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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
hfl/chinese-macbert-base | a986e004d2a7f2a1c2f5a3edef4e20604a974ed1 | 2021-05-19T19:09:45.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | hfl | null | hfl/chinese-macbert-base | 36,823,840 | 43 | transformers | ---
language:
- zh
tags:
- bert
license: "apache-2.0"
---
<p align="center">
<br>
<img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/>
<br>
</p>
<p align="center">
<a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE">
<img alt="GitHub" src="https://img.... | [
-0.12368089705705643,
0.0159906018525362,
0.07993485778570175,
0.010601235553622246,
0.06334946304559708,
0.02685576304793358,
0.021748080849647522,
0.007624071557074785,
0.0053676413372159,
-0.0035076644271612167,
0.08696867525577545,
-0.03400150686502457,
0.06885205209255219,
0.063679352... |
microsoft/deberta-base | 7d4c0126b06bd59dccd3e48e467ed11e37b77f3f | 2022-01-13T13:56:18.000Z | [
"pytorch",
"tf",
"rust",
"deberta",
"en",
"arxiv:2006.03654",
"transformers",
"deberta-v1",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-base | 23,662,412 | 15 | transformers | ---
language: en
tags: deberta-v1
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It... | [
-0.10310466587543488,
-0.1119682714343071,
0.015463879331946373,
0.00776915205642581,
0.017533447593450546,
-0.0014948627213016152,
-0.015752648934721947,
0.038549475371837616,
-0.017899053171277046,
0.048586562275886536,
0.026507191359996796,
-0.007420028559863567,
-0.02895142324268818,
0... |
bert-base-uncased | 418430c3b5df7ace92f2aede75700d22c78a0f95 | 2022-06-06T11:41:24.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-base-uncased | 22,268,934 | 204 | transformers | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](http... | [
-0.10429023206233978,
-0.07568823546171188,
0.0528205968439579,
0.029537051916122437,
0.03592930734157562,
0.06076014041900635,
0.016745345667004585,
-0.011535749770700932,
0.014104398898780346,
-0.024821121245622635,
0.0364287793636322,
-0.055009908974170685,
0.05354085937142372,
0.041097... |
gpt2 | 6c0e6080953db56375760c0471a8c5f2929baf11 | 2021-05-19T16:25:59.000Z | [
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"gpt2",
"text-generation",
"en",
"transformers",
"exbert",
"license:mit"
] | text-generation | false | null | null | gpt2 | 11,350,803 | 164 | transformers | ---
language: en
tags:
- exbert
license: mit
---
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better... | [
-0.05892602726817131,
-0.08013296872377396,
0.060150083154439926,
0.0071877371519804,
0.07087277621030807,
0.028216511011123657,
0.01796095259487629,
0.037697840481996536,
0.03646615520119667,
-0.03444734588265419,
-0.016371622681617737,
-0.00010747667693067342,
0.03819410130381584,
0.0576... |
distilbert-base-uncased | 043235d6088ecd3dd5fb5ca3592b6913fd516027 | 2022-05-31T19:08:36.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"distilbert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | distilbert-base-uncased | 11,250,037 | 70 | transformers | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# DistilBERT base model (uncased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the disti... | [
-0.13866068422794342,
-0.06151656433939934,
0.08515582978725433,
0.01706661842763424,
0.014973160810768604,
-0.052277322858572006,
-0.007684790063649416,
0.061743155121803284,
0.00879606045782566,
-0.05457921698689461,
0.02629678323864937,
0.030604751780629158,
0.04207247868180275,
0.00054... |
Jean-Baptiste/camembert-ner | dbec8489a1c44ecad9da8a9185115bccabd799fe | 2022-04-04T01:13:33.000Z | [
"pytorch",
"camembert",
"token-classification",
"fr",
"dataset:Jean-Baptiste/wikiner_fr",
"transformers",
"autotrain_compatible"
] | token-classification | false | Jean-Baptiste | null | Jean-Baptiste/camembert-ner | 9,833,060 | 11 | transformers | ---
language: fr
datasets:
- Jean-Baptiste/wikiner_fr
widget:
- text: "Je m'appelle jean-baptiste et je vis à montréal"
- text: "george washington est allé à washington"
---
# camembert-ner: model fine-tuned from camemBERT for NER task.
## Introduction
[camembert-ner] is a NER model that was fine-tuned from camemBER... | [
-0.11113610863685608,
-0.03646893799304962,
0.059876054525375366,
-0.003222051775082946,
0.012641402892768383,
-0.04548738896846771,
-0.01905476488173008,
0.06513305753469467,
0.05475887656211853,
-0.036326371133327484,
0.01774558238685131,
-0.053867973387241364,
0.0843958929181099,
0.0142... |
bert-base-cased | a8d257ba9925ef39f3036bfc338acf5283c512d9 | 2021-09-06T08:07:18.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-base-cased | 7,598,326 | 30 | transformers | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT base model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https:... | [
-0.089531309902668,
-0.07718607783317566,
0.05249395594000816,
0.018646515905857086,
0.0447709858417511,
0.06499486416578293,
0.01449884008616209,
0.013737021014094353,
0.022442683577537537,
-0.017234643921256065,
0.04510723799467087,
-0.0327119417488575,
0.06715553998947144,
0.04273235425... |
roberta-base | 251c3c36356d3ad6845eb0554fdb9703d632c6cc | 2021-07-06T10:34:50.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"roberta",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1907.11692",
"arxiv:1806.02847",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | null | null | roberta-base | 7,254,067 | 45 | transformers | ---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa base model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com... | [
-0.08212313055992126,
-0.10390054434537888,
-0.027432728558778763,
0.05361437052488327,
-0.008713857270777225,
0.08931197971105576,
0.012921089306473732,
-0.0319417305290699,
0.03874579817056656,
0.02321513742208481,
0.06043674796819687,
-0.031464289873838425,
0.07827506959438324,
0.029055... |
SpanBERT/spanbert-large-cased | a49cba45de9565a5d3e7b089a94dbae679e64e79 | 2021-05-19T11:31:33.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
] | null | false | SpanBERT | null | SpanBERT/spanbert-large-cased | 7,120,559 | 3 | 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.... |
xlm-roberta-base | f6d161e8f5f6f2ed433fb4023d6cb34146506b3f | 2022-06-06T11:40:43.000Z | [
"pytorch",
"tf",
"jax",
"xlm-roberta",
"fill-mask",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha"... | fill-mask | false | null | null | xlm-roberta-base | 6,960,013 | 42 | transformers | ---
tags:
- exbert
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
... | [
-0.08281730860471725,
0.003249414497986436,
-0.041813094168901443,
-0.047962404787540436,
0.060015056282281876,
0.03705935552716255,
0.03477831184864044,
0.03974342346191406,
-0.008022570051252842,
0.021637847647070885,
0.08089376986026764,
-0.04877686873078346,
0.08540689200162888,
-0.035... |
distilbert-base-uncased-finetuned-sst-2-english | 00c3f1ef306e837efb641eaca05d24d161d9513c | 2022-07-22T08:00:55.000Z | [
"pytorch",
"tf",
"rust",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"dataset:glue",
"transformers",
"license:apache-2.0",
"model-index"
] | text-classification | false | null | null | distilbert-base-uncased-finetuned-sst-2-english | 5,401,984 | 77 | transformers | ---
language: en
license: apache-2.0
datasets:
- sst2
- glue
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: sst2
split: validation
metrics:
... | [
-0.034528184682130814,
-0.04061563313007355,
-0.060734909027814865,
0.035735320299863815,
0.08161136507987976,
0.04066551476716995,
0.0003900852461811155,
0.05125672370195389,
-0.01557959709316492,
-0.04621459171175957,
0.06463723629713058,
-0.0745718702673912,
0.007900773547589779,
-0.046... |
distilroberta-base | c1149320821601524a8d373726ed95bbd2bc0dc2 | 2022-07-22T08:13:21.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"roberta",
"fill-mask",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | distilroberta-base | 5,192,102 | 21 | transformers | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- openwebtext
---
# Model Card for DistilRoBERTa base
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluat... | [
-0.1325169950723648,
-0.03109726682305336,
0.035468652844429016,
0.0266091488301754,
0.027898555621504784,
-0.04462097957730293,
-0.04662588611245155,
0.06629492342472076,
-0.08276285231113434,
-0.04405451565980911,
0.023149758577346802,
-0.024889251217246056,
0.013186859898269176,
-0.0024... |
distilgpt2 | ca98be8f8f0994e707b944a9ef55e66fbcf9e586 | 2022-07-22T08:12:56.000Z | [
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"gpt2",
"text-generation",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:2201.08542",
"arxiv:2203.12574",
"arxiv:1910.09700",
"arxiv:1503.02531",
"transformers",
"exbert",
"license:apache-2.0",
"model-index",
"co2_eq_emissions"
... | text-generation | false | null | null | distilgpt2 | 4,525,173 | 77 | transformers | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- openwebtext
model-index:
- name: distilgpt2
results:
- task:
type: text-generation
name: Text Generation
dataset:
type: wikitext
name: WikiText-103
metrics:
- type: perplexity
name: Perplexity
... | [
-0.12711955606937408,
-0.02593734860420227,
0.022106902673840523,
0.04674231633543968,
0.011120681650936604,
-0.0465211421251297,
0.0013098361669108272,
0.08374807238578796,
-0.028656257316470146,
-0.08449400216341019,
0.015780135989189148,
-0.07485173642635345,
-0.02609829604625702,
0.017... |
cross-encoder/ms-marco-MiniLM-L-12-v2 | 97f7dcbdd6ab58fe7f44368c795fc5200b48fcbe | 2021-08-05T08:39:01.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/ms-marco-MiniLM-L-12-v2 | 3,951,063 | 10 | transformers | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | [
-0.06551434844732285,
-0.07030782848596573,
-0.004193244501948357,
0.05925549939274788,
-0.008339117281138897,
0.08594850450754166,
-0.029806630685925484,
0.0668809562921524,
-0.0017081426922231913,
-0.053372517228126526,
-0.029908085241913795,
0.04448296129703522,
0.032882269471883774,
0.... |
albert-base-v2 | 51dbd9db43a0c6eba97f74b91ce26fface509e0b | 2021-08-30T12:04:48.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | albert-base-v2 | 3,862,051 | 15 | transformers | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT Base v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-rese... | [
0.0007209334871731699,
-0.040847085416316986,
-0.0007834088755771518,
0.05562419071793556,
0.026085415855050087,
0.06654420495033264,
-0.03512277826666832,
-0.035424113273620605,
0.04950462654232979,
-0.05898051708936691,
0.05823838338255882,
-0.05316125601530075,
0.054170671850442886,
-0.... |
bert-base-chinese | 38fda776740d17609554e879e3ac7b9837bdb5ee | 2022-07-22T08:09:06.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"transformers",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-base-chinese | 3,660,463 | 107 | transformers | ---
language: zh
---
# Bert-base-chinese
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
# Model Detai... | [
-0.1257508248090744,
-0.012285888195037842,
0.0526764802634716,
0.03729807585477829,
0.047670330852270126,
0.0854436531662941,
0.026564601808786392,
-0.009043475612998009,
0.01670899987220764,
-0.019426235929131508,
0.03087518736720085,
-0.07265433669090271,
0.087125264108181,
0.0327844023... |
bert-base-multilingual-cased | aff660c4522e466f4d0de19eaf94f91e4e2e7375 | 2021-05-18T16:18:16.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-base-multilingual-cased | 3,089,919 | 40 | transformers | ---
language: multilingual
license: apache-2.0
datasets:
- wikipedia
---
# BERT multilingual base model (cased)
Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released ... | [
-0.0880918800830841,
-0.08954025059938431,
0.05024603381752968,
0.02027062512934208,
0.02424587681889534,
0.05601003021001816,
-0.009814261458814144,
-0.006841807160526514,
0.03744438290596008,
-0.031194061040878296,
0.022091440856456757,
-0.05183573439717293,
0.0506647489964962,
0.0512378... |
xlm-roberta-large-finetuned-conll03-english | 33a83d9855a119c0453ce450858c07835a0bdbed | 2022-07-22T08:04:08.000Z | [
"pytorch",
"rust",
"xlm-roberta",
"token-classification",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
... | token-classification | false | null | null | xlm-roberta-large-finetuned-conll03-english | 2,851,282 | 23 | transformers | ---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
... | [
-0.10246257483959198,
0.04425393044948578,
-0.012316679581999779,
-0.025751229375600815,
0.0764460638165474,
0.03561047092080116,
0.0360226072371006,
0.010814625769853592,
0.045203957706689835,
0.0420810803771019,
0.07108546048402786,
-0.02943423204123974,
0.04923563078045845,
-0.026391703... |
tals/albert-xlarge-vitaminc-mnli | 4c79eb5353f6104eb148d9221560c913f45677c7 | 2022-06-24T01:33:47.000Z | [
"pytorch",
"tf",
"albert",
"text-classification",
"python",
"dataset:fever",
"dataset:glue",
"dataset:multi_nli",
"dataset:tals/vitaminc",
"transformers"
] | text-classification | false | tals | null | tals/albert-xlarge-vitaminc-mnli | 2,529,752 | null | transformers | ---
language: python
datasets:
- fever
- glue
- multi_nli
- tals/vitaminc
---
# Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When ... | [
-0.1226511225104332,
-0.010432502254843712,
-0.0084984814748168,
0.02337195724248886,
0.06423482298851013,
-0.00009322218102170154,
-0.00042406650027260184,
0.10444913804531097,
0.013196569867432117,
-0.002656024880707264,
0.027341550216078758,
-0.052648305892944336,
0.052716560661792755,
... |
bert-large-uncased | 3835a195d41f7ddc47d5ecab84b64f71d6f144e9 | 2021-05-18T16:40:29.000Z | [
"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 | 2,362,221 | 9 | transformers | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com... | [
-0.089927539229393,
-0.0816199854016304,
0.0626024678349495,
0.043510835617780685,
0.032939936965703964,
0.05772925913333893,
0.012369436211884022,
-0.022393228486180305,
0.034775275737047195,
-0.026343373581767082,
0.03744859620928764,
-0.044184569269418716,
0.05464019253849983,
0.0297626... |
valhalla/t5-small-qa-qg-hl | a9d81e686f2169360fd59d8329235d3c4ba74f4f | 2021-06-23T14:42:41.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"dataset:squad",
"arxiv:1910.10683",
"transformers",
"question-generation",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/t5-small-qa-qg-hl | 2,171,047 | 5 | transformers | ---
datasets:
- squad
tags:
- question-generation
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>"
license: mit
---
## T5 for multi-task QA and QG
This is multi-... | [
-0.09922609478235245,
0.028051195666193962,
-0.03504813835024834,
0.041627272963523865,
0.03275827690958977,
0.03436599299311638,
0.013137323781847954,
0.0001289481297135353,
0.05512295663356781,
-0.008591915480792522,
0.014779935590922832,
-0.13871879875659943,
0.0689271092414856,
-0.0048... |
google/t5-v1_1-xl | a9e51c46bd6f3893213c51edf9498be6f0426797 | 2020-11-19T19:55:34.000Z | [
"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-xl | 1,980,571 | 3 | 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... |
sentence-transformers/all-MiniLM-L6-v2 | 717413c64de70e37b55cf53c9cdff0e2d331fac3 | 2022-07-11T21:08:45.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"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_... | sentence-similarity | false | sentence-transformers | null | sentence-transformers/all-MiniLM-L6-v2 | 1,933,749 | 60 | 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... | [
-0.08548831194639206,
-0.06260090321302414,
-0.011101335287094116,
-0.022259490564465523,
0.056814294308423996,
0.07366131246089935,
-0.029393881559371948,
0.02135009691119194,
0.01766866445541382,
-0.0356454998254776,
0.05661066994071007,
-0.019041549414396286,
0.0534217394888401,
0.04904... |
sentence-transformers/paraphrase-MiniLM-L6-v2 | 68b97aaedb0c72be3c88c1af64296b3bbb8001fa | 2022-06-15T18:39:43.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/paraphrase-MiniLM-L6-v2 | 1,710,481 | 16 | sentence-transformers | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dens... | [
-0.028167856857180595,
-0.05239181965589523,
-0.009377066977322102,
0.040452685207128525,
0.03128011152148247,
0.06838051974773407,
-0.054357923567295074,
0.03204594552516937,
0.006394308991730213,
-0.07616591453552246,
0.06814518570899963,
-0.0010415782453492284,
0.05818144232034683,
0.04... |
t5-small | d78aea13fa7ecd06c29e3e46195d6341255065d5 | 2022-07-22T08:11:14.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"t5",
"text2text-generation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.12885",
"arxiv:1905.10044",
"arxiv:1910.09700",
"transformers",
... | translation | false | null | null | t5-small | 1,707,833 | 20 | transformers | ---
language:
- en
- fr
- ro
- de
datasets:
- c4
tags:
- summarization
- translation
license: apache-2.0
---
# Model Card for T5 Small
![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876... | [
-0.05660082772374153,
-0.000966046005487442,
-0.04522397369146347,
0.05273234471678734,
0.13198721408843994,
0.019032752141356468,
-0.004690529778599739,
0.07759352773427963,
0.026721565052866936,
-0.04503154382109642,
0.032610055059194565,
-0.04494328796863556,
0.024699600413441658,
0.027... |
facebook/bart-large-mnli | c626438eeca63a93bd6024b0a0fbf8b3c0c30d7b | 2021-08-09T08:25:07.000Z | [
"pytorch",
"jax",
"rust",
"bart",
"text-classification",
"dataset:multi_nli",
"arxiv:1910.13461",
"arxiv:1909.00161",
"transformers",
"license:mit",
"zero-shot-classification"
] | zero-shot-classification | false | facebook | null | facebook/bart-large-mnli | 1,668,146 | 147 | transformers | ---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
pipeline_tag: zero-shot-classification
datasets:
- multi_nli
---
# bart-large-mnli
This is the checkpoint for [bart-large](https://huggingface.co/facebook/bart-large) after being trained on the [MultiNLI (MNLI)](https://huggingface.co/da... | [
-0.07644176483154297,
-0.043402399867773056,
0.05649516358971596,
-0.028029777109622955,
0.06512340158224106,
0.0230893362313509,
-0.0759434774518013,
-0.030554726719856262,
-0.020933518186211586,
-0.053710583597421646,
0.046663254499435425,
-0.05096018686890602,
-0.015758709982037544,
-0.... |
cardiffnlp/twitter-xlm-roberta-base-sentiment | f3e34b6c30bf27b6649f72eca85d0bbe79df1e55 | 2022-06-22T19:15:32.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"multilingual",
"arxiv:2104.12250",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/twitter-xlm-roberta-base-sentiment | 1,479,744 | 25 | transformers | ---
language: multilingual
widget:
- text: "🤗"
- text: "T'estimo! ❤️"
- text: "I love you!"
- text: "I hate you 🤮"
- text: "Mahal kita!"
- text: "사랑해!"
- text: "난 너가 싫어"
- text: "😍😍😍"
---
# twitter-XLM-roBERTa-base for Sentiment Analysis
This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and ... | [
-0.10031694173812866,
-0.02794644981622696,
-0.014125077985227108,
-0.00975096970796585,
-0.004203904885798693,
0.0024737271014600992,
0.008777450770139694,
0.024569865316152573,
0.06987903267145157,
-0.003512149676680565,
0.03865955024957657,
-0.035966381430625916,
0.0726815015077591,
-0.... |
roberta-large | 619fd8c2ca2bc7ac3959b7f71b6c426c897ba407 | 2021-05-21T08:57:02.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1907.11692",
"arxiv:1806.02847",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | null | null | roberta-large | 1,479,252 | 39 | transformers | ---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa large model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](htt... | [
-0.07912211120128632,
-0.10217892378568649,
-0.02644594945013523,
0.0550602488219738,
-0.00734882615506649,
0.08770351111888885,
0.00936263706535101,
-0.03351088985800743,
0.03828578069806099,
0.022800803184509277,
0.06117379665374756,
-0.03063245862722397,
0.07556978613138199,
0.027471099... |
DeepPavlov/rubert-base-cased-conversational | 645946ce91842a52eaacb2705c77e59194145ffa | 2021-11-08T13:06:54.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"transformers"
] | feature-extraction | false | DeepPavlov | null | DeepPavlov/rubert-base-cased-conversational | 1,418,924 | 5 | transformers | ---
language:
- ru
---
# rubert-base-cased-conversational
Conversational RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\]. We assembled a new vocabulary f... | [
-0.07024551928043365,
-0.13161787390708923,
0.024362895637750626,
-0.043754514306783676,
0.08567774295806885,
0.07290639728307724,
0.0071761030703783035,
-0.009646309539675713,
0.03384178876876831,
-0.034751225262880325,
0.03113185241818428,
-0.022719744592905045,
0.01884695142507553,
0.04... |
microsoft/codebert-base | 3b0952feddeffad0063f274080e3c23d75e7eb39 | 2022-02-11T19:59:44.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"roberta",
"feature-extraction",
"arxiv:2002.08155",
"transformers"
] | feature-extraction | false | microsoft | null | microsoft/codebert-base | 1,347,269 | 30 | transformers | ## CodeBERT-base
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 bi-modal data (documents & code) of [CodeSearchNet](https://github.com/github/CodeSearchNet)
### Training Objective
This model is i... | [
-0.1369808167219162,
-0.10500998049974442,
-0.036125168204307556,
0.05996467173099518,
-0.01003129780292511,
0.08013205230236053,
-0.01008650567382574,
-0.008504729717969894,
-0.051955025643110275,
-0.0021893756929785013,
0.06277790665626526,
0.015850992873311043,
0.03458425775170326,
-0.0... |
ProsusAI/finbert | 5ea63b3d0c737ad6f06e061d9af36b1f7bbd1a4b | 2022-06-03T06:34:37.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"en",
"arxiv:1908.10063",
"transformers",
"financial-sentiment-analysis",
"sentiment-analysis"
] | text-classification | false | ProsusAI | null | ProsusAI/finbert | 1,254,493 | 81 | transformers | ---
language: "en"
tags:
- financial-sentiment-analysis
- sentiment-analysis
widget:
- text: "Stocks rallied and the British pound gained."
---
FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financi... | [
-0.024671152234077454,
-0.06598435342311859,
-0.07246746122837067,
0.007539877202361822,
0.03852372616529465,
0.034074846655130386,
0.06207428500056267,
0.0459100604057312,
0.0375867560505867,
-0.06407948583364487,
-0.010915640741586685,
0.0014108888572081923,
-0.019794268533587456,
0.0332... |
t5-base | 23aa4f41cb7c08d4b05c8f327b22bfa0eb8c7ad9 | 2022-07-22T08:10:56.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"t5",
"text2text-generation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.12885",
"arxiv:1905.10044",
"arxiv:1910.09700",
"transformers",
... | translation | false | null | null | t5-base | 1,234,008 | 53 | transformers | ---
language:
- en
- fr
- ro
- de
datasets:
- c4
tags:
- summarization
- translation
license: apache-2.0
---
# Model Card for T5 Base
![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e516638767... | [
-0.06001785770058632,
-0.0032114647328853607,
-0.043597523123025894,
0.04925158619880676,
0.13157330453395844,
0.017546458169817924,
-0.0037867906503379345,
0.07551992684602737,
0.02609967440366745,
-0.04440973699092865,
0.03008037619292736,
-0.046874769032001495,
0.024552587419748306,
0.0... |
deepset/roberta-base-squad2 | d3c3bb6f2aaec6bf057fbf3796af9c5b9b939758 | 2022-07-22T11:42:08.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/roberta-base-squad2 | 1,111,876 | 92 | transformers | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
model-index:
- name: deepset/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: Exac... | [
-0.11695931106805801,
-0.0684281513094902,
-0.02039109356701374,
0.052642595022916794,
0.036685723811388016,
0.05338458716869354,
0.014029116369783878,
0.021648414433002472,
-0.009836599230766296,
-0.04259449988603592,
-0.016081688925623894,
-0.11886314302682877,
-0.0033667238894850016,
0.... |
distilbert-base-cased-distilled-squad | 1b9d42b637aed70c9f3cd27e13b66ee9f847ed03 | 2022-07-22T07:57:01.000Z | [
"pytorch",
"tf",
"rust",
"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-cased-distilled-squad | 1,064,466 | 15 | transformers | ---
language: en
datasets:
- squad
metrics:
- squad
license: apache-2.0
---
# DistilBERT base cased distilled SQuAD
## 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-... | [
-0.14533667266368866,
-0.026285884901881218,
0.008701134473085403,
0.04142187163233757,
0.058554768562316895,
-0.013294125907123089,
-0.04081675037741661,
0.17894130945205688,
-0.03330094739794731,
-0.043892886489629745,
-0.0002285213122377172,
0.07188321650028229,
0.00904697272926569,
0.0... |
xlm-roberta-large | b2a6150f8be56457baf80c74342cc424080260f0 | 2022-06-27T11:25:40.000Z | [
"pytorch",
"tf",
"jax",
"xlm-roberta",
"fill-mask",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha"... | fill-mask | false | null | null | xlm-roberta-large | 1,017,218 | 24 | transformers | ---
tags:
- exbert
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
-... | [
-0.08172927796840668,
0.005226295441389084,
-0.04117339476943016,
-0.04801236838102341,
0.05737524852156639,
0.031084084883332253,
0.03243193402886391,
0.03622283786535263,
-0.0067443703301250935,
0.019056109711527824,
0.08277745544910431,
-0.043742552399635315,
0.08112572878599167,
-0.036... |
facebook/wav2vec2-base-960h | 706111756296bc76512407a11e69526cf4e22aae | 2022-06-30T00:05:41.000Z | [
"pytorch",
"tf",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"transformers",
"audio",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-base-960h | 986,202 | 57 | transformers | ---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.... | [
-0.1060151606798172,
-0.14686547219753265,
-0.01560901664197445,
-0.047346293926239014,
0.04207441955804825,
0.006387645378708839,
0.011943151243031025,
-0.04540662840008736,
-0.04757222533226013,
-0.08122971653938293,
0.0399002879858017,
-0.1235450878739357,
-0.05642370507121086,
-0.00941... |
daigo/bert-base-japanese-sentiment | 51ac2d2c0a5645d77ca26078fc5f02c349fbb93d | 2021-05-19T14:36:34.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"ja",
"transformers"
] | text-classification | false | daigo | null | daigo/bert-base-japanese-sentiment | 972,842 | 7 | transformers | ---
language:
- ja
---
binary classification
# Usage
```
print(pipeline("sentiment-analysis",model="daigo/bert-base-japanese-sentiment",tokenizer="daigo/bert-base-japanese-sentiment")("私は幸福である。"))
[{'label': 'ポジティブ', 'score': 0.98430425}]
```
| [
-0.04349634051322937,
0.12685701251029968,
-0.0252006184309721,
0.021490514278411865,
-0.008598824962973595,
0.042314764112234116,
0.037573881447315216,
0.03974836319684982,
0.016339000314474106,
-0.07068994641304016,
0.036761365830898285,
-0.07984782010316849,
-0.020948681980371475,
0.012... |
bert-base-multilingual-uncased | 99406b9f2cfa046409626308a01da45a2a078f62 | 2021-05-18T16:19:22.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-base-multilingual-uncased | 970,081 | 13 | transformers | ---
language: en
license: apache-2.0
datasets:
- wikipedia
---
# BERT multilingual base model (uncased)
Pretrained model on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
[this... | [
-0.08508127182722092,
-0.07772396504878998,
0.052124522626399994,
0.023885423317551613,
0.02731981873512268,
0.05022984370589256,
-0.014638576656579971,
-0.01630280539393425,
0.01909637451171875,
-0.032029204070568085,
0.0286273006349802,
-0.05059870332479477,
0.05735894665122032,
0.047481... |
sentence-transformers/all-mpnet-base-v2 | bd44305fd6a1b43c16baf96765e2ecb20bca8e1d | 2022-07-11T21:01:04.000Z | [
"pytorch",
"mpnet",
"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",
"dataset... | sentence-similarity | false | sentence-transformers | null | sentence-transformers/all-mpnet-base-v2 | 966,231 | 43 | 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... | [
-0.0859527662396431,
-0.06226963922381401,
-0.018229903653264046,
-0.016796143725514412,
0.060666605830192566,
0.07571472227573395,
-0.02144172601401806,
0.015986090525984764,
0.014631819911301136,
-0.030871329829096794,
0.04278617352247238,
-0.022306712344288826,
0.05234656110405922,
0.05... |
sentence-transformers/all-MiniLM-L12-v2 | 9e16800aed25dbd1a96dfa6949c68c4d81d5dded | 2022-07-11T21:05:39.000Z | [
"pytorch",
"rust",
"bert",
"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",
"dataset:wikih... | sentence-similarity | false | sentence-transformers | null | sentence-transformers/all-MiniLM-L12-v2 | 954,345 | 5 | 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... | [
-0.08374010771512985,
-0.06014135852456093,
-0.010806024074554443,
-0.02427024208009243,
0.05708928406238556,
0.07425114512443542,
-0.028894850984215736,
0.021960189566016197,
0.016340626403689384,
-0.03585219755768776,
0.05689363181591034,
-0.01657729037106037,
0.053780630230903625,
0.050... |
EleutherAI/gpt-j-6B | 918ad376364058dee23512629bc385380c98e57d | 2022-03-15T13:34:01.000Z | [
"pytorch",
"tf",
"jax",
"gptj",
"text-generation",
"en",
"dataset:The Pile",
"arxiv:2104.09864",
"arxiv:2101.00027",
"transformers",
"causal-lm",
"license:apache-2.0"
] | text-generation | false | EleutherAI | null | EleutherAI/gpt-j-6B | 945,885 | 243 | transformers | ---
language:
- en
tags:
- pytorch
- causal-lm
license: apache-2.0
datasets:
- The Pile
---
# GPT-J 6B
## Model Description
GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represent... | [
-0.12672092020511627,
0.005716377403587103,
-0.010990752838551998,
0.010420054197311401,
0.029339727014303207,
-0.019964296370744705,
-0.06083495914936066,
0.02669130079448223,
-0.05095050483942032,
-0.07086796313524246,
0.023609910160303116,
-0.03130849823355675,
0.02748296409845352,
-0.0... |
prithivida/parrot_paraphraser_on_T5 | 9f32aa1e456e8e8a90d97e8673365f3090fa49fa | 2021-05-18T07:53:27.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | prithivida | null | prithivida/parrot_paraphraser_on_T5 | 870,393 | 20 | transformers | # Parrot
## 1. What is Parrot?
Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. For more details on the library and usage please refer to the [github page](https://github.com/PrithivirajDamodar... | [
-0.03259994462132454,
-0.0861857458949089,
0.0016837017610669136,
0.0013438478345051408,
0.013536225073039532,
0.042183469980955124,
0.035633981227874756,
-0.051882416009902954,
0.023064566776156425,
-0.07208341360092163,
0.0177255030721426,
-0.05516481027007103,
-0.03346610814332962,
0.02... |
openai/clip-vit-base-patch32 | f4881ba48ee4d21b7ed5602603b9e3e92eb1b346 | 2022-03-14T17:58:13.000Z | [
"pytorch",
"tf",
"jax",
"clip",
"feature-extraction",
"arxiv:2103.00020",
"arxiv:1908.04913",
"transformers",
"vision"
] | feature-extraction | false | openai | null | openai/clip-vit-base-patch32 | 854,364 | 49 | transformers | ---
tags:
- vision
---
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robu... | [
-0.05266270786523819,
-0.029511218890547752,
-0.01378261111676693,
-0.016498874872922897,
0.0789661630988121,
-0.019893504679203033,
-0.024232693016529083,
0.048260241746902466,
0.0682869404554367,
-0.09571637958288193,
0.027020670473575592,
-0.017727835103869438,
-0.00020476577628869563,
... |
prajjwal1/bert-tiny | 6f75de8b60a9f8a2fdf7b69cbd86d9e64bcb3837 | 2021-10-27T18:29:01.000Z | [
"pytorch",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"transformers",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"license:mit"
] | null | false | prajjwal1 | null | prajjwal1/bert-tiny | 799,875 | 9 | 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 ... | [
-0.10233648121356964,
-0.04423704370856285,
0.06137974560260773,
0.04279656708240509,
0.01968778856098652,
0.06089229881763458,
-0.011356689967215061,
0.07008757442235947,
-0.03280850872397423,
-0.00013833395496476442,
0.04287395998835564,
0.06444608420133591,
-0.04259755089879036,
0.08806... |
Jean-Baptiste/camembert-ner-with-dates | 8c2d77a331733d26e0ca95a8f525e0ca3aa8e909 | 2021-08-30T12:55:48.000Z | [
"pytorch",
"camembert",
"token-classification",
"fr",
"dataset:Jean-Baptiste/wikiner_fr",
"transformers",
"autotrain_compatible"
] | token-classification | false | Jean-Baptiste | null | Jean-Baptiste/camembert-ner-with-dates | 782,295 | 8 | transformers | ---
language: fr
datasets:
- Jean-Baptiste/wikiner_fr
widget:
- text: "Je m'appelle jean-baptiste et j'habite à montréal depuis fevr 2012"
---
# camembert-ner: model fine-tuned from camemBERT for NER task (including DATE tag).
## Introduction
[camembert-ner-with-dates] is an extension of french camembert-ner model w... | [
-0.037811506539583206,
0.0039187888614833355,
0.029471388086676598,
0.06324126571416855,
0.05223608762025833,
-0.02278575301170349,
-0.06069985404610634,
0.09245885163545609,
0.04154825583100319,
-0.05503368377685547,
-0.010640758089721203,
-0.10378167033195496,
-0.0010783277684822679,
0.0... |
bert-large-cased | d9238236d8326ce4bc117132bb3b7e62e95f3a9a | 2021-05-18T16:33:16.000Z | [
"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-cased | 778,414 | 3 | transformers | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/g... | [
-0.09336259216070175,
-0.0772688239812851,
0.06526713073253632,
0.03688047081232071,
0.03932911157608032,
0.06279470771551132,
0.015506204217672348,
-0.017896251752972603,
0.04141203686594963,
-0.015988845378160477,
0.04849627986550331,
-0.04040355235338211,
0.05780552327632904,
0.03051182... |
facebook/bart-large-cnn | 9137060abd52495839d8c5c67ab4e6d0c49254b2 | 2022-07-28T15:16:55.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"bart",
"text2text-generation",
"arxiv:1910.13461",
"transformers",
"summarization",
"license:mit",
"model-index",
"autotrain_compatible"
] | summarization | false | facebook | null | facebook/bart-large-cnn | 766,202 | 72 | transformers | ---
tags:
- summarization
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
model-index:
- name: facebook/bart-large-cnn
results:
- task:
type: summarization
name: Summarization
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: t... | [
-0.05034415051341057,
-0.046601008623838425,
0.07880154997110367,
0.04858936369419098,
0.07095653563737869,
0.01733444444835186,
-0.0126886498183012,
-0.04332607984542847,
-0.008795729838311672,
-0.06552112102508545,
0.020725594833493233,
-0.07976234704256058,
-0.014367977157235146,
0.0262... |
unitary/toxic-bert | 5cc53435803a6e6f1ac8e4b243910d3bf26803ff | 2021-06-07T15:20:33.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1703.04009",
"arxiv:1905.12516",
"transformers"
] | text-classification | false | unitary | null | unitary/toxic-bert | 749,909 | 15 | 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... | [
-0.15935559570789337,
-0.06404557824134827,
0.01823689602315426,
0.076371930539608,
0.12613750994205475,
0.009862762875854969,
0.012756885960698128,
-0.02012854628264904,
0.018156876787543297,
-0.035751380026340485,
0.00043700658716261387,
-0.02509419247508049,
0.06608232110738754,
0.07615... |
cardiffnlp/twitter-roberta-base-sentiment | b636d90b2ed53d7ba6006cefd76f29cd354dd9da | 2022-04-06T08:10:31.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-sentiment | 734,700 | 57 | transformers | # Twitter-roBERTa-base for Sentiment Analysis
This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see [XLM-T](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)).
... | [
-0.08989861607551575,
-0.08094963431358337,
-0.017688533291220665,
0.004349798895418644,
0.02066958136856556,
0.08162334561347961,
0.014570934697985649,
0.06855554133653641,
0.052577290683984756,
0.017106739804148674,
0.022359319031238556,
-0.014590694569051266,
0.046774785965681076,
0.053... |
mrm8488/t5-base-finetuned-question-generation-ap | 7281097a2e51b1b57684b7de9999e32a0250dd83 | 2022-06-06T21:28:57.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:squad",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-finetuned-question-generation-ap | 717,961 | 26 | transformers | ---
language: en
datasets:
- squad
widget:
- text: "answer: Manuel context: Manuel has created RuPERTa-base with the support of HF-Transformers and Google"
---
# T5-base fine-tuned on SQuAD for **Question Generation**
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tune... | [
-0.1065717414021492,
-0.023419689387083054,
-0.0009038501302711666,
0.00982855074107647,
0.052154503762722015,
0.03478077054023743,
0.03118412010371685,
-0.0010152772301808,
0.01159543078392744,
-0.013769790530204773,
0.02345964126288891,
-0.04115462675690651,
0.041412290185689926,
-0.0218... |
google/bert_uncased_L-2_H-128_A-2 | 1ae49ff827beda5996998802695c4cac8e9932c6 | 2021-05-19T17:28:12.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-2_H-128_A-2 | 687,625 | 11 | 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... |
dslim/bert-base-NER | f7c2808a659015eeb8828f3f809a2f1be67a2446 | 2021-09-05T12:00:26.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"en",
"dataset:conll2003",
"arxiv:1810.04805",
"transformers",
"license:mit",
"autotrain_compatible"
] | token-classification | false | dslim | null | dslim/bert-base-NER | 669,498 | 62 | transformers | ---
language: en
datasets:
- conll2003
license: mit
---
# bert-base-NER
## Model description
**bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: locat... | [
-0.08355141431093216,
-0.06715936213731766,
0.06844159960746765,
-0.050951920449733734,
-0.027103595435619354,
-0.008353169076144695,
-0.0054949745535850525,
0.02595699019730091,
0.04241294786334038,
-0.0159318670630455,
-0.019640907645225525,
-0.043519243597984314,
-0.01804971694946289,
0... |
uer/chinese_roberta_L-12_H-768 | b082602ba4eba86f785a6b4e3310eccc394816ee | 2022-07-15T08:16:22.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:1908.08962",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uer | null | uer/chinese_roberta_L-12_H-768 | 649,235 | 2 | transformers | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "北京是[MASK]国的首都。"
---
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658).
[Turc e... | [
-0.07201097905635834,
-0.024355629459023476,
0.05586782842874527,
0.023193588480353355,
-0.03628445044159889,
0.0811743214726448,
-0.027624739333987236,
0.04325660318136215,
-0.03054499253630638,
0.02316826395690441,
0.06062658503651619,
-0.028973720967769623,
0.043243613094091415,
0.04185... |
cl-tohoku/bert-base-japanese-whole-word-masking | ab68bf4a4d55e7772b1fbea6441bdab72aaf949c | 2021-09-23T13:45:34.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ja",
"dataset:wikipedia",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | cl-tohoku | null | cl-tohoku/bert-base-japanese-whole-word-masking | 632,322 | 15 | transformers | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 東北大学で[MASK]の研究をしています。
---
# BERT base Japanese (IPA dictionary, whole word masking enabled)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes in... | [
-0.10098788142204285,
-0.09673559665679932,
0.04363148659467697,
0.021340372040867805,
-0.01171626802533865,
0.08598610013723373,
0.013818634673953056,
0.022945279255509377,
0.01243932917714119,
-0.0005057408707216382,
0.04235122352838516,
-0.03291153535246849,
0.048775240778923035,
0.0368... |
facebook/bart-base | 84358834e73de6a82c22cec1d90eb45ef4f6eba5 | 2022-06-03T09:43:53.000Z | [
"pytorch",
"tf",
"jax",
"bart",
"feature-extraction",
"en",
"arxiv:1910.13461",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/bart-base | 624,921 | 18 | transformers | ---
license: apache-2.0
language: en
---
# BART (base-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first... | [
-0.11465750634670258,
-0.06656375527381897,
0.05786983296275139,
0.008724099956452847,
-0.06584079563617706,
0.04901890456676483,
-0.044143904000520706,
0.008671154268085957,
0.048350218683481216,
-0.06431283056735992,
0.006088439840823412,
-0.013792728073894978,
0.01790396310389042,
-0.00... |
digitalepidemiologylab/covid-twitter-bert | 945b4ea68241df3ccb8554cd1927ba81d2c9ecaa | 2021-05-19T15:52:48.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"en",
"transformers",
"Twitter",
"COVID-19",
"license:mit"
] | null | false | digitalepidemiologylab | null | digitalepidemiologylab/covid-twitter-bert | 608,689 | null | transformers | ---
language: "en"
thumbnail: "https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png"
tags:
- Twitter
- COVID-19
license: mit
---
# COVID-Twitter-BERT (CT-BERT) v1
:warning: _You may want to use the [v2 model](https://huggingface.co/digitalepidemiologyl... | [
-0.08992455899715424,
-0.022449184209108353,
0.01900365948677063,
0.04468945413827896,
0.07851923257112503,
0.037883076816797256,
0.0014895728090777993,
0.06199873238801956,
0.026359664276242256,
0.0018013465451076627,
0.03541679307818413,
-0.026219435036182404,
0.01637023128569126,
0.0679... |
microsoft/layoutlm-base-uncased | ca841ce8d2f46b13b0ac3f635b8eb7d2e1d758d5 | 2021-08-11T05:27:42.000Z | [
"pytorch",
"tf",
"layoutlm",
"arxiv:1912.13318",
"transformers"
] | null | false | microsoft | null | microsoft/layoutlm-base-uncased | 604,081 | 8 | transformers | # LayoutLM
**Multimodal (text + layout/format + image) pre-training for document AI**
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlm)
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document ... | [
-0.0346904881298542,
0.038127411156892776,
0.010263904929161072,
0.01670531928539276,
0.01803665980696678,
0.023607727140188217,
-0.04285275936126709,
0.007251023314893246,
-0.00320497527718544,
-0.02575642429292202,
0.06485345214605331,
0.030597299337387085,
0.04433407634496689,
0.0214996... |
xlnet-base-cased | 593a21e8b79948a7f952811aa44f37d76e23d586 | 2021-09-16T09:43:58.000Z | [
"pytorch",
"tf",
"rust",
"xlnet",
"text-generation",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1906.08237",
"transformers",
"license:mit"
] | text-generation | false | null | null | xlnet-base-cased | 599,543 | 5 | transformers | ---
language: en
license: mit
datasets:
- bookcorpus
- wikipedia
---
# XLNet (base-sized model)
XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in... | [
-0.087545245885849,
-0.06660180538892746,
0.03745153546333313,
-0.008844469673931599,
0.03226645663380623,
0.11173182725906372,
-0.03159862011671066,
0.03530389070510864,
0.03614726662635803,
-0.05470224469900131,
-0.031718652695417404,
-0.008410460315644741,
0.06573323905467987,
0.0371775... |
distilbert-base-multilingual-cased | 6045845d9e2b056487062a98a902d8304d76441f | 2022-07-22T08:13:03.000Z | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | distilbert-base-multilingual-cased | 585,365 | 16 | transformers | ---
language: multilingual
license: apache-2.0
datasets:
- wikipedia
---
# Model Card for DistilBERT base multilingual (cased)
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation... | [
-0.07365205883979797,
-0.09448935091495514,
0.08372806012630463,
-0.0029499151278287172,
0.018073782324790955,
-0.005491136107593775,
-0.01240010466426611,
0.040871065109968185,
0.00942261517047882,
-0.06830482184886932,
-0.0061553907580673695,
-0.03271116688847542,
0.03827552869915962,
0.... |
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | b8ef00830037f9868450f778081ea683e900fe39 | 2022-06-15T18:43:00.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | 584,527 | 43 | sentence-transformers | ---
pipeline_tag: sentence-similarity
language: multilingual
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences &... | [
-0.032052427530288696,
-0.05750957503914833,
-0.018938865512609482,
0.03187490254640579,
0.009913748130202293,
0.06325744837522507,
-0.05041438341140747,
0.03558629751205444,
0.016923602670431137,
-0.07797453552484512,
0.04905039444565773,
-0.0212862528860569,
0.04814773052930832,
0.055095... |
bhadresh-savani/distilbert-base-uncased-emotion | 322caf2a56793969b8221b87bed988f8e7798b8e | 2022-07-06T10:43:55.000Z | [
"pytorch",
"tf",
"jax",
"distilbert",
"text-classification",
"en",
"dataset:emotion",
"arxiv:1910.01108",
"transformers",
"emotion",
"license:apache-2.0",
"model-index"
] | text-classification | false | bhadresh-savani | null | bhadresh-savani/distilbert-base-uncased-emotion | 564,284 | 37 | transformers | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
model-index:
- name: bhadresh-savani/distilbert-base-uncased-emotion
... | [
-0.04636714607477188,
-0.04246510565280914,
-0.041032660752534866,
0.05817602202296257,
0.09009232372045517,
0.0073073990643024445,
-0.012772567570209503,
0.03918372094631195,
-0.016593430191278458,
-0.07036647200584412,
0.025588108226656914,
-0.0977468490600586,
-0.010455331765115261,
-0.... |
sentence-transformers/bert-base-nli-mean-tokens | 18fc720063106176044380e71bad038d01e821d1 | 2022-06-09T12:34:28.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/bert-base-nli-mean-tokens | 528,903 | 9 | sentence-transformers | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
---
**⚠️ 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 ... | [
-0.07522783428430557,
-0.06903247535228729,
0.037927139550447464,
0.030251873657107353,
0.011466043069958687,
0.08804690837860107,
-0.029127534478902817,
0.07562512904405594,
0.016049973666667938,
-0.07408270239830017,
0.026865346357226372,
0.006537787150591612,
0.04384155943989754,
0.0843... |
deepset/minilm-uncased-squad2 | 2f66fe86fb8a3df5b7b07c214a3d33b31d5a133c | 2022-07-25T14:34:52.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/minilm-uncased-squad2 | 515,791 | 8 | transformers | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
model-index:
- name: deepset/minilm-uncased-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- name: Ex... | [
-0.08635865896940231,
-0.00962852779775858,
-0.02705409564077854,
0.026123594492673874,
0.066559799015522,
0.020438177511096,
-0.033974580466747284,
0.004470542073249817,
-0.014128177426755428,
-0.025917278602719307,
0.039885587990283966,
-0.13294433057308197,
-0.011967284604907036,
0.0258... |
gpt2-medium | 8c7ca69f9d24f64c9f3540f9c416d99e16275828 | 2022-07-22T08:01:16.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit"
] | text-generation | false | null | null | gpt2-medium | 515,318 | 4 | transformers | ---
language: en
license: mit
---
# GPT-2 Medium
## 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)
- [Envi... | [
-0.042659010738134384,
-0.022365229204297066,
-0.024635720998048782,
0.016201013699173927,
0.12279670685529709,
-0.010375344194471836,
0.01589481718838215,
0.08776478469371796,
0.026824552565813065,
-0.04380200430750847,
-0.043144773691892624,
0.02396426536142826,
0.035122230648994446,
0.0... |
pysentimiento/robertuito-sentiment-analysis | e3be95c8efad7f480ce8aab2221188ecb78e40f3 | 2022-06-23T13:01:10.000Z | [
"pytorch",
"tf",
"roberta",
"text-classification",
"es",
"arxiv:2106.09462",
"arxiv:2111.09453",
"transformers",
"twitter",
"sentiment-analysis"
] | text-classification | false | pysentimiento | null | pysentimiento/robertuito-sentiment-analysis | 506,297 | 9 | transformers | ---
language:
- es
tags:
- twitter
- sentiment-analysis
---
# Sentiment Analysis in Spanish
## robertuito-sentiment-analysis
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 corpus (around ~5k tweets) of several dial... | [
-0.04791926220059395,
-0.0507703460752964,
-0.016948174685239792,
0.021370219066739082,
0.013451650738716125,
0.046086281538009644,
0.04978032782673836,
0.03664499521255493,
0.06612519919872284,
-0.015097561292350292,
0.059976547956466675,
-0.009501159191131592,
0.012026981450617313,
0.041... |
Helsinki-NLP/opus-mt-fr-en | 967b0840416a86ccf02573c8fedf9dd0e0b42fd6 | 2021-09-09T21:53:38.000Z | [
"pytorch",
"jax",
"marian",
"text2text-generation",
"fr",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fr-en | 490,737 | 5 | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fr-en
* source languages: fr
* target languages: en
* OPUS readme: [fr-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... | [
-0.05996047332882881,
-0.024237744510173798,
0.02489008568227291,
-0.010470354929566383,
0.012844543904066086,
0.10177662968635559,
-0.05968740954995155,
0.037714309990406036,
0.029419874772429466,
-0.011656415648758411,
0.007984437979757786,
-0.04585428163409233,
-0.07401059567928314,
-0.... |
hfl/chinese-roberta-wwm-ext | 5c58d0b8ec1d9014354d691c538661bf00bfdb44 | 2022-03-01T09:13:56.000Z | [
"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 | 485,950 | 51 | 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... |
google/electra-small-discriminator | 153f486d928bcfc213932f8fc91fc2e3c41af769 | 2021-04-29T15:24:16.000Z | [
"pytorch",
"tf",
"jax",
"electra",
"pretraining",
"en",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/electra-small-discriminator | 482,240 | 5 | 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... |
microsoft/layoutlmv2-base-uncased | 5c1ca07c23780c6dc123807def206ae9c4d59aca | 2021-12-23T12:52:53.000Z | [
"pytorch",
"layoutlmv2",
"en",
"arxiv:2012.14740",
"transformers",
"license:cc-by-nc-sa-4.0"
] | null | false | microsoft | null | microsoft/layoutlmv2-base-uncased | 477,930 | 18 | transformers | ---
language: en
license: cc-by-nc-sa-4.0
---
# LayoutLMv2
**Multimodal (text + layout/format + image) pre-training for document AI**
The documentation of this model in the Transformers library can be found [here](https://huggingface.co/docs/transformers/model_doc/layoutlmv2).
[Microsoft Document AI](https://www.mi... | [
-0.10699433833360672,
-0.03563679754734039,
-0.03966660425066948,
0.033492639660835266,
0.09648266434669495,
0.009470691904425621,
-0.06989522278308868,
0.03577287495136261,
0.02407202310860157,
0.007983296178281307,
-0.010976324789226055,
0.019300684332847595,
0.0484066978096962,
0.100375... |
klue/bert-base | 812449f1a6bc736e693db7aa0e513e5e90795a62 | 2021-10-20T15:23:59.000Z | [
"pytorch",
"bert",
"fill-mask",
"ko",
"arxiv:2105.09680",
"transformers",
"korean",
"klue",
"autotrain_compatible"
] | fill-mask | false | klue | null | klue/bert-base | 461,579 | 7 | transformers | ---
language: ko
tags:
- korean
- klue
mask_token: "[MASK]"
widget:
- text: 대한민국의 수도는 [MASK] 입니다.
---
# KLUE BERT base
Pretrained BERT Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.
## How to use
```python
from tra... | [
-0.16914959251880646,
0.012892910279333591,
0.08553741127252579,
0.006892658770084381,
-0.03603089600801468,
0.03273778408765793,
0.027492238208651543,
0.04780479893088341,
0.0014062360860407352,
-0.03538554161787033,
0.0029013738967478275,
-0.06837352365255356,
0.033635981380939484,
0.067... |
sentence-transformers/distilbert-base-nli-mean-tokens | 683b927b0b0f77e70b9a7d15f7f7601a515925a9 | 2022-06-15T19:35:42.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | sentence-transformers | null | sentence-transformers/distilbert-base-nli-mean-tokens | 454,847 | null | sentence-transformers | ---
pipeline_tag: feature-extraction
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 ... | [
-0.05753064900636673,
-0.07321155071258545,
0.01931511051952839,
0.0479629822075367,
0.02940313331782818,
0.07246044278144836,
-0.03149522468447685,
0.06701167672872543,
0.003295734291896224,
-0.0997343510389328,
0.0376710519194603,
0.01934780180454254,
0.04845025762915611,
0.0624055489897... |
sshleifer/distilbart-cnn-12-6 | a4f8f3ea906ed274767e9906dbaede7531d660ff | 2021-06-14T07:51:12.000Z | [
"pytorch",
"jax",
"rust",
"bart",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | sshleifer | null | sshleifer/distilbart-cnn-12-6 | 452,231 | 57 | transformers | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... | [
-0.10242787003517151,
-0.08055665343999863,
0.058801449835300446,
0.001729630515910685,
-0.01020839437842369,
-0.020107010379433632,
-0.09146951138973236,
0.055804893374443054,
-0.03275766223669052,
-0.0814155638217926,
0.047091152518987656,
-0.024555958807468414,
0.02256167307496071,
-0.0... |
SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune | cf3d414acf70f8f8e68108a2efde164b129e6bfa | 2022-06-27T20:56:39.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"feature-extraction",
"arxiv:2104.02443",
"arxiv:1910.09700",
"arxiv:2105.09680",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune | 443,061 | 5 | transformers | ---
tags:
- summarization
widget:
- text: "you are given an array of numbers a and a number b , compute the difference of elements in a and b"
---
# CodeTrans model for program synthesis
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
... | [
-0.09398753941059113,
0.00023620222054887563,
-0.07200116664171219,
-0.025018131360411644,
0.06995216757059097,
0.0002894390490837395,
0.04121631383895874,
0.018724948167800903,
-0.0070031145587563515,
-0.036057427525520325,
-0.012298164889216423,
-0.0036895424127578735,
0.04473763704299927,... |
sentence-transformers/distiluse-base-multilingual-cased-v2 | 896fbacdabde59de4cb8d75dea7b9bff6066015c | 2022-06-15T19:24:30.000Z | [
"pytorch",
"tf",
"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-v2 | 437,878 | 18 | 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-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & ... | [
-0.045195434242486954,
-0.058382563292980194,
-0.0042662229388952255,
0.020876796916127205,
-0.006434501614421606,
0.029410913586616516,
-0.0583835169672966,
0.02599978633224964,
0.029764804989099503,
-0.08146019279956818,
0.035641223192214966,
-0.04075738042593002,
0.05016973987221718,
0.... |
sentence-transformers/paraphrase-xlm-r-multilingual-v1 | 50f7fa9e273db3db51beceacc1b111e4a1a31d34 | 2022-06-15T19:25:39.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/paraphrase-xlm-r-multilingual-v1 | 434,789 | 31 | sentence-transformers | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-xlm-r-multilingual-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensi... | [
-0.042247891426086426,
-0.06216457113623619,
-0.023620810359716415,
0.01813187263906002,
0.022390039637684822,
0.060496531426906586,
-0.04595527797937393,
0.03595336526632309,
0.015518763102591038,
-0.07833635061979294,
0.04575902596116066,
-0.03354158625006676,
0.060676004737615585,
0.043... |
camembert-base | 3f452b6e5a89b0e6c828c9bba2642bc577086eae | 2022-07-22T08:12:31.000Z | [
"pytorch",
"tf",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | null | null | camembert-base | 431,334 | 16 | transformers | ---
language: fr
license: mit
datasets:
- oscar
---
# CamemBERT: a Tasty French Language Model
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-... | [
-0.08638779073953629,
-0.06763307005167007,
-0.025833409279584885,
-0.027342049404978752,
0.03764456510543823,
0.12339358776807785,
-0.02534555271267891,
0.11847586929798126,
0.08251497894525528,
-0.004934415686875582,
-0.004848347045481205,
-0.05448513105511665,
0.059380196034908295,
0.00... |
nlptown/bert-base-multilingual-uncased-sentiment | e06857fdb0325a7798a8fc361b417dfeec3a3b98 | 2022-04-18T16:46:13.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"en",
"nl",
"de",
"fr",
"it",
"es",
"transformers",
"license:mit"
] | text-classification | false | nlptown | null | nlptown/bert-base-multilingual-uncased-sentiment | 429,449 | 57 | transformers | ---
language:
- en
- nl
- de
- fr
- it
- es
license: mit
---
# bert-base-multilingual-uncased-sentiment
This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a n... | [
-0.08215174823999405,
-0.09198115020990372,
-0.0035543793346732855,
0.012485884130001068,
0.013683075085282326,
0.09545306861400604,
0.04701993986964226,
0.052623968571424484,
0.01546592079102993,
-0.021794479340314865,
0.0045181754976511,
-0.04780310392379761,
0.0417378768324852,
0.032698... |
Hate-speech-CNERG/indic-abusive-allInOne-MuRIL | 159b3636af636844106d203e3d8a07f522aaa6e0 | 2022-05-03T08:49:47.000Z | [
"pytorch",
"bert",
"text-classification",
"bn",
"hi",
"hi-en",
"ka-en",
"ma-en",
"mr",
"ta-en",
"ur",
"ur-en",
"en",
"arxiv:2204.12543",
"transformers",
"license:afl-3.0"
] | text-classification | false | Hate-speech-CNERG | null | Hate-speech-CNERG/indic-abusive-allInOne-MuRIL | 425,203 | null | transformers | ---
language: [bn, hi, hi-en, ka-en, ma-en, mr, ta-en, ur, ur-en, en]
license: afl-3.0
---
This model is used detecting **abusive speech** in **Bengali, Devanagari Hindi, Code-mixed Hindi, Code-mixed Kannada, Code-mixed Malayalam, Marathi, Code-mixed Tamil, Urdu, Code-mixed Urdu, and English languages**. The allInOne ... | [
-0.062019575387239456,
-0.04452022537589073,
-0.013807946816086769,
-0.004274517763406038,
0.05298544839024544,
0.007064873352646828,
-0.00453414861112833,
-0.05843058228492737,
-0.012413380667567253,
-0.06542504578828812,
0.02530588209629059,
-0.07623746991157532,
0.04151010140776634,
0.0... |
yiyanghkust/finbert-tone | 69507fb7dad65fd5ee96679690e6336211edc7a5 | 2022-06-09T12:05:27.000Z | [
"pytorch",
"tf",
"text-classification",
"en",
"transformers",
"financial-sentiment-analysis",
"sentiment-analysis"
] | text-classification | false | yiyanghkust | null | yiyanghkust/finbert-tone | 415,031 | 22 | transformers | ---
language: "en"
tags:
- financial-sentiment-analysis
- sentiment-analysis
widget:
- text: "growth is strong and we have plenty of liquidity"
---
`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three ... | [
-0.0377039760351181,
-0.04703419655561447,
-0.019877271726727486,
0.012331523932516575,
0.030871499329805374,
0.05449178069829941,
0.09381328523159027,
0.03868062421679497,
0.07091400027275085,
-0.053262364119291306,
-0.01170447375625372,
0.027527792379260063,
-0.03912686929106712,
0.01071... |
bert-large-uncased-whole-word-masking-finetuned-squad | 242d9dbb66bb5033025196d5678907307f8fb098 | 2021-05-18T16:35:27.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | null | null | bert-large-uncased-whole-word-masking-finetuned-squad | 413,010 | 23 | transformers | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (uncased) whole word masking finetuned on SQuAD
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 i... | [
-0.075973741710186,
-0.10039574652910233,
0.06402284651994705,
0.03697829693555832,
0.010584109462797642,
0.02961229346692562,
0.01904246397316456,
0.008290208876132965,
0.02470485121011734,
-0.013798780739307404,
0.045710984617471695,
-0.003953421488404274,
0.062075424939394,
0.0243134088... |
j-hartmann/emotion-english-distilroberta-base | d23807173703d44b48d60ca252664f60d0d46563 | 2022-06-09T12:43:53.000Z | [
"pytorch",
"tf",
"roberta",
"text-classification",
"en",
"transformers",
"distilroberta",
"sentiment",
"emotion",
"twitter",
"reddit"
] | text-classification | false | j-hartmann | null | j-hartmann/emotion-english-distilroberta-base | 406,862 | 31 | transformers | ---
language: "en"
tags:
- distilroberta
- sentiment
- emotion
- twitter
- reddit
widget:
- text: "Oh wow. I didn't know that."
- text: "This movie always makes me cry.."
- text: "Oh Happy Day"
---
# Emotion English DistilRoBERTa-base
# Description ℹ
With this model, you can classify emotions in English text data.... | [
-0.030246220529079437,
-0.05355704948306084,
0.05665166303515434,
0.03357546031475067,
0.04713347926735878,
-0.0002391643647570163,
0.016336070373654366,
-0.0035251695662736893,
0.0731247067451477,
-0.05102284625172615,
0.04216110333800316,
-0.07010572403669357,
0.02198854461312294,
-0.011... |
sentence-transformers/multi-qa-mpnet-base-dot-v1 | 69cf9082c6abd4f70bdf8fca0ca826b6b5d16ebc | 2022-07-11T21:02:59.000Z | [
"pytorch",
"mpnet",
"fill-mask",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:search_qa",
"dataset:eli5",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/QQP",
"dataset:embedding-... | sentence-similarity | false | sentence-transformers | null | sentence-transformers/multi-qa-mpnet-base-dot-v1 | 398,918 | 9 | sentence-transformers | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- search_qa
- eli5
- natural_questions
- trivia_qa
- embedding-data/QQP
- embedding-data/PAQ_pairs
- embedding-d... | [
-0.03549480810761452,
-0.061646245419979095,
-0.019519338384270668,
0.03635980188846588,
0.04563184082508087,
0.07743945717811584,
-0.031045420095324516,
0.021972540766000748,
-0.0001432829740224406,
-0.05707089230418205,
0.022465497255325317,
-0.011211416684091091,
0.0628516674041748,
0.0... |
openai/clip-vit-large-patch14 | 0993c71e8ad62658387de2714a69f723ddfffacb | 2022-03-14T18:01:04.000Z | [
"pytorch",
"tf",
"jax",
"clip",
"feature-extraction",
"arxiv:2103.00020",
"arxiv:1908.04913",
"transformers",
"vision"
] | feature-extraction | false | openai | null | openai/clip-vit-large-patch14 | 393,559 | 3 | transformers | ---
tags:
- vision
---
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robu... | [
-0.06138596311211586,
-0.0320110097527504,
-0.0014462426770478487,
-0.017546001821756363,
0.08810905367136002,
-0.02513975277543068,
-0.029011132195591927,
0.05683789402246475,
0.0727316364645958,
-0.09636805206537247,
0.03555210307240486,
-0.008171260356903076,
-0.003876835573464632,
0.04... |
valhalla/distilbart-mnli-12-1 | 506336d4214470e3b3b36021358daae28e25ceac | 2021-06-14T10:27:55.000Z | [
"pytorch",
"jax",
"bart",
"text-classification",
"dataset:mnli",
"transformers",
"distilbart",
"distilbart-mnli",
"zero-shot-classification"
] | zero-shot-classification | false | valhalla | null | valhalla/distilbart-mnli-12-1 | 389,752 | 10 | transformers | ---
datasets:
- mnli
tags:
- distilbart
- distilbart-mnli
pipeline_tag: zero-shot-classification
---
# 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/huggingfa... | [
-0.0767815113067627,
-0.005045149940997362,
0.10137208551168442,
-0.0044491710141301155,
-0.08543753623962402,
-0.054864756762981415,
-0.06274072825908661,
0.012675730511546135,
-0.03593569993972778,
-0.08640600740909576,
0.03498023375868797,
-0.07564754039049149,
0.0690762847661972,
-0.06... |
dmis-lab/biosyn-sapbert-bc5cdr-disease | 53d4525fccf15663f19f0d0846c50286a0a01f1e | 2021-10-25T14:46:40.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | dmis-lab | null | dmis-lab/biosyn-sapbert-bc5cdr-disease | 378,648 | 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.... |
dmis-lab/biosyn-sapbert-bc5cdr-chemical | f9b9daf740698ac427bb6532fd456fc18bccdd80 | 2021-10-25T14:47:09.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | dmis-lab | null | dmis-lab/biosyn-sapbert-bc5cdr-chemical | 373,119 | 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.... |
allenai/scibert_scivocab_uncased | 2ab156b969f2dbbd7ecc0080b78bc2cd272c4092 | 2021-05-19T11:41:40.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
] | null | false | allenai | null | allenai/scibert_scivocab_uncased | 369,675 | 21 | 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... |
hfl/chinese-bert-wwm-ext | 2a995a880017c60e4683869e817130d8af548486 | 2021-05-19T19:06:39.000Z | [
"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-ext | 368,889 | 26 | 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.... |
mrm8488/t5-base-finetuned-common_gen | 5c3010b4532b7834039c65580e688e9656626835 | 2021-09-24T08:52:57.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:common_gen",
"arxiv:1910.10683",
"arxiv:1911.03705",
"transformers",
"common sense",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-finetuned-common_gen | 362,815 | 6 | transformers | ---
language: en
tags:
- common sense
datasets:
- common_gen
widget:
- text: "tree plant ground hole dig"
---
# T5-base fine-tuned on CommonGen
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [CommonGen](https://inklab.usc.edu/CommonGen/index.html) for *Generati... | [
-0.09631180018186569,
-0.06494303047657013,
0.10465230792760849,
0.025842756032943726,
0.10590454190969467,
-0.018765488639473915,
-0.0005120787536725402,
0.00932965986430645,
-0.0034256186336278915,
0.03443259373307228,
-0.026334110647439957,
-0.00806945189833641,
0.02498563751578331,
0.0... |
emilyalsentzer/Bio_ClinicalBERT | 41943bf7f983007123c758373c5246305cc536ec | 2022-02-27T13:59:10.000Z | [
"pytorch",
"jax",
"bert",
"en",
"arxiv:1904.03323",
"arxiv:1901.08746",
"transformers",
"fill-mask",
"license:mit"
] | fill-mask | false | emilyalsentzer | null | emilyalsentzer/Bio_ClinicalBERT | 360,523 | 31 | transformers | ---
language: "en"
tags:
- fill-mask
license: mit
---
# ClinicalBERT - Bio + Clinical BERT Model
The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + Pu... | [
-0.13392198085784912,
-0.09290673583745956,
0.0021686838008463383,
-0.037367839366197586,
-0.03704371303319931,
0.06029707193374634,
-0.06899192929267883,
0.12982583045959473,
0.04429659992456436,
-0.015147091820836067,
0.00587890250608325,
0.0380965955555439,
0.01997937075793743,
0.041801... |
dmis-lab/biosyn-sapbert-bc2gn | 28ef41eace90e9aa6a9db372413c145883c72902 | 2022-02-25T13:32:53.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | dmis-lab | null | dmis-lab/biosyn-sapbert-bc2gn | 358,818 | null | transformers | hello
| [
-0.06277179718017578,
0.054958850145339966,
0.05216483399271965,
0.08579003810882568,
-0.08274887502193451,
-0.07457298040390015,
0.06855464726686478,
0.01839636079967022,
-0.08201130479574203,
-0.037384770810604095,
0.012124866247177124,
0.0035182537976652384,
-0.004134270828217268,
-0.04... |
facebook/detr-resnet-50 | 272941311143979e4ade5424ede52fb5e84c9969 | 2022-06-27T08:29:51.000Z | [
"pytorch",
"detr",
"object-detection",
"dataset:coco",
"arxiv:2005.12872",
"transformers",
"vision",
"license:apache-2.0"
] | object-detection | false | facebook | null | facebook/detr-resnet-50 | 355,674 | 48 | transformers | ---
license: apache-2.0
tags:
- object-detection
- vision
datasets:
- coco
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
example_title: Savanna
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
example_title: Football Match
- s... | [
-0.10373219102621078,
-0.008586849085986614,
0.02562749944627285,
-0.021864578127861023,
0.13229957222938538,
-0.054243844002485275,
-0.015863973647356033,
0.014823966659605503,
0.00230921758338809,
-0.012018969282507896,
0.04497916251420975,
-0.04509170725941658,
-0.028047338128089905,
0.... |
google/vit-base-patch16-224 | 5dca96d358b3fcb9d53b3d3881eb1ae20b6752d1 | 2022-06-23T07:42:10.000Z | [
"pytorch",
"tf",
"jax",
"vit",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | google | null | google/vit-base-patch16-224 | 352,185 | 52 | transformers | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
- imagenet-21k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teap... | [
-0.07735730707645416,
0.03693828359246254,
0.019276298582553864,
-0.030712202191352844,
0.07025900483131409,
-0.07447640597820282,
-0.01946285367012024,
0.053060173988342285,
-0.04137694090604782,
-0.021975580602884293,
0.06290626525878906,
-0.034133244305849075,
0.08397805690765381,
0.048... |
sentence-transformers/paraphrase-mpnet-base-v2 | 18df4b22cd35517843308534d066190182ff39ef | 2022-06-15T19:23:23.000Z | [
"pytorch",
"tf",
"mpnet",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/paraphrase-mpnet-base-v2 | 348,258 | 6 | sentence-transformers | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional den... | [
-0.039815086871385574,
-0.0677199587225914,
-0.008281015790998936,
0.031505927443504333,
0.040791913866996765,
0.059537239372730255,
-0.03474612906575203,
0.016238495707511902,
0.0063071660697460175,
-0.06239967420697212,
0.05775539577007294,
0.004985908977687359,
0.055302850902080536,
0.0... |
cross-encoder/nli-distilroberta-base | 99f096e70ef1fb038b8f0aecabc5a0f491684084 | 2021-08-05T08:40:59.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"transformers",
"distilroberta-base",
"license:apache-2.0",
"zero-shot-classification"
] | zero-shot-classification | false | cross-encoder | null | cross-encoder/nli-distilroberta-base | 345,008 | 9 | transformers | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- distilroberta-base
datasets:
- multi_nli
- snli
metrics:
- accuracy
license: apache-2.0
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/example... | [
-0.05010258033871651,
-0.09692981094121933,
-0.046597957611083984,
-0.04686028137803078,
0.060496799647808075,
0.09971753507852554,
-0.05690623074769974,
-0.0018377932719886303,
-0.01317618414759636,
-0.07581351697444916,
0.017678597941994667,
-0.08035063743591309,
-0.008702612482011318,
-... |
finiteautomata/bertweet-base-sentiment-analysis | cf6b0f60e84096e077c171fe3176093674370291 | 2022-06-23T13:01:55.000Z | [
"pytorch",
"tf",
"roberta",
"text-classification",
"en",
"arxiv:2106.09462",
"transformers",
"sentiment-analysis"
] | text-classification | false | finiteautomata | null | finiteautomata/bertweet-base-sentiment-analysis | 338,964 | 18 | transformers | ---
language:
- en
tags:
- sentiment-analysis
---
# Sentiment Analysis in English
## bertweet-sentiment-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is [BERTweet... | [
-0.10483858734369278,
-0.05710936710238457,
-0.007168810814619064,
-0.0029249864164739847,
0.04239180311560631,
0.027894387021660805,
0.06531602889299393,
0.04366088658571243,
0.07999078184366226,
0.007977987639605999,
0.07169503718614578,
-0.007533452473580837,
0.02954231947660446,
0.0595... |
distilbert-base-cased | 8d708decd7afb7bec0af233e5338fe1fca3db705 | 2022-07-22T08:12:05.000Z | [
"pytorch",
"tf",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0"
] | null | false | null | null | distilbert-base-cased | 334,535 | 7 | transformers | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# Model Card for DistilBERT base model (cased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased).
It was introduced in [this paper](https://arxiv.org/abs/1910.01108).
The code for the distillat... | [
-0.11877017468214035,
-0.11439269036054611,
0.10081158578395844,
0.002818940207362175,
-0.03515144810080528,
-0.014470581896603107,
-0.02057008445262909,
0.08113410323858261,
-0.022086936980485916,
-0.07199153304100037,
-0.010488024912774563,
0.05130061134696007,
0.02058270014822483,
0.018... |
yjernite/retribert-base-uncased | aeab2b097862fa41e084db47e0e02229649bbe53 | 2021-03-10T02:54:37.000Z | [
"pytorch",
"retribert",
"feature-extraction",
"transformers"
] | feature-extraction | false | yjernite | null | yjernite/retribert-base-uncased | 332,598 | 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.... |
emilyalsentzer/Bio_Discharge_Summary_BERT | affde836a50e4d333f15dae9270f5a856d59540b | 2022-02-27T13:59:50.000Z | [
"pytorch",
"jax",
"bert",
"en",
"arxiv:1904.03323",
"arxiv:1901.08746",
"transformers",
"fill-mask",
"license:mit"
] | fill-mask | false | emilyalsentzer | null | emilyalsentzer/Bio_Discharge_Summary_BERT | 328,763 | 8 | transformers | ---
language: "en"
tags:
- fill-mask
license: mit
---
# ClinicalBERT - Bio + Discharge Summary BERT Model
The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base ... | [
-0.12280306965112686,
-0.08057813346385956,
0.013495441526174545,
-0.030393457040190697,
-0.015569285489618778,
0.07275798916816711,
-0.05874161794781685,
0.1335667073726654,
0.05952850356698036,
-0.012769686058163643,
0.010991579852998257,
0.014589333906769753,
0.02326119691133499,
0.0653... |
dslim/bert-large-NER | 95c62bc0d4109bd97d0578e5ff482e6b84c2b8b9 | 2022-06-27T20:58:09.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"en",
"dataset:conll2003",
"arxiv:1810.04805",
"transformers",
"license:mit",
"autotrain_compatible"
] | token-classification | false | dslim | null | dslim/bert-large-NER | 327,366 | 10 | transformers | ---
language: en
datasets:
- conll2003
license: mit
---
# bert-base-NER
## Model description
**bert-large-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: loca... | [
-0.09337218105792999,
-0.06468984484672546,
0.06588803976774216,
-0.04721028357744217,
-0.019723959267139435,
-0.009055743925273418,
0.00244380091316998,
0.021299362182617188,
0.04770536348223686,
-0.018067138269543648,
-0.020009981468319893,
-0.04756350815296173,
-0.018890533596277237,
0.... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.