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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
token-classification | stanza | # Stanza model for Urdu (ur)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https... | {"language": "ur", "license": "apache-2.0", "library_name": "stanza", "tags": ["stanza", "token-classification"]} | stanfordnlp/stanza-ur | null | [
"stanza",
"token-classification",
"ur",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ur"
] | TAGS
#stanza #token-classification #ur #license-apache-2.0 #region-us
| # Stanza model for Urdu (ur)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in our website and our... | [
"# Stanza model for Urdu (ur)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.\nFind more about it in our website ... | [
"TAGS\n#stanza #token-classification #ur #license-apache-2.0 #region-us \n",
"# Stanza model for Urdu (ur)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-ar... |
token-classification | stanza | # Stanza model for Vietnamese (vi)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website]... | {"language": "vi", "license": "apache-2.0", "library_name": "stanza", "tags": ["stanza", "token-classification"]} | stanfordnlp/stanza-vi | null | [
"stanza",
"token-classification",
"vi",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#stanza #token-classification #vi #license-apache-2.0 #region-us
| # Stanza model for Vietnamese (vi)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in our website a... | [
"# Stanza model for Vietnamese (vi)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.\nFind more about it in our we... | [
"TAGS\n#stanza #token-classification #vi #license-apache-2.0 #region-us \n",
"# Stanza model for Vietnamese (vi)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-... |
token-classification | stanza | # Stanza model for Wolof (wo)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](http... | {"language": "wo", "license": "apache-2.0", "library_name": "stanza", "tags": ["stanza", "token-classification"]} | stanfordnlp/stanza-wo | null | [
"stanza",
"token-classification",
"wo",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"wo"
] | TAGS
#stanza #token-classification #wo #license-apache-2.0 #region-us
| # Stanza model for Wolof (wo)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in our website and ou... | [
"# Stanza model for Wolof (wo)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.\nFind more about it in our website... | [
"TAGS\n#stanza #token-classification #wo #license-apache-2.0 #region-us \n",
"# Stanza model for Wolof (wo)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-a... |
token-classification | stanza | # Stanza model for Simplified_Chinese (zh-hans)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in ... | {"language": "zh", "license": "apache-2.0", "library_name": "stanza", "tags": ["stanza", "token-classification"]} | stanfordnlp/stanza-zh-hans | null | [
"stanza",
"token-classification",
"zh",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#stanza #token-classification #zh #license-apache-2.0 #region-us
| # Stanza model for Simplified_Chinese (zh-hans)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in ... | [
"# Stanza model for Simplified_Chinese (zh-hans)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.\nFind more about... | [
"TAGS\n#stanza #token-classification #zh #license-apache-2.0 #region-us \n",
"# Stanza model for Simplified_Chinese (zh-hans)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza bri... |
token-classification | stanza | # Stanza model for Traditional_Chinese (zh-hant)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in... | {"language": "zh", "license": "apache-2.0", "library_name": "stanza", "tags": ["stanza", "token-classification"]} | stanfordnlp/stanza-zh-hant | null | [
"stanza",
"token-classification",
"zh",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#stanza #token-classification #zh #license-apache-2.0 #region-us
| # Stanza model for Traditional_Chinese (zh-hant)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in... | [
"# Stanza model for Traditional_Chinese (zh-hant)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.\nFind more abou... | [
"TAGS\n#stanza #token-classification #zh #license-apache-2.0 #region-us \n",
"# Stanza model for Traditional_Chinese (zh-hant)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza br... |
text2text-generation | transformers | T5-base model fine-tuned for question generation from knowledge graphs. Can be used to generate questions from linearized knowledge graphs, meaning graphs in the form of its all its triples listed in the following format:
`<A> answer node(s) <H> head <R> relation <T> tail <H> head <R> relation <T> tail ... etc ...`,
w... | {"language": ["en"], "license": "openrail", "library_name": "transformers", "tags": ["knowledge_graphs", "question_generation"], "datasets": ["web_questions"], "metrics": ["bleu", "bertscore"]} | stanlochten/t5-KGQgen | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"knowledge_graphs",
"question_generation",
"en",
"dataset:web_questions",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #knowledge_graphs #question_generation #en #dataset-web_questions #license-openrail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-base model fine-tuned for question generation from knowledge graphs. Can be used to generate questions from linearized knowledge graphs, meaning graphs in the form of its all its triples listed in the following format:
'<A> answer node(s) <H> head <R> relation <T> tail <H> head <R> relation <T> tail ... etc ...',
w... | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #knowledge_graphs #question_generation #en #dataset-web_questions #license-openrail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This is a tiny random mt5 model used for testing
See `mt5-make-tiny-model.py` for how it was created. | {} | stas/mt5-tiny-random | null | [
"transformers",
"pytorch",
"jax",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a tiny random mt5 model used for testing
See 'URL' for how it was created. | [] | [
"TAGS\n#transformers #pytorch #jax #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This is a tiny random pegasus-cnn_dailymail model used for testing.
See `make-pegasus-cnn_dailymail-tiny-random.py` for how it was created.
| {} | stas/pegasus-cnn_dailymail-tiny-random | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| This is a tiny random pegasus-cnn_dailymail model used for testing.
See 'make-pegasus-cnn_dailymail-URL' for how it was created.
| [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | This is a tiny random t5 model used for testing
See `t5-make-very-small-model.py` for how it was created. | {} | stas/t5-very-small-random | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a tiny random t5 model used for testing
See 'URL' for how it was created. | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Tiny FSMT en-de
This is a tiny model that is used in the `transformers` test suite. It doesn't do anything useful, other than testing that `modeling_fsmt.py` is functional.
Do not try to use it for anything that requires quality.
The model is indeed 1MB in size.
You can see how it was created [here](https://hugg... | {"language": ["en", "de"], "license": "apache-2.0", "tags": ["wmt19", "testing"], "datasets": ["wmt19"], "metrics": ["bleu"]} | stas/tiny-wmt19-en-de | null | [
"transformers",
"pytorch",
"fsmt",
"text2text-generation",
"wmt19",
"testing",
"en",
"de",
"dataset:wmt19",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"de"
] | TAGS
#transformers #pytorch #fsmt #text2text-generation #wmt19 #testing #en #de #dataset-wmt19 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Tiny FSMT en-de
This is a tiny model that is used in the 'transformers' test suite. It doesn't do anything useful, other than testing that 'modeling_fsmt.py' is functional.
Do not try to use it for anything that requires quality.
The model is indeed 1MB in size.
You can see how it was created here.
If you're l... | [
"# Tiny FSMT en-de\n\nThis is a tiny model that is used in the 'transformers' test suite. It doesn't do anything useful, other than testing that 'modeling_fsmt.py' is functional.\n\nDo not try to use it for anything that requires quality.\n\nThe model is indeed 1MB in size.\n\nYou can see how it was created here.\n... | [
"TAGS\n#transformers #pytorch #fsmt #text2text-generation #wmt19 #testing #en #de #dataset-wmt19 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Tiny FSMT en-de\n\nThis is a tiny model that is used in the 'transformers' test suite. It doesn't do anything useful, other than testi... |
text2text-generation | transformers |
# Tiny FSMT en-ru
This is a tiny model that is used in the `transformers` test suite. It doesn't do anything useful, other than testing that `modeling_fsmt.py` is functional.
Do not try to use it for anything that requires quality.
The model is indeed 30KB in size.
You can see how it was created [here](https://hug... | {"language": ["en", "ru"], "license": "apache-2.0", "tags": ["wmt19", "testing"], "datasets": ["wmt19"], "metrics": ["bleu"]} | stas/tiny-wmt19-en-ru | null | [
"transformers",
"pytorch",
"fsmt",
"text2text-generation",
"wmt19",
"testing",
"en",
"ru",
"dataset:wmt19",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"ru"
] | TAGS
#transformers #pytorch #fsmt #text2text-generation #wmt19 #testing #en #ru #dataset-wmt19 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Tiny FSMT en-ru
This is a tiny model that is used in the 'transformers' test suite. It doesn't do anything useful, other than testing that 'modeling_fsmt.py' is functional.
Do not try to use it for anything that requires quality.
The model is indeed 30KB in size.
You can see how it was created here.
If you're l... | [
"# Tiny FSMT en-ru\n\nThis is a tiny model that is used in the 'transformers' test suite. It doesn't do anything useful, other than testing that 'modeling_fsmt.py' is functional.\n\nDo not try to use it for anything that requires quality.\n\nThe model is indeed 30KB in size.\n\nYou can see how it was created here.\... | [
"TAGS\n#transformers #pytorch #fsmt #text2text-generation #wmt19 #testing #en #ru #dataset-wmt19 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Tiny FSMT en-ru\n\nThis is a tiny model that is used in the 'transformers' test suite. It doesn't do anything useful, other than testi... |
null | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
--... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"]} | stefan-it/electra-base-gc4-64k-0-cased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #de #dataset-german-nlp-group/german_common_crawl #license-mit #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in thi... | [
"# GC4LM: A Colossal (Biased) language model for German\n\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #de #dataset-german-nlp-group/german_common_crawl #license-mit #endpoints_compatible #region-us \n",
"# GC4LM: A Colossal (Biased) language model for German\n\nThis repository presents a colossal (and biased) language model for German trained on the recently... |
fill-mask | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"], "widget": [{"text": "Heute ist ein [MASK] Tag"}]} | stefan-it/electra-base-gc4-64k-0-cased-generator | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"fill-mask",
"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #safetensors #electra #fill-mask #de #dataset-german-nlp-group/german_common_crawl #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
"TAGS\n#transformers #pytorch #tf #safetensors #electra #fill-mask #de #dataset-german-nlp-group/german_common_crawl #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for... |
null | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"]} | stefan-it/electra-base-gc4-64k-100000-cased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #de #dataset-german-nlp-group/german_common_crawl #license-mit #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #de #dataset-german-nlp-group/german_common_crawl #license-mit #endpoints_compatible #region-us \n",
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently r... |
fill-mask | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"], "widget": [{"text": "Heute ist ein [MASK] Tag"}]} | stefan-it/electra-base-gc4-64k-100000-cased-generator | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"fill-mask",
"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #safetensors #electra #fill-mask #de #dataset-german-nlp-group/german_common_crawl #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
"TAGS\n#transformers #pytorch #tf #safetensors #electra #fill-mask #de #dataset-german-nlp-group/german_common_crawl #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for... |
null | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"]} | stefan-it/electra-base-gc4-64k-1000000-cased-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
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|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently r... |
fill-mask | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"], "widget": [{"text": "Heute ist ein [MASK] Tag"}]} | stefan-it/electra-base-gc4-64k-1000000-cased-generator | null | [
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|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German train... |
null | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"]} | stefan-it/electra-base-gc4-64k-200000-cased-discriminator | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently r... |
fill-mask | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"], "widget": [{"text": "Heute ist ein [MASK] Tag"}]} | stefan-it/electra-base-gc4-64k-200000-cased-generator | null | [
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|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for... |
null | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"]} | stefan-it/electra-base-gc4-64k-300000-cased-discriminator | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
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|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently r... |
fill-mask | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"], "widget": [{"text": "Heute ist ein [MASK] Tag"}]} | stefan-it/electra-base-gc4-64k-300000-cased-generator | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German train... |
null | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"]} | stefan-it/electra-base-gc4-64k-400000-cased-discriminator | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
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|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently r... |
fill-mask | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"], "widget": [{"text": "Heute ist ein [MASK] Tag"}]} | stefan-it/electra-base-gc4-64k-400000-cased-generator | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
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#transformers #pytorch #tf #electra #fill-mask #de #dataset-german-nlp-group/german_common_crawl #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German train... |
null | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"]} | stefan-it/electra-base-gc4-64k-500000-cased-discriminator | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #de #dataset-german-nlp-group/german_common_crawl #license-mit #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently r... |
fill-mask | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"], "widget": [{"text": "Heute ist ein [MASK] Tag"}]} | stefan-it/electra-base-gc4-64k-500000-cased-generator | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
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|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #de #dataset-german-nlp-group/german_common_crawl #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German train... |
null | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"]} | stefan-it/electra-base-gc4-64k-600000-cased-discriminator | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #de #dataset-german-nlp-group/german_common_crawl #license-mit #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently r... |
fill-mask | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"], "widget": [{"text": "Heute ist ein [MASK] Tag"}]} | stefan-it/electra-base-gc4-64k-600000-cased-generator | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
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|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #de #dataset-german-nlp-group/german_common_crawl #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German train... |
null | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"]} | stefan-it/electra-base-gc4-64k-700000-cased-discriminator | null | [
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"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #de #dataset-german-nlp-group/german_common_crawl #license-mit #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently r... |
fill-mask | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"], "widget": [{"text": "Heute ist ein [MASK] Tag"}]} | stefan-it/electra-base-gc4-64k-700000-cased-generator | null | [
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"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #de #dataset-german-nlp-group/german_common_crawl #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German train... |
null | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"]} | stefan-it/electra-base-gc4-64k-800000-cased-discriminator | null | [
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"electra",
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"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #de #dataset-german-nlp-group/german_common_crawl #license-mit #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently r... |
fill-mask | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"], "widget": [{"text": "Heute ist ein [MASK] Tag"}]} | stefan-it/electra-base-gc4-64k-800000-cased-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #de #dataset-german-nlp-group/german_common_crawl #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German train... |
null | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"]} | stefan-it/electra-base-gc4-64k-900000-cased-discriminator | null | [
"transformers",
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"tf",
"electra",
"pretraining",
"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #de #dataset-german-nlp-group/german_common_crawl #license-mit #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
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"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently r... |
fill-mask | transformers |
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---... | {"language": "de", "license": "mit", "datasets": ["german-nlp-group/german_common_crawl"], "widget": [{"text": "Heute ist ein [MASK] Tag"}]} | stefan-it/electra-base-gc4-64k-900000-cased-generator | null | [
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"de",
"dataset:german-nlp-group/german_common_crawl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #de #dataset-german-nlp-group/german_common_crawl #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
"German colossal, clean Common Crawl corpus" (GC4),
with a total dataset size of ~844GB.
---
Disclaimer: the presented and trained language models in this... | [
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German trained on the recently released\n\"German colossal, clean Common Crawl corpus\" (GC4),\nwith a total dataset size of ~844GB.\n\n---\n\nDisclaimer: the presented and trained language m... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #de #dataset-german-nlp-group/german_common_crawl #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# GC4LM: A Colossal (Biased) language model for German\nThis repository presents a colossal (and biased) language model for German train... |
null | flair |
# CoNLL-2003 NER Model
Imported sequence tagger model for Flair, that was trained on English CoNLL-2003 corpus for NER.
| {"language": "en", "license": "mit", "tags": ["flair", "sequence-tagger-model"]} | stefan-it/flair-ner-conll03 | null | [
"flair",
"pytorch",
"sequence-tagger-model",
"en",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #sequence-tagger-model #en #license-mit #region-us
|
# CoNLL-2003 NER Model
Imported sequence tagger model for Flair, that was trained on English CoNLL-2003 corpus for NER.
| [
"# CoNLL-2003 NER Model\n\nImported sequence tagger model for Flair, that was trained on English CoNLL-2003 corpus for NER."
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"# CoNLL-2003 NER Model\n\nImported sequence tagger model for Flair, that was trained on English CoNLL-2003 corpus for NER."
] |
text-generation | transformers |
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on ~90 GB from the ["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4).
The model is meant to be an entry point for fine-tuning on other texts, and it is definit... | {"language": "de", "license": "mit", "widget": [{"text": "Heute ist sehr sch\u00f6nes Wetter in"}]} | stefan-it/german-gpt2-larger | null | [
"transformers",
"pytorch",
"jax",
"onnx",
"gpt2",
"text-generation",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #onnx #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on ~90 GB from the "German colossal, clean Common Crawl corpus" (GC4).
The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" as the English GPT-3 model. We d... | [
"# German GPT-2 model\nIn this repository we release (yet another) GPT-2 model, that was trained on ~90 GB from the \"German colossal, clean Common Crawl corpus\" (GC4).\n\nThe model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or \"dangerous\" as the English GPT-3 ... | [
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"# German GPT-2 model\nIn this repository we release (yet another) GPT-2 model, that was trained on ~90 GB from the \"German colossal, clean Commo... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Basque
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Basque using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be... | {"language": "eu", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Basque Stefan Schweter", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "d... | stefan-it/wav2vec2-large-xlsr-53-basque | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"eu",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eu"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Basque
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Basque using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as foll... | [
"# Wav2Vec2-Large-XLSR-53-Basque\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Basque using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be e... | [
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"# Wav2Vec2-Large-XLSR-53-Basque\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Basque using the Common Voice... |
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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | stefan-jo/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"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 #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0619
* Precision: 0.9379
* Recall: 0.9527
* F1: 0.9452
* Accuracy: 0.9867
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: 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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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* learning... |
null | keras | # T5
## Overview
The T5 model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
The abstract from the p... | {} | stevenkolawole/T5 | null | [
"keras",
"arxiv:1910.10683",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [] | TAGS
#keras #arxiv-1910.10683 #region-us
| # T5
## Overview
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
The abstract from the paper is the following:
*Transfer learni... | [
"# T5",
"## Overview\n\nThe T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.\n\nThe abstract from the paper is the following:\n\n*... | [
"TAGS\n#keras #arxiv-1910.10683 #region-us \n",
"# T5",
"## Overview\n\nThe T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.\n\n... |
text-generation | transformers |
# astroGPT 🪐
## Model description
This is a GPT-2 model fine-tuned on Western zodiac signs. For more information about GPT-2, take a look at 🤗 Hugging Face's GPT-2 [model card](https://huggingface.co/gpt2). You can use astroGPT to generate a daily horoscope by entering the current date.
## How to use
To use this... | {"language": "en", "thumbnail": "https://raw.githubusercontent.com/stevhliu/satsuma/master/images/astroGPT-thumbnail.png", "widget": [{"text": "Jan 18, 2020"}, {"text": "Feb 14, 2020"}, {"text": "Jul 04, 2020"}]} | stevhliu/astroGPT | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| astroGPT
========
Model description
-----------------
This is a GPT-2 model fine-tuned on Western zodiac signs. For more information about GPT-2, take a look at Hugging Face's GPT-2 model card. You can use astroGPT to generate a daily horoscope by entering the current date.
How to use
----------
To use this mod... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-billsum-ca_test
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the bill... | {"license": "apache-2.0", "tags": ["summarization", "t5"], "datasets": ["billsum"], "metrics": ["rouge"], "widget": [{"text": "The people of the State of California do enact as follows: SECTION 1. The Legislature hereby finds and declares as follows: (a) Many areas of the state are disproportionately impacted by drough... | stevhliu/t5-small-finetuned-billsum-ca_test | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"dataset:billsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #dataset-billsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-billsum-ca\_test
===================================
This model is a fine-tuned version of t5-small on the billsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3376
* Rouge1: 12.6315
* Rouge2: 6.9839
* Rougel: 10.9983
* Rougelsum: 11.9383
* Gen Len: 19.0
Model des... | [
"### 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: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precis... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | stfuowned/nek | null | [
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"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
|
# My Awesome Model | [
"# My Awesome Model"
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"# My Awesome Model"
] |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | stfuowned/rick | null | [
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"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
|
# My Awesome Model | [
"# My Awesome Model"
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-generation | transformers |
# tin bot | {"tags": ["conversational"]} | sthom/DialoGPT-small-tin | 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
|
# tin bot | [
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text2text-generation | transformers |
pretrained model: https://huggingface.co/Salesforce/codet5-small
finetuning dataset: https://huggingface.co/datasets/code_x_glue_ct_code_to_text (only the python split)
official inference check point (for comparison, using base, not small, size): https://storage.googleapis.com/sfr-codet5-data-research/finetuned_mode... | {"language": ["py", "en"], "license": "apache-2.0", "tags": ["Code2TextGeneration", "Code2TextSummarisation"], "datasets": ["code_x_glue_ct_code_to_text", "code_x_glue_ct_code_to_text (python)"], "metrics": ["code-x-bleu"], "thumbnail": "url to a thumbnail used in social sharing"} | stmnk/codet5-small-code-summarization-python | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"py",
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] | TAGS
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|
pretrained model: URL
finetuning dataset: URL (only the python split)
official inference check point (for comparison, using base, not small, size): URL
for fine-tuning process metrics see this w&b report
| [] | [
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] |
fill-mask | transformers |
## LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
**LUKE** (**L**anguage **U**nderstanding with **K**nowledge-based
**E**mbeddings) is a new pre-trained contextualized representation of words and
entities based on transformer. LUKE treats words and entities in a given text as
indepe... | {"language": "en", "license": "apache-2.0", "tags": ["luke", "named entity recognition", "entity typing", "relation classification", "question answering"], "thumbnail": "https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png"} | studio-ousia/luke-base | null | [
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"has_spa... | null | 2022-03-02T23:29:05+00:00 | [
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| LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
---------------------------------------------------------------------------------
LUKE (Language Understanding with Knowledge-based
Embeddings) is a new pre-trained contextualized representation of words and
entities based on transforme... | [
"### Experimental results\n\n\nThe experimental results are provided as follows:\n\n\n\nIf you find LUKE useful for your work, please cite the following paper:"
] | [
"TAGS\n#transformers #pytorch #luke #fill-mask #named entity recognition #entity typing #relation classification #question answering #en #arxiv-1906.08237 #arxiv-1903.07785 #arxiv-2002.01808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Experimental results\n\n\nT... |
null | transformers | # Model Card for luke-large-finetuned-conll-2003
# Model Details
## Model Description
LUKE (Language Understanding with Knowledge-based Embeddings) is a new pretrained contextualized representation of words and entities based on transformer.
- **Developed by:** Studio Ousia
- **Shared by [Optional]:** More ... | {"license": "apache-2.0"} | studio-ousia/luke-large-finetuned-conll-2003 | null | [
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"endpoints_compatible",
"region:us"
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| Model Card for luke-large-finetuned-conll-2003
==============================================
Model Details
=============
Model Description
-----------------
LUKE (Language Understanding with Knowledge-based Embeddings) is a new pretrained contextualized representation of words and entities based on transformer.
... | [
"### Preprocessing\n\n\nMore information needed",
"### Speeds, Sizes, Times\n\n\nMore information needed\n\n\nEvaluation\n==========\n\n\nTesting Data, Factors & Metrics\n-------------------------------",
"### Testing Data\n\n\nMore information needed",
"### Factors",
"### Metrics\n\n\nLUKE achieves state-o... | [
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"### Preprocessing\n\n\nMore information needed",
"### Speeds, Sizes, Times\n\n\nMore information needed\n\n\nEvaluation\n======... |
fill-mask | transformers |
## LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
**LUKE** (**L**anguage **U**nderstanding with **K**nowledge-based
**E**mbeddings) is a new pre-trained contextualized representation of words and
entities based on transformer. LUKE treats words and entities in a given text as
indepe... | {"language": "en", "license": "apache-2.0", "tags": ["luke", "named entity recognition", "entity typing", "relation classification", "question answering"], "thumbnail": "https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png"} | studio-ousia/luke-large | null | [
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| LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
---------------------------------------------------------------------------------
LUKE (Language Understanding with Knowledge-based
Embeddings) is a new pre-trained contextualized representation of words and
entities based on transforme... | [
"### Experimental results\n\n\nThe experimental results are provided as follows:\n\n\n\nIf you find LUKE useful for your work, please cite the following paper:"
] | [
"TAGS\n#transformers #pytorch #luke #fill-mask #named entity recognition #entity typing #relation classification #question answering #en #arxiv-1906.08237 #arxiv-1903.07785 #arxiv-2002.01808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Experimental results\n\n\nThe experime... |
fill-mask | transformers |
## mLUKE
**mLUKE** (multilingual LUKE) is a multilingual extension of LUKE.
Please check the [official repository](https://github.com/studio-ousia/luke) for
more details and updates.
This is the mLUKE base model with 12 hidden layers, 768 hidden size. The total number
of parameters in this model is 585M (278M for t... | {"language": ["multilingual", "ar", "bn", "de", "el", "en", "es", "fi", "fr", "hi", "id", "it", "ja", "ko", "nl", "pl", "pt", "ru", "sv", "sw", "te", "th", "tr", "vi", "zh"], "license": "apache-2.0", "tags": ["luke", "named entity recognition", "relation classification", "question answering"], "thumbnail": "https://git... | studio-ousia/mluke-base | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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|
## mLUKE
mLUKE (multilingual LUKE) is a multilingual extension of LUKE.
Please check the official repository for
more details and updates.
This is the mLUKE base model with 12 hidden layers, 768 hidden size. The total number
of parameters in this model is 585M (278M for the word embeddings and encoder, 307M for the... | [
"## mLUKE\n\nmLUKE (multilingual LUKE) is a multilingual extension of LUKE.\n\nPlease check the official repository for\nmore details and updates.\n\nThis is the mLUKE base model with 12 hidden layers, 768 hidden size. The total number\nof parameters in this model is 585M (278M for the word embeddings and encoder, ... | [
"TAGS\n#transformers #pytorch #luke #fill-mask #named entity recognition #relation classification #question answering #multilingual #ar #bn #de #el #en #es #fi #fr #hi #id #it #ja #ko #nl #pl #pt #ru #sv #sw #te #th #tr #vi #zh #arxiv-2010.01057 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #regio... |
fill-mask | transformers |
## mLUKE
**mLUKE** (multilingual LUKE) is a multilingual extension of LUKE.
Please check the [official repository](https://github.com/studio-ousia/luke) for
more details and updates.
This is the mLUKE large model with 24 hidden layers, 768 hidden size. The total number
of parameters in this model is 868M (561M for ... | {"language": ["multilingual", "ar", "bn", "de", "el", "en", "es", "fi", "fr", "hi", "id", "it", "ja", "ko", "nl", "pl", "pt", "ru", "sv", "sw", "te", "th", "tr", "vi", "zh"], "license": "apache-2.0", "tags": ["luke", "named entity recognition", "relation classification", "question answering"], "thumbnail": "https://git... | studio-ousia/mluke-large | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
"2010.01057"
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"zh"
] | TAGS
#transformers #pytorch #luke #fill-mask #named entity recognition #relation classification #question answering #multilingual #ar #bn #de #el #en #es #fi #fr #hi #id #it #ja #ko #nl #pl #pt #ru #sv #sw #te #th #tr #vi #zh #arxiv-2010.01057 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## mLUKE
mLUKE (multilingual LUKE) is a multilingual extension of LUKE.
Please check the official repository for
more details and updates.
This is the mLUKE large model with 24 hidden layers, 768 hidden size. The total number
of parameters in this model is 868M (561M for the word embeddings and encoder, 307M for th... | [
"## mLUKE\n\nmLUKE (multilingual LUKE) is a multilingual extension of LUKE.\n\nPlease check the official repository for\nmore details and updates.\n\nThis is the mLUKE large model with 24 hidden layers, 768 hidden size. The total number\nof parameters in this model is 868M (561M for the word embeddings and encoder,... | [
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null | null | tes | {} | studios/TES | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
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null | null | tesss | {} | studios/TES2 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| tesss | [] | [
"TAGS\n#region-us \n"
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fill-mask | transformers | hello
| {} | subbareddyiiit/BERT-NLP | null | [
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"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
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] |
text-generation | transformers | hello
| {} | subbareddyiiit/GPT2NLP | null | [
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"pytorch",
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"gpt2",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| hello
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fill-mask | transformers | hello
| {} | subbareddyiiit/RobertaNLP | null | [
"transformers",
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"jax",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
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] |
fill-mask | transformers | hello
| {} | subbareddyiiit/bert_csl_gold8k | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
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] |
text-generation | transformers | hello
| {} | subbareddyiiit/gpt2_csl_gold8k | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| hello
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fill-mask | transformers | hello
| {} | subbareddyiiit/inria_roberta | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
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] |
fill-mask | transformers | hello
| {} | subbareddyiiit/roberta_csl_gold8k | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # Harry Potter DialoGPT Model | {"tags": ["conversational"]} | sudip/bot1 | 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"
] |
text-generation | transformers |
# Dwight DialoGPT Model
You can find the code [here](https://github.com/sudo-apt-Abrar/BearsandBeets) | {"tags": ["conversational"]} | sudoabrar/DialoGPT-small-dwight | null | [
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"text-generation",
"conversational",
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|
# Dwight DialoGPT Model
You can find the code here | [
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] |
text2text-generation | transformers | __PEGASUS FOR COVID 19 LITERATURE SUMMARIZATION__
__Model Description:__
Pegasus-large fine Tuned on Covid 19 literature.
__Dataset:__
The data is the CORD-19 dataset, containing over 400,000 scholarly articles, including over 150,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses.
Among the... | {} | suha1234/pegasus_covid19 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| __PEGASUS FOR COVID 19 LITERATURE SUMMARIZATION__
__Model Description:__
Pegasus-large fine Tuned on Covid 19 literature.
__Dataset:__
The data is the CORD-19 dataset, containing over 400,000 scholarly articles, including over 150,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses.
Among the... | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | suhasjain/DailoGPT-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
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|
#Harry Potter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
# planes_airlines
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/hugg... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | suhnylla/planes_airlines | 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
|
# planes_airlines
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
#### planes cathay pacific
!planes cathay pacific
#### planes delta airlines
!planes delta airlines
#### p... | [
"# planes_airlines\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",
"#### planes cathay pacific\n\n!planes cathay pacific",
"#### planes delta airlines\n\n!plan... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# planes_airlines\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issu... |
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | sukhendrasingh/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"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-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3323
- Accuracy: 0.8733
- F1: 0.8797
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3323\n- Accuracy: 0.8733\n- F1: 0.8797",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
feature-extraction | transformers | ArabicTransformer small model (B6-6-6 with decoder)
# Paper :
[ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective](https://aclanthology.org/2021.findings-emnlp.108/)
# Abstract
Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arab... | {} | sultan/ArabicTransformer-base | null | [
"transformers",
"pytorch",
"funnel",
"feature-extraction",
"arxiv:2006.03236",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [] | TAGS
#transformers #pytorch #funnel #feature-extraction #arxiv-2006.03236 #endpoints_compatible #region-us
| ArabicTransformer small model (B6-6-6 with decoder)
Paper :
=======
ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective
Abstract
========
Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both AraB... | [] | [
"TAGS\n#transformers #pytorch #funnel #feature-extraction #arxiv-2006.03236 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | ArabicTransformer Large model (B8-8-8 with decoder)
<b>Paper</b> : ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective (EMNLP21)
<b>Abstract</b>
Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both Ar... | {} | sultan/ArabicTransformer-large | null | [
"transformers",
"pytorch",
"funnel",
"feature-extraction",
"arxiv:2006.03236",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [] | TAGS
#transformers #pytorch #funnel #feature-extraction #arxiv-2006.03236 #endpoints_compatible #region-us
| ArabicTransformer Large model (B8-8-8 with decoder)
<b>Paper</b> : ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective (EMNLP21)
<b>Abstract</b>
Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both Ar... | [] | [
"TAGS\n#transformers #pytorch #funnel #feature-extraction #arxiv-2006.03236 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | ArabicTransformer small model (B6-6-6 with decoder)
# Paper :
[ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective](https://aclanthology.org/2021.findings-emnlp.108/)
# Abstract
Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arab... | {} | sultan/ArabicTransformer-small | null | [
"transformers",
"pytorch",
"funnel",
"feature-extraction",
"arxiv:2006.03236",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03236"
] | [] | TAGS
#transformers #pytorch #funnel #feature-extraction #arxiv-2006.03236 #endpoints_compatible #region-us
| ArabicTransformer small model (B6-6-6 with decoder)
Paper :
=======
ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective
Abstract
========
Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both AraB... | [] | [
"TAGS\n#transformers #pytorch #funnel #feature-extraction #arxiv-2006.03236 #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | {} | sultan/BioM-ALBERT-xxlarge-PMC | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | [
"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subject for investigation. In\nthis paper, we empirically study biomedical\ndomain adaptation with la... | [
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"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nh... |
question-answering | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer m... | {} | sultan/BioM-ALBERT-xxlarge-SQuAD2 | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us
| # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer m... | [
"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subject for investigation. In\nthis paper, we empirically study biomedical\ndomain adaptation with larg... | [
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"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subje... |
fill-mask | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | {} | sultan/BioM-ALBERT-xxlarge | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | [
"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subject for investigation. In\nthis paper, we empirically study biomedical\ndomain adaptation with la... | [
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"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nh... |
null | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | {} | sultan/BioM-ELECTRA-Base-Discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | [
"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subject for investigation. In\nthis paper, we empirically study biomedical\ndomain adaptation with la... | [
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"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subject f... |
fill-mask | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | {} | sultan/BioM-ELECTRA-Base-Generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | [
"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subject for investigation. In\nthis paper, we empirically study biomedical\ndomain adaptation with la... | [
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"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\n... |
question-answering | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | {} | sultan/BioM-ELECTRA-Base-SQuAD2-BioASQ8B | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #endpoints_compatible #region-us
| BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
==========================================================================================
Abstract
========
The impact of design choices on the performance
of biomedical language models recently
has been a subject for invest... | [] | [
"TAGS\n#transformers #pytorch #electra #question-answering #endpoints_compatible #region-us \n"
] |
question-answering | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | {} | sultan/BioM-ELECTRA-Base-SQuAD2 | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #endpoints_compatible #region-us
| # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | [
"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subject for investigation. In\nthis paper, we empirically study biomedical\ndomain adaptation with la... | [
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"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a su... |
null | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer m... | {} | sultan/BioM-ELECTRA-Large-Discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer m... | [
"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subject for investigation. In\nthis paper, we empirically study biomedical\ndomain adaptation with larg... | [
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n",
"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subject for... |
fill-mask | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | {} | sultan/BioM-ELECTRA-Large-Generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | [
"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subject for investigation. In\nthis paper, we empirically study biomedical\ndomain adaptation with la... | [
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"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\n... |
question-answering | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | {} | sultan/BioM-ELECTRA-Large-SQuAD2-BioASQ8B | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #endpoints_compatible #region-us
| BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
==========================================================================================
Abstract
========
The impact of design choices on the performance
of biomedical language models recently
has been a subject for invest... | [] | [
"TAGS\n#transformers #pytorch #electra #question-answering #endpoints_compatible #region-us \n"
] |
question-answering | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | {} | sultan/BioM-ELECTRA-Large-SQuAD2 | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #endpoints_compatible #has_space #region-us
| # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | [
"# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA",
"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nhas been a subject for investigation. In\nthis paper, we empirically study biomedical\ndomain adaptation with la... | [
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"# Abstract\n\n\nThe impact of design choices on the performance\nof biomedical language models recently\nha... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Marathi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [Open SLR64](http://openslr.org/64/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. This data contains only female voices but... | {"language": "mr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["openslr"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-large-xlsr-53", "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Marathi by Sumedh Khodke", "results": [{"task":... | sumedh/wav2vec2-large-xlsr-marathi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"mr",
"dataset:openslr",
"base_model:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mr #dataset-openslr #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Marathi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using the Open SLR64 dataset. When using this model, make sure that your speech input is sampled at 16kHz. This data contains only female voices but the model works well for male voices too. Trained on Google Colab Pro on Tesla P100... | [
"# Wav2Vec2-Large-XLSR-53-Marathi\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using the Open SLR64 dataset. When using this model, make sure that your speech input is sampled at 16kHz. This data contains only female voices but the model works well for male voices too. Trained on Google Colab Pro on Tesla... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mr #dataset-openslr #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Marathi\nFine-tuned facebook/wav2... |
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. -->
# Trial_3_Results
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "Trial_3_Results", "results": []}]} | sunitha/Trial_3_Results | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"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 #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# Trial_3_Results
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fo... | [
"# Trial_3_Results\n\nThis model is a fine-tuned version of bert-base-cased 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",
"## Training procedure",
"### Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Trial_3_Results\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information need... |
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-3feb-2022-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-3feb-2022-finetuned-squad", "results": []}]} | sunitha/distilbert-base-uncased-3feb-2022-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"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 #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-3feb-2022-finetuned-squad
=================================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1470
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #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\\_s... |
question-answering | transformers | Question Answering - Build - 1 | {} | sunitha/output_files | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
| Question Answering - Build - 1 | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "distilgpt2-finetuned-wikitext2", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | supah-hakah/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6424
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 #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
null | null | # Fine-tuned Model Submission Template
This is a template reprository for the SUPERB benchmark for the _fine-tuned model_ category. In this category, participants are asked to fine-tuned a pretrained model in each of SUPERB's downstream tasks and then store the model weights and hyperparameters in this repo.
There ar... | {} | superb/finetuned-model-upload-template | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Fine-tuned Model Submission Template
This is a template reprository for the SUPERB benchmark for the _fine-tuned model_ category. In this category, participants are asked to fine-tuned a pretrained model in each of SUPERB's downstream tasks and then store the model weights and hyperparameters in this repo.
There ar... | [
"# Fine-tuned Model Submission Template\n\nThis is a template reprository for the SUPERB benchmark for the _fine-tuned model_ category. In this category, participants are asked to fine-tuned a pretrained model in each of SUPERB's downstream tasks and then store the model weights and hyperparameters in this repo.\n\... | [
"TAGS\n#region-us \n",
"# Fine-tuned Model Submission Template\n\nThis is a template reprository for the SUPERB benchmark for the _fine-tuned model_ category. In this category, participants are asked to fine-tuned a pretrained model in each of SUPERB's downstream tasks and then store the model weights and hyperpa... |
audio-classification | transformers |
# Hubert-Base for Emotion Recognition
## Model description
This is a ported version of
[S3PRL's Hubert for the SUPERB Emotion Recognition task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/emotion).
The base model is [hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960), which is pr... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "hubert", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "IEMOCAP clip \"happy\"", "src": "https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav"}, {"example_title": "IEMOCAP clip \"neutral\"... | superb/hubert-base-superb-er | null | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Hubert-Base for Emotion Recognition
===================================
Model description
-----------------
This is a ported version of
S3PRL's Hubert for the SUPERB Emotion Recognition task.
The base model is hubert-base-ls960, which is pretrained on 16kHz
sampled speech audio. When using the model make sure tha... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Hubert-Base for Intent Classification
## Model description
This is a ported version of [S3PRL's Hubert for the SUPERB Intent Classification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/fluent_commands).
The base model is [hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960), ... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio-classification", "hubert"], "datasets": ["superb"]} | superb/hubert-base-superb-ic | null | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"speech",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #hubert #audio-classification #speech #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us
| Hubert-Base for Intent Classification
=====================================
Model description
-----------------
This is a ported version of S3PRL's Hubert for the SUPERB Intent Classification task.
The base model is hubert-base-ls960, which is pretrained on 16kHz
sampled speech audio. When using the model make su... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #hubert #audio-classification #speech #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Hubert-Base for Keyword Spotting
## Model description
This is a ported version of [S3PRL's Hubert for the SUPERB Keyword Spotting task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/speech_commands).
The base model is [hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960), which is p... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "hubert", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "Speech Commands \"down\"", "src": "https://cdn-media.huggingface.co/speech_samples/keyword_spotting_down.wav"}, {"example_title": "Speech Commands \"go\"", "sr... | superb/hubert-base-superb-ks | null | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us
| Hubert-Base for Keyword Spotting
================================
Model description
-----------------
This is a ported version of S3PRL's Hubert for the SUPERB Keyword Spotting task.
The base model is hubert-base-ls960, which is pretrained on 16kHz
sampled speech audio. When using the model make sure that your sp... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Hubert-Base for Speaker Identification
## Model description
This is a ported version of
[S3PRL's Hubert for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1).
The base model is [hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960), whi... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "hubert", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "VoxCeleb Speaker id10003", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav"}, {"example_title": "VoxCeleb Speaker id10004", "src": ... | superb/hubert-base-superb-sid | null | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us
| Hubert-Base for Speaker Identification
======================================
Model description
-----------------
This is a ported version of
S3PRL's Hubert for the SUPERB Speaker Identification task.
The base model is hubert-base-ls960, which is pretrained on 16kHz
sampled speech audio. When using the model make... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Hubert-Large for Emotion Recognition
## Model description
This is a ported version of
[S3PRL's Hubert for the SUPERB Emotion Recognition task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/emotion).
The base model is [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k), which is... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "hubert", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "IEMOCAP clip \"happy\"", "src": "https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav"}, {"example_title": "IEMOCAP clip \"neutral\"... | superb/hubert-large-superb-er | null | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Hubert-Large for Emotion Recognition
====================================
Model description
-----------------
This is a ported version of
S3PRL's Hubert for the SUPERB Emotion Recognition task.
The base model is hubert-large-ll60k, which is pretrained on 16kHz
sampled speech audio. When using the model make sure ... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Hubert-Large for Intent Classification
## Model description
This is a ported version of [S3PRL's Hubert for the SUPERB Intent Classification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/fluent_commands).
The base model is [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "hubert"], "datasets": ["superb"]} | superb/hubert-large-superb-ic | null | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us
| Hubert-Large for Intent Classification
======================================
Model description
-----------------
This is a ported version of S3PRL's Hubert for the SUPERB Intent Classification task.
The base model is hubert-large-ll60k, which is pretrained on 16kHz
sampled speech audio. When using the model make... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Hubert-Large for Keyword Spotting
## Model description
This is a ported version of
[S3PRL's Hubert for the SUPERB Keyword Spotting task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/speech_commands).
The base model is [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k), which ... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "hubert", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "Speech Commands \"down\"", "src": "https://cdn-media.huggingface.co/speech_samples/keyword_spotting_down.wav"}, {"example_title": "Speech Commands \"go\"", "sr... | superb/hubert-large-superb-ks | null | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us
| Hubert-Large for Keyword Spotting
=================================
Model description
-----------------
This is a ported version of
S3PRL's Hubert for the SUPERB Keyword Spotting task.
The base model is hubert-large-ll60k, which is pretrained on 16kHz
sampled speech audio. When using the model make sure that your... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Hubert-Large for Speaker Identification
## Model description
This is a ported version of
[S3PRL's Hubert for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1).
The base model is [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k), ... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "hubert", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "VoxCeleb Speaker id10003", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav"}, {"example_title": "VoxCeleb Speaker id10004", "src": ... | superb/hubert-large-superb-sid | null | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us
| Hubert-Large for Speaker Identification
=======================================
Model description
-----------------
This is a ported version of
S3PRL's Hubert for the SUPERB Speaker Identification task.
The base model is hubert-large-ll60k, which is pretrained on 16kHz
sampled speech audio. When using the model m... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #hubert #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
null | null |
# Fine-tuned s3prl model
Upstream Model: hubert
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
... | {"tags": ["library:s3prl", "benchmark:superb", "type:model"], "datasets": ["superb"]} | superb/hubert__508944ac | null | [
"tensorboard",
"library:s3prl",
"benchmark:superb",
"type:model",
"dataset:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tensorboard #library-s3prl #benchmark-superb #type-model #dataset-superb #region-us
|
# Fine-tuned s3prl model
Upstream Model: hubert
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
... | [
"# Fine-tuned s3prl model\n\nUpstream Model: hubert",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
"## Limitations and bias\n\n[More information needed]",
"## Training data\n\n[More informa... | [
"TAGS\n#tensorboard #library-s3prl #benchmark-superb #type-model #dataset-superb #region-us \n",
"# Fine-tuned s3prl model\n\nUpstream Model: hubert",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information neede... |
null | null | # SUPERB Submission Template
Welcome to the [SUPERB Challenge](https://superbbenchmark.org/challenge-slt2022/challenge_overview)! SUPERB is a collection of benchmarking resources to evaluate the capability of a universal shared representation for speech processing. It comes with a benchmark on the publicly available d... | {} | superb/superb-submission | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # SUPERB Submission Template
Welcome to the SUPERB Challenge! SUPERB is a collection of benchmarking resources to evaluate the capability of a universal shared representation for speech processing. It comes with a benchmark on the publicly available datasets and a challenge on a secret/not released hidden dataset. In ... | [
"# SUPERB Submission Template\n\nWelcome to the SUPERB Challenge! SUPERB is a collection of benchmarking resources to evaluate the capability of a universal shared representation for speech processing. It comes with a benchmark on the publicly available datasets and a challenge on a secret/not released hidden datas... | [
"TAGS\n#region-us \n",
"# SUPERB Submission Template\n\nWelcome to the SUPERB Challenge! SUPERB is a collection of benchmarking resources to evaluate the capability of a universal shared representation for speech processing. It comes with a benchmark on the publicly available datasets and a challenge on a secret/... |
null | null |
# Fine-tuned s3prl model
Upstream Model: superb-test-org/test-submission-with-example-expert
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[Mor... | {"tags": ["library:s3prl", "benchmark:superb", "type:model"], "datasets": ["superb"]} | superb/superb-test-org__test-submission-with-example-expert__d609b3c32044e50e3d5e9067bd97af1b42f04b0e | null | [
"tensorboard",
"library:s3prl",
"benchmark:superb",
"type:model",
"dataset:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tensorboard #library-s3prl #benchmark-superb #type-model #dataset-superb #region-us
|
# Fine-tuned s3prl model
Upstream Model: superb-test-org/test-submission-with-example-expert
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[Mor... | [
"# Fine-tuned s3prl model\n\nUpstream Model: superb-test-org/test-submission-with-example-expert",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
"## Limitations and bias\n\n[More information n... | [
"TAGS\n#tensorboard #library-s3prl #benchmark-superb #type-model #dataset-superb #region-us \n",
"# Fine-tuned s3prl model\n\nUpstream Model: superb-test-org/test-submission-with-example-expert",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]"... |
null | null |
# Fine-tuned s3prl model
Upstream Model: superb-test-org/test-submission-with-weights
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More infor... | {"tags": ["library:s3prl", "benchmark:superb", "type:model"], "datasets": ["superb"]} | superb/superb-test-org__test-submission-with-weights__2323d47e588aa02648ac1770568eeaa203431535 | null | [
"tensorboard",
"library:s3prl",
"benchmark:superb",
"type:model",
"dataset:superb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tensorboard #library-s3prl #benchmark-superb #type-model #dataset-superb #region-us
|
# Fine-tuned s3prl model
Upstream Model: superb-test-org/test-submission-with-weights
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More infor... | [
"# Fine-tuned s3prl model\n\nUpstream Model: superb-test-org/test-submission-with-weights",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
"## Limitations and bias\n\n[More information needed]"... | [
"TAGS\n#tensorboard #library-s3prl #benchmark-superb #type-model #dataset-superb #region-us \n",
"# Fine-tuned s3prl model\n\nUpstream Model: superb-test-org/test-submission-with-weights",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"##... |
audio-classification | transformers |
# Wav2Vec2-Base for Emotion Recognition
## Model description
This is a ported version of
[S3PRL's Wav2Vec2 for the SUPERB Emotion Recognition task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/emotion).
The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), which is pretra... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "IEMOCAP clip \"happy\"", "src": "https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav"}, {"example_title": "IEMOCAP clip \"neutral... | superb/wav2vec2-base-superb-er | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Wav2Vec2-Base for Emotion Recognition
=====================================
Model description
-----------------
This is a ported version of
S3PRL's Wav2Vec2 for the SUPERB Emotion Recognition task.
The base model is wav2vec2-base, which is pretrained on 16kHz
sampled speech audio. When using the model make sure t... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Wav2Vec2-Base for Intent Classification
## Model description
This is a ported version of [S3PRL's Wav2Vec2 for the SUPERB Intent Classification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/fluent_commands).
The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), whic... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2"], "datasets": ["superb"]} | superb/wav2vec2-base-superb-ic | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2-Base for Intent Classification
=======================================
Model description
-----------------
This is a ported version of S3PRL's Wav2Vec2 for the SUPERB Intent Classification task.
The base model is wav2vec2-base, which is pretrained on 16kHz
sampled speech audio. When using the model make ... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Wav2Vec2-Base for Keyword Spotting
## Model description
This is a ported version of
[S3PRL's Wav2Vec2 for the SUPERB Keyword Spotting task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/speech_commands).
The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), which is pret... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "Speech Commands \"down\"", "src": "https://cdn-media.huggingface.co/speech_samples/keyword_spotting_down.wav"}, {"example_title": "Speech Commands \"go\"", "... | superb/wav2vec2-base-superb-ks | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Wav2Vec2-Base for Keyword Spotting
==================================
Model description
-----------------
This is a ported version of
S3PRL's Wav2Vec2 for the SUPERB Keyword Spotting task.
The base model is wav2vec2-base, which is pretrained on 16kHz
sampled speech audio. When using the model make sure that your ... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
audio-classification | transformers |
# Wav2Vec2-Base for Speaker Identification
## Model description
This is a ported version of
[S3PRL's Wav2Vec2 for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1).
The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), which i... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "VoxCeleb Speaker id10003", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav"}, {"example_title": "VoxCeleb Speaker id10004", "src"... | superb/wav2vec2-base-superb-sid | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"speech",
"audio",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Wav2Vec2-Base for Speaker Identification
========================================
Model description
-----------------
This is a ported version of
S3PRL's Wav2Vec2 for the SUPERB Speaker Identification task.
The base model is wav2vec2-base, which is pretrained on 16kHz
sampled speech audio. When using the model ma... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #speech #audio #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
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