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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-generation | transformers | ## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 256.
Since this was a clas... | {} | textattack/xlnet-base-cased-WNLI | null | [
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 256.
Since this was a clas... | [
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 3e-05, and a maximum sequence length of 256. \nSince this ... | [
"TAGS\n#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the glue dataset loaded using the 'nlp' library. The model was fin... |
text-generation | transformers | ## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 512.
Since this was a clas... | {} | textattack/xlnet-base-cased-imdb | null | [
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 512.
Since this was a clas... | [
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the imdb dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 32, a learning \nrate of 2e-05, and a maximum sequence length of 512. \nSince this ... | [
"TAGS\n#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the imdb dataset loaded using the 'nlp' library. The model was fin... |
text-generation | transformers | ## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this... | {} | textattack/xlnet-base-cased-rotten-tomatoes | null | [
"transformers",
"pytorch",
"xlnet",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## TextAttack Model Card
This 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack
and the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this... | [
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the rotten_tomatoes dataset loaded using the 'nlp' library. The model was fine-tuned \nfor 5 epochs with a batch size of 16, a learning \nrate of 2e-05, and a maximum sequence length of 128. \n... | [
"TAGS\n#transformers #pytorch #xlnet #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextAttack Model Card\nThis 'xlnet-base-cased' model was fine-tuned for sequence classification using TextAttack \nand the rotten_tomatoes dataset loaded using the 'nlp' library. The mo... |
null | transformers |
# ALBERT Base v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/albert-base-v1 | null | [
"transformers",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us
| ALBERT Base v1
==============
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe ALBERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpubl... | [
"TAGS\n#transformers #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nThis bias will also affect all fine-tune... |
null | transformers |
# ALBERT Base v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/albert-base-v2 | null | [
"transformers",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us
| ALBERT Base v2
==============
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe ALBERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders... | [
"TAGS\n#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe ALBERT mod... |
null | transformers |
# ALBERT XLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not ma... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/albert-xlarge-v1 | null | [
"transformers",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us
| ALBERT XLarge v1
================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The tea... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe ALBERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders... | [
"TAGS\n#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe ALBERT mod... |
null | transformers |
# ALBERT XLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not ma... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/albert-xlarge-v2 | null | [
"transformers",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us
| ALBERT XLarge v2
================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The tea... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe ALBERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders... | [
"TAGS\n#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe ALBERT mod... |
null | transformers |
# ALBERT XXLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not m... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/albert-xxlarge-v1 | null | [
"transformers",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us
| ALBERT XXLarge v1
=================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The t... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe ALBERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders... | [
"TAGS\n#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe ALBERT mod... |
null | transformers |
# ALBERT XXLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not m... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/albert-xxlarge-v2 | null | [
"transformers",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.11942"
] | [
"en"
] | TAGS
#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us
| ALBERT XXLarge v2
=================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between english and English.
Disclaimer: The t... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe ALBERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders... | [
"TAGS\n#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe ALBERT mod... |
null | transformers |
# BART (base-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://gi... | {"language": "en", "license": "apache-2.0"} | tftransformers/bart-base | null | [
"transformers",
"en",
"arxiv:1910.13461",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461"
] | [
"en"
] | TAGS
#transformers #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #region-us
|
# BART (base-sized model)
BART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository.
Disclaimer: The team releasing BART did not w... | [
"# BART (base-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. \n\nDisclaimer: The team releasing BART d... | [
"TAGS\n#transformers #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BART (base-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Compreh... |
null | transformers |
# BART (large-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://g... | {"language": "en", "license": "apache-2.0"} | tftransformers/bart-large | null | [
"transformers",
"en",
"arxiv:1910.13461",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461"
] | [
"en"
] | TAGS
#transformers #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #region-us
|
# BART (large-sized model)
BART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository.
Disclaimer: The team releasing BART did not ... | [
"# BART (large-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. \n\nDisclaimer: The team releasing BART ... | [
"TAGS\n#transformers #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BART (large-sized model) \n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Compre... |
null | transformers |
# BERT base model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference bet... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/bert-base-cased | null | [
"transformers",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us
| BERT base model (cased)
=======================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it makes a difference between
english and English.
Disclaimer: The team releasin... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders).... | [
"TAGS\n#transformers #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BE... |
null | transformers |
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference b... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/bert-base-uncased | null | [
"transformers",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us
| BERT base model (uncased)
=========================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it makes a difference between
english and English.
Disclaimer: The team rele... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders).... | [
"TAGS\n#transformers #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BE... |
null | transformers |
# BERT large model (uncased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does no... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/bert-large-cased-whole-word-masking | null | [
"transformers",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us
| BERT large model (uncased) whole word masking
=============================================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders).... | [
"TAGS\n#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BERT model... |
null | transformers |
# BERT Large model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference be... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/bert-large-cased | null | [
"transformers",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Large model (cased)
========================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it makes a difference between
english and English.
Disclaimer: The team releas... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders).... | [
"TAGS\n#transformers #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BE... |
null | transformers |
# BERT large model (uncased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does no... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/bert-large-uncased-whole-word-masking | null | [
"transformers",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us
| BERT large model (uncased) whole word masking
=============================================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders).... | [
"TAGS\n#transformers #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BERT model... |
null | transformers |
# BERT Large model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference ... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | tftransformers/bert-large-uncased | null | [
"transformers",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Large model (uncased)
==========================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it makes a difference between
english and English.
Disclaimer: The team re... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders).... | [
"TAGS\n#transformers #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\nIn tf\\_transformers\n\n\nTraining data\n-------------\n\n\nThe BE... |
null | transformers |
# GPT-2 Large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-... | {"language": "en", "license": "mit", "tags": ["exbert"]} | tftransformers/gpt2-large | null | [
"transformers",
"exbert",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #exbert #en #license-mit #endpoints_compatible #region-us
| GPT-2 Large
===========
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model card
has been written by the Hugging F... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:",
"### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contain... | [
"TAGS\n#transformers #exbert #en #license-mit #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:",
"### Limitations and bias\n\n\nThe training data use... |
null | transformers |
# GPT-2
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-langua... | {"language": "en", "license": "mit", "tags": ["exbert"]} | tftransformers/gpt2-medium | null | [
"transformers",
"exbert",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #exbert #en #license-mit #endpoints_compatible #region-us
| GPT-2
=====
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model card
has been written by the Hugging Face team to ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:",
"### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contain... | [
"TAGS\n#transformers #exbert #en #license-mit #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:",
"### Limitations and bias\n\n\nThe training data use... |
null | transformers |
# GPT-2
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-langua... | {"language": "en", "license": "mit", "tags": ["exbert"]} | tftransformers/gpt2 | null | [
"transformers",
"exbert",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #exbert #en #license-mit #endpoints_compatible #region-us
| GPT-2
=====
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model card
has been written by the Hugging Face team to ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:",
"### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contain... | [
"TAGS\n#transformers #exbert #en #license-mit #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:",
"### Limitations and bias\n\n\nThe training data use... |
null | transformers |
[Google's mT5](https://github.com/google-research/multilingual-t5)
mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebua... | {"language": "multilingual", "license": "apache-2.0", "datasets": ["mc4"]} | tftransformers/mt5-base | null | [
"transformers",
"multilingual",
"dataset:mc4",
"arxiv:2010.11934",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11934"
] | [
"multilingual"
] | TAGS
#transformers #multilingual #dataset-mc4 #arxiv-2010.11934 #license-apache-2.0 #endpoints_compatible #region-us
|
Google's mT5
mT5 is pretrained on the mC4 corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galicia... | [
"## Abstract\n\nThe recent \"Text-to-Text Transfer Transformer\" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based d... | [
"TAGS\n#transformers #multilingual #dataset-mc4 #arxiv-2010.11934 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Abstract\n\nThe recent \"Text-to-Text Transfer Transformer\" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-langua... |
null | transformers |
[Google's mT5](https://github.com/google-research/multilingual-t5)
mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebua... | {"language": "multilingual", "license": "apache-2.0", "datasets": ["mc4"]} | tftransformers/mt5-small | null | [
"transformers",
"multilingual",
"dataset:mc4",
"arxiv:2010.11934",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11934"
] | [
"multilingual"
] | TAGS
#transformers #multilingual #dataset-mc4 #arxiv-2010.11934 #license-apache-2.0 #endpoints_compatible #region-us
|
Google's mT5
mT5 is pretrained on the mC4 corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galicia... | [
"## Abstract\n\nThe recent \"Text-to-Text Transfer Transformer\" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based d... | [
"TAGS\n#transformers #multilingual #dataset-mc4 #arxiv-2010.11934 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Abstract\n\nThe recent \"Text-to-Text Transfer Transformer\" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-langua... |
translation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)
Other Community Checkpoints: [here](https://huggingface.co/models?search=t5)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transfor... | {"language": ["en", "fr", "ro", "de"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | tftransformers/t5-base | null | [
"transformers",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en",
"fr",
"ro",
"de"
] | TAGS
#transformers #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #endpoints_compatible #region-us
|
Google's T5
Pretraining Dataset: C4
Other Community Checkpoints: here
Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfe... | [
"## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and pract... | [
"TAGS\n#transformers #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful... |
translation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)
Other Community Checkpoints: [here](https://huggingface.co/models?search=t5)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transfor... | {"language": ["en", "fr", "ro", "de"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | tftransformers/t5-large | null | [
"transformers",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en",
"fr",
"ro",
"de"
] | TAGS
#transformers #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #endpoints_compatible #region-us
|
Google's T5
Pretraining Dataset: C4
Other Community Checkpoints: here
Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfe... | [
"## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and pract... | [
"TAGS\n#transformers #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful... |
translation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)
Other Community Checkpoints: [here](https://huggingface.co/models?search=t5)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transfor... | {"language": ["en", "fr", "ro", "de"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | tftransformers/t5-small | null | [
"transformers",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"en",
"fr",
"ro",
"de"
] | TAGS
#transformers #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #endpoints_compatible #region-us
|
Google's T5
Pretraining Dataset: C4
Other Community Checkpoints: here
Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfe... | [
"## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and pract... | [
"TAGS\n#transformers #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful... |
text-generation | transformers | # DialoGPT small - Jurandir
Este é Jurandir, o GPT-2 baseado no DialoGPT que fala português. Ele foi treinado com datasets baseados na Wikipédia e no (Brazilian Portuguese Literature Corpus)[https://www.kaggle.com/rtatman/brazilian-portuguese-literature-corpus]. O propósito deste modelo, inicialmente, é para ser usado ... | {} | thaalesalves/jurandir | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # DialoGPT small - Jurandir
Este é Jurandir, o GPT-2 baseado no DialoGPT que fala português. Ele foi treinado com datasets baseados na Wikipédia e no (Brazilian Portuguese Literature Corpus)[URL O propósito deste modelo, inicialmente, é para ser usado com o servidor do KoboldAI em combinação com o bot de Discord Jurand... | [
"# DialoGPT small - Jurandir\nEste é Jurandir, o GPT-2 baseado no DialoGPT que fala português. Ele foi treinado com datasets baseados na Wikipédia e no (Brazilian Portuguese Literature Corpus)[URL O propósito deste modelo, inicialmente, é para ser usado com o servidor do KoboldAI em combinação com o bot de Discord ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT small - Jurandir\nEste é Jurandir, o GPT-2 baseado no DialoGPT que fala português. Ele foi treinado com datasets baseados na Wikipédia e no (Brazilian Portuguese ... |
image-classification | transformers |
# goan-fish-fry
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggin... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | thak123/goan-fish-fry | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# goan-fish-fry
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### king fish fry
!king fish fry
#### mackerel fry
!mackerel fry
#### pomfret fry
!pomfret fry
#### prawn... | [
"# goan-fish-fry\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### king fish fry\n\n!king fish fry",
"#### mackerel fry\n\n!mackerel fry",
"#### pomfret ... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# goan-fish-fry\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues... |
image-classification | transformers |
# indian-snacks
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggin... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | thak123/indian-snacks | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# indian-snacks
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### chalk
!chalk
#### crayon
!crayon
#### marker
!marker
#### pencil
!pencil
#### pens
!pens | [
"# indian-snacks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### chalk\n\n!chalk",
"#### crayon\n\n!crayon",
"#### marker\n\n!marker",
"#### pencil\n... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# indian-snacks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues... |
question-answering | transformers | # roberta-base + DAPT + Task Transfer for Domain-Specific QA
Objective:
This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then trained for the NER task using MIT Movie Dataset --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie doma... | {"language": ["English"], "license": "cc-by-4.0", "tags": ["roberta", "roberta-base", "question-answering", "qa", "movies"], "datasets": ["imdb", "cornell_movie_dialogue", "MIT Movie"]} | thatdramebaazguy/movie-roberta-MITmovie-squad | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"question-answering",
"roberta-base",
"qa",
"movies",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #license-cc-by-4.0 #endpoints_compatible #region-us
| # roberta-base + DAPT + Task Transfer for Domain-Specific QA
Objective:
This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then trained for the NER task using MIT Movie Dataset --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie doma... | [
"# roberta-base + DAPT + Task Transfer for Domain-Specific QA\n\nObjective:\n This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then trained for the NER task using MIT Movie Dataset --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the mo... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# roberta-base + DAPT + Task Transfer for Domain-Specific QA\n\nObjective:\n This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then trai... |
token-classification | transformers | # Movie Roberta + Movies NER Task
Objective:
This is Roberta Base + Movie DAPT --> trained for the NER task using MIT Movie Dataset
https://huggingface.co/thatdramebaazguy/movie-roberta-base was used as the MovieRoberta.
```
model_name = "thatdramebaazguy/movie-roberta-MITmovieroberta-base-MITmovie"
pipeline(mo... | {"language": ["English"], "license": "cc-by-4.0", "tags": ["roberta", "roberta-base", "token-classification", "NER", "named-entities", "BIO", "movies", "DAPT"], "datasets": ["imdb", "cornell_movie_dialogue", "MIT Movie"]} | thatdramebaazguy/movie-roberta-MITmovie | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"token-classification",
"roberta-base",
"NER",
"named-entities",
"BIO",
"movies",
"DAPT",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #tf #jax #roberta #token-classification #roberta-base #NER #named-entities #BIO #movies #DAPT #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # Movie Roberta + Movies NER Task
Objective:
This is Roberta Base + Movie DAPT --> trained for the NER task using MIT Movie Dataset
URL was used as the MovieRoberta.
## Overview
Language model: roberta-base
Language: English
Downstream-task: NER
Training data: MIT Movie
Eval data: MIT Movie
Infrastr... | [
"# Movie Roberta + Movies NER Task\n\nObjective:\n This is Roberta Base + Movie DAPT --> trained for the NER task using MIT Movie Dataset\n URL was used as the MovieRoberta.",
"## Overview\nLanguage model: roberta-base \nLanguage: English \nDownstream-task: NER \nTraining data: MIT Movie \nEval data: MIT Mo... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #token-classification #roberta-base #NER #named-entities #BIO #movies #DAPT #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Movie Roberta + Movies NER Task\n\nObjective:\n This is Roberta Base + Movie DAPT --> trained for the NER t... |
fill-mask | transformers | # roberta-base for MLM
Objective: To make a Roberta Base for the Movie Domain by using various Movie Datasets as simple text for Masked Language Modeling.
This is the Movie Roberta to be used in Movie Domain applications.
```
model_name = "thatdramebaazguy/movie-roberta-base"
pipeline(model=model_name, tokenizer=... | {"language": ["English"], "license": "cc-by-4.0", "tags": ["roberta", "roberta-base", "masked-language-modeling", "masked-lm"], "datasets": ["imdb", "cornell_movie_dialogue", "polarity_movie_data", "25mlens_movie_data"]} | thatdramebaazguy/movie-roberta-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"roberta-base",
"masked-language-modeling",
"masked-lm",
"dataset:imdb",
"dataset:cornell_movie_dialogue",
"dataset:polarity_movie_data",
"dataset:25mlens_movie_data",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoint... | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #roberta-base #masked-language-modeling #masked-lm #dataset-imdb #dataset-cornell_movie_dialogue #dataset-polarity_movie_data #dataset-25mlens_movie_data #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # roberta-base for MLM
Objective: To make a Roberta Base for the Movie Domain by using various Movie Datasets as simple text for Masked Language Modeling.
This is the Movie Roberta to be used in Movie Domain applications.
## Overview
Language model: roberta-base
Language: English
Downstream-task: Fill-Mask ... | [
"# roberta-base for MLM \n\nObjective: To make a Roberta Base for the Movie Domain by using various Movie Datasets as simple text for Masked Language Modeling. \n This is the Movie Roberta to be used in Movie Domain applications.",
"## Overview\nLanguage model: roberta-base \nLanguage: English \nDownstream-tas... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #roberta-base #masked-language-modeling #masked-lm #dataset-imdb #dataset-cornell_movie_dialogue #dataset-polarity_movie_data #dataset-25mlens_movie_data #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base for ML... |
question-answering | transformers | # roberta-base + DAPT + Domain-Specific QA
Objective:
This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain.
https://huggingface.co/thatdramebaazguy/movie-roberta-base was used ... | {"language": ["English"], "license": "cc-by-4.0", "tags": ["roberta", "roberta-base", "question-answering", "qa", "movies"], "datasets": ["imdb", "cornell_movie_dialogue", "SQuAD"]} | thatdramebaazguy/movie-roberta-squad | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"question-answering",
"roberta-base",
"qa",
"movies",
"dataset:imdb",
"dataset:cornell_movie_dialogue",
"dataset:SQuAD",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #dataset-imdb #dataset-cornell_movie_dialogue #dataset-SQuAD #license-cc-by-4.0 #endpoints_compatible #region-us
| # roberta-base + DAPT + Domain-Specific QA
Objective:
This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain.
URL was used as the MovieRoberta.
## Overview
Language model: r... | [
"# roberta-base + DAPT + Domain-Specific QA\n\nObjective:\n This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain. \n URL was used as the MovieRoberta.",
"## Overview\nLanguag... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #dataset-imdb #dataset-cornell_movie_dialogue #dataset-SQuAD #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# roberta-base + DAPT + Domain-Specific QA\n\nObjective:\n This is Roberta Base with Domain Adaptive... |
question-answering | transformers | # roberta-base + Task Transfer (NER) --> Domain-Specific QA
Objective:
This is Roberta Base without any Domain Adaptive Pretraining --> Then trained for the NER task using MIT Movie Dataset --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain, with ad... | {"language": ["English"], "license": "cc-by-4.0", "tags": ["roberta", "roberta-base", "question-answering", "qa", "movies"], "datasets": ["MIT Movie", "SQuAD"]} | thatdramebaazguy/roberta-base-MITmovie-squad | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"question-answering",
"roberta-base",
"qa",
"movies",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #license-cc-by-4.0 #endpoints_compatible #region-us
| # roberta-base + Task Transfer (NER) --> Domain-Specific QA
Objective:
This is Roberta Base without any Domain Adaptive Pretraining --> Then trained for the NER task using MIT Movie Dataset --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain, with ad... | [
"# roberta-base + Task Transfer (NER) --> Domain-Specific QA\n\nObjective:\n This is Roberta Base without any Domain Adaptive Pretraining --> Then trained for the NER task using MIT Movie Dataset --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain,... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #movies #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# roberta-base + Task Transfer (NER) --> Domain-Specific QA\n\nObjective:\n This is Roberta Base without any Domain Adaptive Pretraining --> Then trained for the... |
token-classification | transformers | # roberta-base + Movies NER Task
Objective:
This is Roberta Base trained for the NER task using MIT Movie Dataset
```
model_name = "thatdramebaazguy/roberta-base-MITmovie"
pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="ner")
```
## Overview
**Language model:** roberta-base
**Language:*... | {"language": ["English"], "license": "cc-by-4.0", "tags": ["roberta", "roberta-base", "token-classification", "NER", "named-entities", "BIO", "movies"], "datasets": ["MIT Movie"]} | thatdramebaazguy/roberta-base-MITmovie | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"token-classification",
"roberta-base",
"NER",
"named-entities",
"BIO",
"movies",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #tf #jax #roberta #token-classification #roberta-base #NER #named-entities #BIO #movies #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # roberta-base + Movies NER Task
Objective:
This is Roberta Base trained for the NER task using MIT Movie Dataset
## Overview
Language model: roberta-base
Language: English
Downstream-task: NER
Training data: MIT Movie
Eval data: MIT Movie
Infrastructure: 2x Tesla v100
Code: See example
## Hy... | [
"# roberta-base + Movies NER Task\n\nObjective:\n This is Roberta Base trained for the NER task using MIT Movie Dataset",
"## Overview\nLanguage model: roberta-base \nLanguage: English \nDownstream-task: NER \nTraining data: MIT Movie \nEval data: MIT Movie \nInfrastructure: 2x Tesla v100 \nCode: See exa... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #token-classification #roberta-base #NER #named-entities #BIO #movies #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base + Movies NER Task\n\nObjective:\n This is Roberta Base trained for the NER task using MIT Movie Data... |
question-answering | transformers | # roberta-base + SQuAD QA
Objective:
This is Roberta Base trained to do the SQuAD Task. This makes a QA model capable of answering questions.
```
model_name = "thatdramebaazguy/roberta-base-squad"
pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering")
```
## Overview
**Lang... | {"language": ["English"], "license": "cc-by-4.0", "tags": ["roberta", "roberta-base", "question-answering", "qa"], "datasets": ["SQuAD"]} | thatdramebaazguy/roberta-base-squad | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"question-answering",
"roberta-base",
"qa",
"dataset:SQuAD",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #dataset-SQuAD #license-cc-by-4.0 #endpoints_compatible #region-us
| # roberta-base + SQuAD QA
Objective:
This is Roberta Base trained to do the SQuAD Task. This makes a QA model capable of answering questions.
## Overview
Language model: roberta-base
Language: English
Downstream-task: QA
Training data: SQuADv1
Eval data: SQuAD
Infrastructure: 2x Tesla v100
Code: ... | [
"# roberta-base + SQuAD QA\n\nObjective:\n This is Roberta Base trained to do the SQuAD Task. This makes a QA model capable of answering questions.",
"## Overview\nLanguage model: roberta-base \nLanguage: English \nDownstream-task: QA \nTraining data: SQuADv1 \nEval data: SQuAD \nInfrastructure: 2x Tesla v1... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #question-answering #roberta-base #qa #dataset-SQuAD #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# roberta-base + SQuAD QA\n\nObjective:\n This is Roberta Base trained to do the SQuAD Task. This makes a QA model capable of answering questions.",
"##... |
fill-mask | transformers | # roberta-base for MLM
```
model_name = "thatdramebaazguy/roberta-base-wikimovies"
pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="Fill-Mask")
```
## Overview
**Language model:** roberta-base
**Language:** English
**Downstream-task:** Fill-Mask
**Training data:** wikimovies
**Eval data... | {"language": ["English"], "license": "cc-by-4.0", "tags": ["roberta", "roberta-base", "masked-language-modeling"], "datasets": ["wikimovies"]} | thatdramebaazguy/roberta-base-wikimovies | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"roberta-base",
"masked-language-modeling",
"dataset:wikimovies",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"English"
] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #roberta-base #masked-language-modeling #dataset-wikimovies #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # roberta-base for MLM
## Overview
Language model: roberta-base
Language: English
Downstream-task: Fill-Mask
Training data: wikimovies
Eval data: wikimovies
Infrastructure: 2x Tesla v100
Code: See example
## Hyperparameters
## Performance
perplexity = 4.3808
Some of my work:
- Domain-Adaptati... | [
"# roberta-base for MLM",
"## Overview\nLanguage model: roberta-base \nLanguage: English \nDownstream-task: Fill-Mask \nTraining data: wikimovies \nEval data: wikimovies \nInfrastructure: 2x Tesla v100 \nCode: See example",
"## Hyperparameters",
"## Performance\n\nperplexity = 4.3808\n\nSome of my wor... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #roberta-base #masked-language-modeling #dataset-wikimovies #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base for MLM",
"## Overview\nLanguage model: roberta-base \nLanguage: English \nDownstream-task: Fill... |
text-generation | null |
# Oscar Mendez DialoGPT Model | {"tags": ["conversational"]} | thatoneguy267/DialoGPT-small-Oscar | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Oscar Mendez DialoGPT Model | [
"# Oscar Mendez DialoGPT Model"
] | [
"TAGS\n#conversational #region-us \n",
"# Oscar Mendez DialoGPT Model"
] |
text-generation | null | - PyTorch
#Oscar DialoGPT Model | {"tags": ["conversational"]} | thatoneguy267/bruhpleasehelpme | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
| - PyTorch
#Oscar DialoGPT Model | [] | [
"TAGS\n#conversational #region-us \n"
] |
text-generation | transformers |
# Chandler Bing DialoGPT Model | {"tags": ["conversational"]} | theChanChanMan/DialoGPT-small-chandler | 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
|
# Chandler Bing DialoGPT Model | [
"# Chandler Bing DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Chandler Bing DialoGPT Model"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-hindi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) hindi using the [Multilingual and code-switching ASR challenges for low resource Indian languages](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html).
When using this mode... | {"language": ["hi"]} | theainerd/Wav2Vec2-large-xlsr-hindi | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"hi",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hi #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-hindi
Fine-tuned facebook/wav2vec2-large-xlsr-53 hindi using the Multilingual and code-switching ASR challenges for low resource Indian languages.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) ... | [
"# Wav2Vec2-Large-XLSR-53-hindi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 hindi using the Multilingual and code-switching ASR challenges for low resource Indian languages.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a lan... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hi #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-hindi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 hindi using the Multilingual and code-switching ASR challenges for low resource Indian languages.\nW... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Odia
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) odia using the [Multilingual and code-switching ASR challenges for low resource Indian languages](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html).
When using this model,... | {"language": "or", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["OpenSLR"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Odia by Shyam Sunder Kumar", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speec... | theainerd/wav2vec2-large-xlsr-53-odia | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"or",
"dataset:OpenSLR",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"or"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #or #dataset-OpenSLR #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Odia
Fine-tuned facebook/wav2vec2-large-xlsr-53 odia using the Multilingual and code-switching ASR challenges for low resource Indian languages.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as... | [
"# Wav2Vec2-Large-XLSR-53-Odia\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 odia using the Multilingual and code-switching ASR challenges for low resource Indian languages.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a langu... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #or #dataset-OpenSLR #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Odia\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 odia using the Multilingua... |
text-generation | transformers |
# Chat Boi | {"tags": ["conversational"]} | thefryingpan/gpt-neo-125M-splishy | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #conversational #autotrain_compatible #endpoints_compatible #region-us
|
# Chat Boi | [
"# Chat Boi"
] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #conversational #autotrain_compatible #endpoints_compatible #region-us \n",
"# Chat Boi"
] |
text-generation | transformers |
# Hermione Granger Model | {"tags": ["conversational"]} | theiconik/hermione-granger | 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
|
# Hermione Granger Model | [
"# Hermione Granger Model"
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"# Hermione Granger Model"
] |
text-generation | transformers |
#Sonic DialoGPT Model | {"tags": ["conversational"]} | thesamuelpena/Dialog-medium-Sonic | 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
|
#Sonic DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Master Chief DialoGPT Model | {"tags": ["conversational"]} | thesamuelpena/Dialog-medium-masterchief | 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
|
# Master Chief DialoGPT Model | [
"# Master Chief DialoGPT Model"
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"# Master Chief DialoGPT Model"
] |
text-generation | transformers |
# Ironman DialoGPT Model (small) | {"tags": ["conversational"]} | thetlwin/DialoGPT-small-ironman | 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
|
# Ironman DialoGPT Model (small) | [
"# Ironman DialoGPT Model (small)"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ironman DialoGPT Model (small)"
] |
translation | transformers | # mt5-sinhalese-english
## Model description
An mT5-base model fine-tuned on the Sinhalese-English dataset in the Tatoeba Challenge. Can be used to translate from Sinhalese to English and vice versa.
## Training details
- English - Sinhala dataset from the Tatoeba Challenge [Datasets](https://github.com/Helsinki-NLP... | {"language": ["si", "en"], "license": "apache-2.0", "tags": ["translation"], "metrics": ["sacrebleu"]} | thilina/mt5-sinhalese-english | null | [
"transformers",
"pytorch",
"tf",
"mt5",
"text2text-generation",
"translation",
"si",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"si",
"en"
] | TAGS
#transformers #pytorch #tf #mt5 #text2text-generation #translation #si #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # mt5-sinhalese-english
## Model description
An mT5-base model fine-tuned on the Sinhalese-English dataset in the Tatoeba Challenge. Can be used to translate from Sinhalese to English and vice versa.
## Training details
- English - Sinhala dataset from the Tatoeba Challenge Datasets
- mT5-base pre-trained weights
#... | [
"# mt5-sinhalese-english",
"## Model description\n\nAn mT5-base model fine-tuned on the Sinhalese-English dataset in the Tatoeba Challenge. Can be used to translate from Sinhalese to English and vice versa.",
"## Training details\n- English - Sinhala dataset from the Tatoeba Challenge Datasets\n- mT5-base pre-t... | [
"TAGS\n#transformers #pytorch #tf #mt5 #text2text-generation #translation #si #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt5-sinhalese-english",
"## Model description\n\nAn mT5-base model fine-tuned on the Sinhalese-English dataset in the Ta... |
null | transformers | fintuned the kykim/bert-kor-base model as a dense passage retrieval context encoder by KLUE dataset
this link is experiment result. https://wandb.ai/thingsu/DenseRetrieval
Corpus : Korean Wikipedia Corpus
Trained Strategy :
- Pretrained Model : kykim/bert-kor-base
- Inverse Cloze Task : 16 Epoch, by korquad v 1.... | {} | thingsu/koDPR_context | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| fintuned the kykim/bert-kor-base model as a dense passage retrieval context encoder by KLUE dataset
this link is experiment result. URL
Corpus : Korean Wikipedia Corpus
Trained Strategy :
- Pretrained Model : kykim/bert-kor-base
- Inverse Cloze Task : 16 Epoch, by korquad v 1.0, KLUE MRC dataset
- In-batch Nega... | [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n"
] |
null | transformers | fintuned the kykim/bert-kor-base model as a dense passage retrieval context encoder by KLUE dataset
this link is experiment result. https://wandb.ai/thingsu/DenseRetrieval
Corpus : Korean Wikipedia Corpus
Trained Strategy :
- Pretrained Model : kykim/bert-kor-base
- Inverse Cloze Task : 16 Epoch, by korquad v 1.... | {} | thingsu/koDPR_question | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| fintuned the kykim/bert-kor-base model as a dense passage retrieval context encoder by KLUE dataset
this link is experiment result. URL
Corpus : Korean Wikipedia Corpus
Trained Strategy :
- Pretrained Model : kykim/bert-kor-base
- Inverse Cloze Task : 16 Epoch, by korquad v 1.0, KLUE MRC dataset
- In-batch Nega... | [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Joey from Friends | {"tags": ["conversational"]} | thinhda/chatbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Joey from Friends | [
"# Joey from Friends"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Joey from Friends"
] |
fill-mask | transformers | - model: klue/roberta-large
- learning rate: 1e-4
- lr scheduler type: linear
- weight decay: 0.01
- epochs: 5
- checkpoint: 2700 | {} | this-is-real/mrc-pretrained-roberta-large-1 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| - model: klue/roberta-large
- learning rate: 1e-4
- lr scheduler type: linear
- weight decay: 0.01
- epochs: 5
- checkpoint: 2700 | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | thomaszz/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0623
* Precision: 0.9245
* Recall: 0.9365
* F1: 0.9304
* Accuracy: 0.9834
Model des... | [
"### 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 #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the m... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "IceBERT-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "typ... | thorduragust/IceBERT-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:gpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-finetuned-ner
=====================
This model is a fine-tuned version of vesteinn/IceBERT on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0787
* Precision: 0.8948
* Recall: 0.8622
* F1: 0.8782
* Accuracy: 0.9852
Model description
-----------------
More ... | [
"### 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 #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "XLMR-ENIS-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "mim_gold_ner", "... | thorduragust/XLMR-ENIS-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"license:agpl-3.0",
"model-index",
"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 #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLMR-ENIS-finetuned-ner
=======================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0916
* Precision: 0.8708
* Recall: 0.8475
* F1: 0.8590
* Accuracy: 0.9829
Model description
-----------------
... | [
"### 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 #xlm-roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
text-generation | transformers |
## Chinese pre-trained dialogue model (CDial-GPT)
This project provides a large-scale Chinese GPT model pre-trained on the dataset [LCCC](https://huggingface.co/datasets/silver/lccc).
We present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC data... | {"license": "mit", "tags": ["conversational"], "datasets": "silver/lccc"} | thu-coai/CDial-GPT2_LCCC-base | null | [
"transformers",
"pytorch",
"safetensors",
"conversational",
"dataset:silver/lccc",
"arxiv:1901.08149",
"arxiv:2008.03946",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1901.08149",
"2008.03946"
] | [] | TAGS
#transformers #pytorch #safetensors #conversational #dataset-silver/lccc #arxiv-1901.08149 #arxiv-2008.03946 #license-mit #endpoints_compatible #has_space #region-us
|
## Chinese pre-trained dialogue model (CDial-GPT)
This project provides a large-scale Chinese GPT model pre-trained on the dataset LCCC.
We present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC dataset.
Similar to TransferTransfo, we concatenat... | [
"## Chinese pre-trained dialogue model (CDial-GPT)\n\nThis project provides a large-scale Chinese GPT model pre-trained on the dataset LCCC.\n\nWe present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC dataset.\n\nSimilar to TransferTransfo, we ... | [
"TAGS\n#transformers #pytorch #safetensors #conversational #dataset-silver/lccc #arxiv-1901.08149 #arxiv-2008.03946 #license-mit #endpoints_compatible #has_space #region-us \n",
"## Chinese pre-trained dialogue model (CDial-GPT)\n\nThis project provides a large-scale Chinese GPT model pre-trained on the dataset L... |
text-generation | transformers |
## Chinese pre-trained dialogue model (CDial-GPT)
This project provides a large-scale Chinese GPT model pre-trained on the dataset [LCCC](https://huggingface.co/datasets/silver/lccc).
We present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC data... | {"license": "mit", "tags": ["conversational"], "datasets": "silver/lccc"} | thu-coai/CDial-GPT_LCCC-base | null | [
"transformers",
"pytorch",
"safetensors",
"conversational",
"dataset:silver/lccc",
"arxiv:1901.08149",
"arxiv:2008.03946",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1901.08149",
"2008.03946"
] | [] | TAGS
#transformers #pytorch #safetensors #conversational #dataset-silver/lccc #arxiv-1901.08149 #arxiv-2008.03946 #license-mit #endpoints_compatible #region-us
|
## Chinese pre-trained dialogue model (CDial-GPT)
This project provides a large-scale Chinese GPT model pre-trained on the dataset LCCC.
We present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC dataset.
Similar to TransferTransfo, we concatenat... | [
"## Chinese pre-trained dialogue model (CDial-GPT)\n\nThis project provides a large-scale Chinese GPT model pre-trained on the dataset LCCC.\n\nWe present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC dataset.\n\nSimilar to TransferTransfo, we ... | [
"TAGS\n#transformers #pytorch #safetensors #conversational #dataset-silver/lccc #arxiv-1901.08149 #arxiv-2008.03946 #license-mit #endpoints_compatible #region-us \n",
"## Chinese pre-trained dialogue model (CDial-GPT)\n\nThis project provides a large-scale Chinese GPT model pre-trained on the dataset LCCC.\n\nWe ... |
text-generation | transformers |
## Chinese pre-trained dialogue model (CDial-GPT)
This project provides a large-scale Chinese GPT model pre-trained on the dataset [LCCC](https://huggingface.co/datasets/silver/lccc).
We present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC data... | {"license": "mit", "tags": ["conversational"], "datasets": ["silver/lccc"]} | thu-coai/CDial-GPT_LCCC-large | null | [
"transformers",
"pytorch",
"safetensors",
"conversational",
"dataset:silver/lccc",
"arxiv:1901.08149",
"arxiv:2008.03946",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1901.08149",
"2008.03946"
] | [] | TAGS
#transformers #pytorch #safetensors #conversational #dataset-silver/lccc #arxiv-1901.08149 #arxiv-2008.03946 #license-mit #endpoints_compatible #region-us
|
## Chinese pre-trained dialogue model (CDial-GPT)
This project provides a large-scale Chinese GPT model pre-trained on the dataset LCCC.
We present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC dataset.
Similar to TransferTransfo, we concatenat... | [
"## Chinese pre-trained dialogue model (CDial-GPT)\n\nThis project provides a large-scale Chinese GPT model pre-trained on the dataset LCCC.\n\nWe present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC dataset.\n\nSimilar to TransferTransfo, we ... | [
"TAGS\n#transformers #pytorch #safetensors #conversational #dataset-silver/lccc #arxiv-1901.08149 #arxiv-2008.03946 #license-mit #endpoints_compatible #region-us \n",
"## Chinese pre-trained dialogue model (CDial-GPT)\n\nThis project provides a large-scale Chinese GPT model pre-trained on the dataset LCCC.\n\nWe ... |
text2text-generation | transformers | ## LongLM
### 1. Parameters
| Versions | $d_m$ | $d_{ff}$ | $d_{kv}$ | $n_h$ | $n_e/n_d$ | \# P |
| ------------ | ----- | -------- | -------- | ----- | --------- | ---- |
| LongLM-small | 512 | 2,048 | 64 | 8 | 6/6 | 60M |
| LongLM-base | 768 | 3,072 | 64 | 12 | 12/12 |... | {"language": ["zh"], "tags": ["pytorch", "lm-head", "zh"], "thumbnail": "http://coai.cs.tsinghua.edu.cn/coai/img/logo.png?v=13923", "widget": [{"text": "\u5c0f\u5495\u565c\u5bf9\u9773\u53f8\u5bd2\u5b8c\u5168\u662f\u4e2a\u81ea\u6765\u719f\uff0c\u5c0f\u5bb6\u4f19\u722c\u8fdb\u4ed6\u6000\u91cc\u5c0f\u624b\u6402\u7740\u4ed... | thu-coai/LongLM-base | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lm-head",
"zh",
"arxiv:2108.12960",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.12960"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #lm-head #zh #arxiv-2108.12960 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| LongLM
------
### 1. Parameters
* $d\_m$: the dimension of hidden states
* $d\_{ff}$: the dimension of feed forward layers
* $d\_{kv}$: the dimension of the keys/values in the self-attention layers
* $n\_h$: the number of attention heads
* $n\_e$: the number of hidden layers of the encoder
* $n\_d$: the number of ... | [
"### 1. Parameters\n\n\n\n* $d\\_m$: the dimension of hidden states\n* $d\\_{ff}$: the dimension of feed forward layers\n* $d\\_{kv}$: the dimension of the keys/values in the self-attention layers\n* $n\\_h$: the number of attention heads\n* $n\\_e$: the number of hidden layers of the encoder\n* $n\\_d$: the number... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #lm-head #zh #arxiv-2108.12960 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### 1. Parameters\n\n\n\n* $d\\_m$: the dimension of hidden states\n* $d\\_{ff}$: the dimension of feed forward layers\n* $d\\... |
text2text-generation | transformers | ## LongLM
### 1. Parameters
| Versions | $d_m$ | $d_{ff}$ | $d_{kv}$ | $n_h$ | $n_e/n_d$ | \# P |
| ------------ | ----- | -------- | -------- | ----- | --------- | ---- |
| LongLM-small | 512 | 2,048 | 64 | 8 | 6/6 | 60M |
| LongLM-base | 768 | 3,072 | 64 | 12 | 12/12 |... | {"language": ["zh"], "tags": ["pytorch", "lm-head", "zh"], "thumbnail": "http://coai.cs.tsinghua.edu.cn/coai/img/logo.png?v=13923", "widget": [{"text": "\u5c0f\u5495\u565c\u5bf9\u9773\u53f8\u5bd2\u5b8c\u5168\u662f\u4e2a\u81ea\u6765\u719f\uff0c\u5c0f\u5bb6\u4f19\u722c\u8fdb\u4ed6\u6000\u91cc\u5c0f\u624b\u6402\u7740\u4ed... | thu-coai/LongLM-large | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lm-head",
"zh",
"arxiv:2108.12960",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.12960"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #lm-head #zh #arxiv-2108.12960 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| LongLM
------
### 1. Parameters
* $d\_m$: the dimension of hidden states
* $d\_{ff}$: the dimension of feed forward layers
* $d\_{kv}$: the dimension of the keys/values in the self-attention layers
* $n\_h$: the number of attention heads
* $n\_e$: the number of hidden layers of the encoder
* $n\_d$: the number of ... | [
"### 1. Parameters\n\n\n\n* $d\\_m$: the dimension of hidden states\n* $d\\_{ff}$: the dimension of feed forward layers\n* $d\\_{kv}$: the dimension of the keys/values in the self-attention layers\n* $n\\_h$: the number of attention heads\n* $n\\_e$: the number of hidden layers of the encoder\n* $n\\_d$: the number... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #lm-head #zh #arxiv-2108.12960 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### 1. Parameters\n\n\n\n* $d\\_m$: the dimension of hidden states\n* $d\\_{ff}$: the dimension of feed forward layers\n* $d\\... |
text2text-generation | transformers | ## LongLM
### 1. Parameters
| Versions | $d_m$ | $d_{ff}$ | $d_{kv}$ | $n_h$ | $n_e/n_d$ | \# P |
| ------------ | ----- | -------- | -------- | ----- | --------- | ---- |
| LongLM-small | 512 | 2,048 | 64 | 8 | 6/6 | 60M |
| LongLM-base | 768 | 3,072 | 64 | 12 | 12/12 |... | {"language": ["zh"], "tags": ["pytorch", "lm-head", "zh"], "thumbnail": "http://coai.cs.tsinghua.edu.cn/coai/img/logo.png?v=13923", "widget": [{"text": "\u5c0f\u5495\u565c\u5bf9\u9773\u53f8\u5bd2\u5b8c\u5168\u662f\u4e2a\u81ea\u6765\u719f\uff0c\u5c0f\u5bb6\u4f19\u722c\u8fdb\u4ed6\u6000\u91cc\u5c0f\u624b\u6402\u7740\u4ed... | thu-coai/LongLM-small | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lm-head",
"zh",
"arxiv:2108.12960",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.12960"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #lm-head #zh #arxiv-2108.12960 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| LongLM
------
### 1. Parameters
* $d\_m$: the dimension of hidden states
* $d\_{ff}$: the dimension of feed forward layers
* $d\_{kv}$: the dimension of the keys/values in the self-attention layers
* $n\_h$: the number of attention heads
* $n\_e$: the number of hidden layers of the encoder
* $n\_d$: the number of ... | [
"### 1. Parameters\n\n\n\n* $d\\_m$: the dimension of hidden states\n* $d\\_{ff}$: the dimension of feed forward layers\n* $d\\_{kv}$: the dimension of the keys/values in the self-attention layers\n* $n\\_h$: the number of attention heads\n* $n\\_e$: the number of hidden layers of the encoder\n* $n\\_d$: the number... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #lm-head #zh #arxiv-2108.12960 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### 1. Parameters\n\n\n\n* $d\\_m$: the dimension of hidden states\n* $d\\_{ff}$: the dimension of feed forward layers\n* $d\\... |
fill-mask | transformers | ## Lawformer
### Introduction
This repository provides the source code and checkpoints of the paper "Lawformer: A Pre-trained Language Model forChinese Legal Long Documents". You can download the checkpoint from the [huggingface model hub](https://huggingface.co/xcjthu/Lawformer) or from [here](https://data.thunlp.org... | {} | thunlp/Lawformer | null | [
"transformers",
"pytorch",
"longformer",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #longformer #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ## Lawformer
### Introduction
This repository provides the source code and checkpoints of the paper "Lawformer: A Pre-trained Language Model forChinese Legal Long Documents". You can download the checkpoint from the huggingface model hub or from here.
### Easy Start
We have uploaded our model to the huggingface mod... | [
"## Lawformer",
"### Introduction\nThis repository provides the source code and checkpoints of the paper \"Lawformer: A Pre-trained Language Model forChinese Legal Long Documents\". You can download the checkpoint from the huggingface model hub or from here.",
"### Easy Start\nWe have uploaded our model to the ... | [
"TAGS\n#transformers #pytorch #longformer #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## Lawformer",
"### Introduction\nThis repository provides the source code and checkpoints of the paper \"Lawformer: A Pre-trained Language Model forChinese Legal Long Documents\". You can download ... |
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. -->
# bert-base-cased-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-wikitext2", "results": []}]} | thyagosme/bert-base-cased-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-wikitext2
=========================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.8517
Model description
-----------------
More information needed
Intended uses & limitations
-----------------------... | [
"### 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: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #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... |
multiple-choice | 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. -->
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["swag"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-swag", "results": []}]} | thyagosme/bert-base-uncased-finetuned-swag | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-swag
================================
This model is a fine-tuned version of bert-base-uncased on the swag dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0438
* Accuracy: 0.7915
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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 #bert #multiple-choice #generated_from_trainer #dataset-swag #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: 5e-05\n* train\\_batch\\_size: 16\n*... |
text-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. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the fo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | thyagosme/gpt2-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wikitext2
==============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.1095
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### 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: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #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: 2e-05\n*... |
automatic-speech-recognition | 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. -->
# wav2vec2-base-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-demo-colab", "results": []}]} | thyagosme/wav2vec2-base-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-demo-colab
========================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4657
* Wer: 0.3422
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 3... |
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. -->
# opus-mt-de-en-finetuned-de-to-en-first
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-de-en-finetuned-de-to-en-first", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt1... | tiagohatta/opus-mt-de-en-finetuned-de-to-en-first | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-de-en-finetuned-de-to-en-first
======================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-de-en on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1465
* Bleu: 39.8122
* Gen Len: 25.579
Model description
-----------------
More ... | [
"### 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 #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
# opus-mt-de-en-finetuned-de-to-en-second
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-de-en-finetuned-de-to-en-second", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt... | tiagohatta/opus-mt-de-en-finetuned-de-to-en-second | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-de-en-finetuned-de-to-en-second
=======================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-de-en on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1719
* Bleu: 38.959
* Gen Len: 25.2812
Model description
-----------------
Mor... | [
"### 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: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
text-generation | transformers |
# Bobby Hill DialoGPT Model | {"tags": ["conversational"]} | ticet11/DialoGPT-small-BOBBY | 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
|
# Bobby Hill DialoGPT Model | [
"# Bobby Hill DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Bobby Hill DialoGPT Model"
] |
translation | transformers |
### en-he
* source group: English
* target group: Hebrew
* OPUS readme: [eng-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-heb/README.md)
* model: transformer
* source language(s): eng
* target language(s): heb
* model: transformer
* pre-processing: normalization + SentencePiece (... | {"language": ["en", "he"], "license": "apache-2.0", "tags": ["translation"]} | tiedeman/opus-mt-en-he | null | [
"transformers",
"pytorch",
"rust",
"marian",
"text2text-generation",
"translation",
"en",
"he",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"he"
] | TAGS
#transformers #pytorch #rust #marian #text2text-generation #translation #en #he #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### en-he
* source group: English
* target group: Hebrew
* OPUS readme: eng-heb
* model: transformer
* source language(s): eng
* target language(s): heb
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores:... | [
"### en-he\n\n\n* source group: English\n* target group: Hebrew\n* OPUS readme: eng-heb\n* model: transformer\n* source language(s): eng\n* target language(s): heb\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n... | [
"TAGS\n#transformers #pytorch #rust #marian #text2text-generation #translation #en #he #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### en-he\n\n\n* source group: English\n* target group: Hebrew\n* OPUS readme: eng-heb\n* model: transformer\n* source language(s): eng\n* target ... |
translation | transformers |
### he-en
* source group: Hebrew
* target group: English
* OPUS readme: [heb-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-eng/README.md)
* model: transformer
* source language(s): heb
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (... | {"language": ["he", "en"], "license": "apache-2.0", "tags": ["translation"]} | tiedeman/opus-mt-he-en | null | [
"transformers",
"pytorch",
"rust",
"marian",
"text2text-generation",
"translation",
"he",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"he",
"en"
] | TAGS
#transformers #pytorch #rust #marian #text2text-generation #translation #he #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### he-en
* source group: Hebrew
* target group: English
* OPUS readme: heb-eng
* model: transformer
* source language(s): heb
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: URL
* test set translations: URL
* test set scores:... | [
"### he-en\n\n\n* source group: Hebrew\n* target group: English\n* OPUS readme: heb-eng\n* model: transformer\n* source language(s): heb\n* target language(s): eng\n* model: transformer\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: URL\n* test set translations: URL\n... | [
"TAGS\n#transformers #pytorch #rust #marian #text2text-generation #translation #he #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### he-en\n\n\n* source group: Hebrew\n* target group: English\n* OPUS readme: heb-eng\n* model: transformer\n* source language(s): heb\n* target ... |
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. -->
# bert-base-uncased-finetuned-docvqa
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-docvqa", "results": []}]} | tiennvcs/bert-base-uncased-finetuned-docvqa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-docvqa
==================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9146
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batc... |
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. -->
# bert-base-uncased-finetuned-infovqa
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | tiennvcs/bert-base-uncased-finetuned-infovqa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-infovqa
===================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8276
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batc... |
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. -->
# bert-base-uncased-finetuned-vi-infovqa
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-vi-infovqa", "results": []}]} | tiennvcs/bert-base-uncased-finetuned-vi-infovqa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-vi-infovqa
======================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5470
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batc... |
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. -->
# bert-large-uncased-finetuned-docvqa
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | tiennvcs/bert-large-uncased-finetuned-docvqa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-large-uncased-finetuned-docvqa
===================================
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6367
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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\\_size: 2\n* eval\\_batc... |
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. -->
# bert-large-uncased-finetuned-infovqa
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-larg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | tiennvcs/bert-large-uncased-finetuned-infovqa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-large-uncased-finetuned-infovqa
====================================
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 6.3170
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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\\_size: 2\n* eval\\_batc... |
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. -->
# bert-large-uncased-finetuned-vi-infovqa
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-l... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-large-uncased-finetuned-vi-infovqa", "results": []}]} | tiennvcs/bert-large-uncased-finetuned-vi-infovqa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-large-uncased-finetuned-vi-infovqa
=======================================
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 7.4878
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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\\_size: 2\n* eval\\_batc... |
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-infovqa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-infovqa", "results": []}]} | tiennvcs/distilbert-base-uncased-finetuned-infovqa | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"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 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-infovqa
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8872
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 4\n* eval... |
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"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | tiennvcs/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #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 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description... |
document-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. -->
# layoutlmv2-base-uncased-finetuned-docvqa
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggi... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-base-uncased-finetuned-docvqa", "results": []}]} | tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"document-question-answering",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #document-question-answering #generated_from_trainer #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
| layoutlmv2-base-uncased-finetuned-docvqa
========================================
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1940
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #document-question-answering #generated_from_trainer #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_ba... |
document-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. -->
# layoutlmv2-base-uncased-finetuned-infovqa
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://hugg... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-base-uncased-finetuned-infovqa", "results": []}]} | tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"document-question-answering",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #document-question-answering #generated_from_trainer #license-cc-by-sa-4.0 #endpoints_compatible #region-us
| layoutlmv2-base-uncased-finetuned-infovqa
=========================================
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0870
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #document-question-answering #generated_from_trainer #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size:... |
document-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. -->
# layoutlmv2-base-uncased-finetuned-vi-infovqa
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://h... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-base-uncased-finetuned-vi-infovqa", "results": []}]} | tiennvcs/layoutlmv2-base-uncased-finetuned-vi-infovqa | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"document-question-answering",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #document-question-answering #generated_from_trainer #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
| layoutlmv2-base-uncased-finetuned-vi-infovqa
============================================
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.3332
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #document-question-answering #generated_from_trainer #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_si... |
document-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. -->
# layoutlmv2-large-uncased-finetuned-infovqa
This model is a fine-tuned version of [microsoft/layoutlmv2-large-uncased](https://hu... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-large-uncased-finetuned-infovqa", "results": []}]} | tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"document-question-answering",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #document-question-answering #generated_from_trainer #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us
| layoutlmv2-large-uncased-finetuned-infovqa
==========================================
This model is a fine-tuned version of microsoft/layoutlmv2-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2207
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #document-question-answering #generated_from_trainer #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\... |
document-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. -->
# layoutlmv2-large-uncased-finetuned-vi-infovqa
This model is a fine-tuned version of [microsoft/layoutlmv2-large-uncased](https:/... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-large-uncased-finetuned-vi-infovqa", "results": []}]} | tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"document-question-answering",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #document-question-answering #generated_from_trainer #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
| layoutlmv2-large-uncased-finetuned-vi-infovqa
=============================================
This model is a fine-tuned version of microsoft/layoutlmv2-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 8.5806
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: 2\n* eval\\_batch\\_size: 2\n* seed: 250500\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #document-question-answering #generated_from_trainer #license-cc-by-nc-sa-4.0 #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\\_si... |
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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | tiesan/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1658
* Accuracy: 0.928
* F1: 0.9284
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
image-classification | timm |
# ECA-NFNet-L0
Pretrained model on [ImageNet](http://www.image-net.org/), this is a variant of the [NFNet (Normalization Free)](https://arxiv.org/abs/2102.06171) model family.
## Model description
This model variant was slimmed down from the original F0 variant in the paper for improved runtime characteristics (thr... | {"license": "apache-2.0", "library_name": "timm", "tags": ["image-classification", "timm", "normalization-free", "efficient-channel-attention"], "datasets": ["imagenet"]} | timm/eca_nfnet_l0 | null | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"normalization-free",
"efficient-channel-attention",
"dataset:imagenet",
"arxiv:2102.06171",
"arxiv:1910.03151",
"arxiv:1903.10520",
"arxiv:1906.02659",
"arxiv:2010.15052",
"arxiv:1909.13719",
"license:apache-2.0",
"has_space",
... | null | 2022-03-02T23:29:05+00:00 | [
"2102.06171",
"1910.03151",
"1903.10520",
"1906.02659",
"2010.15052",
"1909.13719"
] | [] | TAGS
#timm #pytorch #safetensors #image-classification #normalization-free #efficient-channel-attention #dataset-imagenet #arxiv-2102.06171 #arxiv-1910.03151 #arxiv-1903.10520 #arxiv-1906.02659 #arxiv-2010.15052 #arxiv-1909.13719 #license-apache-2.0 #has_space #region-us
|
# ECA-NFNet-L0
Pretrained model on ImageNet, this is a variant of the NFNet (Normalization Free) model family.
## Model description
This model variant was slimmed down from the original F0 variant in the paper for improved runtime characteristics (throughput, memory use) in PyTorch, on a GPU accelerator. It utilize... | [
"# ECA-NFNet-L0\n\nPretrained model on ImageNet, this is a variant of the NFNet (Normalization Free) model family.",
"## Model description\n\nThis model variant was slimmed down from the original F0 variant in the paper for improved runtime characteristics (throughput, memory use) in PyTorch, on a GPU accelerator... | [
"TAGS\n#timm #pytorch #safetensors #image-classification #normalization-free #efficient-channel-attention #dataset-imagenet #arxiv-2102.06171 #arxiv-1910.03151 #arxiv-1903.10520 #arxiv-1906.02659 #arxiv-2010.15052 #arxiv-1909.13719 #license-apache-2.0 #has_space #region-us \n",
"# ECA-NFNet-L0\n\nPretrained model... |
image-classification | timm |
# ViT-H/14 (ImageNet-21k)
...
| {"license": "apache-2.0", "tags": ["image-classification", "timm", "vision-transformer"], "datasets": ["imagenet_21k"], "inference": false} | timm/vit_huge_patch14_224_in21k | null | [
"timm",
"pytorch",
"image-classification",
"vision-transformer",
"dataset:imagenet_21k",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #vision-transformer #dataset-imagenet_21k #license-apache-2.0 #has_space #region-us
|
# ViT-H/14 (ImageNet-21k)
...
| [
"# ViT-H/14 (ImageNet-21k)\n..."
] | [
"TAGS\n#timm #pytorch #image-classification #vision-transformer #dataset-imagenet_21k #license-apache-2.0 #has_space #region-us \n",
"# ViT-H/14 (ImageNet-21k)\n..."
] |
text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | timslams666/DialoGPT-small-rick | 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
|
# Rick Sanchez DialoGPT Model | [
"# Rick Sanchez DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Sanchez DialoGPT Model"
] |
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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | timtarusov/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2274
* Accuracy: 0.921
* F1: 0.9211
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-generation | transformers |
# Harry Potter DialoGPT MOdel | {"tags": ["conversational"]} | tinega/DialoGPT-small-harrypotter | 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
|
# Harry Potter DialoGPT MOdel | [
"# Harry Potter DialoGPT MOdel"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT MOdel"
] |
automatic-speech-recognition | 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. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | tingtingyuli/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4371
* Wer: 0.3402
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 3... |
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. -->
# bert-base-uncased-finetuned-Pisa
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-Pisa", "results": []}]} | tizaino/bert-base-uncased-finetuned-Pisa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-Pisa
================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1132
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### 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: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #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. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | tkesonia/xlm-roberta-base-finetuned-marc-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9211
* Mae: 0.5122
Model description
-----------------
More information needed
... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
null | null |
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, in... | {"title": "ArcaneGAN", "emoji": "\ud83d\ude80", "colorFrom": "blue", "colorTo": "blue", "sdk": "gradio", "app_file": "app.py", "pinned": false} | tlanfer/arc | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
|
# Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blue, in... | [
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbnail gradient (red, yellow,... | [
"TAGS\n#region-us \n",
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbna... |
token-classification | transformers |
# sd-ner
## Model description
This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang... | {"language": ["english"], "tags": ["token classification"], "datasets": ["EMBO/sd-panels"], "metrics": []} | tlemberger/sd-ner | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"token classification",
"dataset:EMBO/sd-panels",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"english"
] | TAGS
#transformers #pytorch #jax #roberta #token-classification #token classification #dataset-EMBO/sd-panels #autotrain_compatible #endpoints_compatible #region-us
|
# sd-ner
## Model description
This model is a RoBERTa base model that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the BioLang dataset and fine-tuned for token classification on the SourceData sd-panels dataset to perfor... | [
"# sd-ner",
"## Model description\n\nThis model is a RoBERTa base model that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the BioLang dataset and fine-tuned for token classification on the SourceData sd-panels dataset... | [
"TAGS\n#transformers #pytorch #jax #roberta #token-classification #token classification #dataset-EMBO/sd-panels #autotrain_compatible #endpoints_compatible #region-us \n",
"# sd-ner",
"## Model description\n\nThis model is a RoBERTa base model that was further trained using a masked language modeling task on a ... |
null | transformers | To run the model, you would need dependencies (e.g., vocab extracted from CoNLL corpus). For details, please refer to our [repo](https://github.com/utahnlp/structured_tuning_srl). | {} | tli8hf/robertabase-structured-tuning-srl-conll2012 | null | [
"transformers",
"pytorch",
"roberta",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #endpoints_compatible #region-us
| To run the model, you would need dependencies (e.g., vocab extracted from CoNLL corpus). For details, please refer to our repo. | [] | [
"TAGS\n#transformers #pytorch #roberta #endpoints_compatible #region-us \n"
] |
token-classification | flair | ## Test model | {"tags": ["flair", "token-classification"], "widget": [{"text": "does this work"}]} | tmagajna/test | null | [
"flair",
"pytorch",
"token-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#flair #pytorch #token-classification #region-us
| ## Test model | [
"## Test model"
] | [
"TAGS\n#flair #pytorch #token-classification #region-us \n",
"## Test model"
] |
text-generation | transformers |
# Hank Hill ChatBot
This is an instance of microsoft/DialoGPT-small trained on a tv show character, Hank Hill from King of The Hill. The data comes from a csv file that contains character lines from the first 5 seasons of the show. Updated some portion of the data to accurately show Hank's famous pronunciation of the... | {"tags": ["conversational"]} | tngo/DialoGPT-small-HankHill | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Hank Hill ChatBot
This is an instance of microsoft/DialoGPT-small trained on a tv show character, Hank Hill from King of The Hill. The data comes from a csv file that contains character lines from the first 5 seasons of the show. Updated some portion of the data to accurately show Hank's famous pronunciation of the... | [
"# Hank Hill ChatBot\n\nThis is an instance of microsoft/DialoGPT-small trained on a tv show character, Hank Hill from King of The Hill. The data comes from a csv file that contains character lines from the first 5 seasons of the show. Updated some portion of the data to accurately show Hank's famous pronunciation ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Hank Hill ChatBot\n\nThis is an instance of microsoft/DialoGPT-small trained on a tv show character, Hank Hill from King of The Hill. The data c... |
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. -->
# TESDFEEEE
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an un... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "TESDFEEEE", "results": []}]} | toasterboy/TESDFEEEE | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| TESDFEEEE
=========
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: ... |
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. -->
# Facebook_Mit_HPS
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) o... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Facebook_Mit_HPS", "results": []}]} | toasthans/Facebook_Mit_HPS | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Facebook\_Mit\_HPS
==================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3681
* Accuracy: 0.9281
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3.906763521176542e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 30\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3.906763521176542e-05\n* train\... |
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. -->
# Facebook_Mit_HPS_5_Epoch
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Facebook_Mit_HPS_5_Epoch", "results": []}]} | toasthans/Facebook_Mit_HPS_5_Epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Facebook\_Mit\_HPS\_5\_Epoch
============================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4774
* Accuracy: 0.9315
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.546392051994155e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 5\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5"... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.546392051994155e-05\n* train\... |
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