modelId stringlengths 4 112 | sha stringlengths 40 40 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringclasses 29
values | private bool 1
class | author stringlengths 2 38 ⌀ | config null | id stringlengths 4 112 | downloads float64 0 36.8M ⌀ | likes float64 0 712 ⌀ | library_name stringclasses 17
values | readme stringlengths 0 186k | embedding list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
google/mobilebert-uncased | 1f90a6c24c7879273a291d34a849033eba2dbc0f | 2021-04-19T13:32:58.000Z | [
"pytorch",
"tf",
"rust",
"mobilebert",
"pretraining",
"en",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/mobilebert-uncased | 115,430 | 4 | transformers | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
## MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attention... | [
-0.12261100858449936,
0.03984051197767258,
0.07277359068393707,
-0.03157862275838852,
0.011924276128411293,
-0.02036237344145775,
0.0073683904483914375,
0.05539952591061592,
-0.013074208982288837,
-0.010816708207130432,
0.049461472779512405,
-0.00693269819021225,
0.07679829001426697,
0.021... |
hf-internal-testing/tiny-random-electra | e5efa6ccdff6b9cd0c387c90ff4af92686bc0c12 | 2021-09-17T19:22:20.000Z | [
"pytorch",
"tf",
"electra",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-electra | 114,761 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
hf-internal-testing/tiny-random-albert | 213e37e1c19981fbcee4f5f9ebefee8db74c8db1 | 2021-09-17T19:23:59.000Z | [
"pytorch",
"tf",
"albert",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-albert | 114,473 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
sentence-transformers/all-mpnet-base-v1 | 5db7848555eaffcf26ac367f5dc9e0711acb2106 | 2021-08-31T07:35:23.000Z | [
"pytorch",
"mpnet",
"fill-mask",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/all-mpnet-base-v1 | 113,021 | 3 | sentence-transformers | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
---
# all-mpnet-base-v1
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... | [
-0.040558479726314545,
-0.056745272129774094,
-0.0016802854370325804,
0.026851162314414978,
0.038565367460250854,
0.05819757655262947,
-0.038308341056108475,
0.0019918561447411776,
0.018393995240330696,
-0.06838390231132507,
0.04733388125896454,
-0.00930288527160883,
0.03995739668607712,
0... |
philschmid/bart-large-cnn-samsum | 78e20b3792d507739ebb9e5a417bcc87606d3293 | 2022-07-04T13:10:54.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:samsum",
"transformers",
"sagemaker",
"summarization",
"model-index",
"autotrain_compatible"
] | summarization | false | philschmid | null | philschmid/bart-large-cnn-samsum | 112,471 | 16 | transformers |
---
language: en
tags:
- sagemaker
- bart
- summarization
datasets:
- samsum
widget:
- text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n\
Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:\
\ ok.\nJeff: and how can I get started? \nJeff: where can I f... | [
-0.11892198771238327,
-0.06765516847372055,
0.0056907301768660545,
0.010915056802332401,
0.0549592599272728,
0.026301495730876923,
-0.0520167276263237,
-0.007060013711452484,
-0.05334704741835594,
-0.01107962615787983,
0.017916420474648476,
-0.07910022139549255,
0.002345561282709241,
-0.04... |
cross-encoder/stsb-roberta-large | 9e35bf01ec28b309411c8903d0d4165567303eb4 | 2021-08-05T08:42:03.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/stsb-roberta-large | 111,228 | 3 | transformers | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset]... | [
-0.05867493897676468,
-0.10378813743591309,
-0.054619889706373215,
0.01839994080364704,
-0.024363433942198753,
0.05517164617776871,
-0.018026946112513542,
-0.010603397153317928,
0.005246789660304785,
-0.09420166909694672,
0.07769262790679932,
-0.08185577392578125,
0.055262017995119095,
0.0... |
Helsinki-NLP/opus-mt-ru-en | a06887d8d700245d4b10a20b6d89c1ad778f33c2 | 2022-07-14T08:56:05.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"ru",
"en",
"transformers",
"translation",
"license:cc-by-4.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ru-en | 110,930 | 6 | transformers | ---
tags:
- translation
license: cc-by-4.0
---
### opus-mt-ru-en
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [How to Get Sta... | [
-0.1155688539147377,
-0.03590115159749985,
-0.06292258948087692,
0.004806174896657467,
0.0031903560739010572,
0.10450379550457001,
-0.039205096662044525,
0.06709692627191544,
0.028500284999608994,
0.000059739653806900606,
-0.012441059574484825,
0.026428526267409325,
-0.0014203302562236786,
... |
sentence-transformers/paraphrase-distilroberta-base-v1 | de91d53e03a544451c0e18312391a3f279f7f4ef | 2022-06-15T20:03:06.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/paraphrase-distilroberta-base-v1 | 110,780 | null | sentence-transformers | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-distilroberta-base-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensi... | [
-0.038107383996248245,
-0.076657734811306,
0.0061357771046459675,
0.02914765104651451,
0.012500281445682049,
0.04586440697312355,
-0.030210858210921288,
0.03603936359286308,
-0.0029893594328314066,
-0.06911613792181015,
0.059985700994729996,
-0.0033894057851284742,
0.05699296295642853,
0.0... |
EleutherAI/gpt-neo-1.3B | 797174552ae47f449ab70b684cabcb6603e5e85e | 2021-12-31T13:48:33.000Z | [
"pytorch",
"jax",
"rust",
"gpt_neo",
"text-generation",
"en",
"dataset:the_pile",
"transformers",
"text generation",
"causal-lm",
"license:apache-2.0"
] | text-generation | false | EleutherAI | null | EleutherAI/gpt-neo-1.3B | 110,054 | 40 | transformers | ---
language:
- en
tags:
- text generation
- pytorch
- causal-lm
license: apache-2.0
datasets:
- the_pile
---
# GPT-Neo 1.3B
## Model Description
GPT-Neo 1.3B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 1.3B represents the num... | [
-0.11510144174098969,
-0.08287110924720764,
-0.005334376823157072,
0.03979632630944252,
0.08445019274950027,
-0.013419519178569317,
-0.018398486077785492,
0.003779219463467598,
0.00007733275560894981,
-0.06361331790685654,
-0.06316548585891724,
-0.030629461631178856,
0.0486488863825798,
0.... |
hfl/chinese-electra-180g-small-discriminator | 19998e79c480fa7f3892b3c035e4362fe497efcf | 2021-03-03T01:04:26.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0"
] | null | false | hfl | null | hfl/chinese-electra-180g-small-discriminator | 109,135 | 7 | transformers | ---
language:
- zh
license: "apache-2.0"
---
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compa... | [
-0.09022209793329239,
-0.06908009946346283,
0.044281091541051865,
0.05305112898349762,
-0.009731532074511051,
0.055254384875297546,
-0.02344399318099022,
0.0328807607293129,
-0.026894984766840935,
-0.015258828178048134,
0.028422411531209946,
-0.013105235062539577,
-0.019453970715403557,
0.... |
seyonec/ChemBERTa-zinc-base-v1 | 761d6a18cf99db371e0b43baf3e2d21b3e865a20 | 2021-05-20T20:55:33.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"chemistry",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/ChemBERTa-zinc-base-v1 | 109,134 | 1 | transformers | ---
tags:
- chemistry
---
# ChemBERTa: Training a BERT-like transformer model for masked language modelling of chemical SMILES strings.
Deep learning for chemistry and materials science remains a novel field with lots of potiential. However, the popularity of transfer learning based methods in areas such as NLP and ... | [
-0.0813416913151741,
-0.07240394502878189,
0.011160604655742645,
0.04748031869530678,
0.06129339709877968,
0.04848739877343178,
0.006175683811306953,
0.056120213121175766,
0.002443299861624837,
-0.040323130786418915,
-0.024465953931212425,
-0.06596793979406357,
0.022704850882291794,
0.0441... |
vinai/bertweet-base | f9365bd897a63399564af0859aa981deb6deb0f3 | 2022-06-08T04:43:30.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vinai | null | vinai/bertweet-base | 108,322 | 11 | transformers | # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.... | [
-0.06569212675094604,
-0.08119648694992065,
0.05748256295919418,
0.04152612015604973,
0.0379173643887043,
0.05656646192073822,
-0.01779285818338394,
0.11296234279870987,
0.08408951014280319,
0.024929603561758995,
0.013933742418885231,
0.029724139720201492,
0.01992405392229557,
0.0383222699... |
Sahajtomar/German_Zeroshot | d5b0a26665b8538bcb3faa1e63a634cca4c8ee1b | 2021-05-18T22:22:18.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"dataset:xnli",
"transformers",
"nli",
"xnli",
"de",
"zero-shot-classification"
] | zero-shot-classification | false | Sahajtomar | null | Sahajtomar/German_Zeroshot | 108,175 | 9 | transformers | ---
language: multilingual
tags:
- text-classification
- pytorch
- nli
- xnli
- de
datasets:
- xnli
pipeline_tag: zero-shot-classification
widget:
- text: "Letzte Woche gab es einen Selbstmord in einer nahe gelegenen kolonie"
candidate_labels: "Verbrechen,Tragödie,Stehlen"
hypothesis_template: "In deisem geht es u... | [
-0.0344291627407074,
-0.012087993323802948,
0.045680928975343704,
-0.035287875682115555,
0.08552531152963638,
0.005076405126601458,
-0.028688961640000343,
0.028281506150960922,
0.018721528351306915,
-0.06362123787403107,
0.04842083156108856,
-0.14200611412525177,
-0.024452639743685722,
0.0... |
bert-base-german-dbmdz-uncased | 78540bb3887dee683eb7d1dc01abe831fe2d1b6d | 2022-07-18T20:04:08.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-base-german-dbmdz-uncased | 106,927 | 2 | transformers | ---
language: de
license: mit
---
This model is the same as [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-uncased) for details on the model.
| [
-0.1357068121433258,
-0.03634311631321907,
0.05395318940281868,
-0.03713318333029747,
0.006664438638836145,
0.040192633867263794,
-0.0038049593567848206,
0.05351388081908226,
0.00047776056453585625,
-0.049386877566576004,
0.03711847960948944,
0.005193071439862251,
0.0064083547331392765,
0.... |
smanjil/German-MedBERT | b4e8a3e260ca938390616816402ab23d98775b07 | 2022-06-13T16:52:46.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"German",
"autotrain_compatible"
] | fill-mask | false | smanjil | null | smanjil/German-MedBERT | 106,645 | 3 | transformers | ---
language: de
tags:
- exbert
- German
---
<a href="https://huggingface.co/exbert/?model=smanjil/German-MedBERT">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
# German Medical BERT
This is a fine-tuned model on the Medical domain for the German language and based on German B... | [
-0.12565124034881592,
-0.038943324238061905,
0.006589245516806841,
0.012073183432221413,
0.03546380624175072,
0.034386347979307175,
0.051677409559488297,
0.05742354691028595,
-0.017070000991225243,
-0.04339378699660301,
-0.04357986897230148,
-0.06243818998336792,
0.006701474543660879,
0.03... |
facebook/contriever | 2bd46a25019aeea091fd42d1f0fd4801675cf699 | 2022-01-19T17:23:28.000Z | [
"pytorch",
"bert",
"arxiv:2112.09118",
"transformers"
] | null | false | facebook | null | facebook/contriever | 104,025 | 1 | transformers | This model has been trained without supervision following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever.
## Usage (HuggingFace Tr... | [
-0.05657369643449783,
-0.021640293300151825,
0.02512027695775032,
0.10172487795352936,
0.03167951479554176,
0.03517802432179451,
-0.08030934631824493,
0.007716708816587925,
0.0022110058926045895,
-0.09741324931383133,
0.011567743495106697,
-0.06298261880874634,
0.030807092785835266,
0.0103... |
T-Systems-onsite/cross-en-de-roberta-sentence-transformer | 17bdd111a862ec99279be149fc9efa4f9122bcc1 | 2022-06-16T18:13:54.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"de",
"en",
"dataset:STSbenchmark",
"arxiv:1908.10084",
"transformers",
"sentence_embedding",
"search",
"roberta",
"xlm-r-distilroberta-base-paraphrase-v1",
"paraphrase",
"license:mit"
] | feature-extraction | false | T-Systems-onsite | null | T-Systems-onsite/cross-en-de-roberta-sentence-transformer | 103,768 | 4 | transformers | ---
language:
- de
- en
license: mit
tags:
- sentence_embedding
- search
- pytorch
- xlm-roberta
- roberta
- xlm-r-distilroberta-base-paraphrase-v1
- paraphrase
datasets:
- STSbenchmark
metrics:
- Spearman’s rank correlation
- cosine similarity
---
# Cross English & German RoBERTa for Sentence Embeddings
This model... | [
-0.04831644147634506,
-0.126850426197052,
-0.045794256031513214,
-0.0041112652979791164,
0.03964565694332123,
0.10871448367834091,
-0.00349864037707448,
0.09788155555725098,
0.018154669553041458,
-0.03142061084508896,
0.05150989070534706,
-0.012458089739084244,
0.0703083798289299,
0.087783... |
typeform/distilbert-base-uncased-mnli | 996dacf8ea284d96ea21f88a345fd7d597de1f1f | 2022-06-24T15:43:48.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"en",
"dataset:multi_nli",
"arxiv:1910.09700",
"arxiv:2105.09680",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | false | typeform | null | typeform/distilbert-base-uncased-mnli | 102,610 | 11 | transformers | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- distilbert
datasets:
- multi_nli
metrics:
- accuracy
---
# DistilBERT base model (uncased)
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitat... | [
-0.060269664973020554,
-0.0802665501832962,
0.041652075946331024,
0.023911003023386,
0.11187083274126053,
0.019362909719347954,
-0.024097135290503502,
0.02622562274336815,
-0.02960740588605404,
-0.05765555799007416,
-0.008194196037948132,
-0.13099536299705505,
-0.03553055599331856,
0.03455... |
cardiffnlp/twitter-roberta-base-offensive | afebc0c0c4c58177a8e6ab683c25beffeb351135 | 2021-05-20T15:05:00.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-offensive | 101,518 | 4 | transformers | # Twitter-roBERTa-base for Offensive Language Identification
This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval of... | [
-0.11479626595973969,
-0.021624604240059853,
-0.030052293092012405,
-0.0000351412090822123,
0.009456763043999672,
0.029818087816238403,
0.029994076117873192,
0.02051388844847679,
-0.009232142940163612,
-0.04400203749537468,
-0.009318134747445583,
-0.05021286383271217,
0.04367575794458389,
... |
openai-gpt | b3ab1942f7090e287d001cec22331dfc2764acf0 | 2022-07-22T07:57:33.000Z | [
"pytorch",
"tf",
"rust",
"openai-gpt",
"text-generation",
"en",
"arxiv:1705.11168",
"arxiv:1803.02324",
"arxiv:1910.09700",
"transformers",
"license:mit"
] | text-generation | false | null | null | openai-gpt | 100,975 | 7 | transformers | ---
language: en
license: mit
---
# OpenAI GPT
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Enviro... | [
-0.1161234900355339,
-0.01394845824688673,
-0.07760491222143173,
0.05700996145606041,
0.09085343033075333,
0.0009555137366987765,
-0.004831818863749504,
0.09073350578546524,
0.023012226447463036,
-0.05179548263549805,
0.001585855963639915,
0.05573728308081627,
0.0191801730543375,
0.0259901... |
DeepPavlov/distilrubert-base-cased-conversational | 9b4f8c20bdc51934ef2ef586ef9afee85549cccb | 2022-05-06T11:58:43.000Z | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | false | DeepPavlov | null | DeepPavlov/distilrubert-base-cased-conversational | 100,957 | 1 | transformers | ---
language:
- ru
---
# distilrubert-base-cased-conversational
Conversational DistilRuBERT \(Russian, cased, 6‑layer, 768‑hidden, 12‑heads, 135.4M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational R... | [
-0.07091540098190308,
-0.08183331042528152,
0.06094484031200409,
-0.055882468819618225,
-0.014533385634422302,
0.007880049757659435,
0.043203290551900864,
0.010193640366196632,
0.06719712167978287,
-0.07926148176193237,
-0.014701633714139462,
-0.05027836188673973,
0.017756387591362,
0.0468... |
Helsinki-NLP/opus-mt-en-fr | a8fbc1c711cb6263e8a20c5229b210cc05c57ff0 | 2021-09-09T21:35:24.000Z | [
"pytorch",
"jax",
"marian",
"text2text-generation",
"en",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-fr | 100,160 | 3 | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-fr
* source languages: en
* target languages: fr
* OPUS readme: [en-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... | [
-0.05990223586559296,
-0.025185512378811836,
0.025098320096731186,
-0.011482293717563152,
0.011111912317574024,
0.1011611595749855,
-0.05854257196187973,
0.036521341651678085,
0.027134954929351807,
-0.011700334958732128,
0.007930274121463299,
-0.04669066146016121,
-0.0748048722743988,
-0.0... |
hf-internal-testing/tiny-random-bart | f2efe525625e508121ef8e13b7c37e6324073378 | 2021-11-18T11:36:51.000Z | [
"pytorch",
"tf",
"bart",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-bart | 100,122 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
microsoft/mpnet-base | 5b7474c98ab5f1801502f9d2348485acf4cbbe71 | 2020-12-03T15:59:01.000Z | [
"pytorch",
"tf",
"mpnet",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/mpnet-base | 96,566 | 8 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
ramsrigouthamg/t5-large-paraphraser-diverse-high-quality | 443d721ecccee1cb38cce6f50cfd6c15e44e6ea0 | 2021-09-21T05:21:49.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ramsrigouthamg | null | ramsrigouthamg/t5-large-paraphraser-diverse-high-quality | 96,120 | 11 | transformers | Blog post with more details as well as easy to use Google Colab link: https://towardsdatascience.com/high-quality-sentence-paraphraser-using-transformers-in-nlp-c33f4482856f
!pip install transformers==4.10.2
!pip install sentencepiece==0.1.96
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model =... | [
-0.005755807738751173,
-0.05416945368051529,
0.012997898273169994,
0.033411744982004166,
-0.004421730525791645,
0.04164272919297218,
0.03511586785316467,
0.0584840402007103,
-0.008097563870251179,
-0.04418794810771942,
0.018334807828068733,
-0.06398232281208038,
-0.014875317923724651,
0.02... |
dbmdz/distilbert-base-turkish-cased | 2fffa20b389e66113ec7182349efdec00fce1ff5 | 2021-01-24T01:01:22.000Z | [
"pytorch",
"tf",
"distilbert",
"tr",
"arxiv:1910.01108",
"transformers",
"license:mit"
] | null | false | dbmdz | null | dbmdz/distilbert-base-turkish-cased | 95,636 | 5 | transformers | ---
language: tr
license: mit
---
# 🤗 + 📚 dbmdz Distilled Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a (cased) distilled model for Turkish 🎉
# 🇹🇷 DistilBERTurk
DistilBERTurk is a community-driven cased distilled BERT model for Turkish.
... | [
-0.16550016403198242,
-0.04142226651310921,
0.09789126366376877,
0.03333541378378868,
-0.041793908923864365,
-0.03142624348402023,
-0.029281994327902794,
0.07391227036714554,
0.011119754984974861,
0.005246177781373262,
-0.01358326431363821,
0.0446816012263298,
0.03053460642695427,
0.041921... |
Vamsi/T5_Paraphrase_Paws | 3bbf07dc42d5ddc9ca77c5589ce7239b0b731832 | 2021-06-23T11:39:51.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"transformers",
"paraphrase-generation",
"text-generation",
"Conditional Generation",
"autotrain_compatible"
] | text-generation | false | Vamsi | null | Vamsi/T5_Paraphrase_Paws | 95,004 | 10 | transformers | ---
language: "en"
tags:
- paraphrase-generation
- text-generation
- Conditional Generation
inference: false
---
# Paraphrase-Generation
## Model description
T5 Model for generating paraphrases of english sentences. Trained on the [Google PAWS](https://github.com/google-research-datasets/paws) dataset.
## How t... | [
-0.08339658379554749,
-0.055045221000909805,
-0.0016387255163863301,
0.03436153009533882,
0.02389637753367424,
0.031027978286147118,
0.028065722435712814,
0.021822508424520493,
-0.011508576571941376,
-0.03279034048318863,
0.053110163658857346,
-0.10455069690942764,
0.006744164973497391,
-0... |
unc-nlp/lxmert-base-uncased | 628572c96242d1496147beec1c13a1bb7869605d | 2021-03-10T02:39:25.000Z | [
"pytorch",
"tf",
"lxmert",
"feature-extraction",
"transformers"
] | feature-extraction | false | unc-nlp | null | unc-nlp/lxmert-base-uncased | 94,868 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
ThatSkyFox/DialoGPT-small-joshua | b42987a9651745cfa1112354b16a1c454492045f | 2021-10-24T17:12:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ThatSkyFox | null | ThatSkyFox/DialoGPT-small-joshua | 94,281 | null | transformers | ---
tags:
- conversational
---
#This is a chatbot trained on the transcript of the game "The World Ends with You" | [
-0.06860075891017914,
0.0067749531008303165,
0.013390419073402882,
-0.04155468940734863,
0.12091764807701111,
-0.049388106912374496,
0.11982014030218124,
0.0010361233726143837,
0.015452701598405838,
-0.03549348935484886,
-0.03986160084605217,
-0.03426223248243332,
-0.04668206349015236,
0.0... |
RUCAIBox/mvp | c1d9aeb879f3079101f716f1f6e7109fdd18b4e9 | 2022-06-27T02:27:44.000Z | [
"pytorch",
"mvp",
"en",
"arxiv:2206.12131",
"transformers",
"text-generation",
"text2text-generation",
"summarization",
"conversational",
"license:apache-2.0"
] | text2text-generation | false | RUCAIBox | null | RUCAIBox/mvp | 93,960 | 1 | transformers | ---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
- summarization
- conversational
pipeline_tag: text2text-generation
widget:
- text: "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons."
example_title: "Summarization"
- t... | [
-0.04416974261403084,
0.0939088836312294,
-0.02074822410941124,
0.02610909566283226,
0.0117813590914011,
0.0019433783600106835,
0.063712477684021,
-0.02597101591527462,
0.008997143246233463,
-0.027342600747942924,
-0.026317814365029335,
-0.08198592811822891,
0.06761923432350159,
0.00539092... |
Davlan/bert-base-multilingual-cased-ner-hrl | 6f69c39cadcdba0ab1401fb1f164964e7557e471 | 2022-06-25T17:01:26.000Z | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"de",
"en",
"es",
"fr",
"it",
"lv",
"nl",
"pt",
"zh",
"multilingual",
"transformers",
"autotrain_compatible"
] | token-classification | false | Davlan | null | Davlan/bert-base-multilingual-cased-ner-hrl | 93,549 | 9 | transformers | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# bert-base-multilingual-cased-ner-hrl
## Model description
**bert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French,... | [
-0.10604339092969894,
-0.0215167086571455,
0.03517872095108032,
-0.060515500605106354,
0.0018764589913189411,
0.01565885916352272,
0.013401291333138943,
-0.012043947353959084,
0.07034943997859955,
-0.04905080795288086,
-0.01047432515770197,
-0.07247880846261978,
0.034372761845588684,
0.064... |
google/mt5-base | d86816880b5acc27e697e52bc237e816dc828b17 | 2022-05-27T15:05:12.000Z | [
"pytorch",
"tf",
"jax",
"mt5",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
... | text2text-generation | false | google | null | google/mt5-base | 93,236 | 29 | transformers | ---
language:
- multilingual
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- hi
- hmn
- ht
- hu
- hy
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
-... | [
-0.08226951956748962,
0.05192454159259796,
-0.016503630205988884,
-0.09610544890165329,
0.06950594484806061,
-0.05049009621143341,
0.05426013469696045,
-0.043981023132801056,
-0.01146115455776453,
0.004685561638325453,
0.12800902128219604,
-0.053880609571933746,
0.06841910630464554,
-0.065... |
microsoft/MiniLM-L12-H384-uncased | 44acabbec0ef496f6dbc93adadea57f376b7c0ec | 2021-05-19T23:29:48.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"arxiv:2002.10957",
"arxiv:1810.04805",
"transformers",
"text-classification",
"license:mit"
] | text-classification | false | microsoft | null | microsoft/MiniLM-L12-H384-uncased | 92,474 | 14 | transformers | ---
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression o... | [
-0.06456600874662399,
0.004887367598712444,
0.030243154615163803,
0.05303221940994263,
0.034675393253564835,
0.03362272307276726,
-0.043396949768066406,
0.02993580512702465,
-0.04191944748163223,
0.02595207467675209,
0.04943547025322914,
-0.011186229065060616,
0.00046814687084406614,
0.028... |
sentence-transformers/distiluse-base-multilingual-cased | 47709c6fb2c53d30c871b05b8fb2693a5428d96a | 2022-06-21T14:55:22.000Z | [
"pytorch",
"tf",
"rust",
"distilbert",
"feature-extraction",
"multilingual",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/distiluse-base-multilingual-cased | 91,678 | 2 | sentence-transformers | ---
pipeline_tag: sentence-similarity
language: multilingual
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/distiluse-base-multilingual-cased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & par... | [
-0.04108471795916557,
-0.061429865658283234,
-0.006932356394827366,
0.01849515549838543,
-0.003646940691396594,
0.028386162593960762,
-0.05174849182367325,
0.032794129103422165,
0.021134624257683754,
-0.08399990946054459,
0.03827538713812828,
-0.03729553520679474,
0.04909021034836769,
0.07... |
joeddav/bart-large-mnli-yahoo-answers | d836606b3cf20652cf30283d6884ae26a11e5392 | 2021-06-14T10:44:33.000Z | [
"pytorch",
"jax",
"bart",
"text-classification",
"en",
"dataset:yahoo-answers",
"arxiv:1909.00161",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | false | joeddav | null | joeddav/bart-large-mnli-yahoo-answers | 91,462 | 3 | transformers | ---
language: en
tags:
- text-classification
- pytorch
datasets:
- yahoo-answers
pipeline_tag: zero-shot-classification
---
# bart-lage-mnli-yahoo-answers
## Model Description
This model takes [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) and fine-tunes it on Yahoo Answers topic classif... | [
-0.01935061439871788,
-0.015434669330716133,
-0.021476812660694122,
0.011097761802375317,
0.13664862513542175,
0.04893501475453377,
0.011849159374833107,
-0.02042365074157715,
-0.004281753208488226,
-0.08898960798978806,
0.0035826319362968206,
-0.06178048625588417,
0.014457127079367638,
0.... |
squeezebert/squeezebert-uncased | 7978b0c163f11850ec35d5cd541828159313ac41 | 2020-12-11T22:02:17.000Z | [
"pytorch",
"squeezebert",
"arxiv:2006.11316",
"arxiv:1904.00962",
"transformers"
] | null | false | squeezebert | null | squeezebert/squeezebert-uncased | 90,492 | 0 | transformers | language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in [this paper](https://arx... | [
-0.09591016918420792,
-0.12046685814857483,
0.07926211506128311,
-0.0030952917877584696,
0.02112545259296894,
0.05768368020653725,
-0.027904780581593513,
-0.013985192403197289,
0.033310938626527786,
-0.05282970890402794,
0.06370357424020767,
0.044342607259750366,
0.03635277599096298,
0.025... |
philschmid/distilbart-cnn-12-6-samsum | a823ff6685caf2db51911e2ad903aa55e5defb29 | 2022-07-26T20:01:15.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:samsum",
"transformers",
"sagemaker",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | philschmid | null | philschmid/distilbart-cnn-12-6-samsum | 89,618 | 4 | transformers |
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
datasets:
- samsum
widget:
- text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n\
Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:\
\ ok.\nJeff: and how can I get started? \... | [
-0.11430894583463669,
-0.06454987823963165,
0.0032303265761584044,
0.004364465828984976,
0.042118385434150696,
0.03602240979671478,
-0.06017529219388962,
-0.020737679675221443,
-0.04520484432578087,
-0.005396725609898567,
0.03522118926048279,
-0.07559877634048462,
0.013287895359098911,
-0.... |
distilbert-base-uncased-distilled-squad | 3ab4b58da34f3a23bbdd2626fc58ee6c91f9890b | 2022-07-22T08:03:29.000Z | [
"pytorch",
"tf",
"tflite",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | null | null | distilbert-base-uncased-distilled-squad | 89,003 | 10 | transformers | ---
language: en
datasets:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also know... | [
0.15358218550682068,
-0.03427616506814957,
-0.02505485527217388,
-0.031192146241664886,
0.09076983481645584,
-0.016289588063955307,
-0.041570983827114105,
-0.011634811758995056,
0.07473573088645935,
0.09780681133270264,
-0.0027801471296697855,
-0.08701010048389435,
-0.03494945541024208,
0.... |
facebook/dpr-reader-single-nq-base | 5114ec3299284784702848f7d4e598c59df1a35c | 2020-11-25T16:59:53.000Z | [
"pytorch",
"tf",
"dpr",
"transformers"
] | null | false | facebook | null | facebook/dpr-reader-single-nq-base | 88,755 | 1 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
Davlan/xlm-roberta-large-ner-hrl | 690312971788985228c98683d4b816fbf026b346 | 2022-06-27T10:49:56.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"ar",
"de",
"en",
"es",
"fr",
"it",
"lv",
"nl",
"pt",
"zh",
"multilingual",
"transformers",
"autotrain_compatible"
] | token-classification | false | Davlan | null | Davlan/xlm-roberta-large-ner-hrl | 88,660 | 3 | transformers | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# xlm-roberta-large-ner-hrl
## Model description
**xlm-roberta-large-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dut... | [
-0.09928802400827408,
-0.02726970985531807,
-0.015378586016595364,
-0.021642033010721207,
-0.0013988417340442538,
0.025329966098070145,
-0.0006954127456992865,
-0.031166501343250275,
0.043237730860710144,
-0.050213512033224106,
-0.0022258777171373367,
-0.06777866929769516,
0.0329110175371170... |
patrickvonplaten/t5-tiny-random | a3735d6adf1f23b7c32e6622fd6da7bc46d7f123 | 2021-11-03T17:13:16.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | patrickvonplaten | null | patrickvonplaten/t5-tiny-random | 88,297 | 1 | transformers | [
-0.11883839219808578,
0.04829875007271767,
-0.0025480713229626417,
-0.011011119931936264,
0.05195086821913719,
0.010291781276464462,
0.11543325334787369,
0.0007007101085036993,
-0.08592551946640015,
-0.07065412402153015,
0.0013317831326276064,
-0.03547239303588867,
0.018434111028909683,
-0... | |
hf-internal-testing/tiny-random-mbart | 63c7077c54936948aeb5a675e10489c945957824 | 2021-12-28T12:24:41.000Z | [
"pytorch",
"tf",
"mbart",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-mbart | 88,207 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
EleutherAI/gpt-neo-2.7B | 51568a6e0ae813a3f2a9da558ab7beac5e3acc24 | 2021-12-31T13:46:21.000Z | [
"pytorch",
"jax",
"rust",
"gpt_neo",
"text-generation",
"en",
"dataset:The Pile",
"transformers",
"text generation",
"causal-lm",
"license:apache-2.0"
] | text-generation | false | EleutherAI | null | EleutherAI/gpt-neo-2.7B | 87,789 | 86 | transformers | ---
language:
- en
tags:
- text generation
- pytorch
- causal-lm
license: apache-2.0
datasets:
- The Pile
---
# GPT-Neo 2.7B
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the num... | [
-0.11035904288291931,
-0.0736161544919014,
0.0017591755604371428,
0.049754057079553604,
0.07570783793926239,
-0.0033338149078190327,
-0.028603561222553253,
0.017043255269527435,
-0.0011142523726448417,
-0.06447353959083557,
-0.07025180011987686,
-0.012435778975486755,
0.044310782104730606,
... |
allenai/scibert_scivocab_cased | 9be298ced05121c9e6e2b2cb9f508b47b8eae650 | 2021-05-19T11:40:28.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
] | null | false | allenai | null | allenai/scibert_scivocab_cased | 86,580 | 4 | transformers | # SciBERT
This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text.
The training corpus was papers taken from [Semantic Scholar](https://www.semanticscholar.org). Corpus size is 1.... | [
-0.06226271763443947,
-0.03768712654709816,
0.038678526878356934,
0.012727038934826851,
0.007553716655820608,
0.03183666244149208,
-0.033963095396757126,
0.052286069840192795,
0.0109660429880023,
-0.00013924833911005408,
0.031278062611818314,
0.026570413261651993,
0.033382706344127655,
0.0... |
mfeb/albert-xxlarge-v2-squad2 | e734d3529f402760605a24af13e02f6f092e96a0 | 2020-04-24T16:10:08.000Z | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | mfeb | null | mfeb/albert-xxlarge-v2-squad2 | 86,458 | 2 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
cmarkea/distilcamembert-base-sentiment | b7804e295dc3cf2aa8ce8cff83f22e0bdd249558 | 2022-05-24T15:56:45.000Z | [
"pytorch",
"tf",
"camembert",
"text-classification",
"fr",
"dataset:amazon_reviews_multi",
"dataset:allocine",
"transformers",
"license:mit"
] | text-classification | false | cmarkea | null | cmarkea/distilcamembert-base-sentiment | 86,119 | 7 | transformers | ---
language: fr
license: mit
datasets:
- amazon_reviews_multi
- allocine
widget:
- text: "Je pensais lire un livre nul, mais finalement je l'ai trouvé super !"
- text: "Cette banque est très bien, mais elle n'offre pas les services de paiements sans contact."
- text: "Cette banque est très bien et elle offre en plus l... | [
-0.08509080111980438,
-0.04192579165101051,
0.03317994251847267,
-0.04399668797850609,
0.06721599400043488,
0.06851593405008316,
0.02805706299841404,
0.019604304805397987,
0.11798758059740067,
-0.011242646723985672,
0.04094197228550911,
-0.060880355536937714,
0.07020800560712814,
-0.050841... |
dmis-lab/biobert-v1.1 | 551ca18efd7f052c8dfa0b01c94c2a8e68bc5488 | 2021-05-19T16:03:17.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | dmis-lab | null | dmis-lab/biobert-v1.1 | 85,159 | 6 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
DeepPavlov/rubert-base-cased-sentence | 78b5122d6365337dd4114281b0d08cd1edbb3bc8 | 2021-05-18T18:18:43.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1508.05326",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
] | feature-extraction | false | DeepPavlov | null | DeepPavlov/rubert-base-cased-sentence | 84,680 | 2 | transformers | ---
language:
- ru
---
# rubert-base-cased-sentence
Sentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) is a representation‑based sentence encoder for Russian. It is initialized with RuBERT and fine‑tuned on SNLI\[1\] google-translated to russian and on russian part of XNLI dev set\[2\... | [
-0.1096625030040741,
-0.08813684433698654,
0.015785368159413338,
0.018870815634727478,
0.06271202117204666,
0.09231587499380112,
0.04556342586874962,
0.06430148333311081,
0.00961476843804121,
-0.03852849453687668,
-0.04729653149843216,
-0.02376796118915081,
0.012658814899623394,
0.10643056... |
sshleifer/tiny-gpt2 | 5f91d94bd9cd7190a9f3216ff93cd1dd95f2c7be | 2021-05-23T12:55:11.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | sshleifer | null | sshleifer/tiny-gpt2 | 84,146 | 4 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
neuralmind/bert-large-portuguese-cased | aa302f6ea73b759f7df9cad58bd272127b67ec28 | 2021-05-20T01:31:09.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"pt",
"dataset:brWaC",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | neuralmind | null | neuralmind/bert-large-portuguese-cased | 83,959 | 13 | transformers | ---
language: pt
license: mit
tags:
- bert
- pytorch
datasets:
- brWaC
---
# BERTimbau Large (aka "bert-large-portuguese-cased")
## Introduction
BERTimbau Large is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performa... | [
-0.05875744670629501,
-0.07558168470859528,
0.02786586619913578,
-0.004597230348736048,
-0.015285303816199303,
0.12218151241540909,
-0.06774542480707169,
0.11221165955066681,
0.002904575550928712,
0.02148512937128544,
0.0008097945828922093,
-0.06468662619590759,
-0.07042493671178818,
0.075... |
google/bert_uncased_L-12_H-768_A-12 | cc478efa3482fefe275eb2733363db9713d499ef | 2021-05-19T17:27:43.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-12_H-768_A-12 | 83,694 | 2 | transformers | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... | [
-0.02777470275759697,
-0.02693094126880169,
0.07438826560974121,
0.03228488564491272,
-0.0023304771166294813,
0.018128493800759315,
-0.06253628432750702,
0.0994548574090004,
-0.014644814655184746,
0.018868697807192802,
-0.015814494341611862,
0.03585591912269592,
0.03645862638950348,
0.0455... |
Helsinki-NLP/opus-mt-ja-en | 6282eb0555cd0253dc9fac00c5fafb2825ad04b4 | 2021-09-10T13:53:12.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ja",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ja-en | 83,179 | 6 | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ja-en
* source languages: ja
* target languages: en
* OPUS readme: [ja-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ja-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... | [
-0.06432453542947769,
-0.014627905562520027,
0.028839394450187683,
-0.015565412119030952,
0.004527274519205093,
0.1018657237291336,
-0.05753038451075554,
0.03512683883309364,
0.018153801560401917,
-0.015504163689911366,
0.0149066848680377,
-0.03349577635526657,
-0.0594843365252018,
-0.0303... |
sonoisa/sentence-bert-base-ja-mean-tokens | c5db458007569cea1374d4b5766193832c3fc285 | 2021-12-14T11:43:43.000Z | [
"pytorch",
"fill-mask",
"ja",
"sentence-transformers",
"sentence-bert",
"feature-extraction",
"sentence-similarity",
"license:cc-by-sa-4.0"
] | feature-extraction | false | sonoisa | null | sonoisa/sentence-bert-base-ja-mean-tokens | 82,730 | 3 | sentence-transformers | ---
language: ja
license: cc-by-sa-4.0
tags:
- sentence-transformers
- sentence-bert
- feature-extraction
- sentence-similarity
---
This is a Japanese sentence-BERT model.
日本語用Sentence-BERTモデル（バージョン1）です。
※: 精度が1.5ポイントほど向上した[バージョン2モデル](https://huggingface.co/sonoisa/sentence-bert-base-ja-mean-tokens-v2)もあります。
# 解説
... | [
-0.11660256236791611,
0.0061948467046022415,
0.06344430148601532,
0.008880695328116417,
-0.012143644504249096,
0.030758801847696304,
0.05637551471590996,
0.0679168850183487,
0.006136115174740553,
-0.06035778298974037,
0.07822864502668381,
-0.06868240237236023,
0.011276966892182827,
0.07586... |
pyannote/embedding | 09a3ed256d0fddbf5616fd9fb5db917fcf002708 | 2022-03-23T09:24:30.000Z | [
"pytorch",
"tensorboard",
"dataset:voxceleb",
"pyannote-audio",
"pyannote",
"pyannote-audio-model",
"audio",
"voice",
"speech",
"speaker",
"speaker-recognition",
"speaker-verification",
"speaker-identification",
"speaker-embedding",
"license:mit"
] | null | false | pyannote | null | pyannote/embedding | 82,716 | 4 | pyannote-audio | ---
tags:
- pyannote
- pyannote-audio
- pyannote-audio-model
- audio
- voice
- speech
- speaker
- speaker-recognition
- speaker-verification
- speaker-identification
- speaker-embedding
datasets:
- voxceleb
license: mit
inference: false
---
# 🎹 Speaker embedding
Relies on pyannote.audio 2.0 currently in development... | [
-0.11622641235589981,
-0.07014607638120651,
-0.06214850768446922,
-0.10208682715892792,
0.040338486433029175,
0.01847350411117077,
-0.01817064732313156,
-0.03924247995018959,
-0.008549445308744907,
-0.06353478878736496,
0.01645846664905548,
-0.03550684079527855,
-0.05862465873360634,
0.054... |
sentence-transformers/all-distilroberta-v1 | 57dd5d5be528ba968ef928103d92f95afc487e68 | 2022-07-11T21:04:19.000Z | [
"pytorch",
"rust",
"roberta",
"fill-mask",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:MS Marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:code_search_net",
"dataset:search_qa",
"dataset:eli5",
"dataset:snli",
"dataset:multi_nli"... | sentence-similarity | false | sentence-transformers | null | sentence-transformers/all-distilroberta-v1 | 81,479 | 4 | sentence-transformers | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- MS Marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- nat... | [
-0.08612792193889618,
-0.060601916164159775,
-0.00894971750676632,
-0.01770220510661602,
0.05812236666679382,
0.0735861137509346,
-0.014823758974671364,
0.017490435391664505,
0.020216738805174828,
-0.036188364028930664,
0.05180221050977707,
-0.01903282292187214,
0.050146929919719696,
0.058... |
sentence-transformers/distilbert-base-nli-stsb-mean-tokens | 888433dbfd0f07dc22d9e038d9acb20e5ca7e0d5 | 2022-06-15T20:07:20.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/distilbert-base-nli-stsb-mean-tokens | 81,248 | 4 | sentence-transformers | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... | [
-0.05621979013085365,
-0.06729768216609955,
0.010952773503959179,
0.03578962758183479,
0.015302282758057117,
0.06508829444646835,
-0.02364686131477356,
0.06663063913583755,
0.01284108217805624,
-0.09828194230794907,
0.04485175758600235,
0.011912762187421322,
0.056274425238370895,
0.0615364... |
vinai/phobert-base | 667b55927a1571811539f27c0f374429a1c75759 | 2022-06-08T04:44:26.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2003.00744",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vinai | null | vinai/phobert-base | 81,225 | 6 | transformers | # <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first publ... | [
-0.02316584624350071,
-0.06331945210695267,
0.016042284667491913,
0.03249581903219223,
0.019429268315434456,
0.09637048095464706,
-0.06447488814592361,
0.03405490517616272,
0.030829444527626038,
0.014013518579304218,
0.046114854514598846,
-0.019820937886834145,
-0.04403949901461601,
0.0508... |
Helsinki-NLP/opus-mt-en-es | c8b63c83f30e46417ce423a585f9b9e20e1b877d | 2021-07-13T16:24:56.000Z | [
"pytorch",
"jax",
"marian",
"text2text-generation",
"en",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-es | 80,022 | 7 | transformers | ---
language:
- en
- es
tags:
- translation
license: apache-2.0
---
### eng-spa
* source group: English
* target group: Spanish
* OPUS readme: [eng-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-spa/README.md)
* model: transformer
* source language(s): eng
* target language(s): ... | [
-0.06966234743595123,
-0.034144289791584015,
0.004877227358520031,
0.013180157169699669,
-0.00041228102054446936,
0.02620140090584755,
-0.03311961516737938,
-0.018366381525993347,
0.027249740436673164,
-0.02891036495566368,
0.023400193080306053,
-0.08813272416591644,
-0.026132328435778618,
... |
nreimers/BERT-Tiny_L-2_H-128_A-2 | 9cb03776b08d300ae73aa6ba4860a760c606f62d | 2021-05-28T11:05:21.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | nreimers | null | nreimers/BERT-Tiny_L-2_H-128_A-2 | 79,688 | null | transformers | This is the BERT-Medium model from Google: https://github.com/google-research/bert#bert. A BERT model with 2 layers, 128 hidden unit size, and 2 attention heads. | [
-0.06408871710300446,
-0.0558897964656353,
-0.000056679651606827974,
0.04218868911266327,
-0.026718584820628166,
0.020316045731306076,
0.015228485688567162,
0.06567509472370148,
0.02856004238128662,
0.00876308511942625,
0.06492961198091507,
-0.02582830749452114,
0.015215352177619934,
0.054... |
cointegrated/rubert-tiny | f191937ca7511ae76b44b06a460f18ab1699c54b | 2022-01-28T11:42:37.000Z | [
"pytorch",
"bert",
"pretraining",
"ru",
"en",
"transformers",
"russian",
"fill-mask",
"embeddings",
"masked-lm",
"tiny",
"feature-extraction",
"sentence-similarity",
"license:mit"
] | feature-extraction | false | cointegrated | null | cointegrated/rubert-tiny | 79,161 | 9 | transformers | ---
language: ["ru", "en"]
tags:
- russian
- fill-mask
- pretraining
- embeddings
- masked-lm
- tiny
- feature-extraction
- sentence-similarity
license: mit
widget:
- text: "Миниатюрная модель для [MASK] разных задач."
---
This is a very small distilled version of the [bert-base-multilingual-cased](https://huggingface.... | [
-0.0647537037730217,
-0.0959482342004776,
-0.004717791453003883,
-0.00015283514221664518,
-0.009959021583199501,
0.042953941971063614,
0.031484272330999374,
0.07571440190076828,
-0.001052135950885713,
-0.03104425221681595,
-0.02722579799592495,
0.03990322723984718,
0.02363264001905918,
0.1... |
hfl/chinese-bert-wwm | ab0aa81da273504efc8540aa4d0bbaa3016a1bb5 | 2021-05-19T19:07:49.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | hfl | null | hfl/chinese-bert-wwm | 78,869 | 18 | transformers | ---
language:
- zh
license: "apache-2.0"
---
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cu... | [
-0.13572391867637634,
-0.0475379079580307,
0.08086962252855301,
0.03534784913063049,
-0.030565138906240463,
0.011088831350207329,
-0.00047960912343114614,
-0.031159551814198494,
-0.03220145404338837,
0.007688344921916723,
0.046967726200819016,
-0.040497053414583206,
0.02268795855343342,
0.... |
facebook/wav2vec2-base | 0b5b8e868dd84f03fd87d01f9c4ff0f080fecfe8 | 2021-12-28T12:44:31.000Z | [
"pytorch",
"wav2vec2",
"pretraining",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"transformers",
"speech",
"license:apache-2.0"
] | null | false | facebook | null | facebook/wav2vec2-base | 78,091 | 14 | transformers | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Base
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech... | [
-0.04661073163151741,
-0.14737346768379211,
-0.022453952580690384,
-0.06520354747772217,
-0.03890654072165489,
0.016162358224391937,
-0.03662024810910225,
-0.04336774721741676,
-0.04944242164492607,
-0.06637085974216461,
0.006718456745147705,
-0.060948893427848816,
-0.016698786988854408,
-... |
microsoft/deberta-v3-small | 23bfba973812a80178eb6c2c600e85cc461ffc2c | 2022-01-13T17:59:25.000Z | [
"pytorch",
"tf",
"deberta-v2",
"en",
"arxiv:2006.03654",
"arxiv:2111.09543",
"transformers",
"deberta",
"deberta-v3",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-v3-small | 78,067 | 14 | transformers | ---
language: en
tags:
- deberta
- deberta-v3
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa m... | [
-0.11638691276311874,
-0.1071627289056778,
0.016267353668808937,
-0.002027192385867238,
0.03834981471300125,
0.021732037886977196,
-0.03556564450263977,
-0.009589710272848606,
0.006394130643457174,
0.022164423018693924,
-0.003562737489119172,
0.02519911900162697,
-0.0384259931743145,
0.012... |
clue/albert_chinese_tiny | 654acaf73c361ad56e4f4b1e2bb0023cbb1872b2 | 2020-12-11T21:35:55.000Z | [
"pytorch",
"albert",
"zh",
"transformers"
] | null | false | clue | null | clue/albert_chinese_tiny | 77,354 | 5 | transformers | ---
language: zh
---
## albert_chinese_tiny
### Overview
**Language model:** albert-tiny
**Model size:** 16M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on do... | [
-0.06317080557346344,
-0.0023192905355244875,
0.01165094505995512,
0.08090061694383621,
0.016087960451841354,
0.041357699781656265,
0.029506178572773933,
0.07897283136844635,
-0.06243324652314186,
-0.07728925347328186,
0.04331088438630104,
-0.08255154639482498,
-0.0487600639462471,
0.04196... |
facebook/mbart-large-en-ro | 2534e987b9ed03c416bbbaefa1a39e3441439bdd | 2021-03-10T03:46:29.000Z | [
"pytorch",
"tf",
"mbart",
"en",
"ro",
"transformers",
"translation",
"license:mit"
] | translation | false | facebook | null | facebook/mbart-large-en-ro | 76,917 | null | transformers | ---
tags:
- translation
language:
- en
- ro
license: mit
---
### mbart-large-en-ro
This is mbart-large-cc25, finetuned on wmt_en_ro.
It scores BLEU 28.1 without post processing and BLEU 38 with postprocessing. Instructions in `romanian_postprocessing.md`
Original Code: https://github.com/pytorch/fairseq/tree/master... | [
-0.007621801923960447,
-0.01703798398375511,
-0.08073578774929047,
0.04123009741306305,
-0.04652228578925133,
-0.020721185952425003,
-0.10060228407382965,
0.03902320936322212,
-0.08615412563085556,
-0.004541575908660889,
0.006475736852735281,
-0.020761197432875633,
0.017573758959770203,
-0... |
Helsinki-NLP/opus-mt-ar-en | 9c5efffe6f69dcb65d7156f40dfa27b54be34258 | 2021-09-09T21:26:15.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"ar",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ar-en | 76,095 | 5 | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ar-en
* source languages: ar
* target languages: en
* OPUS readme: [ar-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ar-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... | [
-0.06272033601999283,
-0.017573334276676178,
0.017940470948815346,
-0.01280292309820652,
-0.0024173606652766466,
0.09677304327487946,
-0.05317144840955734,
0.021177278831601143,
0.014774358831346035,
-0.00803383719176054,
0.008448613807559013,
-0.033677179366350174,
-0.07955210655927658,
-... |
bert-large-uncased-whole-word-masking | 90d3333009848fae4860a5338419d17f70be940c | 2021-05-18T16:37:36.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-large-uncased-whole-word-masking | 74,654 | 3 | transformers | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (uncased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository]... | [
-0.09330075234174728,
-0.09092521667480469,
0.07883816212415695,
0.03750195354223251,
0.025480758398771286,
0.027773417532444,
0.0043360209092497826,
0.006080521270632744,
0.04459067061543465,
-0.009725834242999554,
0.04516946151852608,
-0.0059538534842431545,
0.05672499164938927,
0.038383... |
google/reformer-crime-and-punishment | 0e6c3decb8211d49bf881013425dc8b0448b3f5a | 2021-02-01T17:53:38.000Z | [
"pytorch",
"rust",
"reformer",
"text-generation",
"transformers"
] | text-generation | false | google | null | google/reformer-crime-and-punishment | 74,293 | null | transformers | ## Reformer Model trained on "Crime and Punishment"
Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English.
Crime and Punishment training data was taken from `gs://trax-ml/reformer/crime-and-punishment-2554.txt` and contains
roughly 0.5M tokens.
The ReformerLM model was trai... | [
-0.09176437556743622,
-0.0676511824131012,
-0.12951013445854187,
0.032020941376686096,
0.01595294289290905,
0.03415663540363312,
-0.04698619991540909,
0.06785151362419128,
0.03886549919843674,
-0.02937512844800949,
0.05438841134309769,
0.03398013114929199,
0.0456855446100235,
-0.0122421924... |
vennify/t5-base-grammar-correction | 9e4a09d21dca1072a69302df9261289d03c3ed78 | 2022-01-14T16:35:23.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:jfleg",
"arxiv:1702.04066",
"transformers",
"grammar",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | vennify | null | vennify/t5-base-grammar-correction | 74,162 | 16 | transformers | ---
language: en
tags:
- grammar
- text2text-generation
license: cc-by-nc-sa-4.0
datasets:
- jfleg
---
# T5 Grammar Correction
This model generates a revised version of inputted text with the goal of containing fewer grammatical errors.
It was trained with [Happy Transformer](https://github.com/EricFillion/happy-tr... | [
-0.01891413703560829,
0.0013203424168750644,
0.06944957375526428,
0.023219941183924675,
-0.046593450009822845,
-0.02740631066262722,
-0.01799030415713787,
0.025745172053575516,
-0.012283911928534508,
-0.04474150761961937,
0.013626646250486374,
-0.029597405344247818,
0.04667035490274429,
-0... |
deepset/roberta-large-squad2 | faa13e991d03fba7f6eb6a75356c4c0806e2588a | 2022-07-25T10:34:40.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/roberta-large-squad2 | 74,021 | 11 | transformers | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
--- | [
-0.02740214765071869,
-0.03384599834680557,
-0.04259829968214035,
-0.030564099550247192,
0.06767307221889496,
0.040373336523771286,
0.04175208508968353,
-0.03964615240693092,
-0.036700259894132614,
0.01088388729840517,
0.0984257310628891,
-0.1500645875930786,
-0.02741895616054535,
0.006139... |
Helsinki-NLP/opus-mt-ROMANCE-en | dd27a5df7623594b19ab50244084e2beddc2181c | 2021-09-09T21:25:31.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"roa",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ROMANCE-en | 73,229 | null | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ROMANCE-en
* source languages: fr,fr_BE,fr_CA,fr_FR,wa,frp,oc,ca,rm,lld,fur,lij,lmo,es,es_AR,es_CL,es_CO,es_CR,es_DO,es_EC,es_ES,es_GT,es_HN,es_MX,es_NI,es_PA,es_PE,es_PR,es_SV,es_UY,es_VE,pt,pt_br,pt_BR,pt_PT,gl,lad,an,mwl,it,it_IT,co,nap,scn,vec,sc,ro,la
*... | [
-0.04340076819062233,
-0.011738399043679237,
0.03005216456949711,
-0.05907583609223366,
0.06247638538479805,
0.0531720295548439,
0.05944265425205231,
0.020297039300203323,
0.05283963307738304,
0.027144793421030045,
-0.004421210382133722,
-0.04271046072244644,
-0.02203620783984661,
-0.02040... |
monologg/koelectra-small-v2-distilled-korquad-384 | 70c28f5b9e6b2bd05bb609f6be1f9f8ff918cd6f | 2020-06-04T17:39:49.000Z | [
"pytorch",
"tflite",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | monologg | null | monologg/koelectra-small-v2-distilled-korquad-384 | 72,726 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
Helsinki-NLP/opus-mt-en-zh | 93db7712e4698309ac17a80605adbf54dea5c8ee | 2021-09-09T21:40:41.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"marian",
"text2text-generation",
"en",
"zh",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-zh | 71,580 | 19 | transformers | ---
language:
- en
- zh
tags:
- translation
license: apache-2.0
---
### eng-zho
* source group: English
* target group: Chinese
* OPUS readme: [eng-zho](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-zho/README.md)
* model: transformer
* source language(s): eng
* target language(s): cjy... | [
-0.10756716132164001,
0.008526080287992954,
-0.007776136044412851,
-0.016940409317612648,
-0.003156449878588319,
0.024193666875362396,
-0.013154472224414349,
0.007966340519487858,
0.029635844752192497,
-0.02338504046201706,
0.06851661950349808,
-0.10727731883525848,
0.036675211042165756,
-... |
monologg/koelectra-small-v3-discriminator | 7488f8db0f208beff4a1f3f9bb3ed04650a89ed7 | 2020-12-26T16:24:33.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | monologg | null | monologg/koelectra-small-v3-discriminator | 70,824 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
deepset/xlm-roberta-base-squad2 | 8962e174ec8ad665bbf11edc2130487d2f7ea22a | 2022-07-25T07:17:34.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/xlm-roberta-base-squad2 | 70,545 | 10 | transformers | ---
datasets:
- squad_v2
license: cc-by-4.0
model-index:
- name: deepset/xlm-roberta-base-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- name: Exact Match
... | [
-0.09271793812513351,
-0.06556104123592377,
-0.04029972106218338,
0.004843738861382008,
0.018827809020876884,
0.03874773532152176,
-0.045168619602918625,
0.005241851322352886,
0.015665756538510323,
-0.025757595896720886,
0.01612682268023491,
-0.15172308683395386,
-0.015381364151835442,
0.0... |
roberta-large-mnli | 0dcbcf20673c006ac2d1e324954491b96f0c0015 | 2022-07-22T08:02:16.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"en",
"dataset:multi_nli",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:1806.02847",
"arxiv:1804.07461",
"arxiv:1704.05426",
"arxiv:1508.05326",
"arxiv:1809.05053",
"arxiv:1910.09700",
"transformers",
... | text-classification | false | null | null | roberta-large-mnli | 69,490 | 25 | transformers | ---
language:
- en
license: mit
tags:
- autogenerated-modelcard
datasets:
- multi_nli
- wikipedia
- bookcorpus
---
# roberta-large-mnli
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#ri... | [
-0.05529342591762543,
-0.05466151982545853,
0.01425489503890276,
0.050123561173677444,
0.09375286847352982,
0.05856576934456825,
0.004283801652491093,
0.010782178491353989,
-0.008327623829245567,
-0.04508131742477417,
-0.03435446321964264,
-0.06969651579856873,
-0.015518602915108204,
0.015... |
google/t5-v1_1-xxl | 6e80045f023a868fdb58e0b697d1ace5fe4880be | 2020-11-19T19:55:45.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-v1_1-xxl | 69,217 | 2 | transformers | ---
language: en
datasets:
- c4
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1
## Version 1.1
[T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the f... | [
-0.08697672933340073,
-0.03702358901500702,
0.06502795964479446,
0.006211201194673777,
0.059899959713220596,
0.04369475692510605,
-0.025000816211104393,
-0.05999826639890671,
-0.010968467220664024,
-0.09833592176437378,
0.03334726393222809,
0.0184017401188612,
-0.024979878216981888,
-0.026... |
hfl/chinese-roberta-wwm-ext-large | a25cc9e05974bd9687e528edd516f2cfdb3f5db9 | 2022-03-01T09:15:16.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | hfl | null | hfl/chinese-roberta-wwm-ext-large | 68,277 | 18 | transformers | ---
language:
- zh
tags:
- bert
license: "apache-2.0"
---
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word ... | [
-0.13892720639705658,
-0.03527410328388214,
0.06445440649986267,
0.030523041263222694,
0.0028055051807314157,
0.012470254674553871,
0.0003026134509127587,
-0.0201879795640707,
-0.03373470902442932,
-0.0031854051630944014,
0.07240064442157745,
-0.05555472522974014,
0.039492979645729065,
0.0... |
prajjwal1/bert-small | 0ec5f86f27c1a77d704439db5e01c307ea11b9d4 | 2021-10-27T18:31:52.000Z | [
"pytorch",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"transformers",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"license:mit"
] | null | false | prajjwal1 | null | prajjwal1/bert-small | 67,809 | 5 | transformers | ---
language:
- en
license:
- mit
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
---
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert).
This is one of the smaller ... | [
-0.09775897115468979,
-0.05270969867706299,
0.05881834775209427,
0.042393166571855545,
0.015701496973633766,
0.05437036231160164,
-0.011593913659453392,
0.06600289791822433,
-0.032027002424001694,
0.002709813416004181,
0.03395364060997963,
0.062217582017183304,
-0.04905374348163605,
0.0879... |
sshleifer/tiny-marian-en-de | 7a6b5b34785930445aeb20a9a34543b72de6e267 | 2020-06-25T02:27:15.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/tiny-marian-en-de | 67,303 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
microsoft/codebert-base-mlm | 5c927614b8750b556dcf569cf8a211fbe20f688a | 2021-05-20T17:47:48.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2002.08155",
"transformers",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/codebert-base-mlm | 66,627 | 8 | transformers | ## CodeBERT-base-mlm
Pretrained weights for [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://arxiv.org/abs/2002.08155).
### Training Data
The model is trained on the code corpus of [CodeSearchNet](https://github.com/github/CodeSearchNet)
### Training Objective
This model is initialized wi... | [
-0.10968580842018127,
-0.08500976115465164,
-0.03272177651524544,
0.04580762982368469,
-0.04101911559700966,
0.04154600575566292,
0.009745294228196144,
0.004895903170108795,
-0.035460591316223145,
-0.014520184136927128,
0.0050820475444197655,
-0.04072875902056694,
0.03522548824548721,
-0.0... |
nyust-eb210/braslab-bert-drcd-384 | abb9294fe9c0605d2f498a3228bfc6a30e8d2fbb | 2021-05-31T14:47:20.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"zh-tw",
"dataset:DRCD",
"transformers",
"autotrain_compatible"
] | question-answering | false | nyust-eb210 | null | nyust-eb210/braslab-bert-drcd-384 | 65,856 | null | transformers | ---
language: zh-tw
datasets: DRCD
tasks: Question Answering
---
# BERT DRCD 384
This model is a fine-tune checkpoint of [bert-base-chinese](https://huggingface.co/bert-base-chinese), fine-tuned on DRCD dataset.
This model reaches a F1 score of 86.
This model reaches a EM score of 83.
Training Arguments:
- length:... | [
-0.04130730405449867,
0.0011377865448594093,
-0.0055031790398061275,
-0.004470398649573326,
0.0033558662980794907,
-0.03352126106619835,
-0.021290775388479233,
0.07019874453544617,
-0.07218516618013382,
-0.010279553011059761,
0.02407163754105568,
-0.0297709908336401,
0.016106178984045982,
... |
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext | eaa409b6b7c9380a5f2ba7a59aa97712ff30f386 | 2021-09-22T20:09:56.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"en",
"arxiv:2007.15779",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext | 65,440 | 32 | transformers | ---
language: en
tags:
- exbert
license: mit
widget:
- text: "[MASK] is a tumor suppressor gene."
---
## PubMedBERT (abstracts + full text)
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on ge... | [
-0.0562528558075428,
-0.11965169757604599,
0.04586523771286011,
-0.022685816511511803,
0.07718706876039505,
0.0493830144405365,
-0.007534889504313469,
0.034528013318777084,
0.04667988792061806,
-0.02408837154507637,
-0.035582222044467926,
0.05982271581888199,
0.001686572446487844,
0.054318... |
sshleifer/tiny-distilroberta-base | d305c58110158c865cb6746c62d4511d4148a934 | 2021-10-22T16:10:44.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sshleifer | null | sshleifer/tiny-distilroberta-base | 64,865 | 2 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
microsoft/DialoGPT-small | f9c829d0285e7addb0667aeb6e33956916ec6cd0 | 2021-05-23T09:14:00.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"arxiv:1911.00536",
"transformers",
"conversational",
"license:mit"
] | conversational | false | microsoft | null | microsoft/DialoGPT-small | 64,443 | 8 | transformers | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation... | [
-0.03749089315533638,
-0.038063280284404755,
0.01325487531721592,
0.007241677958518267,
-0.03522462770342827,
-0.053405653685331345,
0.009222707711160183,
0.04326779395341873,
0.04960482940077782,
-0.03252561390399933,
-0.0518563911318779,
-0.05841609090566635,
-0.005250383634120226,
-0.00... |
nlpaueb/bert-base-uncased-contracts | f918d2e0cf491ba2c5fcf9f82d5e9603b8c5f3ea | 2022-04-28T14:43:56.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"en",
"transformers",
"legal",
"license:cc-by-sa-4.0",
"fill-mask"
] | fill-mask | false | nlpaueb | null | nlpaueb/bert-base-uncased-contracts | 64,347 | 5 | transformers | ---
language: en
pipeline_tag: fill-mask
license: cc-by-sa-4.0
thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png
tags:
- legal
widget:
- text: "This [MASK] Agreement is between General Motors and John Murray."
---
# LEGAL-BERT: The Muppets straight out of Law School
<img align="left" src="h... | [
-0.11263040453195572,
0.03710266947746277,
0.021883966401219368,
-0.09976549446582794,
0.027026409283280373,
0.04849156737327576,
0.02794586680829525,
0.050930365920066833,
0.0778391882777214,
0.04879958555102348,
0.008090869523584843,
0.0007032636785879731,
-0.0010577768553048372,
0.05360... |
viktor-enzell/wav2vec2-large-voxrex-swedish-4gram | 3cafa40b3433cf9d1b3b2b65c37c48541f57d1a8 | 2022-06-02T13:40:29.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"sv",
"dataset:common_voice",
"dataset:NST Swedish ASR Database",
"dataset:P4",
"dataset:The Swedish Culturomics Gigaword Corpus",
"transformers",
"audio",
"speech",
"hf-asr-leaderboard",
"license:cc0-1.0",
"model-index"
] | automatic-speech-recognition | false | viktor-enzell | null | viktor-enzell/wav2vec2-large-voxrex-swedish-4gram | 62,723 | 3 | transformers | ---
language: sv
datasets:
- common_voice
- NST Swedish ASR Database
- P4
- The Swedish Culturomics Gigaword Corpus
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- hf-asr-leaderboard
- sv
license: cc0-1.0
model-index:
- name: Wav2vec 2.0 large VoxRex Swedish (C) with 4-gram
results:
- task:
... | [
-0.0679410919547081,
-0.12754788994789124,
-0.003429172560572624,
-0.05934516713023186,
-0.006051825825124979,
0.02955787256360054,
-0.031118694692850113,
-0.0038188695907592773,
0.01692098192870617,
-0.04389169067144394,
-0.01761012151837349,
-0.1268942952156067,
-0.042338982224464417,
0.... |
cmarkea/distilcamembert-base-ner | 21167ca4a0fd71a615e579dc4898c4079e86b014 | 2022-05-24T15:55:53.000Z | [
"pytorch",
"tf",
"camembert",
"token-classification",
"fr",
"dataset:Jean-Baptiste/wikiner_fr",
"transformers",
"license:mit",
"autotrain_compatible"
] | token-classification | false | cmarkea | null | cmarkea/distilcamembert-base-ner | 62,690 | 10 | transformers | ---
language: fr
license: mit
datasets:
- Jean-Baptiste/wikiner_fr
widget:
- text: "Boulanger, habitant à Boulanger et travaillant dans le magasin Boulanger situé dans la ville de Boulanger. Boulanger a écrit le livre éponyme Boulanger édité par la maison d'édition Boulanger."
- text: "Quentin Jerome Tarantino naît le ... | [
-0.047859154641628265,
-0.017419995740056038,
-0.07205899059772491,
-0.029605058953166008,
-0.12393578141927719,
-0.0033345327246934175,
0.04732734337449074,
0.03573530539870262,
-0.05365319550037384,
-0.027201805263757706,
0.050268352031707764,
-0.0955272912979126,
0.040088675916194916,
-... |
dbmdz/bert-base-german-uncased | 26dd62c10449c89b8029c4440855983dfc5a5e83 | 2021-05-19T14:57:28.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/bert-base-german-uncased | 62,613 | 1 | transformers | ---
language: de
license: mit
---
# 🤗 + 📚 dbmdz German BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources another German BERT models 🎉
# German BERT
## Stats
In addition to the recently released [German BERT](https://deepset.ai/german-bert)
model by [d... | [
-0.1263769567012787,
-0.10557122528553009,
0.04468299821019173,
0.012351217679679394,
0.015333450399339199,
0.028427256271243095,
-0.031389269977808,
0.043275825679302216,
0.03880617395043373,
-0.016047313809394836,
-0.013517589308321476,
-0.010923854075372219,
0.020954560488462448,
0.0008... |
facebook/opt-350m | 10517ad5b51c8c6e02db7824d8494721d4874488 | 2022-06-16T15:25:49.000Z | [
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"transformers",
"license:other"
] | text-generation | false | facebook | null | facebook/opt-350m | 62,324 | 10 | transformers | ---
language: en
inference: false
tags:
- text-generation
license: other
commercial: false
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github... | [
-0.10659797489643097,
0.009452736005187035,
-0.0065415226854383945,
0.07719623297452927,
0.03445767983794212,
0.01497737504541874,
-0.022032609209418297,
0.01988007314503193,
0.04918115213513374,
0.003344404511153698,
0.06589271873235703,
-0.003997517749667168,
0.01682063564658165,
0.05590... |
sentence-transformers/msmarco-bert-base-dot-v5 | 668e63a378bc93d76c430af68338e550dc78df09 | 2022-06-15T20:34:19.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-bert-base-dot-v5 | 62,053 | 1 | sentence-transformers | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# msmarco-bert-base-dot-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **sema... | [
-0.053221218287944794,
-0.060649722814559937,
0.03904784098267555,
0.03387981653213501,
0.011063685640692711,
0.08115866035223007,
-0.029631279408931732,
0.08412574231624603,
-0.014959825202822685,
-0.025813443586230278,
-0.01890314184129238,
0.005977277643978596,
0.027383506298065186,
0.0... |
nghuyong/ernie-2.0-en | c18a9f28b99a65011e3a6c61e2109f03833a447b | 2021-05-20T01:42:24.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"en",
"arxiv:1907.12412",
"transformers"
] | null | false | nghuyong | null | nghuyong/ernie-2.0-en | 61,990 | 5 | transformers | ---
language: en
---
# ERNIE-2.0
## Introduction
ERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019,
which builds and learns incrementally pre-training tasks through constant multi-task learning.
Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including... | [
-0.08446931093931198,
-0.05870860442519188,
0.025094227865338326,
-0.015466729179024696,
-0.059619151055812836,
0.04924703389406204,
0.01396530494093895,
-0.019247712567448616,
0.0023524470161646605,
-0.002595726866275072,
0.02094940096139908,
-0.010529479011893272,
0.004545342642813921,
0... |
sentence-transformers/stsb-roberta-base-v2 | b5e9e8dbc4a7d931c766a9113d1a04963a480c06 | 2022-06-15T20:05:42.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/stsb-roberta-base-v2 | 61,846 | null | sentence-transformers | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/stsb-roberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense v... | [
-0.06077510118484497,
-0.05407135188579559,
-0.016179567202925682,
0.04191448912024498,
0.008593971841037273,
0.06620514392852783,
-0.04922949895262718,
0.018254855647683144,
0.030321313068270683,
-0.0647226944565773,
0.049179695546627045,
-0.0029516227077692747,
0.052620090544223785,
0.05... |
facebook/m2m100_1.2B | 90301acb1353eb6623e48973520b486612a57439 | 2022-05-26T22:26:41.000Z | [
"pytorch",
"rust",
"m2m_100",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"ast",
"az",
"ba",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"g... | text2text-generation | false | facebook | null | facebook/m2m100_1.2B | 61,684 | 10 | transformers | ---
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ... | [
-0.10692368447780609,
0.07552546262741089,
-0.044128578156232834,
-0.11650911718606949,
0.07949317246675491,
-0.030942490324378014,
0.11971228569746017,
0.008442494086921215,
0.016834881156682968,
-0.021874411031603813,
0.0967758521437645,
-0.05792298540472984,
0.10661910474300385,
-0.0103... |
microsoft/mdeberta-v3-base | 7d66e84a399b78accc72e0f61cd6d50f02ee1c2c | 2022-01-13T19:41:26.000Z | [
"pytorch",
"tf",
"deberta-v2",
"multilingual",
"arxiv:2006.03654",
"arxiv:2111.09543",
"transformers",
"deberta",
"deberta-v3",
"mdeberta",
"license:mit"
] | null | false | microsoft | null | microsoft/mdeberta-v3-base | 61,660 | 33 | transformers | ---
language: multilingual
tags:
- deberta
- deberta-v3
- mdeberta
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves... | [
-0.11939087510108948,
-0.11226687580347061,
0.003774260403588414,
0.0013722158037126064,
0.02877381443977356,
0.018623869866132736,
-0.039686571806669235,
-0.013982083648443222,
0.019328460097312927,
0.01526631135493517,
-0.007461810018867254,
0.016951480880379677,
-0.04505900293588638,
0.... |
google/electra-small-generator | c04c64e3cca372b13615e71e51bc261f93905212 | 2021-04-29T15:23:28.000Z | [
"pytorch",
"tf",
"jax",
"electra",
"fill-mask",
"en",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | google | null | google/electra-small-generator | 61,641 | 2 | transformers | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks usi... | [
-0.15659932792186737,
-0.013592531904578209,
-0.03288592770695686,
0.026664843782782555,
0.05259827896952629,
0.01947689615190029,
0.018389219418168068,
-0.03876601532101631,
0.011088617146015167,
-0.05713406205177307,
0.024072255939245224,
-0.0444607250392437,
0.024075860157608986,
0.0325... |
valhalla/m2m100_tiny_random | 337a4a691b7e14ad1668f5f4e481eaea6ce59ba1 | 2021-03-05T09:03:18.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/m2m100_tiny_random | 61,625 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
Helsinki-NLP/opus-mt-nl-en | 642ab6dc2d08ca9b5706ff44191dc443eae738e1 | 2021-09-10T13:59:07.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"nl",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-nl-en | 61,618 | 4 | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nl-en
* source languages: nl
* target languages: en
* OPUS readme: [nl-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nl-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... | [
-0.054038695991039276,
-0.022756846621632576,
0.0265913438051939,
-0.009581152349710464,
0.007277315482497215,
0.09881782531738281,
-0.06081283837556839,
0.030017463490366936,
0.019366208463907242,
-0.013667913153767586,
0.007796785328537226,
-0.04403863102197647,
-0.08572422713041306,
-0.... |
Rostlab/prot_bert_bfd | 6c5c8a55a52ff08a664dfd584aa1773f125a0487 | 2020-12-11T21:30:10.000Z | [
"pytorch",
"tf",
"fill-mask",
"protein",
"dataset:BFD",
"transformers",
"protein language model",
"autotrain_compatible"
] | fill-mask | false | Rostlab | null | Rostlab/prot_bert_bfd | 60,576 | 3 | transformers | ---
language: protein
tags:
- protein language model
datasets:
- BFD
---
# ProtBert-BFD model
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://github.com/... | [
-0.09309598803520203,
-0.13018883764743805,
-0.01690264418721199,
-0.02881556563079357,
0.0879664421081543,
0.07551614940166473,
-0.011417614296078682,
0.09910315275192261,
0.02540416456758976,
-0.0508120059967041,
-0.0008755468879826367,
0.008362802676856518,
0.024881890043616295,
0.04625... |
facebook/opt-1.3b | aa6ac1e23bb9a499be2b7400079cd2a7b8a1309a | 2022-06-22T09:53:16.000Z | [
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"transformers",
"license:other"
] | text-generation | false | facebook | null | facebook/opt-1.3b | 60,368 | 9 | transformers | ---
language: en
inference: false
tags:
- text-generation
- opt
license: other
commercial: false
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://g... | [
-0.09430353343486786,
0.010255655273795128,
-0.006349516566842794,
0.079442597925663,
0.024165261536836624,
0.013372854329645634,
-0.011628345586359501,
0.022513514384627342,
0.048179179430007935,
0.007294895127415657,
0.06878720223903656,
0.0030618805903941393,
0.010361358523368835,
0.052... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.