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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 | __index_level_0__ int64 0 38.5k | readme stringlengths 0 186k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 0 | ---
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.... |
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 | 1 | ---
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... |
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 | 2 | ---
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... |
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 | 3 | ---
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... |
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 | 4 | ---
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... |
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 | 5 | ---
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... |
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 | 6 | ---
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:... |
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 | 7 | ---
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... |
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 | 8 | Entry not found |
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 | 9 | ---
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
... |
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 | 10 | ---
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:
... |
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 | 11 | ---
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... |
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 | 12 | ---
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
... |
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 | 13 | ---
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).... |
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 | 14 | ---
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... |
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 | 15 | ---
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... |
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 | 16 | ---
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 ... |
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 | 17 | ---
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
... |
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 | 18 | ---
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 ... |
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 | 19 | ---
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... |
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 | 20 | ---
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-... |
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 | 21 | ---
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... |
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 | 22 | ---
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... |
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 | 23 | ---
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... |
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 | 24 | ---
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... |
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 | 25 | ---
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... |
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 | 26 | ---
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 ... |
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 | 27 | ---
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... |
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 | 28 | ---
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... |
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 | 29 | ## 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... |
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 | 30 | ---
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... |
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 | 31 | ---
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... |
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 | 32 | ---
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... |
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 | 33 | ---
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-... |
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 | 34 | ---
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
-... |
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 | 35 | ---
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.... |
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 | 36 | ---
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}]
```
|
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 | 37 | ---
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... |
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 | 38 | ---
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... |
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 | 39 | ---
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... |
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 | 40 | ---
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... |
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 | 41 | # 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... |
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 | 42 | ---
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... |
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 | 43 | ---
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 ... |
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 | 44 | ---
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... |
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 | 45 | ---
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... |
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 | 46 | ---
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... |
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 | 47 |
<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... |
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 | 48 | # 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)).
... |
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 | 49 | ---
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... |
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 | 50 | ---
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... |
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 | 51 | ---
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... |
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 | 52 | ---
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... |
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 | 53 | ---
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... |
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 | 54 | ---
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... |
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 | 55 | ---
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... |
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 | 56 | # 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 ... |
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 | 57 | ---
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... |
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 | 58 | ---
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... |
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 | 59 | ---
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 &... |
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 | 60 | ---
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
... |
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 | 61 | ---
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 ... |
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 | 62 | ---
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... |
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 | 63 | ---
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... |
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 | 64 | ---
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... |
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 | 65 | ---
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... |
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 | 66 | ---
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 ... |
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 | 67 | ---
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... |
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 | 68 | ---
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... |
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 | 69 | ---
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... |
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 | 70 | ---
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 ... |
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 | 71 | ---
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... |
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 | 72 | ---
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)
... |
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 | 73 | ---
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 & ... |
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 | 74 | ---
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... |
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 | 75 | ---
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-... |
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 | 76 | ---
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... |
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 | 77 | ---
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 ... |
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 | 78 | ---
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 ... |
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 | 79 | ---
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... |
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 | 80 | ---
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.... |
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 | 81 | ---
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... |
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 | 82 | ---
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... |
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 | 83 | ---
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... |
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 | 84 | Entry not found |
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 | 85 | Entry not found |
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 | 86 | # 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.... |
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 | 87 | ---
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... |
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 | 88 | ---
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... |
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 | 89 | ---
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... |
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 | 90 | hello
|
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 | 91 | ---
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... |
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 | 92 | ---
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... |
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 | 93 | ---
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... |
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 | 94 | ---
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... |
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 | 95 | ---
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... |
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 | 96 | ---
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... |
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 | 97 | Entry not found |
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 | 98 | ---
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 ... |
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 | 99 | ---
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... |
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