaderboard",
"ja",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"doi:10.57967/hf/0336",
"license:apache-2.0",
"model-index",
"endpoints_c... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #common-voice #hf-asr-leaderboard #ja #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #doi-10.57967/hf/0336 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Model description
-----------------
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on my collection of Public Japanese Voice datasets for research Common Voice 7.0, JUST (Japanese speech corpus of Saruwatari-lab., University of Tokyo), JSSS (Japanese speech corpus for summarization and simplificatio... | [
"### Total training data:\n\n\n~60 hours",
"### Benchmark WER result:\n\n\nCOMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 10.96\nCOMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 7.98",
"### Benchmark CER result:\n\n\nCOMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 4.28\nCOMMON VOICE 7.0: with 4-grams LM, C... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #common-voice #hf-asr-leaderboard #ja #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #doi-10.57967/hf/0336 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Total training d... |
automatic-speech-recognition | transformers |
## Model description
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset.
### Benchmark WER result:
| | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voic... | {"language": ["ja"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common-voice", "hf-asr-leaderboard", "ja", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xlsr-53-ja", "results": [{"task": {"type": "automatic-speech-recognitio... | vutankiet2901/wav2vec2-large-xlsr-53-ja | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common-voice",
"hf-asr-leaderboard",
"ja",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common-voice #hf-asr-leaderboard #ja #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Model description
-----------------
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - JA dataset.
### Benchmark WER result:
COMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 15.74
COMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 15.37
### Ben... | [
"### Benchmark WER result:\n\n\nCOMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 15.74\nCOMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 15.37",
"### Benchmark CER result:\n\n\nCOMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 9.51\nCOMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 6.91\n\n\nEvaluation\n-----... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common-voice #hf-asr-leaderboard #ja #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Benchmark WER result:\n\n\nCOMMON VOICE 7.0: withou... |
automatic-speech-recognition | transformers | ## Model description
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA
### Benchmark WER result:
| | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VO... | {"language": ["ja"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common-voice", "hf-asr-leaderboard", "ja", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-1b", "results": [{"task": {"type": "automatic-speech-recognition", "nam... | vutankiet2901/wav2vec2-xls-r-1b-ja | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common-voice",
"hf-asr-leaderboard",
"ja",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common-voice #hf-asr-leaderboard #ja #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Model description
-----------------
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - JA
### Benchmark WER result:
COMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 16.97
COMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 11.77
### Benchmark CER res... | [
"### Benchmark WER result:\n\n\nCOMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 16.97\nCOMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 11.77",
"### Benchmark CER result:\n\n\nCOMMON VOICE 7.0: without LM, COMMON VOICE 8.0: 6.82\nCOMMON VOICE 7.0: with 4-grams LM, COMMON VOICE 8.0: 5.22\n\n\nEvaluation\n-----... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common-voice #hf-asr-leaderboard #ja #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Benchmark WER result:\n\n\nCOMMON VOICE 7.0: withou... |
text2text-generation | transformers | Fine-Tuned MarianMT translation model for translating text from English to Dutch. Checkpoint of pre-trained model = Helsinki-NLP/opus-mt-en-nl.
Trained using custom training loop with PyTorch on Colab for 2 epochs. Link to the GitHub repo containing Google Colab notebook: https://github.com/vanadnarayane26/Maverick_2.... | {} | vvn/en-to-dutch-marianmt | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Fine-Tuned MarianMT translation model for translating text from English to Dutch. Checkpoint of pre-trained model = Helsinki-NLP/opus-mt-en-nl.
Trained using custom training loop with PyTorch on Colab for 2 epochs. Link to the GitHub repo containing Google Colab notebook: URL
| [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | Fine-Tuned MarianMT translation model for translating text from English to Italian.
Checkpoint of pre-trained model = Helsinki-NLP/opus-mt-en-it.
Trained using custom training loop with PyTorch on Colab for 2 epochs.
Link to the GitHub repo containing Google Colab notebook: https://github.com/vanadnarayane26/Maverick... | {} | vvn/en-to-it-marianmt | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Fine-Tuned MarianMT translation model for translating text from English to Italian.
Checkpoint of pre-trained model = Helsinki-NLP/opus-mt-en-it.
Trained using custom training loop with PyTorch on Colab for 2 epochs.
Link to the GitHub repo containing Google Colab notebook: URL | [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- 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-small-finetuned-no_paragraph-to-paragraph
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-no_paragraph-to-paragraph", "results": []}]} | vxvxx/t5-small-finetuned-no_paragraph-to-paragraph | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-no\_paragraph-to-paragraph
=============================================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0713
* Bleu: 0.0
* Gen Len: 19.0
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- 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-small-finetuned-no_paragraph-to-yes_paragraph-2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-sm... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-no_paragraph-to-yes_paragraph-2", "results": []}]} | vxvxx/t5-small-finetuned-no_paragraph-to-yes_paragraph-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-no\_paragraph-to-yes\_paragraph-2
====================================================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0001
* Bleu: 0.0
* Gen Len: 19.0
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
null | null | <pre>
----------------------------------------
<span>developing brains!!</span>
----------------------------------------
_---~~(~~-_.
_{ ) )
, ) -~~- ( ,-' )_
( `-,_..`., )-- '_,)
( ` _) ( -~( -_ `, }
(_- _ ~_-~~~~`, ,' )
`~ -^( __;-,((()))
... | {} | vymn/vymn | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| <pre>
----------------------------------------
<span>developing brains!!</span>
----------------------------------------
_---~~(~~-_.
_{ ) )
, ) -~~- ( ,-' )_
( '-,_..'., )-- '_,)
( ' _) ( -~( -_ ', }
(_- _ ~_-~~~~', ,' )
'~ -^( __;-,((()))
... | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
## Indo GPT-2 Small
Indo GPT-2 Small is a language model based on the [GPT-2 model](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). It was trained on the latest (late December 2020) Indonesian Wikipedia articles.
The model was originally HuggingFace's pretrained... | {"language": "id", "license": "mit", "tags": ["indo-gpt2-small"], "datasets": ["wikipedia"], "widget": [{"text": "Nama saya Budi, dari Indonesia"}]} | w11wo/indo-gpt2-small | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"indo-gpt2-small",
"id",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #indo-gpt2-small #id #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Indo GPT-2 Small
----------------
Indo GPT-2 Small is a language model based on the GPT-2 model. It was trained on the latest (late December 2020) Indonesian Wikipedia articles.
The model was originally HuggingFace's pretrained English GPT-2 model and is later fine-tuned on the Indonesian dataset. Many of the techn... | [
"### Load Model and Byte-level Tokenizer",
"### Generate a Sequence\n\n\nDisclaimer\n----------\n\n\nDo remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the result... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #indo-gpt2-small #id #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Load Model and Byte-level Tokenizer",
"### Generate a Sequence\n\n\nDisclaimer\n----------\n... |
fill-mask | transformers |
## Indo RoBERTa Small
Indo RoBERTa Small is a masked language model based on the [RoBERTa model](https://arxiv.org/abs/1907.11692). It was trained on the latest (late December 2020) Indonesian Wikipedia articles.
The model was trained from scratch and achieved a perplexity of 48.27 on the validation dataset (20% of t... | {"language": "id", "license": "mit", "tags": ["indo-roberta-small"], "datasets": ["wikipedia"], "widget": [{"text": "Karena pandemi ini, kita harus <mask> di rumah saja."}]} | w11wo/indo-roberta-small | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"roberta",
"fill-mask",
"indo-roberta-small",
"id",
"dataset:wikipedia",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #roberta #fill-mask #indo-roberta-small #id #dataset-wikipedia #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Indo RoBERTa Small
------------------
Indo RoBERTa Small is a masked language model based on the RoBERTa model. It was trained on the latest (late December 2020) Indonesian Wikipedia articles.
The model was trained from scratch and achieved a perplexity of 48.27 on the validation dataset (20% of the articles). Many... | [
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the results... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #roberta #fill-mask #indo-roberta-small #id #dataset-wikipedia #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo rem... |
text-classification | transformers |
## Indonesian RoBERTa Base IndoLEM Sentiment Classifier
Indonesian RoBERTa Base IndoLEM Sentiment Classifier is a sentiment-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesi... | {"language": "id", "license": "mit", "tags": ["indonesian-roberta-base-indolem-sentiment-classifier-fold-0"], "datasets": ["indolem"], "widget": [{"text": "Pelayanan hotel ini sangat baik."}]} | w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0 | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"indonesian-roberta-base-indolem-sentiment-classifier-fold-0",
"id",
"dataset:indolem",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"id"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #indonesian-roberta-base-indolem-sentiment-classifier-fold-0 #id #dataset-indolem #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Indonesian RoBERTa Base IndoLEM Sentiment Classifier
----------------------------------------------------
Indonesian RoBERTa Base IndoLEM Sentiment Classifier is a sentiment-text-classification model based on the RoBERTa model. The model was originally the pre-trained Indonesian RoBERTa Base model, which is then fine... | [
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which come from both the pre-trained RoBERTa model and 'IndoLEM''s Sentiment Analysis dataset that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nIndonesian RoBERTa Base IndoLEM Sentiment Classifier was tra... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #indonesian-roberta-base-indolem-sentiment-classifier-fold-0 #id #dataset-indolem #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider... |
text-classification | transformers |
## Indonesian RoBERTa Base IndoNLI
Indonesian RoBERTa Base IndoNLI is a natural language inference (NLI) model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-base) model, which is then f... | {"language": "id", "license": "mit", "tags": ["indonesian-roberta-base-indonli"], "datasets": ["indonli"], "widget": [{"text": "Andi tersenyum karena mendapat hasil baik. </s></s> Andi sedih."}], "model-index": [{"name": "w11wo/indonesian-roberta-base-indonli", "results": [{"task": {"type": "natural-language-inference"... | w11wo/indonesian-roberta-base-indonli | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"roberta",
"text-classification",
"indonesian-roberta-base-indonli",
"id",
"dataset:indonli",
"arxiv:1907.11692",
"arxiv:2110.14566",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692",
"2110.14566"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #safetensors #roberta #text-classification #indonesian-roberta-base-indonli #id #dataset-indonli #arxiv-1907.11692 #arxiv-2110.14566 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| Indonesian RoBERTa Base IndoNLI
-------------------------------
Indonesian RoBERTa Base IndoNLI is a natural language inference (NLI) model based on the RoBERTa model. The model was originally the pre-trained Indonesian RoBERTa Base model, which is then fine-tuned on 'IndoNLI''s dataset consisting of Indonesian Wikip... | [
"### As NLI Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which come from both the pre-trained RoBERTa model and the 'IndoNLI' dataset that may be carried over into the results of this model.\n\n\nReferences\n----------\n\n\n[1] Mahendra, R., Aji, A. F., Louvan, S., Rahman, F., & Vania, C. (202... | [
"TAGS\n#transformers #pytorch #tf #safetensors #roberta #text-classification #indonesian-roberta-base-indonli #id #dataset-indonli #arxiv-1907.11692 #arxiv-2110.14566 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### As NLI Classifier\n\n\nDisclaimer\n----------\n\n\nDo co... |
token-classification | transformers |
## Indonesian RoBERTa Base POSP Tagger
Indonesian RoBERTa Base POSP Tagger is a part-of-speech token-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-base) model, whic... | {"language": "id", "license": "mit", "tags": ["indonesian-roberta-base-posp-tagger"], "datasets": ["indonlu"], "widget": [{"text": "Budi sedang pergi ke pasar."}]} | w11wo/indonesian-roberta-base-posp-tagger | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"roberta",
"token-classification",
"indonesian-roberta-base-posp-tagger",
"id",
"dataset:indonlu",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #safetensors #roberta #token-classification #indonesian-roberta-base-posp-tagger #id #dataset-indonlu #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Indonesian RoBERTa Base POSP Tagger
-----------------------------------
Indonesian RoBERTa Base POSP Tagger is a part-of-speech token-classification model based on the RoBERTa model. The model was originally the pre-trained Indonesian RoBERTa Base model, which is then fine-tuned on 'indonlu''s 'POSP' dataset consisti... | [
"### As Token Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which come from both the pre-trained RoBERTa model and the 'POSP' dataset that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nIndonesian RoBERTa Base POSP Tagger was trained and evaluated by Wilson Wongso.... | [
"TAGS\n#transformers #pytorch #tf #safetensors #roberta #token-classification #indonesian-roberta-base-posp-tagger #id #dataset-indonlu #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Token Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which ... |
text-classification | transformers |
## Indonesian RoBERTa Base Sentiment Classifier
Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-base)... | {"language": "id", "license": "mit", "tags": ["indonesian-roberta-base-sentiment-classifier"], "datasets": ["indonlu"], "widget": [{"text": "Jangan sampai saya telpon bos saya ya!"}]} | w11wo/indonesian-roberta-base-sentiment-classifier | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"roberta",
"text-classification",
"indonesian-roberta-base-sentiment-classifier",
"id",
"dataset:indonlu",
"arxiv:1907.11692",
"doi:10.57967/hf/0644",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"... | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #safetensors #roberta #text-classification #indonesian-roberta-base-sentiment-classifier #id #dataset-indonlu #arxiv-1907.11692 #doi-10.57967/hf/0644 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| Indonesian RoBERTa Base Sentiment Classifier
--------------------------------------------
Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the RoBERTa model. The model was originally the pre-trained Indonesian RoBERTa Base model, which is then fine-tuned on 'indonlu''s 'S... | [
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which come from both the pre-trained RoBERTa model and the 'SmSA' dataset that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nIndonesian RoBERTa Base Sentiment Classifier was trained and evaluated by Wilson... | [
"TAGS\n#transformers #pytorch #tf #safetensors #roberta #text-classification #indonesian-roberta-base-sentiment-classifier #id #dataset-indonlu #arxiv-1907.11692 #doi-10.57967/hf/0644 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### As Text Classifier\n\n\nDisclaimer\n-----... |
text-classification | transformers |
## Javanese BERT Small IMDB Classifier
Javanese BERT Small IMDB Classifier is a movie-classification model based on the [BERT model](https://arxiv.org/abs/1810.04805). It was trained on Javanese IMDB movie reviews.
The model was originally [`w11wo/javanese-bert-small-imdb`](https://huggingface.co/w11wo/javanese-bert... | {"language": "jv", "license": "mit", "tags": ["javanese-bert-small-imdb-classifier"], "datasets": ["w11wo/imdb-javanese"], "widget": [{"text": "Dhuh Gusti, film iki elek banget. Aku getun ndelok !!!"}]} | w11wo/javanese-bert-small-imdb-classifier | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"text-classification",
"javanese-bert-small-imdb-classifier",
"jv",
"dataset:w11wo/imdb-javanese",
"arxiv:1810.04805",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"jv"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #text-classification #javanese-bert-small-imdb-classifier #jv #dataset-w11wo/imdb-javanese #arxiv-1810.04805 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Javanese BERT Small IMDB Classifier
-----------------------------------
Javanese BERT Small IMDB Classifier is a movie-classification model based on the BERT model. It was trained on Javanese IMDB movie reviews.
The model was originally 'w11wo/javanese-bert-small-imdb' which is then fine-tuned on the 'w11wo/imdb-ja... | [
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which came from the IMDB review that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nJavanese BERT Small IMDB Classifier was trained and evaluated by Wilson Wongso. All computation and development are done o... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #text-classification #javanese-bert-small-imdb-classifier #jv #dataset-w11wo/imdb-javanese #arxiv-1810.04805 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the b... |
fill-mask | transformers |
## Javanese BERT Small IMDB
Javanese BERT Small IMDB is a masked language model based on the [BERT model](https://arxiv.org/abs/1810.04805). It was trained on Javanese IMDB movie reviews.
The model was originally the pretrained [Javanese BERT Small model](https://huggingface.co/w11wo/javanese-bert-small) and is later... | {"language": "jv", "license": "mit", "tags": ["javanese-bert-small-imdb"], "datasets": ["w11wo/imdb-javanese"], "widget": [{"text": "Fast and Furious iku film sing [MASK]."}]} | w11wo/javanese-bert-small-imdb | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"javanese-bert-small-imdb",
"jv",
"dataset:w11wo/imdb-javanese",
"arxiv:1810.04805",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"jv"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #javanese-bert-small-imdb #jv #dataset-w11wo/imdb-javanese #arxiv-1810.04805 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Javanese BERT Small IMDB
------------------------
Javanese BERT Small IMDB is a masked language model based on the BERT model. It was trained on Javanese IMDB movie reviews.
The model was originally the pretrained Javanese BERT Small model and is later fine-tuned on the Javanese IMDB movie review dataset. It achiev... | [
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which came from the IMDB review that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nJavanese BERT Small was trained and evaluated by Wilson Wongso. All computati... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #javanese-bert-small-imdb #jv #dataset-w11wo/imdb-javanese #arxiv-1810.04805 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n---------... |
fill-mask | transformers |
## Javanese BERT Small
Javanese BERT Small is a masked language model based on the [BERT model](https://arxiv.org/abs/1810.04805). It was trained on the latest (late December 2020) Javanese Wikipedia articles.
The model was originally HuggingFace's pretrained [English BERT model](https://huggingface.co/bert-base-unca... | {"language": "jv", "license": "mit", "tags": ["javanese-bert-small"], "datasets": ["wikipedia"], "widget": [{"text": "Aku mangan sate ing [MASK] bareng konco-konco"}]} | w11wo/javanese-bert-small | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"javanese-bert-small",
"jv",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"jv"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #javanese-bert-small #jv #dataset-wikipedia #arxiv-1810.04805 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Javanese BERT Small
-------------------
Javanese BERT Small is a masked language model based on the BERT model. It was trained on the latest (late December 2020) Javanese Wikipedia articles.
The model was originally HuggingFace's pretrained English BERT model and is later fine-tuned on the Javanese dataset. It achi... | [
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the results... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #javanese-bert-small #jv #dataset-wikipedia #arxiv-1810.04805 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo remem... |
text-classification | transformers |
## Javanese DistilBERT Small IMDB Classifier
Javanese DistilBERT Small IMDB Classifier is a movie-classification model based on the [DistilBERT model](https://arxiv.org/abs/1910.01108). It was trained on Javanese IMDB movie reviews.
The model was originally [`w11wo/javanese-distilbert-small-imdb`](https://huggingface... | {"language": "jv", "license": "mit", "tags": ["javanese-distilbert-small-imdb-classifier"], "datasets": ["w11wo/imdb-javanese"], "widget": [{"text": "Aku babar pisan ora nikmati film iki."}]} | w11wo/javanese-distilbert-small-imdb-classifier | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"javanese-distilbert-small-imdb-classifier",
"jv",
"dataset:w11wo/imdb-javanese",
"arxiv:1910.01108",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [
"jv"
] | TAGS
#transformers #pytorch #tf #distilbert #text-classification #javanese-distilbert-small-imdb-classifier #jv #dataset-w11wo/imdb-javanese #arxiv-1910.01108 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Javanese DistilBERT Small IMDB Classifier
-----------------------------------------
Javanese DistilBERT Small IMDB Classifier is a movie-classification model based on the DistilBERT model. It was trained on Javanese IMDB movie reviews.
The model was originally 'w11wo/javanese-distilbert-small-imdb' which is then fi... | [
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which came from the IMDB review that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nJavanese DistilBERT Small IMDB Classifier was trained and evaluated by Wilson Wongso. All computation and development are ... | [
"TAGS\n#transformers #pytorch #tf #distilbert #text-classification #javanese-distilbert-small-imdb-classifier #jv #dataset-w11wo/imdb-javanese #arxiv-1910.01108 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases ... |
fill-mask | transformers |
## Javanese DistilBERT Small IMDB
Javanese DistilBERT Small IMDB is a masked language model based on the [DistilBERT model](https://arxiv.org/abs/1910.01108). It was trained on Javanese IMDB movie reviews.
The model was originally the pretrained [Javanese DistilBERT Small model](https://huggingface.co/w11wo/javanese-... | {"language": "jv", "license": "mit", "tags": ["javanese-distilbert-small-imdb"], "datasets": ["w11wo/imdb-javanese"], "widget": [{"text": "Film favoritku yaiku Interstellar [MASK] Christopher Nolan."}]} | w11wo/javanese-distilbert-small-imdb | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"fill-mask",
"javanese-distilbert-small-imdb",
"jv",
"dataset:w11wo/imdb-javanese",
"arxiv:1910.01108",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [
"jv"
] | TAGS
#transformers #pytorch #tf #distilbert #fill-mask #javanese-distilbert-small-imdb #jv #dataset-w11wo/imdb-javanese #arxiv-1910.01108 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Javanese DistilBERT Small IMDB
------------------------------
Javanese DistilBERT Small IMDB is a masked language model based on the DistilBERT model. It was trained on Javanese IMDB movie reviews.
The model was originally the pretrained Javanese DistilBERT Small model and is later fine-tuned on the Javanese IMDB m... | [
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which came from the IMDB review that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nJavanese DistilBERT Small was trained and evaluated by Wilson Wongso. All com... | [
"TAGS\n#transformers #pytorch #tf #distilbert #fill-mask #javanese-distilbert-small-imdb #jv #dataset-w11wo/imdb-javanese #arxiv-1910.01108 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\... |
fill-mask | transformers |
## Javanese DistilBERT Small
Javanese DistilBERT Small is a masked language model based on the [DistilBERT model](https://arxiv.org/abs/1910.01108). It was trained on the latest (late December 2020) Javanese Wikipedia articles.
The model was originally HuggingFace's pretrained [English DistilBERT model](https://huggi... | {"language": "jv", "license": "mit", "tags": ["javanese-distilbert-small"], "datasets": ["wikipedia"], "widget": [{"text": "Joko [MASK] wis kelas siji SMA."}]} | w11wo/javanese-distilbert-small | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"distilbert",
"fill-mask",
"javanese-distilbert-small",
"jv",
"dataset:wikipedia",
"arxiv:1910.01108",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [
"jv"
] | TAGS
#transformers #pytorch #tf #safetensors #distilbert #fill-mask #javanese-distilbert-small #jv #dataset-wikipedia #arxiv-1910.01108 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Javanese DistilBERT Small
-------------------------
Javanese DistilBERT Small is a masked language model based on the DistilBERT model. It was trained on the latest (late December 2020) Javanese Wikipedia articles.
The model was originally HuggingFace's pretrained English DistilBERT model and is later fine-tuned on... | [
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the results... | [
"TAGS\n#transformers #pytorch #tf #safetensors #distilbert #fill-mask #javanese-distilbert-small #jv #dataset-wikipedia #arxiv-1910.01108 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nD... |
text-classification | transformers |
## Javanese GPT-2 Small IMDB Classifier
Javanese GPT-2 Small IMDB Classifier is a movie-classification model based on the [GPT-2 model](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). It was trained on Javanese IMDB movie reviews.
The model was originally [`w11w... | {"language": "jv", "license": "mit", "tags": ["javanese-gpt2-small-imdb-classifier"], "datasets": ["w11wo/imdb-javanese"], "widget": [{"text": "Film sing apik banget!"}]} | w11wo/javanese-gpt2-small-imdb-classifier | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"gpt2",
"text-classification",
"javanese-gpt2-small-imdb-classifier",
"jv",
"dataset:w11wo/imdb-javanese",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"jv"
] | TAGS
#transformers #pytorch #tf #safetensors #gpt2 #text-classification #javanese-gpt2-small-imdb-classifier #jv #dataset-w11wo/imdb-javanese #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Javanese GPT-2 Small IMDB Classifier
------------------------------------
Javanese GPT-2 Small IMDB Classifier is a movie-classification model based on the GPT-2 model. It was trained on Javanese IMDB movie reviews.
The model was originally 'w11wo/javanese-gpt2-small-imdb' which is then fine-tuned on the 'w11wo/imd... | [
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which came from the IMDB review that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nJavanese GPT-2 Small IMDB Classifier was trained and evaluated by Wilson Wongso. All computation and development are done ... | [
"TAGS\n#transformers #pytorch #tf #safetensors #gpt2 #text-classification #javanese-gpt2-small-imdb-classifier #jv #dataset-w11wo/imdb-javanese #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider t... |
text-generation | transformers |
## Javanese GPT-2 Small IMDB
Javanese GPT-2 Small IMDB is a causal language model based on the [GPT-2 model](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). It was trained on Javanese IMDB movie reviews.
The model was originally the pretrained [Javanese GPT-2 Sm... | {"language": "jv", "license": "mit", "tags": ["javanese-gpt2-small-imdb"], "datasets": ["w11wo/imdb-javanese"], "widget": [{"text": "Train to Busan yaiku film sing digawe ing Korea Selatan"}]} | w11wo/javanese-gpt2-small-imdb | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"javanese-gpt2-small-imdb",
"jv",
"dataset:w11wo/imdb-javanese",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"jv"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #javanese-gpt2-small-imdb #jv #dataset-w11wo/imdb-javanese #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Javanese GPT-2 Small IMDB
-------------------------
Javanese GPT-2 Small IMDB is a causal language model based on the GPT-2 model. It was trained on Javanese IMDB movie reviews.
The model was originally the pretrained Javanese GPT-2 Small model and is later fine-tuned on the Javanese IMDB movie review dataset. It a... | [
"### As Causal Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which came from the IMDB review that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nJavanese GPT-2 Small was trained and evaluated by Wilson Wongso. All computat... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #javanese-gpt2-small-imdb #jv #dataset-w11wo/imdb-javanese #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### As Causal Language Model",
"### Feature Extraction in PyTorch\n\n\nDiscla... |
text-generation | transformers |
## Javanese GPT-2 Small
Javanese GPT-2 Small is a language model based on the [GPT-2 model](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). It was trained on the latest (late December 2020) Javanese Wikipedia articles.
The model was originally HuggingFace's pret... | {"language": "jv", "license": "mit", "tags": ["javanese-gpt2-small"], "datasets": ["wikipedia"], "widget": [{"text": "Jenengku Budi, saka Indonesia"}]} | w11wo/javanese-gpt2-small | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"javanese-gpt2-small",
"jv",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"jv"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #javanese-gpt2-small #jv #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Javanese GPT-2 Small
--------------------
Javanese GPT-2 Small is a language model based on the GPT-2 model. It was trained on the latest (late December 2020) Javanese Wikipedia articles.
The model was originally HuggingFace's pretrained English GPT-2 model and is later fine-tuned on the Javanese dataset. Many of t... | [
"### Load Model and Byte-level Tokenizer",
"### Generate a Sequence\n\n\nDisclaimer\n----------\n\n\nDo remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the result... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #javanese-gpt2-small #jv #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Load Model and Byte-level Tokenizer",
"### Generate a Sequence\n\n\nDisclaimer\n--------... |
text-classification | transformers |
## Javanese RoBERTa Small IMDB Classifier
Javanese RoBERTa Small IMDB Classifier is a movie-classification model based on the [RoBERTa model](https://arxiv.org/abs/1907.11692). It was trained on Javanese IMDB movie reviews.
The model was originally [`w11wo/javanese-roberta-small-imdb`](https://huggingface.co/w11wo/ja... | {"language": "jv", "license": "mit", "tags": ["javanese-roberta-small-imdb-classifier"], "datasets": ["w11wo/imdb-javanese"], "widget": [{"text": "Aku bakal menehi rating film iki 1 bintang."}]} | w11wo/javanese-roberta-small-imdb-classifier | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"javanese-roberta-small-imdb-classifier",
"jv",
"dataset:w11wo/imdb-javanese",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"jv"
] | TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #javanese-roberta-small-imdb-classifier #jv #dataset-w11wo/imdb-javanese #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Javanese RoBERTa Small IMDB Classifier
--------------------------------------
Javanese RoBERTa Small IMDB Classifier is a movie-classification model based on the RoBERTa model. It was trained on Javanese IMDB movie reviews.
The model was originally 'w11wo/javanese-roberta-small-imdb' which is then fine-tuned on the... | [
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which came from the IMDB review that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nJavanese RoBERTa Small IMDB Classifier was trained and evaluated by Wilson Wongso. All computation and development are don... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #javanese-roberta-small-imdb-classifier #jv #dataset-w11wo/imdb-javanese #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases w... |
fill-mask | transformers |
## Javanese RoBERTa Small IMDB
Javanese RoBERTa Small IMDB is a masked language model based on the [RoBERTa model](https://arxiv.org/abs/1907.11692). It was trained on Javanese IMDB movie reviews.
The model was originally the pretrained [Javanese RoBERTa Small model](https://huggingface.co/w11wo/javanese-roberta-smal... | {"language": "jv", "license": "mit", "tags": ["javanese-roberta-small-imdb"], "datasets": ["w11wo/imdb-javanese"], "widget": [{"text": "Aku bakal menehi rating film iki 5 <mask>."}]} | w11wo/javanese-roberta-small-imdb | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"javanese-roberta-small-imdb",
"jv",
"dataset:w11wo/imdb-javanese",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"jv"
] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #javanese-roberta-small-imdb #jv #dataset-w11wo/imdb-javanese #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Javanese RoBERTa Small IMDB
---------------------------
Javanese RoBERTa Small IMDB is a masked language model based on the RoBERTa model. It was trained on Javanese IMDB movie reviews.
The model was originally the pretrained Javanese RoBERTa Small model and is later fine-tuned on the Javanese IMDB movie review dat... | [
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which came from the IMDB review that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nJavanese RoBERTa Small was trained and evaluated by Wilson Wongso. All comput... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #javanese-roberta-small-imdb #jv #dataset-w11wo/imdb-javanese #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\n... |
fill-mask | transformers |
## Javanese RoBERTa Small
Javanese RoBERTa Small is a masked language model based on the [RoBERTa model](https://arxiv.org/abs/1907.11692). It was trained on the latest (late December 2020) Javanese Wikipedia articles.
The model was originally HuggingFace's pretrained [English RoBERTa model](https://huggingface.co/ro... | {"language": "jv", "license": "mit", "tags": ["javanese-roberta-small"], "datasets": ["wikipedia"], "widget": [{"text": "Ing mangsa rendheng awakedhewe kudu pinter njaga <mask>."}]} | w11wo/javanese-roberta-small | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"javanese-roberta-small",
"jv",
"dataset:wikipedia",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"jv"
] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #javanese-roberta-small #jv #dataset-wikipedia #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Javanese RoBERTa Small
----------------------
Javanese RoBERTa Small is a masked language model based on the RoBERTa model. It was trained on the latest (late December 2020) Javanese Wikipedia articles.
The model was originally HuggingFace's pretrained English RoBERTa model and is later fine-tuned on the Javanese d... | [
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the results... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #javanese-roberta-small #jv #dataset-wikipedia #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Masked Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo remember tha... |
token-classification | transformers |
## Lao RoBERTa Base POS Tagger
Lao RoBERTa Base POS Tagger is a part-of-speech token-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Lao RoBERTa Base](https://huggingface.co/w11wo/lao-roberta-base) model, which is then fine-tuned on the [`... | {"language": "lo", "license": "mit", "tags": ["lao-roberta-base-pos-tagger"], "widget": [{"text": "\u0eae\u0ec9\u0ead\u0e87 \u0ea1\u0ec8\u0ea7\u0e99 \u0ec1\u0e97\u0ec9 \u0eaa\u0ebd\u0e87\u0e94\u0eb5 \u0ead\u0eb4\u0eab\u0ebc\u0eb5"}]} | w11wo/lao-roberta-base-pos-tagger | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"token-classification",
"lao-roberta-base-pos-tagger",
"lo",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"lo"
] | TAGS
#transformers #pytorch #safetensors #roberta #token-classification #lao-roberta-base-pos-tagger #lo #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Lao RoBERTa Base POS Tagger
---------------------------
Lao RoBERTa Base POS Tagger is a part-of-speech token-classification model based on the RoBERTa model. The model was originally the pre-trained Lao RoBERTa Base model, which is then fine-tuned on the 'Yunshan Cup 2020' dataset consisting of tag-labelled Lao corp... | [
"### As Token Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which come from both the pre-trained RoBERTa model and the 'Yunshan Cup 2020' dataset that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nLao RoBERTa Base POS Tagger was trained and evaluated by Wilson Won... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #lao-roberta-base-pos-tagger #lo #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Token Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which come from both the pre-traine... |
fill-mask | transformers |
## Lao RoBERTa Base
Lao RoBERTa Base is a masked language model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. It was trained on the [OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset, specifically the `deduplicated_lo` subset. The model was trained from scratch and achieve... | {"language": "lo", "license": "mit", "tags": ["lao-roberta-base"], "datasets": ["oscar-corpus/OSCAR-2109"]} | w11wo/lao-roberta-base | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"lao-roberta-base",
"lo",
"dataset:oscar-corpus/OSCAR-2109",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"lo"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #fill-mask #lao-roberta-base #lo #dataset-oscar-corpus/OSCAR-2109 #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Lao RoBERTa Base
----------------
Lao RoBERTa Base is a masked language model based on the RoBERTa model. It was trained on the OSCAR-2109 dataset, specifically the 'deduplicated\_lo' subset. The model was trained from scratch and achieved an evaluation loss of 1.4556 and an evaluation perplexity of 4.287.
This mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* distributed\\_type: tpu\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 1024\n* total\\_eval\\_batch\\_size: 1024\n*... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #fill-mask #lao-roberta-base #lo #dataset-oscar-corpus/OSCAR-2109 #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.