pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (Italian version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://n... | {"language": "it", "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-it | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"lexical normalization",
"it",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"it"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #lexical normalization #it #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (Italian version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11 langu... | [
"# Fine-tuned ByT5-small for MultiLexNorm (Italian version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #lexical normalization #it #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fine-tuned ByT5-small for Multi... |
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (Dutch version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://noi... | {"language": "nl", "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-nl | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lexical normalization",
"nl",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generat... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"nl"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #nl #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (Dutch version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11 languag... | [
"# Fine-tuned ByT5-small for MultiLexNorm (Dutch version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #nl #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fine-tuned ByT5-sm... |
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (Slovenian version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https:/... | {"language": "sl", "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-sl | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"lexical normalization",
"sl",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"sl"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #lexical normalization #sl #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (Slovenian version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11 lan... | [
"# Fine-tuned ByT5-small for MultiLexNorm (Slovenian version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets i... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #lexical normalization #sl #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fine-tuned ByT5-small for Multi... |
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (Serbian version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://n... | {"language": "sr", "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-sr | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"lexical normalization",
"sr",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"sr"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #lexical normalization #sr #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (Serbian version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11 langu... | [
"# Fine-tuned ByT5-small for MultiLexNorm (Serbian version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #lexical normalization #sr #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fine-tuned ByT5-small for Multi... |
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (Turkish version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://n... | {"language": "tr", "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-tr | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lexical normalization",
"tr",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generat... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"tr"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #tr #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (Turkish version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 11 langu... | [
"# Fine-tuned ByT5-small for MultiLexNorm (Turkish version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in ... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #tr #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fine-tuned ByT5-sm... |
text2text-generation | transformers |
# Fine-tuned ByT5-small for MultiLexNorm (Turkish-German version)
This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](ht... | {"language": ["tr", "de", "multilingual"], "license": "apache-2.0", "tags": ["lexical normalization"], "datasets": ["mc4", "wikipedia", "multilexnorm"]} | ufal/byt5-small-multilexnorm2021-trde | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lexical normalization",
"tr",
"de",
"multilingual",
"dataset:mc4",
"dataset:wikipedia",
"dataset:multilexnorm",
"arxiv:2105.13626",
"arxiv:1907.06292",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_co... | null | 2022-03-02T23:29:05+00:00 | [
"2105.13626",
"1907.06292"
] | [
"tr",
"de",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #tr #de #multilingual #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned ByT5-small for MultiLexNorm (Turkish-German version)
!model image
This is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datasets in 1... | [
"# Fine-tuned ByT5-small for MultiLexNorm (Turkish-German version)\n\n!model image\n\nThis is the official release of the fine-tuned models for the winning entry to the *W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task, which evaluates lexical-normalization systems on 12 social media datas... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #lexical normalization #tr #de #multilingual #dataset-mc4 #dataset-wikipedia #dataset-multilexnorm #arxiv-2105.13626 #arxiv-1907.06292 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ... |
fill-mask | transformers |
# Model Card for RobeCzech
## Version History
- **version 1.1**: Version 1.1 was released in Jan 2024, with a change to the
tokenizer described below; the model parameters were mostly kept the same, but
(a) the embeddings were enlarged (by copying suitable rows) to correspond to
the updated tokenizer, (b) the ... | {"language": "cs", "license": "cc-by-nc-sa-4.0", "tags": ["RobeCzech", "Czech", "RoBERTa", "\u00daFAL"]} | ufal/robeczech-base | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"roberta",
"fill-mask",
"RobeCzech",
"Czech",
"RoBERTa",
"ÚFAL",
"cs",
"arxiv:2105.11314",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.11314"
] | [
"cs"
] | TAGS
#transformers #pytorch #tf #safetensors #roberta #fill-mask #RobeCzech #Czech #RoBERTa #ÚFAL #cs #arxiv-2105.11314 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model Card for RobeCzech
========================
Version History
---------------
* version 1.1: Version 1.1 was released in Jan 2024, with a change to the
tokenizer described below; the model parameters were mostly kept the same, but
(a) the embeddings were enlarged (by copying suitable rows) to correspond to
the ... | [
"### Preprocessing\n\n\nThe texts are tokenized into subwords with a byte-level BPE (BBPE) tokenizer,\nwhich was trained on the entire corpus and we limit its vocabulary size to\n52,000 items.",
"### Speeds, Sizes, Times\n\n\nThe model creators note in the associated paper:\n\n\n\n> \n> The training batch size is... | [
"TAGS\n#transformers #pytorch #tf #safetensors #roberta #fill-mask #RobeCzech #Czech #RoBERTa #ÚFAL #cs #arxiv-2105.11314 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Preprocessing\n\n\nThe texts are tokenized into subwords with a byte-level BPE (BBPE) token... |
token-classification | transformers |
# Model Description
This model is a fine-tuned version of BioBERT on the NCBI disease dataset for named entity recognition (NER) of diseases. It can be used to extract disease mentions from unstructured text in the medical and biological domains.
# Intended Use
This model is intended for use in extracting disease men... | {"language": ["en"], "license": "openrail", "tags": ["disease", "biology", "medical"], "datasets": ["ncbi_disease"], "widget": [{"text": "The patient was diagnosed with lung cancer and started chemotherapy."}, {"text": "The patient has a history of heart disease and high blood pressure."}, {"text": "The patient was dia... | ugaray96/biobert_ncbi_disease_ner | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"token-classification",
"disease",
"biology",
"medical",
"en",
"dataset:ncbi_disease",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #token-classification #disease #biology #medical #en #dataset-ncbi_disease #license-openrail #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Description
This model is a fine-tuned version of BioBERT on the NCBI disease dataset for named entity recognition (NER) of diseases. It can be used to extract disease mentions from unstructured text in the medical and biological domains.
# Intended Use
This model is intended for use in extracting disease men... | [
"# Model Description\nThis model is a fine-tuned version of BioBERT on the NCBI disease dataset for named entity recognition (NER) of diseases. It can be used to extract disease mentions from unstructured text in the medical and biological domains.",
"# Intended Use\nThis model is intended for use in extracting d... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #token-classification #disease #biology #medical #en #dataset-ncbi_disease #license-openrail #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Description\nThis model is a fine-tuned version of BioBERT on the NCBI disease data... |
text-generation | transformers |
# Ginger DialoGPT Model | {"tags": ["conversational"]} | ughvom/Ginger | 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
|
# Ginger DialoGPT Model | [
"# Ginger DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ginger DialoGPT Model"
] |
text-generation | transformers |
# britnayBOTMAIN Model | {"tags": ["conversational"]} | ughvom/britnayBOTMAIN | 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
|
# britnayBOTMAIN Model | [
"# britnayBOTMAIN Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# britnayBOTMAIN Model"
] |
fill-mask | transformers |
[](https://github.com/uhh-lt/amharicmodels)
# Introduction
This is the Amharic RoBERTa transformer-based LM. It is part of the effort to build benchmark datasets and models for Amharic NLP.
# Examples
If yo... | {"language": ["am"], "license": "mit", "tags": ["Amharic", "Semetic language"], "datasets": ["Amharic_corpus_from_LT_group_UHH"], "thumbnail": "https://raw.githubusercontent.com/uhh-lt/amharicmodels/master/logo.png?token=AAIB2MYMI6TSIK7CHWYGHKTBQ3FQS", "widget": [{"text": "\u12a0\u1260\u1260 <mask> \u1260\u120b \u1362"... | uhhlt/am-roberta | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"Amharic",
"Semetic language",
"am",
"dataset:Amharic_corpus_from_LT_group_UHH",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"am"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #Amharic #Semetic language #am #dataset-Amharic_corpus_from_LT_group_UHH #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
Example 1:
'አበበ <mask> በላ ። '
Example 2... | [
"# Introduction\nThis is the Amharic RoBERTa transformer-based LM. It is part of the effort to build benchmark datasets and models for Amharic NLP.",
"# Examples\nIf you want to test the model in the 'Hosted inference API', copy the following texts to the box (right side)\n\nExample 1:\n\n'አበበ <mask> በላ ። '\n\nEx... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #Amharic #Semetic language #am #dataset-Amharic_corpus_from_LT_group_UHH #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Introduction\nThis is the Amharic RoBERTa transformer-based LM. It is part of the effort to build benc... |
text-classification | transformers |
# bert-based-uncased-hatespeech-movies:
A hatespeech model used to classify text as **normal**, **offensive**, **hatespeech** in Movie subtitles. The model is initially a pre-trained transformer model(bert-based-uncased) which is further trained on Twitter comments which can be normal, offensive and hate to learn the... | {"language": "en", "datasets": ["twitter", "movies subtitles"], "tag": "text-classification"} | uhhlt/bert-based-uncased-hatespeech-movies | null | [
"transformers",
"tf",
"bert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #tf #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
|
# bert-based-uncased-hatespeech-movies:
A hatespeech model used to classify text as normal, offensive, hatespeech in Movie subtitles. The model is initially a pre-trained transformer model(bert-based-uncased) which is further trained on Twitter comments which can be normal, offensive and hate to learn the context fro... | [
"# bert-based-uncased-hatespeech-movies: \nA hatespeech model used to classify text as normal, offensive, hatespeech in Movie subtitles. The model is initially a pre-trained transformer model(bert-based-uncased) which is further trained on Twitter comments which can be normal, offensive and hate to learn the contex... | [
"TAGS\n#transformers #tf #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-based-uncased-hatespeech-movies: \nA hatespeech model used to classify text as normal, offensive, hatespeech in Movie subtitles. The model is initially a pre-trained transformer model(bert-b... |
fill-mask | transformers | # Gottbert-base
BERT model trained solely on the German portion of the OSCAR data set.
[Paper: GottBERT: a pure German Language Model](https://arxiv.org/abs/2012.02110)
Authors: Raphael Scheible, Fabian Thomczyk, Patric Tippmann, Victor Jaravine, Martin Boeker | {} | uklfr/gottbert-base | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"arxiv:2012.02110",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.02110"
] | [] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #arxiv-2012.02110 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Gottbert-base
BERT model trained solely on the German portion of the OSCAR data set.
Paper: GottBERT: a pure German Language Model
Authors: Raphael Scheible, Fabian Thomczyk, Patric Tippmann, Victor Jaravine, Martin Boeker | [
"# Gottbert-base\n\nBERT model trained solely on the German portion of the OSCAR data set.\n\nPaper: GottBERT: a pure German Language Model\n\nAuthors: Raphael Scheible, Fabian Thomczyk, Patric Tippmann, Victor Jaravine, Martin Boeker"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #arxiv-2012.02110 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Gottbert-base\n\nBERT model trained solely on the German portion of the OSCAR data set.\n\nPaper: GottBERT: a pure German Language Model\n\nAuthors: Rapha... |
text-generation | transformers |
# Peppa Pig DialogGPT-small Model | {"tags": ["conversational"]} | umr55766/DialogGPT-small-peppa-pig | 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
|
# Peppa Pig DialogGPT-small Model | [
"# Peppa Pig DialogGPT-small Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Peppa Pig DialogGPT-small Model"
] |
text-classification | transformers | # mMiniLM-L6 Reranker finetuned on English MS MARCO
## Introduction
mMiniLM-L6-v2-en-msmarco is a multilingual miniLM-based model fine-tuned on English MS MARCO passage dataset. Further information about the dataset or the translation method can be found on our [**mMARCO: A Multilingual Version of MS MARCO Passage Rank... | {"language": "pt", "license": "mit", "tags": ["msmarco", "miniLM", "pytorch", "tensorflow", "en"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00c3\u00aas"}], "inference": false} | unicamp-dl/mMiniLM-L6-v2-en-msmarco | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"msmarco",
"miniLM",
"tensorflow",
"en",
"pt",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #en #pt #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us
| # mMiniLM-L6 Reranker finetuned on English MS MARCO
## Introduction
mMiniLM-L6-v2-en-msmarco is a multilingual miniLM-based model fine-tuned on English MS MARCO passage dataset. Further information about the dataset or the translation method can be found on our mMARCO: A Multilingual Version of MS MARCO Passage Ranking... | [
"# mMiniLM-L6 Reranker finetuned on English MS MARCO",
"## Introduction\nmMiniLM-L6-v2-en-msmarco is a multilingual miniLM-based model fine-tuned on English MS MARCO passage dataset. Further information about the dataset or the translation method can be found on our mMARCO: A Multilingual Version of MS MARCO Pass... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #en #pt #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us \n",
"# mMiniLM-L6 Reranker finetuned on English MS MARCO",
"## Introduction\nmMiniLM-L6-v2-en-msmarco is a multilingual miniLM-ba... |
text-classification | transformers | # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-en-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In t... | {"language": "pt", "license": "mit", "tags": ["msmarco", "miniLM", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v1 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"msmarco",
"miniLM",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us
| # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-en-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In t... | [
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-en-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated ver... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us \n",
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-en-pt-msmarco-v1 is a multilingual miniLM-... |
text-classification | transformers | # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-en-pt-msmarco-v2 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In t... | {"language": "pt", "license": "mit", "tags": ["msmarco", "miniLM", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"msmarco",
"miniLM",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us
| # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-en-pt-msmarco-v2 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In t... | [
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-en-pt-msmarco-v2 is a multilingual miniLM-based model finetuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated ver... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us \n",
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-en-pt-msmarco-v2 is a multilingual miniLM-... |
text-classification | transformers | # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-mmarco-v1 is a multilingual miniLM-based model finetuned on a multilingual version of MS MARCO passage dataset. This dataset, named mMARCO, is formed by passages in 9 different languages, translated from English MS MARCO passages collection.
In ... | {"language": "pt", "license": "mit", "tags": ["msmarco", "miniLM", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/mMiniLM-L6-v2-mmarco-v1 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"msmarco",
"miniLM",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us
| # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-mmarco-v1 is a multilingual miniLM-based model finetuned on a multilingual version of MS MARCO passage dataset. This dataset, named mMARCO, is formed by passages in 9 different languages, translated from English MS MARCO passages collection.
In ... | [
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-mmarco-v1 is a multilingual miniLM-based model finetuned on a multilingual version of MS MARCO passage dataset. This dataset, named mMARCO, is formed by passages in 9 different languages, translated from English MS MARCO passages coll... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us \n",
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-mmarco-v1 is a multilingual miniLM-based m... |
text-classification | transformers | # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-mmarco-v2 is a multilingual miniLM-based model finetuned on a multilingual version of MS MARCO passage dataset. This dataset, named mMARCO, is formed by passages in 9 different languages, translated from English MS MARCO passages collection.
In ... | {"language": "pt", "license": "mit", "tags": ["msmarco", "miniLM", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/mMiniLM-L6-v2-mmarco-v2 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"msmarco",
"miniLM",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #region-us
| # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-mmarco-v2 is a multilingual miniLM-based model finetuned on a multilingual version of MS MARCO passage dataset. This dataset, named mMARCO, is formed by passages in 9 different languages, translated from English MS MARCO passages collection.
In ... | [
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-mmarco-v2 is a multilingual miniLM-based model finetuned on a multilingual version of MS MARCO passage dataset. This dataset, named mMARCO, is formed by passages in 9 different languages, translated from English MS MARCO passages coll... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #region-us \n",
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-mmarco-v2 is a multilingual min... |
text-classification | transformers | # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using [Helsinki](https://huggingface.co/Helsinki-NLP) NMT model... | {"language": "pt", "license": "mit", "tags": ["msmarco", "miniLM", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/mMiniLM-L6-v2-pt-msmarco-v1 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"msmarco",
"miniLM",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us
| # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using Helsinki NMT model. Further information about the dataset... | [
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using Helsinki NMT model. Further information about ... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us \n",
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-pt-msmarco-v1 is a multilingual miniLM-bas... |
text-classification | transformers | # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-pt-msmarco-v2 is a multilingual miniLM-based model finetuned on a Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate.
Further information about the dataset ... | {"language": "pt", "license": "mit", "tags": ["msmarco", "miniLM", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/mMiniLM-L6-v2-pt-v2 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"msmarco",
"miniLM",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us
| # mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-pt-msmarco-v2 is a multilingual miniLM-based model finetuned on a Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate.
Further information about the dataset ... | [
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-pt-msmarco-v2 is a multilingual miniLM-based model finetuned on a Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. \nFurther information about ... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #msmarco #miniLM #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #region-us \n",
"# mMiniLM-L6-v2 Reranker finetuned on mMARCO",
"## Introduction\nmMiniLM-L6-v2-pt-msmarco-v2 is a multilingual miniLM-bas... |
text2text-generation | transformers | # mt5-base Reranker finetuned on MS MARCO
## Introduction
mT5-base-en-msmarco-v1 is a mT5-based model finetuned on English MS MARCO passage dataset.
Further information about the dataset or the translation method can be found on our paper [**mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset**](htt... | {"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "en"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/mt5-base-en-msmarco | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"msmarco",
"t5",
"tensorflow",
"en",
"pt",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #msmarco #t5 #tensorflow #en #pt #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| # mt5-base Reranker finetuned on MS MARCO
## Introduction
mT5-base-en-msmarco-v1 is a mT5-based model finetuned on English MS MARCO passage dataset.
Further information about the dataset or the translation method can be found on our paper mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset and mMARC... | [
"# mt5-base Reranker finetuned on MS MARCO",
"## Introduction\nmT5-base-en-msmarco-v1 is a mT5-based model finetuned on English MS MARCO passage dataset. \nFurther information about the dataset or the translation method can be found on our paper mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Datas... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #msmarco #t5 #tensorflow #en #pt #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# mt5-base Reranker finetuned on MS MARCO",
"## Introduction\nmT5-base-en-msmarco-v1 is a mT5-bas... |
text2text-generation | transformers | # mt5-base Reranker finetuned on mMARCO
## Introduction
mT5-base-en-pt-msmarco-v1 is a mT5-based model fine-tuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In the version v1, the Portugu... | {"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/mt5-base-en-pt-msmarco-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"msmarco",
"t5",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #msmarco #t5 #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| # mt5-base Reranker finetuned on mMARCO
## Introduction
mT5-base-en-pt-msmarco-v1 is a mT5-based model fine-tuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In the version v1, the Portugu... | [
"# mt5-base Reranker finetuned on mMARCO",
"## Introduction\nmT5-base-en-pt-msmarco-v1 is a mT5-based model fine-tuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In the version v1, ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #msmarco #t5 #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# mt5-base Reranker finetuned on mMARCO",
"## Introduction\nmT5-base-en-pt-msmarco-v1 is a mT5... |
text2text-generation | transformers | # mt5-base Reranker finetuned on mMARCO
## Introduction
mT5-base-en-pt-msmarco-v2 is a mT5-based model fine-tuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In the v2 version, the Portugu... | {"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/mt5-base-en-pt-msmarco-v2 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"msmarco",
"t5",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #msmarco #t5 #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| # mt5-base Reranker finetuned on mMARCO
## Introduction
mT5-base-en-pt-msmarco-v2 is a mT5-based model fine-tuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In the v2 version, the Portugu... | [
"# mt5-base Reranker finetuned on mMARCO",
"## Introduction\nmT5-base-en-pt-msmarco-v2 is a mT5-based model fine-tuned on a bilingual version of MS MARCO passage dataset. This bilingual dataset version is formed by the original MS MARCO dataset (in English) and a Portuguese translated version. In the v2 version, ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #msmarco #t5 #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# mt5-base Reranker finetuned on mMARCO",
"## Introduction\nmT5-base-en-pt-msmarco-v2 is a mT5... |
text2text-generation | transformers | # mt5-base Reranker finetuned on mMARCO
## Introduction
mt5-base-mmarco-v1 is a mT5-based model fine-tuned on a multilingual translated version of MS MARCO passage dataset. This dataset, named Multi MS MARCO, is formed by 9 complete MS MARCO passages collection in 9 different languages. In the version v1, the datasets... | {"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00c3\u00aas"}], "inference": false} | unicamp-dl/mt5-base-mmarco-v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"msmarco",
"t5",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #msmarco #t5 #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| # mt5-base Reranker finetuned on mMARCO
## Introduction
mt5-base-mmarco-v1 is a mT5-based model fine-tuned on a multilingual translated version of MS MARCO passage dataset. This dataset, named Multi MS MARCO, is formed by 9 complete MS MARCO passages collection in 9 different languages. In the version v1, the datasets... | [
"# mt5-base Reranker finetuned on mMARCO",
"## Introduction\nmt5-base-mmarco-v1 is a mT5-based model fine-tuned on a multilingual translated version of MS MARCO passage dataset. This dataset, named Multi MS MARCO, is formed by 9 complete MS MARCO passages collection in 9 different languages. In the version v1, t... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #msmarco #t5 #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# mt5-base Reranker finetuned on mMARCO",
"## Introduction\nmt5-base-mmarco-v1 is a mT5-based ... |
text2text-generation | transformers | # mt5-base Reranker finetuned on mMARCO
## Introduction
mt5-base-mmarco-v2 is a mT5-based model fine-tuned on a multilingual translated version of MS MARCO passage dataset. This dataset, named Multi MS MARCO, is formed by 9 complete MS MARCO passages collection in 9 different languages. In the v2 version, the datasets... | {"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00c3\u00aas"}], "inference": false} | unicamp-dl/mt5-base-mmarco-v2 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"msmarco",
"t5",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #msmarco #t5 #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| # mt5-base Reranker finetuned on mMARCO
## Introduction
mt5-base-mmarco-v2 is a mT5-based model fine-tuned on a multilingual translated version of MS MARCO passage dataset. This dataset, named Multi MS MARCO, is formed by 9 complete MS MARCO passages collection in 9 different languages. In the v2 version, the datasets... | [
"# mt5-base Reranker finetuned on mMARCO",
"## Introduction\nmt5-base-mmarco-v2 is a mT5-based model fine-tuned on a multilingual translated version of MS MARCO passage dataset. This dataset, named Multi MS MARCO, is formed by 9 complete MS MARCO passages collection in 9 different languages. In the v2 version, t... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #msmarco #t5 #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# mt5-base Reranker finetuned on mMARCO",
"## Introduction\nmt5-base-mmarco-v2 is a mT5-based ... |
text2text-generation | transformers | # PTT5-base Reranker finetuned on both English and Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-en-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated us... | {"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-base-en-pt-msmarco-100k-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"msmarco",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| # PTT5-base Reranker finetuned on both English and Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-en-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated us... | [
"# PTT5-base Reranker finetuned on both English and Portuguese MS MARCO",
"## Introduction\nptt5-base-msmarco-en-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was tr... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# PTT5-base Reranker finetuned on both English and Portuguese MS MARCO",
"## Introduction\nptt5-bas... |
text2text-generation | transformers | # PTT5-base Reranker finetuned on both English and Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-en-pt-10k-v1 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated usi... | {"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-base-en-pt-msmarco-10k-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"msmarco",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| # PTT5-base Reranker finetuned on both English and Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-en-pt-10k-v1 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated usi... | [
"# PTT5-base Reranker finetuned on both English and Portuguese MS MARCO",
"## Introduction\nptt5-base-msmarco-en-pt-10k-v1 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was tra... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# PTT5-base Reranker finetuned on both English and Portuguese MS MARCO",
"## Introduction\nptt5-bas... |
text2text-generation | transformers |
# Portuguese T5 (aka "PTT5")
## Introduction
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) and two vocabularies (Google's T5 ... | {"language": "pt", "license": "mit", "tags": ["t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["brWaC"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-base-portuguese-vocab | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"tensorflow",
"pt",
"pt-br",
"dataset:brWaC",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| Portuguese T5 (aka "PTT5")
==========================
Introduction
------------
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) a... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | # PTT5-base Reranker finetuned on Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-pt-100k-v1 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using [Helsinki](https://huggingface.co/... | {"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-base-pt-msmarco-100k-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"msmarco",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| # PTT5-base Reranker finetuned on Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-pt-100k-v1 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using Helsinki NMT model. This model was... | [
"# PTT5-base Reranker finetuned on Portuguese MS MARCO",
"## Introduction\nptt5-base-msmarco-pt-100k-v1 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using Helsinki NMT model. Thi... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# PTT5-base Reranker finetuned on Portuguese MS MARCO",
"## Introduction\nptt5-base-msmarco-pt-100k... |
text2text-generation | transformers | # PTT5-base Reranker finetuned on Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was f... | {"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-base-pt-msmarco-100k-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"msmarco",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| # PTT5-base Reranker finetuned on Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was f... | [
"# PTT5-base Reranker finetuned on Portuguese MS MARCO",
"## Introduction\nptt5-base-msmarco-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# PTT5-base Reranker finetuned on Portuguese MS MARCO",
"## Introduction\nptt5-base-msmarco-pt-100k... |
text2text-generation | transformers | # PTT5-base Reranker finetuned on Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-pt-10k-v1 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using [Helsinki](https://huggingface.co/H... | {"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-base-pt-msmarco-10k-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"msmarco",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| # PTT5-base Reranker finetuned on Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-pt-10k-v1 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using Helsinki NMT model. This model was ... | [
"# PTT5-base Reranker finetuned on Portuguese MS MARCO",
"## Introduction\nptt5-base-msmarco-pt-10k-v1 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using Helsinki NMT model. This... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# PTT5-base Reranker finetuned on Portuguese MS MARCO",
"## Introduction\nptt5-base-msmarco-pt-10k-... |
text2text-generation | transformers | # PTT5-base Reranker finetuned on Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-pt-10k-v2 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was fi... | {"language": "pt", "license": "mit", "tags": ["msmarco", "t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["msmarco"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-base-pt-msmarco-10k-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"msmarco",
"tensorflow",
"pt",
"pt-br",
"dataset:msmarco",
"arxiv:2108.13897",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.13897"
] | [
"pt"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us
| # PTT5-base Reranker finetuned on Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-pt-10k-v2 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This model was fi... | [
"# PTT5-base Reranker finetuned on Portuguese MS MARCO",
"## Introduction\nptt5-base-msmarco-pt-10k-v2 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated using Google Translate. This m... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #msmarco #tensorflow #pt #pt-br #dataset-msmarco #arxiv-2108.13897 #license-mit #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# PTT5-base Reranker finetuned on Portuguese MS MARCO",
"## Introduction\nptt5-base-msmarco-pt-10k-... |
text2text-generation | transformers |
# Portuguese T5 (aka "PTT5")
## Introduction
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) and two vocabularies (Google's T5 ... | {"language": "pt", "license": "mit", "tags": ["t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["brWaC"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-base-t5-vocab | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"tensorflow",
"pt",
"pt-br",
"dataset:brWaC",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #text-generation-inference #region-us
| Portuguese T5 (aka "PTT5")
==========================
Introduction
------------
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) a... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Portuguese T5 (aka "PTT5")
## Introduction
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) and two vocabularies (Google's T5 ... | {"language": "pt", "license": "mit", "tags": ["t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["brWaC"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-large-portuguese-vocab | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"tensorflow",
"pt",
"pt-br",
"dataset:brWaC",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #text-generation-inference #region-us
| Portuguese T5 (aka "PTT5")
==========================
Introduction
------------
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) a... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Portuguese T5 (aka "PTT5")
## Introduction
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) and two vocabularies (Google's T5 ... | {"language": "pt", "license": "mit", "tags": ["t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["brWaC"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-large-t5-vocab | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"tensorflow",
"pt",
"pt-br",
"dataset:brWaC",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #text-generation-inference #region-us
| Portuguese T5 (aka "PTT5")
==========================
Introduction
------------
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) a... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Portuguese T5 (aka "PTT5")
## Introduction
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) and two vocabularies (Google's T5 ... | {"language": "pt", "license": "mit", "tags": ["t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["brWaC"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-small-portuguese-vocab | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"tensorflow",
"pt",
"pt-br",
"dataset:brWaC",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #text-generation-inference #region-us
| Portuguese T5 (aka "PTT5")
==========================
Introduction
------------
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) a... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Portuguese T5 (aka "PTT5")
## Introduction
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) and two vocabularies (Google's T5 ... | {"language": "pt", "license": "mit", "tags": ["t5", "pytorch", "tensorflow", "pt", "pt-br"], "datasets": ["brWaC"], "widget": [{"text": "Texto de exemplo em portugu\u00eas"}], "inference": false} | unicamp-dl/ptt5-small-t5-vocab | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"tensorflow",
"pt",
"pt-br",
"dataset:brWaC",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #text-generation-inference #region-us
| Portuguese T5 (aka "PTT5")
==========================
Introduction
------------
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) a... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #tensorflow #pt #pt-br #dataset-brWaC #license-mit #autotrain_compatible #text-generation-inference #region-us \n"
] |
translation | transformers |
# Introduction
This repository brings an implementation of T5 for translation in EN-PT tasks using a modest hardware setup. We propose some changes in tokenizator and post-processing that improves the result and used a Portuguese pretrained model for the translation. You can collect more informations in [our reposit... | {"language": ["en", "pt"], "tags": ["translation"], "datasets": ["EMEA", "ParaCrawl 99k", "CAPES", "Scielo", "JRC-Acquis", "Biomedical Domain Corpora"], "metrics": ["bleu"]} | unicamp-dl/translation-en-pt-t5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"en",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"pt"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #translation #en #pt #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Introduction
This repository brings an implementation of T5 for translation in EN-PT tasks using a modest hardware setup. We propose some changes in tokenizator and post-processing that improves the result and used a Portuguese pretrained model for the translation. You can collect more informations in our reposito... | [
"# Introduction\n\nThis repository brings an implementation of T5 for translation in EN-PT tasks using a modest hardware setup. We propose some changes in tokenizator and post-processing that improves the result and used a Portuguese pretrained model for the translation. You can collect more informations in our rep... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #translation #en #pt #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Introduction\n\nThis repository brings an implementation of T5 for translation in EN-PT tasks using a modest hardware setup. We propose ... |
translation | transformers |
# Introduction
This repository brings an implementation of T5 for translation in PT-EN tasks using a modest hardware setup. We propose some changes in tokenizator and post-processing that improves the result and used a Portuguese pretrained model for the translation. You can collect more informations in [our reposito... | {"language": ["en", "pt"], "tags": ["translation"], "datasets": ["EMEA", "ParaCrawl 99k", "CAPES", "Scielo", "JRC-Acquis", "Biomedical Domain Corpora"], "metrics": ["bleu"]} | unicamp-dl/translation-pt-en-t5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"en",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"pt"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #translation #en #pt #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Introduction
This repository brings an implementation of T5 for translation in PT-EN tasks using a modest hardware setup. We propose some changes in tokenizator and post-processing that improves the result and used a Portuguese pretrained model for the translation. You can collect more informations in our repositor... | [
"# Introduction\n\nThis repository brings an implementation of T5 for translation in PT-EN tasks using a modest hardware setup. We propose some changes in tokenizator and post-processing that improves the result and used a Portuguese pretrained model for the translation. You can collect more informations in our rep... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #translation #en #pt #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Introduction\n\nThis repository brings an implementation of T5 for translation in PT-EN tasks using a modest hardware setup. We propose ... |
text-classification | transformers |
# 🤗 + polibert_SA - POLItic BERT based Sentiment Analysis
## Model description
This model performs sentiment analysis on Italian political twitter sentences. It was trained starting from an instance of "bert-base-italian-uncased-xxl" and fine-tuned on an Italian dataset of tweets. You can try it out at https:... | {"language": "it", "license": "mit", "tags": ["sentiment", "Italian"], "widget": [{"text": "Giuseppe Rossi \u00e8 un ottimo politico"}]} | gbarone77/polibert_sa | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"sentiment",
"Italian",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #sentiment #Italian #it #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# + polibert_SA - POLItic BERT based Sentiment Analysis
## Model description
This model performs sentiment analysis on Italian political twitter sentences. It was trained starting from an instance of "bert-base-italian-uncased-xxl" and fine-tuned on an Italian dataset of tweets. You can try it out at URL (in ... | [
"# + polibert_SA - POLItic BERT based Sentiment Analysis",
"## Model description \n \nThis model performs sentiment analysis on Italian political twitter sentences. It was trained starting from an instance of \"bert-base-italian-uncased-xxl\" and fine-tuned on an Italian dataset of tweets. You can try it out a... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #sentiment #Italian #it #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# + polibert_SA - POLItic BERT based Sentiment Analysis",
"## Model description \n \nThis model performs sentiment analysis on Italian political ... |
text-classification | transformers | <div align="center">
**⚠️ Disclaimer:**
The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify
# 🙊 Detoxify
## Tox... | {"license": "apache-2.0", "pipeline_tag": "text-classification"} | unitary/multilingual-toxic-xlm-roberta | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"arxiv:1703.04009",
"arxiv:1905.12516",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1703.04009",
"1905.12516"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #arxiv-1703.04009 #arxiv-1905.12516 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
️ Disclaimer:
The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from URL
Detoxify
========
Toxic Comment Classification with Pytorch Lightning and Transformers
----------------------------------------------... | [
"### Toxic Comment Classification Challenge\n\n\nThis challenge includes the following labels:\n\n\n* 'toxic'\n* 'severe\\_toxic'\n* 'obscene'\n* 'threat'\n* 'insult'\n* 'identity\\_hate'",
"### Jigsaw Unintended Bias in Toxicity Classification\n\n\nThis challenge has 2 types of labels: the main toxicity labels a... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #arxiv-1703.04009 #arxiv-1905.12516 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Toxic Comment Classification Challenge\n\n\nThis challenge includes the following labels:\n\n\n* 'toxic'\n* 'severe\\_... |
text-classification | transformers |
<div align="center">
**⚠️ Disclaimer:**
The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify
# 🙊 Detoxify... | {"license": "apache-2.0"} | unitary/toxic-bert | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1703.04009",
"arxiv:1905.12516",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1703.04009",
"1905.12516"
] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #arxiv-1703.04009 #arxiv-1905.12516 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
️ Disclaimer:
The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from URL
Detoxify
========
Toxic Comment Classification with Pytorch Lightning and Transformers
----------------------------------------------... | [
"### Toxic Comment Classification Challenge\n\n\nThis challenge includes the following labels:\n\n\n* 'toxic'\n* 'severe\\_toxic'\n* 'obscene'\n* 'threat'\n* 'insult'\n* 'identity\\_hate'",
"### Jigsaw Unintended Bias in Toxicity Classification\n\n\nThis challenge has 2 types of labels: the main toxicity labels a... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-1703.04009 #arxiv-1905.12516 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Toxic Comment Classification Challenge\n\n\nThis challenge includes the following labels:\n\n\n* 'toxic'\n* 'severe\\_to... |
text-classification | transformers | <div align="center">
**⚠️ Disclaimer:**
The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify
# 🙊 Detoxify
## To... | {"license": "apache-2.0"} | unitary/unbiased-toxic-roberta | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"arxiv:1703.04009",
"arxiv:1905.12516",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1703.04009",
"1905.12516"
] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #arxiv-1703.04009 #arxiv-1905.12516 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
️ Disclaimer:
The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from URL
Detoxify
========
Toxic Comment Classification with Pytorch Lightning and Transformers
----------------------------------------------... | [
"### Toxic Comment Classification Challenge\n\n\nThis challenge includes the following labels:\n\n\n* 'toxic'\n* 'severe\\_toxic'\n* 'obscene'\n* 'threat'\n* 'insult'\n* 'identity\\_hate'",
"### Jigsaw Unintended Bias in Toxicity Classification\n\n\nThis challenge has 2 types of labels: the main toxicity labels a... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #arxiv-1703.04009 #arxiv-1905.12516 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Toxic Comment Classification Challenge\n\n\nThis challenge includes the following labels:\n\n\n* 'toxic'\n* 'severe\\... |
automatic-speech-recognition | transformers | Bad Modell for Research Purposes! | {} | unknownTransformer/wav2vec2-large-xlsr-german | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Bad Modell for Research Purposes! | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | ## roberta-urdu-small
[](https://github.com/urduhack/urduhack/blob/master/LICENSE)
### Overview
**Language model:** roberta-urdu-small
**Model size:** 125M
**Language:** Urdu
**Training data:** News data from urdu news resources in Pakistan
### About ro... | {"language": "ur", "license": "mit", "tags": ["roberta-urdu-small", "urdu", "transformers"], "thumbnail": "https://raw.githubusercontent.com/urduhack/urduhack/master/docs/_static/urduhack.png"} | urduhack/roberta-urdu-small | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"roberta-urdu-small",
"urdu",
"ur",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ur"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #roberta-urdu-small #urdu #ur #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ## roberta-urdu-small
![License: MIT](URL
### Overview
Language model: roberta-urdu-small
Model size: 125M
Language: Urdu
Training data: News data from urdu news resources in Pakistan
### About roberta-urdu-small
roberta-urdu-small is a language model for urdu language.
## Training procedure
roberta-urdu-small was tr... | [
"## roberta-urdu-small\n\n![License: MIT](URL",
"### Overview\nLanguage model: roberta-urdu-small\nModel size: 125M\nLanguage: Urdu\nTraining data: News data from urdu news resources in Pakistan",
"### About roberta-urdu-small\nroberta-urdu-small is a language model for urdu language.",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #roberta-urdu-small #urdu #ur #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## roberta-urdu-small\n\n![License: MIT](URL",
"### Overview\nLanguage model: roberta-urdu-small\nModel size: 125M\nLanguage: Urdu\nTraining data: News d... |
text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | usamazaheer/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | usami/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"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-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7767
* Matthews Correlation: 0.5492
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #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... |
sentence-similarity | sentence-transformers |
# sbert-roberta-large-anli-mnli-snli
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
The model is weight initialized by RoBERTa-large and trained on ANLI (Nie et al., 2... | {"language": ["en"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["anli", "multi_nli", "snli"], "pipeline_tag": "sentence-similarity"} | usc-isi/sbert-roberta-large-anli-mnli-snli | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:anli",
"dataset:multi_nli",
"dataset:snli",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #en #dataset-anli #dataset-multi_nli #dataset-snli #endpoints_compatible #has_space #region-us
|
# sbert-roberta-large-anli-mnli-snli
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
The model is weight initialized by RoBERTa-large and trained on ANLI (Nie et al., 2020), MNLI (Williams et a... | [
"# sbert-roberta-large-anli-mnli-snli\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nThe model is weight initialized by RoBERTa-large and trained on ANLI (Nie et al., 2020), MNLI (Willi... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #en #dataset-anli #dataset-multi_nli #dataset-snli #endpoints_compatible #has_space #region-us \n",
"# sbert-roberta-large-anli-mnli-snli\n\nThis is a sentence-transformers model: It maps sentences & paragraphs ... |
token-classification | flair | ## Test model README
Some test README description
| {"tags": ["flair", "token-classification"], "widget": [{"text": "does this work"}]} | usernamtadejm/flairbookmodel1234 | null | [
"flair",
"pytorch",
"token-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#flair #pytorch #token-classification #region-us
| ## Test model README
Some test README description
| [
"## Test model README\nSome test README description"
] | [
"TAGS\n#flair #pytorch #token-classification #region-us \n",
"## Test model README\nSome test README description"
] |
text-generation | transformers |
# yuyuyui-chatbot
This model is based on [rinna/japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2-medium) and finetuned on Yuyuyui scenario corpus.
## Usage
The model takes a sequence of utterances (context) to generate a subsequent utterance (response). Each utterance begins with a **character token... | {"language": "ja", "inference": false} | ushikado/yuyuyui-chatbot | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"ja",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #ja #autotrain_compatible #text-generation-inference #region-us
|
# yuyuyui-chatbot
This model is based on rinna/japanese-gpt2-medium and finetuned on Yuyuyui scenario corpus.
## Usage
The model takes a sequence of utterances (context) to generate a subsequent utterance (response). Each utterance begins with a character token and ends with an EOS token. Use the unspecified charac... | [
"# yuyuyui-chatbot\n\nThis model is based on rinna/japanese-gpt2-medium and finetuned on Yuyuyui scenario corpus.",
"## Usage\n\nThe model takes a sequence of utterances (context) to generate a subsequent utterance (response). Each utterance begins with a character token and ends with an EOS token. Use the unspec... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #ja #autotrain_compatible #text-generation-inference #region-us \n",
"# yuyuyui-chatbot\n\nThis model is based on rinna/japanese-gpt2-medium and finetuned on Yuyuyui scenario corpus.",
"## Usage\n\nThe model takes a sequence of utterances (context) to ge... |
text-generation | transformers |
#ut friend | {"tags": ["conversational"]} | uutkras/Pandabot | 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
|
#ut friend | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers |
# QuReTec: query resolution model
QuReTeC is a query resolution model. It finds the relevant terms in a question history.
It is based on **bert-large-uncased** with a max sequence length of 300.
# Config details
Training and evaluation was done using the following BertConfig:
```json
BertConfig {
"_name_or_path":... | {"language": ["en"], "tags": ["conversational-search"], "datasets": ["uva-irlab/canard_quretec"], "metrics": ["f1"], "model-index": [{"name": "QuReTec", "results": [{"task": {"type": "conversational", "name": "Conversational search"}, "dataset": {"name": "CANARD", "type": "canard"}, "metrics": [{"type": "f1", "value": ... | uva-irlab/quretec | null | [
"transformers",
"pytorch",
"bert",
"conversational-search",
"en",
"dataset:uva-irlab/canard_quretec",
"arxiv:2005.11723",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.11723"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #conversational-search #en #dataset-uva-irlab/canard_quretec #arxiv-2005.11723 #model-index #endpoints_compatible #region-us
|
# QuReTec: query resolution model
QuReTeC is a query resolution model. It finds the relevant terms in a question history.
It is based on bert-large-uncased with a max sequence length of 300.
# Config details
Training and evaluation was done using the following BertConfig:
# Original authors
QuReTeC model from th... | [
"# QuReTec: query resolution model\n\nQuReTeC is a query resolution model. It finds the relevant terms in a question history.\nIt is based on bert-large-uncased with a max sequence length of 300.",
"# Config details\nTraining and evaluation was done using the following BertConfig:",
"# Original authors\n\nQuReT... | [
"TAGS\n#transformers #pytorch #bert #conversational-search #en #dataset-uva-irlab/canard_quretec #arxiv-2005.11723 #model-index #endpoints_compatible #region-us \n",
"# QuReTec: query resolution model\n\nQuReTeC is a query resolution model. It finds the relevant terms in a question history.\nIt is based on bert-l... |
text-generation | transformers |
# Polyjuice
## Model description
This is a ported version of [Polyjuice](https://homes.cs.washington.edu/~wtshuang/static/papers/2021-arxiv-polyjuice.pdf), the general-purpose counterfactual generator.
For more code release, please refer to [this github page](https://github.com/tongshuangwu/polyjuice).
#### How to ... | {"language": "en", "tags": ["counterfactual generation"], "widget": [{"text": "It is great for kids. <|perturb|> [negation] It [BLANK] great for kids. [SEP]"}]} | uw-hai/polyjuice | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"counterfactual generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #counterfactual generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Polyjuice
## Model description
This is a ported version of Polyjuice, the general-purpose counterfactual generator.
For more code release, please refer to this github page.
#### How to use
### BibTeX entry and citation info
| [
"# Polyjuice",
"## Model description\n\nThis is a ported version of Polyjuice, the general-purpose counterfactual generator.\nFor more code release, please refer to this github page.",
"#### How to use",
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #counterfactual generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Polyjuice",
"## Model description\n\nThis is a ported version of Polyjuice, the general-purpose counterfactual generator.\nFor more c... |
fill-mask | transformers | # Nyströmformer
Nyströmformer model for masked language modeling (MLM) pretrained on BookCorpus and English Wikipedia for sequence length 512.
## About Nyströmformer
The Nyströmformer model was proposed in [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) b... | {} | uw-madison/nystromformer-512 | null | [
"transformers",
"pytorch",
"nystromformer",
"fill-mask",
"arxiv:2102.03902",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.03902"
] | [] | TAGS
#transformers #pytorch #nystromformer #fill-mask #arxiv-2102.03902 #autotrain_compatible #endpoints_compatible #region-us
| # Nyströmformer
Nyströmformer model for masked language modeling (MLM) pretrained on BookCorpus and English Wikipedia for sequence length 512.
## About Nyströmformer
The Nyströmformer model was proposed in Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention by Yunyang Xiong, Zhanpeng Zeng, Rudr... | [
"# Nyströmformer\n\nNyströmformer model for masked language modeling (MLM) pretrained on BookCorpus and English Wikipedia for sequence length 512.",
"## About Nyströmformer\n\nThe Nyströmformer model was proposed in Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention by Yunyang Xiong, Zhanpe... | [
"TAGS\n#transformers #pytorch #nystromformer #fill-mask #arxiv-2102.03902 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Nyströmformer\n\nNyströmformer model for masked language modeling (MLM) pretrained on BookCorpus and English Wikipedia for sequence length 512.",
"## About Nyströmformer\n\nTh... |
fill-mask | transformers | # YOSO
YOSO model for masked language modeling (MLM) for sequence length 4096.
## About YOSO
The YOSO model was proposed in [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fun... | {} | uw-madison/yoso-4096 | null | [
"transformers",
"pytorch",
"yoso",
"fill-mask",
"arxiv:2111.09714",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.09714"
] | [] | TAGS
#transformers #pytorch #yoso #fill-mask #arxiv-2111.09714 #autotrain_compatible #endpoints_compatible #region-us
| # YOSO
YOSO model for masked language modeling (MLM) for sequence length 4096.
## About YOSO
The YOSO model was proposed in You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
The abstract from t... | [
"# YOSO\n\nYOSO model for masked language modeling (MLM) for sequence length 4096.",
"## About YOSO\n\nThe YOSO model was proposed in You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.\n\nThe ... | [
"TAGS\n#transformers #pytorch #yoso #fill-mask #arxiv-2111.09714 #autotrain_compatible #endpoints_compatible #region-us \n",
"# YOSO\n\nYOSO model for masked language modeling (MLM) for sequence length 4096.",
"## About YOSO\n\nThe YOSO model was proposed in You Only Sample (Almost) Once: Linear Cost Self-Atten... |
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": []} | uyeongjae/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: 3.6426
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... |
text-generation | transformers |
# Rick and Morty DialoGPT Model (small) | {"tags": ["conversational"]} | uyharold86/DialoGPT-small-RickAndMorty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model (small) | [
"# Rick and Morty DialoGPT Model (small)"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model (small)"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-da-colab
This model is a fine-tuned version of [Alvenir/wav2vec2-base-da](https://huggingface.co/Alven... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-da-colab", "results": []}]} | vachonni/wav2vec2-large-xls-r-300m-da-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-da-colab
This model is a fine-tuned version of Alvenir/wav2vec2-base-da on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Train... | [
"# wav2vec2-large-xls-r-300m-da-colab\n\nThis model is a fine-tuned version of Alvenir/wav2vec2-base-da on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-da-colab\n\nThis model is a fine-tuned version of Alvenir/wav2vec2-base-da on the None dataset.",
"## Model descriptio... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-dansk-CV-80
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-dansk-CV-80", "results": []}]} | vachonni/wav2vec2-large-xls-r-300m-dansk-CV-80 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-dansk-CV-80
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Danish, using the mozilla-foundation/common_voice_8_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6394
- eval_wer: 0.3682
- eval_runtime: 104.0466
- eval_samples_per_s... | [
"# wav2vec2-large-xls-r-300m-dansk-CV-80\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Danish, using the mozilla-foundation/common_voice_8_0 dataset.\n\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.6394\n- eval_wer: 0.3682\n- eval_runtime: 104.0466\n- eval_sam... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-dansk-CV-80\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Danish, usin... |
text2text-generation | transformers | [www.github.com/vahmohh/masters-thesis](https://www.github.com/vahmohh/masters-thesis)
The model has been built upon the pre-trained T5 model by fine-tuning it on SQuAD dataset for the porpuse of automatic question and answer generation.
The following format should be used for generating questions.
```sh
generate... | {} | vahmohh/t5-qag-base | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| URL
The model has been built upon the pre-trained T5 model by fine-tuning it on SQuAD dataset for the porpuse of automatic question and answer generation.
The following format should be used for generating questions.
Output:
The following format should be used for generating answers.
Output:
| [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers |
# IndicBERT
IndicBERT is a multilingual ALBERT model pretrained exclusively on 12 major Indian languages. It is pre-trained on our novel monolingual corpus of around 9 billion tokens and subsequently evaluated on a set of diverse tasks. IndicBERT has much fewer parameters than other multilingual models (mBERT, XLM-R ... | {"language": "en", "license": "mit", "datasets": ["AI4Bharat IndicNLP Corpora"]} | vaishnavi/indic-bert-512 | null | [
"transformers",
"pytorch",
"albert",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #albert #en #license-mit #endpoints_compatible #region-us
| IndicBERT
=========
IndicBERT is a multilingual ALBERT model pretrained exclusively on 12 major Indian languages. It is pre-trained on our novel monolingual corpus of around 9 billion tokens and subsequently evaluated on a set of diverse tasks. IndicBERT has much fewer parameters than other multilingual models (mBERT... | [
"#### IndicGLUE",
"#### Additional Tasks\n\n\n\n\\* Note: all models have been restricted to a max\\_seq\\_length of 128.\n\n\nDownloads\n---------\n\n\nThe model can be downloaded here. Both tf checkpoints and pytorch binaries are included in the archive. Alternatively, you can also download it from Huggingface.... | [
"TAGS\n#transformers #pytorch #albert #en #license-mit #endpoints_compatible #region-us \n",
"#### IndicGLUE",
"#### Additional Tasks\n\n\n\n\\* Note: all models have been restricted to a max\\_seq\\_length of 128.\n\n\nDownloads\n---------\n\n\nThe model can be downloaded here. Both tf checkpoints and pytorch ... |
text-generation | transformers |
# Patrick Bateman DialoGPT Model
| {"tags": ["conversational"]} | valarikv/DialoGPT-small-bateman | 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
|
# Patrick Bateman DialoGPT Model
| [
"# Patrick Bateman DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Patrick Bateman DialoGPT Model"
] |
question-answering | transformers |
# BART-LARGE finetuned on SQuADv1
This is bart-large model finetuned on SQuADv1 dataset for question answering task
## Model details
BART was propsed in the [paper](https://arxiv.org/abs/1910.13461) **BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**.... | {"datasets": ["squad"]} | valhalla/bart-large-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"jax",
"bart",
"question-answering",
"dataset:squad",
"arxiv:1910.13461",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461"
] | [] | TAGS
#transformers #pytorch #jax #bart #question-answering #dataset-squad #arxiv-1910.13461 #endpoints_compatible #has_space #region-us
| BART-LARGE finetuned on SQuADv1
===============================
This is bart-large model finetuned on SQuADv1 dataset for question answering task
Model details
-------------
BART was propsed in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehens... | [] | [
"TAGS\n#transformers #pytorch #jax #bart #question-answering #dataset-squad #arxiv-1910.13461 #endpoints_compatible #has_space #region-us \n"
] |
zero-shot-classification | transformers |
# DistilBart-MNLI
distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingface/transformers/tree/master/examples/seq2seq#distilbart).
We just copy alternating layers from `bart... | {"tags": ["distilbart", "distilbart-mnli"], "datasets": ["mnli"], "pipeline_tag": "zero-shot-classification"} | valhalla/distilbart-mnli-12-1 | null | [
"transformers",
"pytorch",
"jax",
"bart",
"text-classification",
"distilbart",
"distilbart-mnli",
"zero-shot-classification",
"dataset:mnli",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bart #text-classification #distilbart #distilbart-mnli #zero-shot-classification #dataset-mnli #autotrain_compatible #endpoints_compatible #has_space #region-us
| DistilBart-MNLI
===============
distilbart-mnli is the distilled version of bart-large-mnli created using the No Teacher Distillation technique proposed for BART summarisation by Huggingface, here.
We just copy alternating layers from 'bart-large-mnli' and finetune more on the same data.
matched acc: bart-large-m... | [] | [
"TAGS\n#transformers #pytorch #jax #bart #text-classification #distilbart #distilbart-mnli #zero-shot-classification #dataset-mnli #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
zero-shot-classification | transformers |
# DistilBart-MNLI
distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingface/transformers/tree/master/examples/seq2seq#distilbart).
We just copy alternating layers from `bart... | {"tags": ["distilbart", "distilbart-mnli"], "datasets": ["mnli"], "pipeline_tag": "zero-shot-classification"} | valhalla/distilbart-mnli-12-3 | null | [
"transformers",
"pytorch",
"jax",
"bart",
"text-classification",
"distilbart",
"distilbart-mnli",
"zero-shot-classification",
"dataset:mnli",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bart #text-classification #distilbart #distilbart-mnli #zero-shot-classification #dataset-mnli #autotrain_compatible #endpoints_compatible #has_space #region-us
| DistilBart-MNLI
===============
distilbart-mnli is the distilled version of bart-large-mnli created using the No Teacher Distillation technique proposed for BART summarisation by Huggingface, here.
We just copy alternating layers from 'bart-large-mnli' and finetune more on the same data.
matched acc: bart-large-m... | [] | [
"TAGS\n#transformers #pytorch #jax #bart #text-classification #distilbart #distilbart-mnli #zero-shot-classification #dataset-mnli #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
zero-shot-classification | transformers |
# DistilBart-MNLI
distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingface/transformers/tree/master/examples/seq2seq#distilbart).
We just copy alternating layers from `bart... | {"tags": ["distilbart", "distilbart-mnli"], "datasets": ["mnli"], "pipeline_tag": "zero-shot-classification"} | valhalla/distilbart-mnli-12-6 | null | [
"transformers",
"pytorch",
"jax",
"bart",
"text-classification",
"distilbart",
"distilbart-mnli",
"zero-shot-classification",
"dataset:mnli",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bart #text-classification #distilbart #distilbart-mnli #zero-shot-classification #dataset-mnli #autotrain_compatible #endpoints_compatible #has_space #region-us
| DistilBart-MNLI
===============
distilbart-mnli is the distilled version of bart-large-mnli created using the No Teacher Distillation technique proposed for BART summarisation by Huggingface, here.
We just copy alternating layers from 'bart-large-mnli' and finetune more on the same data.
matched acc: bart-large-m... | [] | [
"TAGS\n#transformers #pytorch #jax #bart #text-classification #distilbart #distilbart-mnli #zero-shot-classification #dataset-mnli #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
zero-shot-classification | transformers |
# DistilBart-MNLI
distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingface/transformers/tree/master/examples/seq2seq#distilbart).
We just copy alternating layers from `bart... | {"tags": ["distilbart", "distilbart-mnli"], "datasets": ["mnli"], "pipeline_tag": "zero-shot-classification"} | valhalla/distilbart-mnli-12-9 | null | [
"transformers",
"pytorch",
"jax",
"bart",
"text-classification",
"distilbart",
"distilbart-mnli",
"zero-shot-classification",
"dataset:mnli",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bart #text-classification #distilbart #distilbart-mnli #zero-shot-classification #dataset-mnli #autotrain_compatible #endpoints_compatible #has_space #region-us
| DistilBart-MNLI
===============
distilbart-mnli is the distilled version of bart-large-mnli created using the No Teacher Distillation technique proposed for BART summarisation by Huggingface, here.
We just copy alternating layers from 'bart-large-mnli' and finetune more on the same data.
matched acc: bart-large-m... | [] | [
"TAGS\n#transformers #pytorch #jax #bart #text-classification #distilbart #distilbart-mnli #zero-shot-classification #dataset-mnli #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
## DistilT5 for question-generation
This is distilled version of [t5-base-qa-qg-hl](https://huggingface.co/valhalla/t5-base-qa-qg-hl) model trained for question answering and answer aware question generation tasks.
The model is distilled using the **No Teacher Distillation** method proposed by Huggingface, [here](htt... | {"license": "mit", "tags": ["question-generation", "distilt5", "distilt5-qg"], "datasets": ["squad"], "widget": [{"text": "generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "question: What is 42 context: 42 is the answer to life, the universe and everything. </s>"}]} | valhalla/distilt5-qa-qg-hl-12-6 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-generation",
"distilt5",
"distilt5-qg",
"dataset:squad",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #question-generation #distilt5 #distilt5-qg #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DistilT5 for question-generation
--------------------------------
This is distilled version of t5-base-qa-qg-hl model trained for question answering and answer aware question generation tasks.
The model is distilled using the No Teacher Distillation method proposed by Huggingface, here.
We just copy alternating l... | [
"### Model in action\n\n\nYou'll need to clone the repo.\n\n\n![Open In Colab](URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question-generation #distilt5 #distilt5-qg #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Model in action\n\n\nYou'll need to clone the repo.\n\n\n![Open In Colab](URL"
] |
text2text-generation | transformers |
## DistilT5 for question-generation
This is distilled version of [t5-small-qa-qg-hl](https://huggingface.co/valhalla/t5-small-qa-qg-hl) model trained for question answering and answer aware question generation tasks.
The model is distilled using the **No Teacher Distillation** method proposed by Huggingface, [here](h... | {"license": "mit", "tags": ["question-generation", "distilt5", "distilt5-qg"], "datasets": ["squad"], "widget": [{"text": "generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "question: What is 42 context: 42 is the answer to life, the universe and everything. </s>"}]} | valhalla/distilt5-qa-qg-hl-6-4 | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"question-generation",
"distilt5",
"distilt5-qg",
"dataset:squad",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #question-generation #distilt5 #distilt5-qg #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DistilT5 for question-generation
--------------------------------
This is distilled version of t5-small-qa-qg-hl model trained for question answering and answer aware question generation tasks.
The model is distilled using the No Teacher Distillation method proposed by Huggingface, here.
We just copy alternating ... | [
"### Model in action\n\n\nYou'll need to clone the repo.\n\n\n![Open In Colab](URL"
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #question-generation #distilt5 #distilt5-qg #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Model in action\n\n\nYou'll need to clone the repo.\n\n\n![Open In Colab](URL"
] |
text2text-generation | transformers |
## DistilT5 for question-generation
This is distilled version of [t5-base-qg-hl](https://huggingface.co/valhalla/t5-base-qg-hl) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
The model is distilled using the **No Teacher Distill... | {"license": "mit", "tags": ["question-generation", "distilt5", "distilt5-qg"], "datasets": ["squad"], "widget": [{"text": "<hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>"}, {"text": "Although <hl> p... | valhalla/distilt5-qg-hl-12-6 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-generation",
"distilt5",
"distilt5-qg",
"dataset:squad",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #question-generation #distilt5 #distilt5-qg #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DistilT5 for question-generation
--------------------------------
This is distilled version of t5-base-qg-hl model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
The model is distilled using the No Teacher Distillation method propos... | [
"### Model in action\n\n\nYou'll need to clone the repo.\n\n\n![Open In Colab](URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question-generation #distilt5 #distilt5-qg #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Model in action\n\n\nYou'll need to clone the repo.\n\n\n![Open In Colab](URL"
] |
text2text-generation | transformers |
## DistilT5 for question-generation
This is distilled version of [t5-small-qa-qg-hl](https://huggingface.co/valhalla/t5-small-qa-qg-hl) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
The model is distilled using the **No Teacher... | {"license": "mit", "tags": ["question-generation", "distilt5", "distilt5-qg"], "datasets": ["squad"], "widget": [{"text": "<hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>"}, {"text": "Although <hl> p... | valhalla/distilt5-qg-hl-6-4 | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"question-generation",
"distilt5",
"distilt5-qg",
"dataset:squad",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #question-generation #distilt5 #distilt5-qg #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DistilT5 for question-generation
--------------------------------
This is distilled version of t5-small-qa-qg-hl model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
The model is distilled using the No Teacher Distillation method pr... | [
"### Model in action\n\n\nYou'll need to clone the repo.\n\n\n![Open In Colab](URL"
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #question-generation #distilt5 #distilt5-qg #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Model in action\n\n\nYou'll need to clone the repo.\n\n\n![Open In Colab](URL"
] |
question-answering | transformers | # ELECTRA-BASE-DISCRIMINATOR finetuned on SQuADv1
This is electra-base-discriminator model finetuned on SQuADv1 dataset for for question answering task.
## Model details
As mentioned in the original paper: ELECTRA is a new method for self-supervised language representation learning.
It can be used to pre-train transf... | {} | valhalla/electra-base-discriminator-finetuned_squadv1 | 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
| ELECTRA-BASE-DISCRIMINATOR finetuned on SQuADv1
===============================================
This is electra-base-discriminator model finetuned on SQuADv1 dataset for for question answering task.
Model details
-------------
As mentioned in the original paper: ELECTRA is a new method for self-supervised languag... | [] | [
"TAGS\n#transformers #pytorch #electra #question-answering #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers | **This model is uploaded for testing purpose. It's random model not trained on anything** | {} | valhalla/gpt-neo-random-tiny | null | [
"transformers",
"pytorch",
"gpt_neo",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #feature-extraction #endpoints_compatible #region-us
| This model is uploaded for testing purpose. It's random model not trained on anything | [] | [
"TAGS\n#transformers #pytorch #gpt_neo #feature-extraction #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# LONGFORMER-BASE-4096 fine-tuned on SQuAD v1
This is longformer-base-4096 model fine-tuned on SQuAD v1 dataset for question answering task.
[Longformer](https://arxiv.org/abs/2004.05150) model created by Iz Beltagy, Matthew E. Peters, Arman Coha from AllenAI. As the paper explains it
> `Longformer` is a BERT-li... | {"license": "mit", "datasets": ["squad_v1"]} | valhalla/longformer-base-4096-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"tf",
"rust",
"longformer",
"question-answering",
"dataset:squad_v1",
"arxiv:2004.05150",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.05150"
] | [] | TAGS
#transformers #pytorch #tf #rust #longformer #question-answering #dataset-squad_v1 #arxiv-2004.05150 #license-mit #endpoints_compatible #has_space #region-us
| LONGFORMER-BASE-4096 fine-tuned on SQuAD v1
===========================================
This is longformer-base-4096 model fine-tuned on SQuAD v1 dataset for question answering task.
Longformer model created by Iz Beltagy, Matthew E. Peters, Arman Coha from AllenAI. As the paper explains it
>
> 'Longformer' is ... | [] | [
"TAGS\n#transformers #pytorch #tf #rust #longformer #question-answering #dataset-squad_v1 #arxiv-2004.05150 #license-mit #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
# Model Card for KeywordIdentifier
# Model Details
## Model Description
More information needed
- **Developed by:** Facebook
- **Shared by [Optional]:** Suraj Patil
- **Model type:** Text2Text Generation
- **Language(s) (NLP):** More information needed
- **License:** More information needed
- **Parent Model:... | {"language": ["multilingual"], "tags": ["text-2-text-generation", "m2m_100"]} | valhalla/m2m100_tiny_random | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"text-2-text-generation",
"multilingual",
"arxiv:2010.11125",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11125",
"1910.09700"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #text-2-text-generation #multilingual #arxiv-2010.11125 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for KeywordIdentifier
# Model Details
## Model Description
More information needed
- Developed by: Facebook
- Shared by [Optional]: Suraj Patil
- Model type: Text2Text Generation
- Language(s) (NLP): More information needed
- License: More information needed
- Parent Model: [M2M100]URL
- Resourc... | [
"# Model Card for KeywordIdentifier",
"# Model Details",
"## Model Description\n \nMore information needed\n \n- Developed by: Facebook\n- Shared by [Optional]: Suraj Patil\n- Model type: Text2Text Generation\n- Language(s) (NLP): More information needed\n- License: More information needed\n- Parent Model: [M2M... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #text-2-text-generation #multilingual #arxiv-2010.11125 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for KeywordIdentifier",
"# Model Details",
"## Model Description\n \nMore information needed\n \n- De... |
automatic-speech-recognition | transformers |
TODO: [To be filled]
## Evaluation on LibriSpeech Test
The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) *"clean"* and *"other"* test dataset.
```python
from datasets import load_dataset
from transformers import Speech2TextTransformerForCond... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"]} | valhalla/s2t_librispeech_large | null | [
"transformers",
"pytorch",
"speech_to_text_transformer",
"text2text-generation",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #speech_to_text_transformer #text2text-generation #audio #automatic-speech-recognition #en #dataset-librispeech_asr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| TODO: [To be filled]
Evaluation on LibriSpeech Test
------------------------------
The following script shows how to evaluate this model on the LibriSpeech *"clean"* and *"other"* test dataset.
*Result (WER)*:
| [] | [
"TAGS\n#transformers #pytorch #speech_to_text_transformer #text2text-generation #audio #automatic-speech-recognition #en #dataset-librispeech_asr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
TODO: [To be filled]
## Evaluation on LibriSpeech Test
The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) *"clean"* and *"other"* test dataset.
```python
from datasets import load_dataset
from transformers import Speech2TextTransformerForCond... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"]} | valhalla/s2t_librispeech_medium | null | [
"transformers",
"pytorch",
"speech_to_text_transformer",
"text2text-generation",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #speech_to_text_transformer #text2text-generation #audio #automatic-speech-recognition #en #dataset-librispeech_asr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| TODO: [To be filled]
Evaluation on LibriSpeech Test
------------------------------
The following script shows how to evaluate this model on the LibriSpeech *"clean"* and *"other"* test dataset.
*Result (WER)*:
| [] | [
"TAGS\n#transformers #pytorch #speech_to_text_transformer #text2text-generation #audio #automatic-speech-recognition #en #dataset-librispeech_asr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
TODO: [To be filled]
## Evaluation on LibriSpeech Test
The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) *"clean"* and *"other"* test dataset.
```python
from datasets import load_dataset
from transformers import Speech2TextTransformerForCond... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"]} | valhalla/s2t_librispeech_small | null | [
"transformers",
"pytorch",
"speech_to_text_transformer",
"text2text-generation",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #speech_to_text_transformer #text2text-generation #audio #automatic-speech-recognition #en #dataset-librispeech_asr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| TODO: [To be filled]
Evaluation on LibriSpeech Test
------------------------------
The following script shows how to evaluate this model on the LibriSpeech *"clean"* and *"other"* test dataset.
*Result (WER)*:
| [] | [
"TAGS\n#transformers #pytorch #speech_to_text_transformer #text2text-generation #audio #automatic-speech-recognition #en #dataset-librispeech_asr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | This model is uploaded for testing purpose
| {} | valhalla/t5-base-cnn-fp6-test | 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 model is uploaded for testing purpose
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
## T5 for question-generation
This is [t5-base](https://arxiv.org/abs/1910.10683) model trained for end-to-end question generation task. Simply input the text and the model will generate multile questions.
You can play with the model using the inference API, just put the text and see the results!
For more deatils s... | {"license": "mit", "tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "Python is a programming language. It is developed by Guido Van Rossum and released in 1991. </s>"}]} | valhalla/t5-base-e2e-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1910.10683",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## T5 for question-generation
This is t5-base model trained for end-to-end question generation task. Simply input the text and the model will generate multile questions.
You can play with the model using the inference API, just put the text and see the results!
For more deatils see this repo.
### Model in action
... | [
"## T5 for question-generation\nThis is t5-base model trained for end-to-end question generation task. Simply input the text and the model will generate multile questions. \n\nYou can play with the model using the inference API, just put the text and see the results!\n\nFor more deatils see this repo.",
"### Mode... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## T5 for question-generation\nThis is t5-base model trained for end-to-end question generat... |
text2text-generation | transformers |
## T5 for multi-task QA and QG
This is multi-task [t5-base](https://arxiv.org/abs/1910.10683) model trained for question answering and answer aware question generation tasks.
For question generation the answer spans are highlighted within the text with special highlight tokens (`<hl>`) and prefixed with 'generate qu... | {"license": "mit", "tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "question: What is 42 context: 42 is the answer to life, the universe and everything. </s>"}]} | valhalla/t5-base-qa-qg-hl | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1910.10683",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## T5 for multi-task QA and QG
This is multi-task t5-base model trained for question answering and answer aware question generation tasks.
For question generation the answer spans are highlighted within the text with special highlight tokens ('<hl>') and prefixed with 'generate question: '. For QA the input is proce... | [
"## T5 for multi-task QA and QG\nThis is multi-task t5-base model trained for question answering and answer aware question generation tasks. \n\nFor question generation the answer spans are highlighted within the text with special highlight tokens ('<hl>') and prefixed with 'generate question: '. For QA the input i... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## T5 for multi-task QA and QG\nThis is multi-task t5-base model trained for question answer... |
text2text-generation | transformers |
## T5 for question-generation
This is [t5-base](https://arxiv.org/abs/1910.10683) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
You can play with the model using the inference API, just highlight the answer spans with `<hl>` t... | {"license": "mit", "tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "<hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>"}, {"text": "Although <hl> practicality <hl> beats puri... | valhalla/t5-base-qg-hl | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1910.10683",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## T5 for question-generation
This is t5-base model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
You can play with the model using the inference API, just highlight the answer spans with '<hl>' tokens and end the text with '</s>'. ... | [
"## T5 for question-generation\nThis is t5-base model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. \n\nYou can play with the model using the inference API, just highlight the answer spans with '<hl>' tokens and end the text with '... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## T5 for question-generation\nThis is t5-base model trained for answer aware question gener... |
text2text-generation | transformers | # T5 for question-answering
This is T5-base model fine-tuned on SQuAD1.1 for QA using text-to-text approach
## Model training
This model was trained on colab TPU with 35GB RAM for 4 epochs
## Results:
| Metric | #Value |
|-------------|---------|
| Exact Match | 81.5610 |
| F1 | 89.9601 |
## Model in ... | {} | valhalla/t5-base-squad | 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
| T5 for question-answering
=========================
This is T5-base model fine-tuned on SQuAD1.1 for QA using text-to-text approach
Model training
--------------
This model was trained on colab TPU with 35GB RAM for 4 epochs
Results:
--------
Model in Action
---------------
Play with this model ![Open In C... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
## T5 for question-generation
This is [t5-small](https://arxiv.org/abs/1910.10683) model trained for end-to-end question generation task. Simply input the text and the model will generate multile questions.
You can play with the model using the inference API, just put the text and see the results!
For more deatils ... | {"license": "mit", "tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "Python is developed by Guido Van Rossum and released in 1991. </s>"}]} | valhalla/t5-small-e2e-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1910.10683",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## T5 for question-generation
This is t5-small model trained for end-to-end question generation task. Simply input the text and the model will generate multile questions.
You can play with the model using the inference API, just put the text and see the results!
For more deatils see this repo.
### Model in action ... | [
"## T5 for question-generation\nThis is t5-small model trained for end-to-end question generation task. Simply input the text and the model will generate multile questions. \n\nYou can play with the model using the inference API, just put the text and see the results!\n\nFor more deatils see this repo.",
"### Mod... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## T5 for question-generation\nThis is t5-small model trained for end-to-end question genera... |
text2text-generation | transformers |
## T5 for multi-task QA and QG
This is multi-task [t5-small](https://arxiv.org/abs/1910.10683) model trained for question answering and answer aware question generation tasks.
For question generation the answer spans are highlighted within the text with special highlight tokens (`<hl>`) and prefixed with 'generate q... | {"license": "mit", "tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "question: What is 42 context: 42 is the answer to life, the universe and everything. </s>"}]} | valhalla/t5-small-qa-qg-hl | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1910.10683",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## T5 for multi-task QA and QG
This is multi-task t5-small model trained for question answering and answer aware question generation tasks.
For question generation the answer spans are highlighted within the text with special highlight tokens ('<hl>') and prefixed with 'generate question: '. For QA the input is proc... | [
"## T5 for multi-task QA and QG\nThis is multi-task t5-small model trained for question answering and answer aware question generation tasks. \n\nFor question generation the answer spans are highlighted within the text with special highlight tokens ('<hl>') and prefixed with 'generate question: '. For QA the input ... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## T5 for multi-task QA and QG\nThis is multi-task t5-small model trained for question ... |
text2text-generation | transformers |
## T5 for question-generation
This is [t5-small](https://arxiv.org/abs/1910.10683) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
You can play with the model using the inference API, just highlight the answer spans with `<hl>` ... | {"license": "mit", "tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "<hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>"}, {"text": "Simple is better than <hl> complex <hl>. <... | valhalla/t5-small-qg-hl | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1910.10683",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## T5 for question-generation
This is t5-small model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
You can play with the model using the inference API, just highlight the answer spans with '<hl>' tokens and end the text with '</s>'.... | [
"## T5 for question-generation\nThis is t5-small model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. \n\nYou can play with the model using the inference API, just highlight the answer spans with '<hl>' tokens and end the text with ... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## T5 for question-generation\nThis is t5-small model trained for answer aware question... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-allsides
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncas... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-allsides", "results": []}]} | valurank/distilbert-allsides | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us
| distilbert-allsides
===================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9138
* Acc: 0.7094
Model description
-----------------
More information needed
Intended uses & limitations
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 12345\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_st... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n*... |
text-classification | transformers |
# DistilBERT fine-tuned for news classification
This model is based on [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) pretrained weights, with a classification head fine-tuned to classify news articles into 3 categories (bad, medium, good).
## Training data
The dataset used to fine-tune t... | {"language": "en", "license": "other", "datasets": ["valurank/news-small"]} | valurank/distilbert-quality | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:valurank/news-small",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #en #dataset-valurank/news-small #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# DistilBERT fine-tuned for news classification
This model is based on distilbert-base-uncased pretrained weights, with a classification head fine-tuned to classify news articles into 3 categories (bad, medium, good).
## Training data
The dataset used to fine-tune the model is news-small, the 300 article news datas... | [
"# DistilBERT fine-tuned for news classification\n\nThis model is based on distilbert-base-uncased pretrained weights, with a classification head fine-tuned to classify news articles into 3 categories (bad, medium, good).",
"## Training data\n\nThe dataset used to fine-tune the model is news-small, the 300 articl... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #en #dataset-valurank/news-small #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT fine-tuned for news classification\n\nThis model is based on distilbert-base-uncased pretrained weights, with a classification he... |
text-classification | transformers | # DistilROBERTA fine-tuned for bias detection
This model is based on [distilroberta-base](https://huggingface.co/distilroberta-base) pretrained weights, with a classification head fine-tuned to classify text into 2 categories (neutral, biased).
## Training data
The dataset used to fine-tune the model is [wikirev-bias... | {"language": "en", "license": "other", "datasets": ["valurank/wikirev-bias"]} | valurank/distilroberta-bias | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:valurank/wikirev-bias",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-valurank/wikirev-bias #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us
| # DistilROBERTA fine-tuned for bias detection
This model is based on distilroberta-base pretrained weights, with a classification head fine-tuned to classify text into 2 categories (neutral, biased).
## Training data
The dataset used to fine-tune the model is wikirev-bias, extracted from English wikipedia revisions, ... | [
"# DistilROBERTA fine-tuned for bias detection\n\nThis model is based on distilroberta-base pretrained weights, with a classification head fine-tuned to classify text into 2 categories (neutral, biased).",
"## Training data\nThe dataset used to fine-tune the model is wikirev-bias, extracted from English wikipedia... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #dataset-valurank/wikirev-bias #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# DistilROBERTA fine-tuned for bias detection\n\nThis model is based on distilroberta-base pretrained weights, with a classification... |
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. -->
# distilroberta-clickbait
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-clickbait", "results": []}]} | valurank/distilroberta-clickbait | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-clickbait
=======================
This model is a fine-tuned version of distilroberta-base on a dataset of headlines.
It achieves the following results on the evaluation set:
* Loss: 0.0268
* Acc: 0.9963
Training and evaluation data
----------------------------
The following data sources were used... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 12345\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_st... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* ev... |
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. -->
# distilroberta-hatespeech
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) o... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-hatespeech", "results": []}]} | valurank/distilroberta-hatespeech | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-hatespeech
========================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3619
* Acc: 0.8423
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: 32\n* eval\\_batch\\_size: 32\n* seed: 12345\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_st... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* ev... |
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. -->
# distilroberta-mbfc-bias-4class
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-b... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-mbfc-bias-4class", "results": []}]} | valurank/distilroberta-mbfc-bias-4class | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-mbfc-bias-4class
==============================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5336
* Acc: 0.8503
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 12345\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_st... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* ev... |
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. -->
# distilroberta-mbfc-bias
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-mbfc-bias", "results": []}]} | valurank/distilroberta-mbfc-bias | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilroberta-mbfc-bias
=======================
This model is a fine-tuned version of distilroberta-base on the Proppy dataset, using political bias from URL as labels.
It achieves the following results on the evaluation set:
* Loss: 1.4130
* Acc: 0.6348
Training and evaluation data
----------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 12345\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_st... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_siz... |
text-classification | transformers |
# DistilROBERTA fine-tuned for news classification
This model is based on [distilroberta-base](https://huggingface.co/distilroberta-base) pretrained weights, with a classification head fine-tuned to classify news articles into 3 categories (bad, medium, good).
## Training data
The dataset used to fine-tune the mode... | {"language": "en", "license": "other", "datasets": ["valurank/news-small"]} | valurank/distilroberta-news-small | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:valurank/news-small",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-valurank/news-small #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# DistilROBERTA fine-tuned for news classification
This model is based on distilroberta-base pretrained weights, with a classification head fine-tuned to classify news articles into 3 categories (bad, medium, good).
## Training data
The dataset used to fine-tune the model is news-small, the 300 article news dataset... | [
"# DistilROBERTA fine-tuned for news classification\n\nThis model is based on distilroberta-base pretrained weights, with a classification head fine-tuned to classify news articles into 3 categories (bad, medium, good).",
"## Training data\n\nThe dataset used to fine-tune the model is news-small, the 300 article ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #dataset-valurank/news-small #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilROBERTA fine-tuned for news classification\n\nThis model is based on distilroberta-base pretrained weights, with a classification head fi... |
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. -->
# distilroberta-offensive
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-offensive", "results": []}]} | valurank/distilroberta-offensive | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilroberta-offensive
=======================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4526
* Acc: 0.8975
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 12345\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_st... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_siz... |
text-classification | transformers |
# distilroberta-propaganda-2class
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the QCRI propaganda dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5087
- Acc: 0.7424
## Training and evaluation data
Training data is the 19-clas... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-propaganda-2class", "results": []}]} | valurank/distilroberta-propaganda-2class | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilroberta-propaganda-2class
===============================
This model is a fine-tuned version of distilroberta-base on the QCRI propaganda dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5087
* Acc: 0.7424
Training and evaluation data
----------------------------
Training data... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 12345\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_st... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_siz... |
text-classification | transformers |
# distilroberta-proppy
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the proppy corpus.
It achieves the following results on the evaluation set:
- Loss: 0.1838
- Acc: 0.9269
## Training and evaluation data
The training data is the [proppy corpus](https://z... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-proppy", "results": []}]} | valurank/distilroberta-proppy | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-proppy
====================
This model is a fine-tuned version of distilroberta-base on the proppy corpus.
It achieves the following results on the evaluation set:
* Loss: 0.1838
* Acc: 0.9269
Training and evaluation data
----------------------------
The training data is the proppy corpus. See Pro... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 12345\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_s... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* e... |
text-classification | spacy | A Spacy pipeline for counting Part-of-speech articles
| Feature | Description |
| --- | --- |
| **Name** | `en_pos_counter` |
| **Version** | `0.1` |
| **spaCy** | `>=3.4.0,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `attribute_ruler`, `pos_counter` |
| **Components** | `tok2vec`, `tagger`, `attribute_rule... | {"language": ["en"], "tags": ["spacy", "text-classification"]} | valurank/en_pos_counter | null | [
"spacy",
"text-classification",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #text-classification #en #region-us
| A Spacy pipeline for counting Part-of-speech articles
### Label Scheme
View label scheme (50 labels for 1 components)
| [
"### Label Scheme\n\n\n\nView label scheme (50 labels for 1 components)"
] | [
"TAGS\n#spacy #text-classification #en #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (50 labels for 1 components)"
] |
text-classification | spacy | A Spacy pipeline for generating readability scores
| Feature | Description |
| --- | --- |
| **Name** | `en_readability` |
| **Version** | `0.1` |
| **spaCy** | `>=3.4.0,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `readability` |
| **Components** | `tok2vec`, `tagger`, `parser`... | {"language": ["en"], "tags": ["spacy", "text-classification"]} | valurank/en_readability | null | [
"spacy",
"text-classification",
"en",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #text-classification #en #has_space #region-us
| A Spacy pipeline for generating readability scores
### Label Scheme
View label scheme (95 labels for 2 components)
| [
"### Label Scheme\n\n\n\nView label scheme (95 labels for 2 components)"
] | [
"TAGS\n#spacy #text-classification #en #has_space #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (95 labels for 2 components)"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.