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 | __index_level_0__ int64 0 38.5k | readme stringlengths 0 186k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
deepset/xlm-roberta-large-squad2 | 089becf104e1928b27123065f4724e93fcbfd879 | 2022-07-25T09:48:49.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"multilingual",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/xlm-roberta-large-squad2 | 60,309 | 18 | transformers | 300 | ---
language: multilingual
tags:
- question-answering
datasets:
- squad_v2
license: cc-by-4.0
model-index:
- name: deepset/xlm-roberta-large-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split... |
microsoft/layoutlmv3-base | 2b54055895563a60a6f828b15b71b81e58fd6f0f | 2022-07-20T09:35:00.000Z | [
"pytorch",
"layoutlmv3",
"en",
"arxiv:2204.08387",
"transformers",
"license:cc-by-nc-sa-4.0"
] | null | false | microsoft | null | microsoft/layoutlmv3-base | 59,950 | 19 | transformers | 301 | ---
language: en
license: cc-by-nc-sa-4.0
---
# LayoutLMv3
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlmv3)
## Model description
LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The sim... |
typeform/mobilebert-uncased-mnli | b60d566014db63a45a440ee32b3e9e9a01d2a1fc | 2021-02-14T09:11:00.000Z | [
"pytorch",
"mobilebert",
"text-classification",
"en",
"dataset:multi_nli",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | false | typeform | null | typeform/mobilebert-uncased-mnli | 59,703 | 1 | transformers | 302 | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- mobilebert
datasets:
- multi_nli
metrics:
- accuracy
---
# MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
This model is the Multi-Genre Natural Language Inference (MNLI) fine-turned version of the [uncased MobileBERT model](https:/... |
sentence-transformers/LaBSE | 931b5f9a111859fa72549cd1a7cb32168ebbe010 | 2022-06-15T19:56:07.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/LaBSE | 59,438 | 25 | sentence-transformers | 303 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
---
# LaBSE
This is a port of the [LaBSE](https://tfhub.dev/google/LaBSE/1) model to PyTorch. It can be used to map 109 languages to a shared vector space.
## Usage (Sente... |
t5-3b | 7a91dcdb0494b6d21c9aec758dac1f33c8db715c | 2022-07-22T08:11:47.000Z | [
"pytorch",
"tf",
"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",
"summarization",
... | translation | false | null | null | t5-3b | 59,284 | 1 | transformers | 304 | ---
language:
- en
- fr
- ro
- de
datasets:
- c4
tags:
- summarization
- translation
license: apache-2.0
---
# Model Card for T5-3B
![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727... |
valhalla/distilbart-mnli-12-3 | ef9a58ce6a9cd44cd0d4c2f7db1cd67f81019a8b | 2021-06-14T10:29:48.000Z | [
"pytorch",
"jax",
"bart",
"text-classification",
"dataset:mnli",
"transformers",
"distilbart",
"distilbart-mnli",
"zero-shot-classification"
] | zero-shot-classification | false | valhalla | null | valhalla/distilbart-mnli-12-3 | 59,222 | 6 | transformers | 305 | ---
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... |
bigscience/T0_3B | 8794c7177e3a67b8a0ec739d94eecfa6a591c974 | 2022-06-21T01:31:56.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | bigscience | null | bigscience/T0_3B | 59,190 | 42 | transformers | 306 | ---
datasets:
- bigscience/P3
language: en
license: apache-2.0
widget:
- text: "A is the son's of B's uncle. What is the family relationship between A and B?"
- text: "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."
- text: "Task: copy but say the opposite.\n
PSG won its match again... |
Rostlab/prot_t5_xl_uniref50 | d604cdc190f7df5186404c8729934f0ee9a4b0e4 | 2021-03-29T11:47:15.000Z | [
"pytorch",
"t5",
"text2text-generation",
"protein",
"dataset:UniRef50",
"transformers",
"protein language model",
"autotrain_compatible"
] | text2text-generation | false | Rostlab | null | Rostlab/prot_t5_xl_uniref50 | 59,027 | 5 | transformers | 307 | ---
language: protein
tags:
- protein language model
datasets:
- UniRef50
---
# ProtT5-XL-UniRef50 model
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://... |
google/pegasus-large | 51b039cd8c644561432f7bfbe75e65f720b38f66 | 2021-09-14T07:50:56.000Z | [
"pytorch",
"tf",
"jax",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | google | null | google/pegasus-large | 58,783 | 21 | transformers | 308 | ---
language: en
tags:
- summarization
---
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@... |
hf-internal-testing/tiny-random-gpt2 | 937b4d23b6648f5a1a0d1247b939b26981798903 | 2021-09-17T19:24:03.000Z | [
"pytorch",
"tf",
"gpt2",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-gpt2 | 57,934 | null | transformers | 309 | Entry not found |
facebook/blenderbot-400M-distill | a2084cb58dd4810f45302724dd07c68051fe9ed3 | 2022-05-16T19:39:21.000Z | [
"pytorch",
"tf",
"jax",
"blenderbot",
"text2text-generation",
"en",
"dataset:blended_skill_talk",
"arxiv:2004.13637",
"transformers",
"convAI",
"conversational",
"facebook",
"license:apache-2.0",
"autotrain_compatible"
] | conversational | false | facebook | null | facebook/blenderbot-400M-distill | 57,741 | 41 | transformers | 310 | ---
language:
- en
thumbnail:
tags:
- convAI
- conversational
- facebook
license: apache-2.0
datasets:
- blended_skill_talk
metrics:
- perplexity
---
## Model description
+ Paper: [Recipes for building an open-domain chatbot]( https://arxiv.org/abs/2004.13637)
+ [Original PARLAI Code](https://parl.ai/projects/recipe... |
princeton-nlp/unsup-simcse-bert-base-uncased | 6504ae026e02a1464538d443b15e36afc318e034 | 2021-05-20T02:57:45.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | princeton-nlp | null | princeton-nlp/unsup-simcse-bert-base-uncased | 57,366 | null | transformers | 311 | Entry not found |
Michau/t5-base-en-generate-headline | f526532f788c45b6b6288286e5ef929fa768ef6a | 2021-06-23T03:17:34.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Michau | null | Michau/t5-base-en-generate-headline | 57,353 | 18 | transformers | 312 | ## About the model
The model has been trained on a collection of 500k articles with headings. Its purpose is to create a one-line heading suitable for the given article.
Sample code with a WikiNews article:
```python
import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
device = torch.device(... |
unitary/multilingual-toxic-xlm-roberta | 19f5c53459ec9679c675aeead38cab87cf588944 | 2021-05-06T11:04:34.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"arxiv:1703.04009",
"arxiv:1905.12516",
"transformers"
] | text-classification | false | unitary | null | unitary/multilingual-toxic-xlm-roberta | 56,831 | 5 | transformers | 313 | ---
pipeline_tag: "text-classification"
---
<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.c... |
flair/ner-english-fast | 3d3d35790f78a00ef319939b9004209d1d05f788 | 2021-02-26T15:39:34.000Z | [
"pytorch",
"en",
"dataset:conll2003",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-english-fast | 56,353 | 3 | flair | 314 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- conll2003
widget:
- text: "George Washington went to Washington"
---
## English NER in Flair (fast model)
This is the fast 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **9... |
facebook/wav2vec2-large-960h-lv60-self | 54074b1c16f4de6a5ad59affb4caa8f2ea03a119 | 2022-05-23T16:13:42.000Z | [
"pytorch",
"tf",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2010.11430",
"arxiv:2006.11477",
"transformers",
"speech",
"audio",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-large-960h-lv60-self | 56,338 | 19 | transformers | 315 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: wav2vec2-large-960h-lv60
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriS... |
bhadresh-savani/bert-base-go-emotion | 6ecebb2840243665ab089020504c52e086862848 | 2021-11-29T10:43:10.000Z | [
"pytorch",
"bert",
"en",
"dataset:go_emotions",
"transformers",
"text-classification",
"go-emotion",
"license:apache-2.0"
] | text-classification | false | bhadresh-savani | null | bhadresh-savani/bert-base-go-emotion | 55,959 | 3 | transformers | 316 | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- go-emotion
- pytorch
license: apache-2.0
datasets:
- go_emotions
metrics:
- Accuracy
---
# Bert-Base-Uncased-Go-Emotion
## Model description:
## Training ... |
cross-encoder/quora-distilroberta-base | 2f10e5b229ecdb2ca204717607c7635897fd645b | 2021-08-05T08:41:31.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/quora-distilroberta-base | 55,355 | null | transformers | 317 | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questi... |
Narsil/deberta-large-mnli-zero-cls | 47eecd0a22df5e7d6ad4d9ff6fa4b6f322db5700 | 2021-08-23T13:27:24.000Z | [
"pytorch",
"deberta",
"text-classification",
"en",
"arxiv:2006.03654",
"transformers",
"deberta-v1",
"deberta-mnli",
"license:mit",
"zero-shot-classification"
] | zero-shot-classification | false | Narsil | null | Narsil/deberta-large-mnli-zero-cls | 54,966 | 3 | transformers | 318 | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
pipeline_tag: zero-shot-classification
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBE... |
flair/ner-english | 627fd305bf597ea90fa54a50228ccfd4b412caf5 | 2021-03-02T22:11:28.000Z | [
"pytorch",
"en",
"dataset:conll2003",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-english | 54,507 | 4 | flair | 319 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- conll2003
widget:
- text: "George Washington went to Washington"
---
## English NER in Flair (default model)
This is the standard 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Sco... |
siebert/sentiment-roberta-large-english | 6eac71655a474ee4d6d0eee7fa532300c537856d | 2022-07-12T18:48:33.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"en",
"arxiv:1907.11692",
"transformers",
"sentiment",
"twitter",
"reviews",
"siebert"
] | text-classification | false | siebert | null | siebert/sentiment-roberta-large-english | 52,445 | 24 | transformers | 320 | ---
language: "en"
tags:
- sentiment
- twitter
- reviews
- siebert
---
## SiEBERT - English-Language Sentiment Classification
# Overview
This model ("SiEBERT", prefix for "Sentiment in English") is a fine-tuned checkpoint of [RoBERTa-large](https://huggingface.co/roberta-large) ([Liu et al. 2019](https://arxiv.org/pd... |
microsoft/infoxlm-large | d616d637f0720deda963cebbfc630657d2b7d3ae | 2021-08-04T11:43:05.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2007.07834",
"transformers",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/infoxlm-large | 52,422 | 2 | transformers | 321 | # InfoXLM
**InfoXLM** (NAACL 2021, [paper](https://arxiv.org/pdf/2007.07834.pdf), [repo](https://github.com/microsoft/unilm/tree/master/infoxlm), [model](https://huggingface.co/microsoft/infoxlm-base)) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
**MD5**
```
05b95b7d9774... |
cl-tohoku/bert-base-japanese-char | 6aa4c7bc39337858fee3e70f258edeada2e308ea | 2021-09-23T13:45:29.000Z | [
"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-char | 52,290 | 4 | transformers | 322 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 仙台は「[MASK]の都」と呼ばれている。
---
# BERT base Japanese (character tokenization)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-... |
vinai/bertweet-covid19-base-uncased | fd00afc23cbc3c3dba662f913d549453f91cb4d4 | 2022-06-08T04:41:56.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vinai | null | vinai/bertweet-covid19-base-uncased | 52,157 | 1 | transformers | 323 | # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.... |
hf-internal-testing/tiny-random-vit | 1870c862512fd2c5c46337626d3fec558aa816f3 | 2022-03-02T15:34:35.000Z | [
"pytorch",
"tf",
"vit",
"image-classification",
"transformers"
] | image-classification | false | hf-internal-testing | null | hf-internal-testing/tiny-random-vit | 52,105 | null | transformers | 324 | Entry not found |
distilbert-base-german-cased | 06b1dc5ba050ddbf462d060df38f906eedb31b01 | 2022-06-03T09:46:31.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | distilbert-base-german-cased | 51,892 | 4 | transformers | 325 | ---
language: de
license: apache-2.0
---
## distilbert-base-german-cased
|
deepset/bert-base-cased-squad2 | 3eb2ba4d2ff1903c1b71e74a8f3640eef57da82d | 2022-07-25T11:35:36.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/bert-base-cased-squad2 | 51,199 | 9 | transformers | 326 | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
---
This is a BERT base cased model trained on SQuAD v2 |
google/byt5-small | ce8f3a48ed7676af36476a01fb01f95ea529599c | 2022-05-27T15:06:27.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
... | text2text-generation | false | google | null | google/byt5-small | 51,139 | 11 | transformers | 327 | ---
language:
- multilingual
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- hi
- hmn
- ht
- hu
- hy
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
-... |
sshleifer/tiny-mbart | 9d6b9b3b2774b464bb6b14eda4efe30f82846136 | 2021-08-26T10:55:11.000Z | [
"pytorch",
"tf",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/tiny-mbart | 50,936 | 4 | transformers | 328 | Entry not found |
monologg/bert-base-cased-goemotions-original | 13c44c849132f82bb61188d909a574badffb27a3 | 2021-05-19T23:48:33.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | monologg | null | monologg/bert-base-cased-goemotions-original | 50,803 | 2 | transformers | 329 | Entry not found |
dmis-lab/biobert-base-cased-v1.2 | 67c9c25b46986521ca33df05d8540da1210b3256 | 2021-06-24T02:54:58.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | dmis-lab | null | dmis-lab/biobert-base-cased-v1.2 | 50,666 | 4 | transformers | 330 | Entry not found |
deepset/sentence_bert | 496b9b39b227f03c4053a9f5fdac1616773b5112 | 2021-05-19T15:34:03.000Z | [
"pytorch",
"jax",
"bert",
"transformers",
"license:apache-2.0"
] | null | false | deepset | null | deepset/sentence_bert | 50,503 | 5 | transformers | 331 | ---
license: apache-2.0
---
This is an upload of the bert-base-nli-stsb-mean-tokens pretrained model from the Sentence Transformers Repo (https://github.com/UKPLab/sentence-transformers)
|
flair/ner-english-ontonotes-large | 4ffb3596f4359f0c8799ea15bbf5dbb3b0915a53 | 2021-05-08T15:35:21.000Z | [
"pytorch",
"en",
"dataset:ontonotes",
"arxiv:2011.06993",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-english-ontonotes-large | 50,495 | 26 | flair | 332 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- ontonotes
widget:
- text: "On September 1st George won 1 dollar while watching Game of Thrones."
---
## English NER in Flair (Ontonotes large model)
This is the large 18-class NER model for English that ships with [Flair](https:... |
facebook/opt-125m | 934b6a077313f3ee660a918a95313f5d0b136c5a | 2022-06-22T09:52:32.000Z | [
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"transformers",
"license:other"
] | text-generation | false | facebook | null | facebook/opt-125m | 50,484 | 13 | transformers | 333 | ---
language: en
inference: false
tags:
- text-generation
- opt
license: other
commercial: false
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://g... |
sberbank-ai/ruRoberta-large | 29b46edec511391c384dfd0bbd3892cb72495c5f | 2021-09-21T19:45:07.000Z | [
"pytorch",
"roberta",
"fill-mask",
"ru",
"transformers",
"PyTorch",
"Transformers",
"autotrain_compatible"
] | fill-mask | false | sberbank-ai | null | sberbank-ai/ruRoberta-large | 50,365 | 11 | transformers | 334 | ---
language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: "https://github.com/sberbank-ai/model-zoo"
---
# ruRoberta-large
Model was trained by [SberDevices](https://sberdevices.ru/) team.
* Task: `mask filling`
* Type: `encoder`
* Tokenizer: `bbpe`
* Dict size: `50 257`
* Num Parameters: `355 M`
* Training Data... |
sentence-transformers/distiluse-base-multilingual-cased-v1 | 756c7aa7d57c27bd1c71a483367c53966465f450 | 2022-06-15T20:11:01.000Z | [
"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-v1 | 49,802 | 10 | sentence-transformers | 335 | ---
pipeline_tag: sentence-similarity
language: multilingual
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/distiluse-base-multilingual-cased-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & ... |
allenai/led-base-16384 | 25756ed025a94fdf2bc4987af86a58fd999047ec | 2021-01-11T14:51:01.000Z | [
"pytorch",
"tf",
"led",
"text2text-generation",
"en",
"arxiv:2004.05150",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/led-base-16384 | 49,616 | 7 | transformers | 336 | ---
language: en
license: apache-2.0
---
## Introduction
[Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer#longformer).
As described in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-base-16384* wa... |
sshleifer/tiny-distilbert-base-cased-distilled-squad | 33a976c7ab7d41310ea4063d311dbf66c8aaa001 | 2020-05-14T16:54:23.000Z | [
"pytorch",
"tf",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | sshleifer | null | sshleifer/tiny-distilbert-base-cased-distilled-squad | 49,350 | null | transformers | 337 | Entry not found |
nlpaueb/bert-base-greek-uncased-v1 | ec2b8f88dd215b5246f2f850413d5bff90d7540d | 2022-03-02T16:32:57.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"el",
"arxiv:2008.12014",
"transformers",
"fill-mask"
] | fill-mask | false | nlpaueb | null | nlpaueb/bert-base-greek-uncased-v1 | 49,226 | 6 | transformers | 338 | ---
language: el
pipeline_tag: fill-mask
thumbnail: https://github.com/nlpaueb/GreekBERT/raw/master/greek-bert-logo.png
widget:
- text: "Σήμερα είναι μια [MASK] μέρα."
---
# GreekBERT
A Greek version of BERT pre-trained language model.
<img src="https://github.com/nlpaueb/GreekBERT/raw/master/greek-bert-logo.png" w... |
IlyaGusev/mbart_ru_sum_gazeta | 3cba0b42de306923e580d5b8e266cc33b5cb289a | 2022-07-13T15:35:33.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"ru",
"dataset:IlyaGusev/gazeta",
"arxiv:2006.11063",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | IlyaGusev | null | IlyaGusev/mbart_ru_sum_gazeta | 48,196 | 11 | transformers | 339 | ---
language:
- ru
tags:
- summarization
- mbart
datasets:
- IlyaGusev/gazeta
license: apache-2.0
inference:
parameters:
no_repeat_ngram_size: 4
widget:
- text: "Высота башни составляет 324 метра (1063 фута), примерно такая же высота, как у 81-этажного здания, и самое высокое сооружение в Париже. Его основание кв... |
nlpaueb/legal-bert-base-uncased | 15b570cbf88259610b082a167dacc190124f60f6 | 2022-04-28T14:42:50.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"en",
"transformers",
"legal",
"license:cc-by-sa-4.0",
"fill-mask"
] | fill-mask | false | nlpaueb | null | nlpaueb/legal-bert-base-uncased | 48,089 | 25 | transformers | 340 | ---
language: en
pipeline_tag: fill-mask
license: cc-by-sa-4.0
thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png
tags:
- legal
widget:
- text: "The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of police."
---
# LEGAL-BERT: The Mupp... |
cross-encoder/ms-marco-MiniLM-L-2-v2 | f4db9595e5310ba9e0cfbf391154583933b533eb | 2021-08-05T08:39:25.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/ms-marco-MiniLM-L-2-v2 | 47,946 | null | transformers | 341 | ---
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).... |
navervision/KELIP | 027d7a67da81f4d2c092f296c47e6e33344dfede | 2022-03-17T11:04:13.000Z | [
"pytorch",
"kelip",
"transformers"
] | null | false | navervision | null | navervision/KELIP | 47,838 | 4 | transformers | 342 | Entry not found |
Tatyana/rubert-base-cased-sentiment-new | a1ff066aeb2b26b5f1b8d793862e51d77a1090d3 | 2021-05-30T23:12:27.000Z | [
"pytorch",
"bert",
"text-classification",
"ru",
"dataset:Tatyana/ru_sentiment_dataset",
"transformers",
"sentiment"
] | text-classification | false | Tatyana | null | Tatyana/rubert-base-cased-sentiment-new | 47,547 | 1 | transformers | 343 | ---
language:
- ru
tags:
- sentiment
- text-classification
datasets:
- Tatyana/ru_sentiment_dataset
---
# RuBERT for Sentiment Analysis
Russian texts sentiment classification.
Model trained on [Tatyana/ru_sentiment_dataset](https://huggingface.co/datasets/Tatyana/ru_sentiment_dataset)
## Labels meaning
0: NEUTRA... |
allenai/specter | c15597dc3bf1f00444f1c5a59c9bb80c93499635 | 2022-06-25T16:04:29.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"en",
"dataset:SciDocs",
"arxiv:2004.07180",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | allenai | null | allenai/specter | 47,052 | 14 | transformers | 344 | ---
language: en
thumbnail: "https://camo.githubusercontent.com/7d080b7a769f7fdf64ac0ebeb47b039cb50be35287e3071f9d633f0fe33e7596/68747470733a2f2f692e6962622e636f2f33544331576d472f737065637465722d6c6f676f2d63726f707065642e706e67"
license: apache-2.0
datasets:
- SciDocs
metrics:
- F1
- accuracy
- map
- ndcg
---
## SPECT... |
microsoft/layoutxlm-base | b95ef788341ccd507115d74e10c4bb7137559f19 | 2022-06-15T14:51:06.000Z | [
"pytorch",
"layoutlmv2",
"arxiv:2104.08836",
"transformers",
"license:cc-by-nc-sa-4.0"
] | null | false | microsoft | null | microsoft/layoutxlm-base | 46,743 | 22 | transformers | 345 | ---
license: cc-by-nc-sa-4.0
---
# LayoutXLM
**Multimodal (text + layout/format + image) pre-training for document AI**
LayoutXLM is a multilingual variant of LayoutLMv2.
The documentation of this model in the Transformers library can be found [here](https://huggingface.co/docs/transformers/model_doc/layoutxlm).
[... |
Helsinki-NLP/opus-mt-ko-en | 8bf548f19accb8fdc96055608840f5a0c194ec8d | 2020-08-21T14:42:47.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ko",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ko-en | 45,612 | 2 | transformers | 346 | ---
language:
- ko
- en
tags:
- translation
license: apache-2.0
---
### kor-eng
* source group: Korean
* target group: English
* OPUS readme: [kor-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-eng/README.md)
* model: transformer-align
* source language(s): kor kor_Hang kor_Latn... |
cambridgeltl/SapBERT-from-PubMedBERT-fulltext | c1f013fb438445557fa71a012928e233a9c5c777 | 2021-05-24T09:59:06.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"arxiv:2010.11784",
"transformers"
] | feature-extraction | false | cambridgeltl | null | cambridgeltl/SapBERT-from-PubMedBERT-fulltext | 44,769 | 3 | transformers | 347 | ---
language: en
tags:
- biomedical
- lexical-semantics
datasets:
- UMLS
**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
**[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
### SapBERT-PubMedBERT
SapBERT by [Liu et al. (2020)](https... |
BeIR/query-gen-msmarco-t5-large-v1 | 5dd8dd401d24332c17e40015e9792ee31f3ced91 | 2021-06-23T02:12:04.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | BeIR | null | BeIR/query-gen-msmarco-t5-large-v1 | 43,945 | 9 | transformers | 348 | # Query Generation
This model is the t5-base model from [docTTTTTquery](https://github.com/castorini/docTTTTTquery).
The T5-base model was trained on the [MS MARCO Passage Dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking), which consists of about 500k real search queries from Bing together with the releva... |
Xenova/sponsorblock-small | 5261e7056338c5a91dd6e153314536f44a182b03 | 2022-02-08T16:56:09.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Xenova | null | Xenova/sponsorblock-small | 43,756 | 1 | transformers | 349 | Entry not found |
EColi/SB_Classifier | dc4dce65613d29abd9c20b054a0a0c7abd0c6cb6 | 2022-04-20T17:27:13.000Z | [
"pytorch",
"bert",
"text-classification",
"generic"
] | text-classification | false | EColi | null | EColi/SB_Classifier | 43,746 | null | generic | 350 | ---
tags:
- text-classification
- generic
library_name: generic
widget:
- text: 'This video is sponsored by squarespace'
example_title: Sponsor
- text: 'Check out the merch at linustechtips.com'
example_title: Unpaid/self promotion
- text: "Don't forget to like, comment and subscribe"
example_title: Interaction r... |
dmis-lab/biobert-base-cased-v1.1 | 924f12e0c3db7f156a765ad53fb6b11e7afedbc8 | 2020-10-14T07:02:59.000Z | [
"pytorch",
"transformers"
] | null | false | dmis-lab | null | dmis-lab/biobert-base-cased-v1.1 | 43,360 | 7 | transformers | 351 | Entry not found |
indobenchmark/indobert-base-p1 | c2cd0b51ddce6580eb35263b39b0a1e5fb0a39e2 | 2021-05-19T20:22:23.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"transformers",
"indobert",
"indobenchmark",
"indonlu",
"license:mit"
] | feature-extraction | false | indobenchmark | null | indobenchmark/indobert-base-p1 | 42,423 | 1 | transformers | 352 | ---
language: id
tags:
- indobert
- indobenchmark
- indonlu
license: mit
inference: false
datasets:
- Indo4B
---
# IndoBERT Base Model (phase1 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a ma... |
rasa/LaBSE | e615b58364f13c7be81e15ccea2ab27a6c483b76 | 2021-05-20T04:01:27.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | rasa | null | rasa/LaBSE | 42,409 | 7 | transformers | 353 | Entry not found |
microsoft/swin-base-patch4-window7-224-in22k | 790d9b6014f6d157cc34d70afc0604eccc92dadd | 2022-05-16T18:11:16.000Z | [
"pytorch",
"tf",
"swin",
"image-classification",
"dataset:imagenet-21k",
"arxiv:2103.14030",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/swin-base-patch4-window7-224-in22k | 42,311 | 3 | transformers | 354 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- 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: Teapot
- src: http... |
bert-large-cased-whole-word-masking-finetuned-squad | ba9ccd18e456b6c6a63a3ea5b21776f05452d923 | 2021-05-18T16:22:37.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"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-cased-whole-word-masking-finetuned-squad | 42,243 | null | transformers | 355 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (cased) 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 in
... |
flair/ner-english-ontonotes-fast | 38a8eb6a720791da55e15962c36a37dd8d8270b2 | 2021-03-02T22:05:17.000Z | [
"pytorch",
"en",
"dataset:ontonotes",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-english-ontonotes-fast | 42,162 | 7 | flair | 356 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- ontonotes
widget:
- text: "On September 1st George Washington won 1 dollar."
---
## English NER in Flair (Ontonotes fast model)
This is the fast version of the 18-class NER model for English that ships with [Flair](https://githu... |
VietAI/gpt-neo-1.3B-vietnamese-news | fbe35b344fc44b1cd58d0c7a4130310eb8894265 | 2021-10-10T16:44:31.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"vi",
"transformers",
"causal-lm",
"gpt"
] | text-generation | false | VietAI | null | VietAI/gpt-neo-1.3B-vietnamese-news | 41,653 | 2 | transformers | 357 | ---
language:
- vi
tags:
- pytorch
- causal-lm
- gpt
---
# GPT-Neo 1.3B for Vietnamese News
Details will be available soon.
For more information, please contact anhduongng.1001@gmail.com / imthanhlv@gmail.com / nguyenvulebinh@gmail.com. |
google/t5-xxl-lm-adapt | 7c856f0142a6655ee44e2fd00fcc9f6d35fff56f | 2021-11-01T14:23:24.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"transformers",
"t5-lm-adapt",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-xxl-lm-adapt | 41,589 | 3 | transformers | 358 | ---
language: en
datasets:
- c4
tags:
- t5-lm-adapt
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transform... |
sentence-transformers/multi-qa-mpnet-base-cos-v1 | bd0b4f6d767d5cb937b4c1a9611df492a80e891a | 2021-08-24T21:07:06.000Z | [
"pytorch",
"mpnet",
"fill-mask",
"sentence-transformers",
"feature-extraction",
"sentence-similarity"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/multi-qa-mpnet-base-cos-v1 | 41,510 | 6 | sentence-transformers | 359 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-qa-mpnet-base-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**... |
openai/clip-vit-base-patch16 | 6cef4adda11be098f7c823c95de721298611f514 | 2022-03-14T18:00:36.000Z | [
"pytorch",
"jax",
"clip",
"feature-extraction",
"arxiv:2103.00020",
"arxiv:1908.04913",
"transformers",
"vision"
] | feature-extraction | false | openai | null | openai/clip-vit-base-patch16 | 41,138 | 7 | transformers | 360 | ---
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 robust... |
sentence-transformers/roberta-base-nli-stsb-mean-tokens | 903ef0c8897802c3209d82aa46b1c897ac56cf28 | 2022-06-15T20:49:42.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/roberta-base-nli-stsb-mean-tokens | 41,072 | null | sentence-transformers | 361 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... |
airesearch/wangchanberta-base-att-spm-uncased | abe46f39cf2c911a6ad5ec8299bdf7503edc95e4 | 2022-02-16T14:42:32.000Z | [
"pytorch",
"camembert",
"fill-mask",
"th",
"arxiv:1907.11692",
"arxiv:1801.06146",
"arxiv:1808.06226",
"arxiv:2101.09635",
"transformers",
"autotrain_compatible"
] | fill-mask | false | airesearch | null | airesearch/wangchanberta-base-att-spm-uncased | 41,065 | 9 | transformers | 362 | ---
language: th
widget:
- text: "ผู้ใช้งานท่าอากาศยานนานาชาติ<mask>มีกว่าสามล้านคน<pad>"
---
# WangchanBERTa base model: `wangchanberta-base-att-spm-uncased`
<br>
Pretrained RoBERTa BASE model on assorted Thai texts (78.5 GB).
The script and documentation can be found at [this repository](https://github.com/vistec-... |
pdelobelle/robbert-v2-dutch-ner | 64e413ebaf94d058544dd6bce531c66c3116e652 | 2022-07-05T13:23:41.000Z | [
"pytorch",
"jax",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | pdelobelle | null | pdelobelle/robbert-v2-dutch-ner | 40,831 | null | transformers | 363 | Entry not found |
monologg/koelectra-base-v3-discriminator | 68b30cd259f34a4b5aa8786392612ba2a2617fcc | 2021-10-20T16:53:40.000Z | [
"pytorch",
"electra",
"pretraining",
"ko",
"transformers",
"korean",
"license:apache-2.0"
] | null | false | monologg | null | monologg/koelectra-base-v3-discriminator | 40,481 | 13 | transformers | 364 | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA v3 (Base Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-v3-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model a... |
textattack/bert-base-uncased-ag-news | fe417ad660b1657142f66353a184dc0c7e6d2e48 | 2021-05-20T07:40:21.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/bert-base-uncased-ag-news | 40,413 | 2 | transformers | 365 | ## TextAttack Model CardThis `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a c... |
mrm8488/bert-small-finetuned-squadv2 | 3ffb743e93b64bc944f778292a71ebac650834ae | 2021-05-20T00:33:09.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"arxiv:1908.08962",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/bert-small-finetuned-squadv2 | 40,088 | null | transformers | 366 | ---
language: en
thumbnail:
---
# BERT-Small fine-tuned on SQuAD v2
[BERT-Small](https://github.com/google-research/bert/) created by [Google Research](https://github.com/google-research) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
**Mode size** (after trai... |
Helsinki-NLP/opus-mt-fi-en | 7fb1e75696c8b8930df5afae6bb5d22ffca4ed30 | 2021-01-18T08:32:43.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-en | 40,083 | 1 | transformers | 367 | ---
language:
- fi
- en
tags:
- translation
license: apache-2.0
---
### fin-eng
* source group: Finnish
* target group: English
* OPUS readme: [fin-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md)
* model: transformer-align
* source language(s): fin
* target languag... |
albert-large-v2 | c76159dc6b4d18f16d303451ae64b4f34a7d0d63 | 2021-01-13T15:35:47.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | albert-large-v2 | 39,393 | 5 | transformers | 368 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT Large 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-res... |
microsoft/deberta-large | 822a8791fdac38e8086e2731158047e9b63e4521 | 2022-01-13T17:10:16.000Z | [
"pytorch",
"tf",
"deberta",
"en",
"arxiv:2006.03654",
"transformers",
"deberta-v1",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-large | 38,677 | 9 | transformers | 369 | ---
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... |
rinna/japanese-gpt-1b | a3c6e8478d5afa92fe5174b984555e01fe378cd3 | 2022-02-18T04:46:46.000Z | [
"pytorch",
"gpt2",
"text-generation",
"ja",
"dataset:cc100",
"dataset:wikipedia",
"dataset:c4",
"transformers",
"japanese",
"gpt",
"lm",
"nlp",
"license:mit"
] | text-generation | false | rinna | null | rinna/japanese-gpt-1b | 38,593 | 20 | transformers | 370 | ---
language: ja
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
tags:
- ja
- japanese
- gpt
- text-generation
- lm
- nlp
license: mit
datasets:
- cc100
- wikipedia
- c4
widget:
- text: "西田幾多郎は、"
---
# japanese-gpt-1b
This repository provides a 1.3B-p... |
cross-encoder/ms-marco-TinyBERT-L-2-v2 | e9ea2688951463fc2791a2ea2ddfce6762900675 | 2021-08-05T08:39:45.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/ms-marco-TinyBERT-L-2-v2 | 38,423 | 1 | transformers | 371 | ---
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).... |
flair/ner-german-large | d8943c40a867161a5a5b7ce91f31adaea1c3a424 | 2021-05-08T15:36:43.000Z | [
"pytorch",
"de",
"dataset:conll2003",
"arxiv:2011.06993",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-german-large | 38,327 | 6 | flair | 372 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: de
datasets:
- conll2003
widget:
- text: "George Washington ging nach Washington"
---
## German NER in Flair (large model)
This is the large 4-class NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: *... |
csebuetnlp/mT5_multilingual_XLSum | 361416d0a10fe5df7e139081f3b5476fd39c860f | 2021-10-03T13:14:22.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"am",
"ar",
"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
"ha",
"hi",
"ig",
"id",
"ja",
"rn",
"ko",
"ky",
"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",
"si",
"so",
"es",
"sw",
"ta",
"te... | summarization | false | csebuetnlp | null | csebuetnlp/mT5_multilingual_XLSum | 37,992 | 46 | transformers | 373 | ---
tags:
- summarization
- mT5
datasets:
- csebuetnlp/xlsum
language:
- am
- ar
- az
- bn
- my
- zh
- en
- fr
- gu
- ha
- hi
- ig
- id
- ja
- rn
- ko
- ky
- mr
- ne
- om
- ps
- fa
- pcm
- pt
- pa
- ru
- gd
- sr
- si
- so
- es
- sw
- ta
- te
- th
- ti
- tr
- uk
- ur
- uz
- vi
- cy
- yo
licenses:
- cc-by-nc-sa-4.0
widge... |
textattack/albert-base-v2-yelp-polarity | bbb5fb3997de43eedb58f7c74b8fbd63c719b5dd | 2020-07-06T16:37:10.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/albert-base-v2-yelp-polarity | 37,888 | null | transformers | 374 | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 512.
Since this was... |
monologg/kobert | 8ebf2818cfd85570737d31ed8cd7aaa000e7056c | 2021-05-19T23:52:30.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | monologg | null | monologg/kobert | 37,585 | 5 | transformers | 375 | Entry not found |
mrm8488/bert-medium-finetuned-squadv2 | 881ce1995ab82387a14f63cf50c845afb8f6f724 | 2021-05-20T00:25:00.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"arxiv:1908.08962",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/bert-medium-finetuned-squadv2 | 37,108 | 1 | transformers | 376 | ---
language: en
thumbnail:
---
# BERT-Medium fine-tuned on SQuAD v2
[BERT-Medium](https://github.com/google-research/bert/) created by [Google Research](https://github.com/google-research) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
**Mode size** (after tr... |
YituTech/conv-bert-base | 5cb451936b5c4a96562d8b146de85f64f9cf2c22 | 2021-02-24T11:26:14.000Z | [
"pytorch",
"tf",
"convbert",
"feature-extraction",
"transformers"
] | feature-extraction | false | YituTech | null | YituTech/conv-bert-base | 36,924 | null | transformers | 377 | Entry not found |
dangvantuan/sentence-camembert-large | 3c04b3d31c3b8ab520fd9cb474b6f50ad4b7a9a1 | 2022-07-22T22:33:07.000Z | [
"pytorch",
"tf",
"camembert",
"feature-extraction",
"fr",
"dataset:stsb_multi_mt",
"arxiv:1908.10084",
"transformers",
"Text",
"Sentence Similarity",
"Sentence-Embedding",
"camembert-large",
"license:apache-2.0",
"sentence-similarity",
"model-index"
] | sentence-similarity | false | dangvantuan | null | dangvantuan/sentence-camembert-large | 36,830 | 5 | transformers | 378 | ---
pipeline_tag: sentence-similarity
language: fr
datasets:
- stsb_multi_mt
tags:
- Text
- Sentence Similarity
- Sentence-Embedding
- camembert-large
license: apache-2.0
model-index:
- name: sentence-camembert-large by Van Tuan DANG
results:
- task:
name: Sentence-Embedding
type: Text Similarity
d... |
DeepPavlov/bert-base-multilingual-cased-sentence | 403febddd8959ecc1a8d140a83d461a1261c7935 | 2021-05-18T18:16:12.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1704.05426",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
] | feature-extraction | false | DeepPavlov | null | DeepPavlov/bert-base-multilingual-cased-sentence | 36,729 | null | transformers | 379 | ---
language:
- multilingual
---
# bert-base-multilingual-cased-sentence
Sentence Multilingual BERT \(101 languages, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) is a representation‑based sentence encoder for 101 languages of Multilingual BERT. It is initialized with Multilingual BERT and then fine‑tuned ... |
deepset/gbert-base | 4a45e506eccc3405ed2e2a0502995d3f7e483509 | 2022-02-17T14:05:19.000Z | [
"pytorch",
"tf",
"fill-mask",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"arxiv:2010.10906",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | deepset | null | deepset/gbert-base | 36,687 | 13 | transformers | 380 | ---
language: de
license: mit
datasets:
- wikipedia
- OPUS
- OpenLegalData
---
# German BERT base
Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [pap... |
sentence-transformers/msmarco-distilbert-base-v4 | 62b749054617919f8d1e8462a987edea4b998e3c | 2022-06-15T19:32:25.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-distilbert-base-v4 | 36,505 | 1 | sentence-transformers | 381 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-distilbert-base-v4
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional d... |
M-CLIP/M-BERT-Base-ViT-B | 5da718394f8f62314bb080b1e989e61f5e3ce026 | 2021-05-18T21:34:39.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | M-CLIP | null | M-CLIP/M-BERT-Base-ViT-B | 36,232 | 5 | transformers | 382 | <br />
<p align="center">
<h1 align="center">M-BERT Base ViT-B</h1>
<p align="center">
<a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/M-BERT%20Base%20ViT-B">Github Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you nee... |
ntu-spml/distilhubert | 9c4eece5b1dd98770108a416c101096fb04813de | 2021-11-05T12:43:24.000Z | [
"pytorch",
"hubert",
"feature-extraction",
"en",
"dataset:librispeech_asr",
"arxiv:2110.01900",
"transformers",
"speech",
"license:apache-2.0"
] | feature-extraction | false | ntu-spml | null | ntu-spml/distilhubert | 36,130 | 7 | transformers | 383 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# DistilHuBERT
[DistilHuBERT by NTU Speech Processing & Machine Learning Lab](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller)
The base model pretrained on 16kHz sampled speech audio. When using the model make sur... |
bigscience/bloom | d9bf58e6d318c7760664d16167a62debfd237554 | 2022-07-29T09:32:01.000Z | [
"pytorch",
"tensorboard",
"bloom",
"feature-extraction",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
... | text-generation | false | bigscience | null | bigscience/bloom | 36,017 | 712 | transformers | 384 | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
programming_language:
- C
- C++
- C... |
beomi/KcELECTRA-base | 686333e78646593e324d6ad5e955dfb6dc9f0f5d | 2022-06-26T01:49:50.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"transformers"
] | null | false | beomi | null | beomi/KcELECTRA-base | 35,838 | 4 | transformers | 385 | Entry not found |
albert-xxlarge-v2 | aaec31cf649a4d91a96b11f83eb5b2985eaf8ee5 | 2021-01-13T15:33:03.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | albert-xxlarge-v2 | 35,631 | 5 | transformers | 386 | ---
tags:
- exbert
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT XXLarge 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://gith... |
sentence-transformers/nli-mpnet-base-v2 | c388b46d029476cd6611aa9ed44d05272bbbacfb | 2022-06-15T20:14:17.000Z | [
"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/nli-mpnet-base-v2 | 35,533 | 1 | sentence-transformers | 387 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/nli-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vect... |
facebook/mbart-large-cc25 | 2df0e6dd8a0e7f6df056fe4d0d95941a04b64e4f | 2021-03-10T03:48:19.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"en",
"ar",
"cs",
"de",
"et",
"fi",
"fr",
"gu",
"hi",
"it",
"ja",
"kk",
"ko",
"lt",
"lv",
"my",
"ne",
"nl",
"ro",
"ru",
"si",
"tr",
"vi",
"zh",
"multilingual",
"transformers",
"translation",
"autotrain_compatible... | translation | false | facebook | null | facebook/mbart-large-cc25 | 35,330 | 15 | transformers | 388 | ---
tags:
- translation
language:
- en
- ar
- cs
- de
- et
- fi
- fr
- gu
- hi
- it
- ja
- kk
- ko
- lt
- lv
- my
- ne
- nl
- ro
- ru
- si
- tr
- vi
- zh
- multilingual
---
#### mbart-large-cc25
Pretrained (not finetuned) multilingual mbart model.
Original Languages
```
export langs=ar_AR,cs_CZ,de_DE,en_XX,es_XX,et... |
facebook/blenderbot_small-90M | a2a23a425b397872915db19bdee2522877eddc14 | 2021-12-02T08:09:04.000Z | [
"pytorch",
"tf",
"jax",
"blenderbot-small",
"text2text-generation",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"transformers",
"convAI",
"conversational",
"facebook",
"license:apache-2.0",
"autotrain_compatible"
] | conversational | false | facebook | null | facebook/blenderbot_small-90M | 35,264 | 12 | transformers | 389 | ---
language:
- en
thumbnail:
tags:
- convAI
- conversational
- facebook
license: apache-2.0
datasets:
- blended_skill_talk
metrics:
- perplexity
---
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes... |
classla/bcms-bertic-ner | 4bd46a99b73827a3f6a095ceafa08b6933986dc0 | 2022-02-04T14:26:47.000Z | [
"pytorch",
"electra",
"token-classification",
"hr",
"bs",
"sr",
"cnr",
"hbs",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | classla | null | classla/bcms-bertic-ner | 35,225 | 2 | transformers | 390 | ---
language:
- hr
- bs
- sr
- cnr
- hbs
widget:
- text: "Zovem se Marko i živim u Zagrebu. Studirao sam u Beogradu na Filozofskom fakultetu. Obožavam album Moanin."
license: apache-2.0
---
# The [BERTić](https://huggingface.co/classla/bcms-bertic)* [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named e... |
sentence-transformers/paraphrase-distilroberta-base-v2 | d9461390caf1e64923d00bc55fa02d3c1ed2b9e5 | 2022-06-15T19:42:26.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/paraphrase-distilroberta-base-v2 | 35,187 | 3 | sentence-transformers | 391 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-distilroberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensi... |
sentence-transformers/paraphrase-TinyBERT-L6-v2 | 8fe7263a517189c4a11a98f87db8ac964b235b5f | 2022-06-15T20:12:46.000Z | [
"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-TinyBERT-L6-v2 | 35,010 | null | sentence-transformers | 392 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-TinyBERT-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional de... |
valhalla/t5-base-e2e-qg | c652651334cd5516f2bd0f0fb5303a01a678024e | 2021-06-23T14:40:07.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:squad",
"arxiv:1910.10683",
"transformers",
"question-generation",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/t5-base-e2e-qg | 34,949 | 2 | transformers | 393 | ---
datasets:
- squad
tags:
- question-generation
widget:
- text: "Python is a programming language. It is developed by Guido Van Rossum and released in 1991. </s>"
license: mit
---
## T5 for question-generation
This is [t5-base](https://arxiv.org/abs/1910.10683) model trained for end-to-end question generation task. ... |
microsoft/graphcodebert-base | 2ff24803553d2274dd118c7ea20e9b37a5804b11 | 2021-07-21T16:26:39.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/graphcodebert-base | 34,654 | 7 | transformers | 394 | Entry not found |
hf-internal-testing/tiny-random-t5 | 2f582cd79ed5795b71539951d237945bc1c5ac7e | 2022-05-02T14:37:37.000Z | [
"pytorch",
"tf",
"t5",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-t5 | 34,603 | null | transformers | 395 | Entry not found |
hf-internal-testing/tiny-random-bigbird_pegasus | 21ef3274d4148d5299e862b2c80a46713fc688f6 | 2021-09-17T19:22:17.000Z | [
"pytorch",
"bigbird_pegasus",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-bigbird_pegasus | 34,545 | null | transformers | 396 | Entry not found |
deepset/gbert-large | f6bca479ebb46e62ac99c03282a5030139e302f4 | 2022-02-17T14:05:45.000Z | [
"pytorch",
"tf",
"fill-mask",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"dataset:oscar",
"arxiv:2010.10906",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | deepset | null | deepset/gbert-large | 34,526 | 10 | transformers | 397 | ---
language: de
license: mit
datasets:
- wikipedia
- OPUS
- OpenLegalData
- oscar
---
# German BERT large
Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In... |
cahya/xlm-roberta-large-indonesian-NER | d0ef1c27f757b1c21ab299ccfb25fe858ac77ed4 | 2020-09-23T15:55:50.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | cahya | null | cahya/xlm-roberta-large-indonesian-NER | 34,151 | 1 | transformers | 398 | Entry not found |
facebook/detr-resnet-50-panoptic | fc15262cfd4c13cbdad6d1d55ff0cd31a2251a27 | 2022-06-27T08:30:08.000Z | [
"pytorch",
"detr",
"image-segmentation",
"dataset:coco",
"arxiv:2005.12872",
"transformers",
"vision",
"license:apache-2.0"
] | image-segmentation | false | facebook | null | facebook/detr-resnet-50-panoptic | 34,102 | 30 | transformers | 399 | ---
license: apache-2.0
tags:
- image-segmentation
- vision
datasets:
- coco
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
example_title: Football Match
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/dog-cat.jpg
example_title: Dog & Cat... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.