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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ai4bharat/IndicBARTSS | 4b2669d25bc24a46ad2501c2b759451b7a4a1a26 | 2022-03-15T05:48:12.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2109.02903",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ai4bharat | null | ai4bharat/IndicBARTSS | 2,151 | 2 | transformers | 1,300 | IndicBARTSS is a multilingual, sequence-to-sequence pre-trained model focusing on Indic languages and English. It currently supports 11 Indian languages and is based on the mBART architecture. You can use IndicBARTSS model to build natural language generation applications for Indian languages by finetuning the model wi... |
zjukg/OntoProtein | d27d23e56e1b565958a5016eaf82847fa08a427a | 2022-04-12T14:42:54.000Z | [
"pytorch",
"bert",
"fill-mask",
"protein",
"dataset:ProteinKG25",
"transformers",
"protein language model",
"autotrain_compatible"
] | fill-mask | false | zjukg | null | zjukg/OntoProtein | 2,150 | 3 | transformers | 1,301 | ---
language: protein
tags:
- protein language model
datasets:
- ProteinKG25
widget:
- text: "D L I P T S S K L V V [MASK] D T S L Q V K K A F F A L V T"
---
# OntoProtein model
Pretrained model on protein sequences using masked language modeling (MLM) and knowledge embedding (KE) objective objective. It was introdu... |
prajjwal1/bert-medium-mnli | 82e4a3118f63cba6e97875aa1b7e6a674a193063 | 2021-10-05T17:56:07.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1908.08962",
"arxiv:2110.01518",
"transformers"
] | text-classification | false | prajjwal1 | null | prajjwal1/bert-medium-mnli | 2,149 | null | transformers | 1,302 | 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). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](... |
sentence-transformers/all-MiniLM-L12-v1 | c8f1d5b49a00a0b0025e540ceca2c38101fc926f | 2021-08-30T20:01:21.000Z | [
"pytorch",
"bert",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/all-MiniLM-L12-v1 | 2,148 | 2 | sentence-transformers | 1,303 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
---
# all-MiniLM-L12-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used... |
shahrukhx01/roberta-base-boolq | 87b8505e8f651d5aadedb50ea6737871a45a83b8 | 2022-06-02T08:36:14.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"transformers",
"boolean-qa"
] | text-classification | false | shahrukhx01 | null | shahrukhx01/roberta-base-boolq | 2,147 | null | transformers | 1,304 | ---
language: "en"
tags:
- boolean-qa
widget:
- text: "Is Berlin the smallest city of Germany? <s> Berlin is the capital and largest city of Germany by both area and population. Its 3.8 million inhabitants make it the European Union's most populous city, according to the population within city limits "
---
# Labels Ma... |
artemnech/enrut5-base | 13523cbed3ee1390197d050cc52b0e6f9aa3ea45 | 2022-07-25T05:17:35.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"ru",
"en",
"transformers",
"russian",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | artemnech | null | artemnech/enrut5-base | 2,146 | null | transformers | 1,305 | ---
language: ["ru", "en"]
tags:
- russian
license: mit
widget:
- text: "translate ru to en: Интересный момент. Модель не видела русских диалогов, но может их понимать"
---
This pruned model of mt5-base [google/mt5-base](https://huggingface.co/google/mt5-base) with only some Rusian and English embeddings left.
The m... |
whaleloops/phrase-bert | 6f68f4dc2d28aadefa038c79023dc7dfd51f6495 | 2021-11-03T15:04:02.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2109.06304",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | whaleloops | null | whaleloops/phrase-bert | 2,144 | 5 | sentence-transformers | 1,306 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# whaleloops/phrase-bert
This is the official repository for the EMNLP 2021 long paper [Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration](https:/... |
michaelrglass/albert-base-rci-wikisql-col | d51bdace09428c72213107d0fe12709c1d7d5d2f | 2021-06-16T15:58:03.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | false | michaelrglass | null | michaelrglass/albert-base-rci-wikisql-col | 2,143 | null | transformers | 1,307 | Entry not found |
jjzha/jobspanbert-base-cased | 0565591b92a2f8da7094dbf05c3ec6e2b93c0987 | 2022-07-26T08:15:15.000Z | [
"pytorch",
"bert",
"en",
"transformers",
"continuous pretraining",
"job postings",
"JobSpanBERT"
] | null | false | jjzha | null | jjzha/jobspanbert-base-cased | 2,140 | null | transformers | 1,308 | ---
language:
- en
tags:
- continuous pretraining
- job postings
- JobSpanBERT
---
# JobSpanBERT
This is the JobSpanBERT model from:
Mike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill Extraction from Job Postings__. Proceedings of the 2022 Conference of th... |
Laggrif/DialoGPT-medium-Luke | 36fcfc7f3d7209dcb7a349804a7a6f5dab2ddd94 | 2022-06-21T17:50:07.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Laggrif | null | Laggrif/DialoGPT-medium-Luke | 2,138 | null | transformers | 1,309 | ---
tags:
- conversational
---
# Luke DialoGPT Model |
GroNLP/gpt2-small-italian | 9a5b0043f33d9adacd23d53e3a8e9c70f71febc9 | 2021-05-21T09:58:53.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"it",
"arxiv:2012.05628",
"transformers",
"adaption",
"recycled",
"gpt2-small"
] | text-generation | false | GroNLP | null | GroNLP/gpt2-small-italian | 2,136 | null | transformers | 1,310 | ---
language: it
tags:
- adaption
- recycled
- gpt2-small
pipeline_tag: text-generation
---
# GPT-2 recycled for Italian (small)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This m... |
hf-internal-testing/tiny-random-unispeech | a90315fed34d4891a62a539637210f2bdd30e68f | 2022-01-26T13:56:35.000Z | [
"pytorch",
"unispeech",
"audio-classification",
"transformers"
] | audio-classification | false | hf-internal-testing | null | hf-internal-testing/tiny-random-unispeech | 2,136 | null | transformers | 1,311 | Entry not found |
mrsinghania/asr-question-detection | 90b29f15265e6819044d484039b1ae9ca683342d | 2021-09-21T06:44:23.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | mrsinghania | null | mrsinghania/asr-question-detection | 2,136 | 2 | transformers | 1,312 | <i>Question vs Statement classifier</i> trained on more than 7k samples which were coming from spoken data in an interview setting
<b>Code for using in Transformers:</b>
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mrsinghania/asr-question-de... |
ktrapeznikov/biobert_v1.1_pubmed_squad_v2 | 351a8218e59777dcb0a1b454ead77a0c39014bc5 | 2021-05-19T21:10:03.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | ktrapeznikov | null | ktrapeznikov/biobert_v1.1_pubmed_squad_v2 | 2,135 | 1 | transformers | 1,313 | ### Model
**[`monologg/biobert_v1.1_pubmed`](https://huggingface.co/monologg/biobert_v1.1_pubmed)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
This model is case... |
aware-ai/wav2vec2-xls-r-1b-5gram-german | 4bfed40b06b3286744027db0cf211efdfb1c7aa6 | 2022-06-01T13:33:48.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | aware-ai | null | aware-ai/wav2vec2-xls-r-1b-5gram-german | 2,127 | 1 | transformers | 1,314 | ---
language: de
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: wav2vec2-xls-r-1b-5gram-german with LM by Florian Zimmermeister @A\\Ware
results:
- task:
name: Speech Recognition
type: aut... |
readerbench/RoBERT-large | 2677d2cc3bc009380161e71eda03515abfb5feb4 | 2021-05-20T04:07:47.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"ro",
"transformers"
] | null | false | readerbench | null | readerbench/RoBERT-large | 2,125 | null | transformers | 1,315 | Model card for RoBERT-large
---
language:
- ro
---
# RoBERT-large
## Pretrained BERT model for Romanian
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this [paper](https://www.aclweb.org/anthology/2020.coling-main.5... |
IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment | 21ff55d0bd2f7904d2a5380165ac2fd6d0d74b81 | 2022-05-27T07:59:44.000Z | [
"pytorch",
"bert",
"text-classification",
"zh",
"transformers",
"NLU",
"Sentiment",
"Chinese",
"license:apache-2.0"
] | text-classification | false | IDEA-CCNL | null | IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment | 2,124 | 4 | transformers | 1,316 | ---
language:
- zh
license: apache-2.0
tags:
- bert
- NLU
- Sentiment
- Chinese
inference: true
widget:
- text: "今天心情不好"
---
# Erlangshen-Roberta-110M-Semtiment, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
We collect 8 sentiment datasets in the Chinese domain fo... |
facebook/contriever-msmarco | abe8c1493371369031bcb1e02acb754cf4e162fa | 2022-06-25T17:19:59.000Z | [
"pytorch",
"bert",
"arxiv:2112.09118",
"transformers",
"feature-extraction"
] | feature-extraction | false | facebook | null | facebook/contriever-msmarco | 2,121 | null | transformers | 1,317 | ---
tags:
- feature-extraction
pipeline_tag: feature-extraction
---
This model is the finetuned version of the pre-trained contriever model available here https://huggingface.co/facebook/contriever, following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://... |
textattack/distilbert-base-cased-CoLA | 73fd8dc841293aab1caea98581bb57481c87ff55 | 2020-06-09T16:45:43.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/distilbert-base-cased-CoLA | 2,120 | null | transformers | 1,318 | Entry not found |
SEBIS/legal_t5_small_trans_fr_en | 2940039b5f8da8f9a6f3c09be0c9667be9d7a9a9 | 2021-06-23T09:52:57.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"French English",
"dataset:dcep europarl jrc-acquis",
"transformers",
"translation French English model",
"autotrain_compatible"
] | text2text-generation | false | SEBIS | null | SEBIS/legal_t5_small_trans_fr_en | 2,119 | null | transformers | 1,319 |
---
language: French English
tags:
- translation French English model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "quels montants ont été attribués et quelles sommes ont été effectivement utilisées dans chaque État membre? 4."
---
# legal_t5_small_trans_fr_en model
Model on translating legal text from F... |
dbmdz/convbert-base-turkish-cased | 6d9b09e4e6f249c477aac7b73f3bcf9aa78ed1a8 | 2021-03-15T23:29:04.000Z | [
"pytorch",
"tf",
"convbert",
"feature-extraction",
"tr",
"arxiv:2008.02496",
"transformers",
"license:mit"
] | feature-extraction | false | dbmdz | null | dbmdz/convbert-base-turkish-cased | 2,119 | null | transformers | 1,320 | ---
language: tr
license: mit
---
# 🤗 + 📚 dbmdz Turkish ConvBERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased ConvBERT model for Turkish 🎉
# 🇹🇷 ConvBERTurk
ConvBERTurk is a community-driven cased ConvBERT model for Turkish.
In addition to the... |
valurank/distilroberta-bias | c1e4a2773522c3acc929a7b2c9af2b7e4137b96d | 2022-06-08T20:44:39.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:valurank/wikirev-bias",
"transformers",
"license:other"
] | text-classification | false | valurank | null | valurank/distilroberta-bias | 2,114 | null | transformers | 1,321 | ---
license: other
language: en
datasets:
- valurank/wikirev-bias
---
# DistilROBERTA fine-tuned for bias detection
This model is based on [distilroberta-base](https://huggingface.co/distilroberta-base) pretrained weights, with a classification head fine-tuned to classify text into 2 categories (neutral, biased).
## ... |
fhswf/bert_de_ner | 97b17ba2e2bfe2e9d1b8d6e348cb60e0e82fc0b4 | 2021-05-19T16:49:54.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"de",
"dataset:germeval_14",
"transformers",
"German",
"NER",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | token-classification | false | fhswf | null | fhswf/bert_de_ner | 2,113 | 2 | transformers | 1,322 | ---
language: de
license: cc-by-sa-4.0
datasets:
- germeval_14
tags:
- German
- de
- NER
---
# BERT-DE-NER
## What is it?
This is a German BERT model fine-tuned for named entity recognition.
## Base model & training
This model is based on [bert-base-german-dbmdz-cased](https://huggingface.co/bert-base-german-... |
ixa-ehu/SciBERT-SQuAD-QuAC | df352e10c506e443875447c166a679b6a5ee34e9 | 2021-06-29T22:55:53.000Z | [
"pytorch",
"bert",
"question-answering",
"en",
"arxiv:1808.07036",
"transformers",
"autotrain_compatible"
] | question-answering | false | ixa-ehu | null | ixa-ehu/SciBERT-SQuAD-QuAC | 2,110 | null | transformers | 1,323 | ---
language: en
---
# SciBERT-SQuAD-QuAC
This is the [SciBERT language representation model](https://huggingface.co/allenai/scibert_scivocab_uncased) fine tuned for Question Answering. SciBERT is a pre-trained language model based on BERT that has been trained on a large corpus of scientific text. When fine tuning f... |
hf-internal-testing/tiny-random-blenderbot | 9432cd260adf10352afc43e7080b154ca0313105 | 2021-09-17T19:25:13.000Z | [
"pytorch",
"tf",
"blenderbot",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-blenderbot | 2,106 | null | transformers | 1,324 | Entry not found |
JamesStratford/PLord-bot-DialoGPT-medium | a1f8100aa348ae0b41363d6089d81529e0ac3484 | 2022-07-08T01:37:51.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | JamesStratford | null | JamesStratford/PLord-bot-DialoGPT-medium | 2,105 | null | transformers | 1,325 | ---
tags:
- conversational
---
# PlordBot - medium |
ktrapeznikov/gpt2-medium-topic-news | d079f5fb6ab7eaf5a38dc2a72bd708a60879d23c | 2021-05-23T06:18:56.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers"
] | text-generation | false | ktrapeznikov | null | ktrapeznikov/gpt2-medium-topic-news | 2,101 | 1 | transformers | 1,326 | ---
language:
- en
thumbnail:
widget:
- text: "topic: climate article:"
---
# GPT2-medium-topic-news
## Model description
GPT2-medium fine tuned on a large news corpus conditioned on a topic
## Intended uses & limitations
#### How to use
To generate a news article text conditioned on a topic, prompt model with:... |
Qishuai/distilbert_punctuator_en | 3b5050a2775440ef59b76082ad75eb9574973ad3 | 2021-12-13T14:47:49.000Z | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Qishuai | null | Qishuai/distilbert_punctuator_en | 2,098 | 5 | transformers | 1,327 | # Punctuator for Uncased English
The model is fine-tuned based on `DistilBertForTokenClassification` for adding punctuations to plain text (uncased English)
## Usage
```python
from transformers import DistilBertForTokenClassification, DistilBertTokenizerFast
model = DistilBertForTokenClassification.from_pretrained(... |
johngiorgi/declutr-base | 3a644f1c78aae97f6e7ed0e2463bcbbaef2e7383 | 2022-03-11T14:47:09.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:2006.03659",
"transformers",
"autotrain_compatible"
] | fill-mask | false | johngiorgi | null | johngiorgi/declutr-base | 2,095 | 1 | transformers | 1,328 | # DeCLUTR-base
## Model description
The "DeCLUTR-base" model from our paper: [DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations](https://arxiv.org/abs/2006.03659).
## Intended uses & limitations
The model is intended to be used as a universal sentence encoder, similar to [Google's Universa... |
LeBenchmark/wav2vec2-FR-7K-large | 970d57910b508c27e9cafd52b781fee76cebfc8b | 2021-11-23T17:54:37.000Z | [
"pytorch",
"wav2vec2",
"feature-extraction",
"fr",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | LeBenchmark | null | LeBenchmark/wav2vec2-FR-7K-large | 2,092 | 3 | transformers | 1,329 | ---
language: "fr"
thumbnail:
tags:
- wav2vec2
license: "apache-2.0"
---
# LeBenchmark: wav2vec2 large model trained on 7K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. For more information o... |
hustvl/yolos-small | 5f960fd774250e41a01086ccbbf5e44d9d603c14 | 2022-06-27T08:37:45.000Z | [
"pytorch",
"yolos",
"object-detection",
"dataset:coco",
"arxiv:2106.00666",
"transformers",
"vision",
"license:apache-2.0"
] | object-detection | false | hustvl | null | hustvl/yolos-small | 2,089 | 10 | transformers | 1,330 | ---
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... |
hyunwoongko/kobart | b5a881942b2536ed7851752a77d7da36d58f2e49 | 2022-04-11T01:19:27.000Z | [
"pytorch",
"bart",
"text2text-generation",
"ko",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | hyunwoongko | null | hyunwoongko/kobart | 2,083 | 1 | transformers | 1,331 | ---
language: ko
tags:
- bart
license: mit
---
## KoBART-base-v2
With the addition of chatting data, the model is trained to handle the semantics of sequences longer than KoBART.
```python
from transformers import PreTrainedTokenizerFast, BartModel
tokenizer = PreTrainedTokenizerFast.from_pretrained('hyunwoongko/ko... |
richielleisart/Childe | 5da9de8d7f9e9cfde2c126b6ac6531b3ddff606a | 2022-01-19T18:52:50.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | richielleisart | null | richielleisart/Childe | 2,080 | null | transformers | 1,332 | ---
tags:
- conversational
---
# Childe Chatbot Model |
nvidia/mit-b5 | 9707ed6bec8a37b67fc9b6d03fe6fbb0e8020f76 | 2022-07-29T13:15:56.000Z | [
"pytorch",
"tf",
"segformer",
"image-classification",
"dataset:imagenet_1k",
"arxiv:2105.15203",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | nvidia | null | nvidia/mit-b5 | 2,076 | null | transformers | 1,333 | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet_1k
widget:
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
example_title: House
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg
exampl... |
microsoft/deberta-v2-xlarge-mnli | 5272422ce68b8d61766079390b96b033a64414d2 | 2021-05-21T20:08:15.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"en",
"arxiv:2006.03654",
"transformers",
"deberta",
"deberta-mnli",
"license:mit"
] | text-classification | false | microsoft | null | microsoft/deberta-v2-xlarge-mnli | 2,075 | 2 | transformers | 1,334 | ---
language: en
tags:
- deberta
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves... |
hf-internal-testing/tiny-random-longformer | 5690941b3c077e091b13b5f992b42e2ead18b35d | 2021-09-17T19:24:34.000Z | [
"pytorch",
"tf",
"longformer",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-longformer | 2,070 | 1 | transformers | 1,335 | Entry not found |
CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment | 6ef16021f303c8a2bac02fd5af16601593e665d2 | 2021-10-17T12:09:14.000Z | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | false | CAMeL-Lab | null | CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment | 2,064 | 2 | transformers | 1,336 | ---
language:
- ar
license: apache-2.0
widget:
- text: "أنا بخير"
---
# CAMeLBERT Mix SA Model
## Model description
**CAMeLBERT Mix SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuni... |
flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A | dce2aa07b3e1e5c91c2f411c5534b399462f7b16 | 2021-07-25T21:33:06.000Z | [
"pytorch",
"mpnet",
"fill-mask",
"arxiv:2102.07033",
"arxiv:2104.08727",
"sentence-transformers",
"feature-extraction",
"sentence-similarity"
] | sentence-similarity | false | flax-sentence-embeddings | null | flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A | 2,064 | 1 | sentence-transformers | 1,337 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-QA_v1-mpnet-asymmetric-A
## Model Description
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentenc... |
transfaeries/DialoGPT-medium-Discord-1.0 | fdd1f5fa445bd30233a0a2d854d89741fad3fa80 | 2021-09-02T04:19:36.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | transfaeries | null | transfaeries/DialoGPT-medium-Discord-1.0 | 2,062 | null | transformers | 1,338 | ---
tags:
- conversational
---
# Discord Model Medium 7 epochs |
hf-internal-testing/tiny-random-t5-v1.1 | 95197e7dc6c034b9ae97b124952afb5e15ed0fb2 | 2021-11-02T21:08:45.000Z | [
"pytorch",
"tf",
"t5",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-t5-v1.1 | 2,060 | null | transformers | 1,339 | Entry not found |
M-CLIP/M-BERT-Distil-40 | ff20c09c1a088589cb65a169d165b5ddcbe792ca | 2021-03-21T15:39:15.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"transformers"
] | feature-extraction | false | M-CLIP | null | M-CLIP/M-BERT-Distil-40 | 2,056 | 1 | transformers | 1,340 | <br />
<p align="center">
<h1 align="center">M-BERT Distil 40</h1>
<p align="center">
<a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/M-BERT%20Distil%2040">Github Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you need ... |
voidful/albert_chinese_base | 549e8a023d81bd68e70cf3e2b4aa621e145695ed | 2021-08-03T05:02:21.000Z | [
"pytorch",
"albert",
"fill-mask",
"zh",
"transformers",
"autotrain_compatible"
] | fill-mask | false | voidful | null | voidful/albert_chinese_base | 2,054 | 4 | transformers | 1,341 | ---
language: zh
pipeline_tag: fill-mask
widget:
- text: "今天[MASK]情很好"
---
# albert_chinese_base
This a albert_chinese_base model from [Google's github](https://github.com/google-research/ALBERT)
converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_alb... |
M-CLIP/XLM-Roberta-Large-Vit-B-32 | cfb9f55a6aad08a948167a8360fc11bce171d941 | 2022-06-02T23:23:21.000Z | [
"pytorch",
"tf",
"M-CLIP",
"multilingual",
"transformers"
] | null | false | M-CLIP | null | M-CLIP/XLM-Roberta-Large-Vit-B-32 | 2,052 | null | transformers | 1,342 | ---
language: multilingual
---
## Multilingual-clip: XLM-Roberta-Large-Vit-B-32
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `ViT-B-32` can be retrieved via instructions found on OpenAI's ... |
textattack/roberta-base-MNLI | 6f2e633322381bc5897405e417ec531ea3633a3f | 2021-05-20T22:06:43.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/roberta-base-MNLI | 2,037 | 1 | transformers | 1,343 | Entry not found |
philschmid/MiniLM-L6-H384-uncased-sst2 | 0c0ecdc39368f87291727ec084111e89e30b45b2 | 2021-09-24T09:53:36.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | philschmid | null | philschmid/MiniLM-L6-H384-uncased-sst2 | 2,034 | null | transformers | 1,344 | Entry not found |
lidiya/bart-base-samsum | eeb19117db15f1388c7188cb455e7a98af647792 | 2022-07-20T14:56:27.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:samsum",
"transformers",
"seq2seq",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | lidiya | null | lidiya/bart-base-samsum | 2,030 | 1 | transformers | 1,345 | ---
language: en
tags:
- bart
- seq2seq
- summarization
license: apache-2.0
datasets:
- samsum
widget:
- text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n\
Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:\
\ ok.\nJeff: and how can I get started? \nJe... |
akdeniz27/roberta-large-cuad | 32cd27aa93ae12e576f214c40c558bdcc5081220 | 2021-11-14T08:43:30.000Z | [
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:cuad",
"transformers",
"autotrain_compatible"
] | question-answering | false | akdeniz27 | null | akdeniz27/roberta-large-cuad | 2,024 | null | transformers | 1,346 | ---
language: en
datasets:
- cuad
---
# RoBERTa Large Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "RoBERTa Large"
using CUAD dataset https://huggingface.co/datasets/cuad
Link for model checkpoint: https://github.com/TheAtticusProject/cuad
For the use of the model with CUAD: https://git... |
deepset/tapas-large-nq-hn-reader | 3b9b9fcfd1789686d05a3b63d8492ac162c7d9fc | 2022-01-23T14:58:08.000Z | [
"pytorch",
"tapas",
"en",
"transformers",
"license:apache-2.0"
] | null | false | deepset | null | deepset/tapas-large-nq-hn-reader | 2,024 | null | transformers | 1,347 | ---
language: en
tags:
- tapas
license: apache-2.0
---
This model contains the converted PyTorch checkpoint of the original Tensorflow model available in the [TaPas repository](https://github.com/google-research/tapas/blob/master/DENSE_TABLE_RETRIEVER.md#reader-models).
It is described in Herzig et al.'s (2021) [paper... |
facebook/wav2vec2-large-robust | 2493a2c576276145c3e066d9243b0e391fab673a | 2021-11-05T12:45:27.000Z | [
"pytorch",
"wav2vec2",
"pretraining",
"en",
"dataset:libri_light",
"dataset:common_voice",
"dataset:switchboard",
"dataset:fisher",
"arxiv:2104.01027",
"transformers",
"speech",
"license:apache-2.0"
] | null | false | facebook | null | facebook/wav2vec2-large-robust | 2,021 | 9 | transformers | 1,348 | ---
language: en
datasets:
- libri_light
- common_voice
- switchboard
- fisher
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Large-Robust
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained on 16kHz sampled speech audio.
Spee... |
hf-internal-testing/tiny-random-deberta | 449491e17107f61f2e8df35a0e20a55e9c4afd3c | 2021-09-17T19:22:32.000Z | [
"pytorch",
"tf",
"deberta",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-deberta | 2,019 | null | transformers | 1,349 | Entry not found |
Helsinki-NLP/opus-mt-ur-en | c803d32b6f7a3a7a8cb1ba91d2947de0009f8cdc | 2020-08-21T14:42:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ur",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ur-en | 2,017 | 1 | transformers | 1,350 | ---
language:
- ur
- en
tags:
- translation
license: apache-2.0
---
### urd-eng
* source group: Urdu
* target group: English
* OPUS readme: [urd-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/urd-eng/README.md)
* model: transformer-align
* source language(s): urd
* target language(s... |
aychang/roberta-base-imdb | cb6bcadd0540b61c9623bd6295d51ac445ceb135 | 2021-05-20T14:25:56.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"dataset:imdb",
"transformers",
"license:mit"
] | text-classification | false | aychang | null | aychang/roberta-base-imdb | 2,017 | 1 | transformers | 1,351 | ---
language:
- en
thumbnail:
tags:
- text-classification
license: mit
datasets:
- imdb
metrics:
---
# IMDB Sentiment Task: roberta-base
## Model description
A simple base roBERTa model trained on the "imdb" dataset.
## Intended uses & limitations
#### How to use
##### Transformers
```python
# Load model and ... |
stas/pegasus-cnn_dailymail-tiny-random | 600d1e9bb307c4c4c7361688317e80fc2612bc5c | 2021-07-01T05:33:00.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | stas | null | stas/pegasus-cnn_dailymail-tiny-random | 2,015 | null | transformers | 1,352 | This is a tiny random pegasus-cnn_dailymail model used for testing.
See `make-pegasus-cnn_dailymail-tiny-random.py` for how it was created.
|
hf-internal-testing/tiny-random-big_bird | 0ab074a1d464a4cc6846332560f1f2abca400a71 | 2022-03-25T17:49:02.000Z | [
"pytorch",
"big_bird",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-big_bird | 2,010 | null | transformers | 1,353 | Entry not found |
stas/tiny-m2m_100 | 4df2a26e27b5f4823e2e797424de47f14c2e1b27 | 2022-04-29T23:57:25.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"transformers",
"testing",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | stas | null | stas/tiny-m2m_100 | 2,009 | null | transformers | 1,354 | ---
language:
- en
thumbnail:
tags:
- testing
license: apache-2.0
---
# Tiny M2M100 model
This is a tiny model that is used in the `transformers` test suite. It doesn't do anything useful beyond functional testing.
Do not try to use it for anything that requires quality.
The model is indeed 4MB in size.
You can se... |
hf-internal-testing/tiny-random-mpnet | 490e676cf9e1714ddd21f9169dc14652e9a9e7f4 | 2021-09-17T19:25:01.000Z | [
"pytorch",
"tf",
"mpnet",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-mpnet | 2,008 | null | transformers | 1,355 | Entry not found |
hf-internal-testing/tiny-electra | 7479c5defabc4a550d08c170f7f4fb0b0e6be19b | 2021-07-16T01:27:58.000Z | [
"pytorch",
"electra",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | hf-internal-testing | null | hf-internal-testing/tiny-electra | 2,005 | null | transformers | 1,356 | This is a tiny-electra random model to be used for basic testing.
|
hf-internal-testing/tiny-random-funnel | ec246a681806cada4b3c073569afba96f7ac8eb8 | 2021-09-17T19:25:04.000Z | [
"pytorch",
"tf",
"funnel",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-funnel | 2,002 | null | transformers | 1,357 | Entry not found |
Helsinki-NLP/opus-mt-en-sv | 13d9f7f708dd86e1edf61f0cd438298267b83850 | 2021-09-09T21:39:27.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"en",
"sv",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-sv | 1,998 | 1 | transformers | 1,358 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-sv
* source languages: en
* target languages: sv
* OPUS readme: [en-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
hf-internal-testing/tiny-random-prophetnet | d5071e4655fd0413b0e71405a91dfb4280e31b81 | 2021-09-17T19:24:57.000Z | [
"pytorch",
"prophetnet",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-prophetnet | 1,998 | null | transformers | 1,359 | Entry not found |
hf-internal-testing/tiny-random-mobilebert | 1f919a6d77ef448d41e0de29f79f854ace43bc4c | 2021-09-17T19:24:24.000Z | [
"pytorch",
"tf",
"mobilebert",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-mobilebert | 1,996 | null | transformers | 1,360 | Entry not found |
hf-internal-testing/tiny-random-squeezebert | da3eaaeb3b2fa22836d34097046f192db387e961 | 2021-09-17T19:25:10.000Z | [
"pytorch",
"squeezebert",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-squeezebert | 1,995 | null | transformers | 1,361 | Entry not found |
Helsinki-NLP/opus-mt-tc-big-en-tr | dc016b6b79636a066052e581101c734ca5934667 | 2022-06-01T13:01:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"tr",
"transformers",
"translation",
"opus-mt-tc",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tc-big-en-tr | 1,995 | 1 | transformers | 1,362 | ---
language:
- en
- tr
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-tr
results:
- task:
name: Translation eng-tur
type: translation
args: eng-tur
dataset:
name: flores101-devtest
type: flores_101
args: eng tur devtest
metrics... |
hf-internal-testing/tiny-layoutlm | 7bc6366344bf3e7363a5e0e2f4fdd3087ab68e4a | 2021-08-04T04:33:04.000Z | [
"pytorch",
"layoutlm",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | hf-internal-testing | null | hf-internal-testing/tiny-layoutlm | 1,992 | null | transformers | 1,363 | This is a tiny-layoutlm random model to be used for basic testing.
|
hf-internal-testing/tiny-random-gpt_neo | b95a8110971dfc560caa02c286c3b8aa0118941a | 2021-09-17T19:25:26.000Z | [
"pytorch",
"gpt_neo",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-gpt_neo | 1,992 | null | transformers | 1,364 | Entry not found |
hf-internal-testing/tiny-random-led | 2774e58f25d3fdda4c0d86b140cca8e049ee6a9f | 2021-09-17T19:24:21.000Z | [
"pytorch",
"tf",
"led",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-led | 1,992 | null | transformers | 1,365 | Entry not found |
hf-internal-testing/tiny-random-deberta-v2 | 924b47948998e199d88e95e1df46ab125e0f325a | 2021-09-17T19:23:17.000Z | [
"pytorch",
"tf",
"deberta-v2",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-deberta-v2 | 1,991 | null | transformers | 1,366 | Entry not found |
lincoln/mbart-mlsum-automatic-summarization | 17a3a2e474932a90e664ef2c75c5e46ef964fc1a | 2021-09-07T08:21:55.000Z | [
"pytorch",
"tf",
"mbart",
"text2text-generation",
"fr",
"dataset:MLSUM",
"arxiv:2004.14900",
"transformers",
"summarization",
"bart",
"license:mit",
"autotrain_compatible"
] | summarization | false | lincoln | null | lincoln/mbart-mlsum-automatic-summarization | 1,991 | 3 | transformers | 1,367 | ---
language:
- fr
license: mit
datasets:
- MLSUM
pipeline_tag: "summarization"
widget:
- text: « La veille de l’ouverture, je vais faire venir un coach pour les salariés qui reprendront le travail. Cela va me coûter 300 euros, mais après des mois d’oisiveté obligatoire, la reprise n’est pas simple. Certains sont ... |
hf-internal-testing/tiny-random-camembert | 8fa65c628a3f475b1ed4e8dff6adf09db1b6bb83 | 2022-07-27T10:07:32.000Z | [
"pytorch",
"camembert",
"feature-extraction",
"transformers"
] | feature-extraction | false | hf-internal-testing | null | hf-internal-testing/tiny-random-camembert | 1,990 | null | transformers | 1,368 | Entry not found |
microsoft/DialogRPT-human-vs-rand | 7206b425c2c016dd5533e2a99e665ba3546e5ce0 | 2021-05-23T09:18:07.000Z | [
"pytorch",
"gpt2",
"text-classification",
"arxiv:2009.06978",
"transformers"
] | text-classification | false | microsoft | null | microsoft/DialogRPT-human-vs-rand | 1,986 | 1 | transformers | 1,369 | # Demo
Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
| Context | Response | `human_vs_rand` score |
| :------ | :------- | :------------: |
| I love NLP! | He is a great basketball player. | 0.027 |
| I love NLP! | C... |
dbmdz/bert-base-italian-uncased | d91243bae3a97a72691e9a6bfdf5d9f8fa4be9e4 | 2021-05-19T15:00:42.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"it",
"dataset:wikipedia",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/bert-base-italian-uncased | 1,984 | 2 | transformers | 1,370 | ---
language: it
license: mit
datasets:
- wikipedia
---
# 🤗 + 📚 dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models 🎉
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikiped... |
MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli | 1b7b1b212ea53c7a64546076569fbb01c3df8fbd | 2022-07-28T16:24:07.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"en",
"dataset:multi_nli",
"dataset:anli",
"dataset:fever",
"dataset:lingnli",
"dataset:alisawuffles/WANLI",
"arxiv:2104.07179",
"arxiv:2111.09543",
"transformers",
"zero-shot-classification",
"license:mit",
"model-index"
] | zero-shot-classification | false | MoritzLaurer | null | MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli | 1,980 | 4 | transformers | 1,371 | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
license: mit
metrics:
- accuracy
datasets:
- multi_nli
- anli
- fever
- lingnli
- alisawuffles/WANLI
pipeline_tag: zero-shot-classification
#- text-classification
#widget:
#- text: "I first thought that I really liked the movie, but upon second ... |
kamalkraj/bioelectra-base-discriminator-pubmed | b08ce00d5a23e2682a20e9d33356730530bdecd1 | 2021-09-07T13:52:16.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | kamalkraj | null | kamalkraj/bioelectra-base-discriminator-pubmed | 1,978 | 3 | transformers | 1,372 | ## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model that ada... |
mbartolo/roberta-large-synqa | 1ae8322fd562c2b2193a7d2b8d0887177b616d62 | 2022-07-25T23:36:39.000Z | [
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:adversarial_qa",
"dataset:mbartolo/synQA",
"dataset:squad",
"arxiv:2002.00293",
"arxiv:2104.08678",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | mbartolo | null | mbartolo/roberta-large-synqa | 1,973 | null | transformers | 1,373 | ---
language:
- en
tags:
- question-answering
license: apache-2.0
datasets:
- adversarial_qa
- mbartolo/synQA
- squad
metrics:
- exact_match
- f1
model-index:
- name: mbartolo/roberta-large-synqa
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type... |
sberbank-ai/ruT5-large | 4d14102f32e730d68b1950bfaeb7a4988c978737 | 2021-09-28T15:56:17.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ru",
"transformers",
"PyTorch",
"Transformers",
"autotrain_compatible"
] | text2text-generation | false | sberbank-ai | null | sberbank-ai/ruT5-large | 1,962 | 7 | transformers | 1,374 | ---
language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: "https://github.com/sberbank-ai/model-zoo"
---
# ruT5-large
Model was trained by [SberDevices](https://sberdevices.ru/) team.
* Task: `text2text generation`
* Type: `encoder-decoder`
* Tokenizer: `bpe`
* Dict size: `32 101 `
* Num Parameters: `737 M`
* Tra... |
mrm8488/GPT-2-finetuned-covid-bio-medrxiv | 9f18ece8499d11cd7e0679e14be9e32ac9148f5e | 2021-08-25T21:38:35.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers"
] | text-generation | false | mrm8488 | null | mrm8488/GPT-2-finetuned-covid-bio-medrxiv | 1,961 | null | transformers | 1,375 | ---
language: en
thumbnail:
widget:
- text: "Old people with COVID-19 tends to suffer"
---
# GPT-2 + bio/medrxiv files from CORD19: 🦠 ✍ ⚕
**GPT-2** fine-tuned on **biorxiv_medrxiv** files from [CORD-19](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) dataset.
## Datasets details:
| Datas... |
Jiva/xlm-roberta-large-it-mnli | c6e64469ec4aa17fedbd1b2522256f90a90b5b86 | 2021-12-10T14:56:38.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"it",
"dataset:multi_nli",
"dataset:glue",
"arxiv:1911.02116",
"transformers",
"tensorflow",
"license:mit",
"zero-shot-classification"
] | zero-shot-classification | false | Jiva | null | Jiva/xlm-roberta-large-it-mnli | 1,960 | 4 | transformers | 1,376 | ---
language: it
tags:
- text-classification
- pytorch
- tensorflow
datasets:
- multi_nli
- glue
license: mit
pipeline_tag: zero-shot-classification
widget:
- text: "La seconda guerra mondiale vide contrapporsi, tra il 1939 e il 1945, le cosiddette potenze dell'Asse e gli Alleati che, come già accaduto ai belligeranti ... |
ltgoslo/norbert | 44815f7e109b53547cccdf3c6847f4c28b989816 | 2022-03-25T16:02:00.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"no",
"arxiv:2104.06546",
"transformers",
"norwegian",
"license:cc-by-4.0",
"autotrain_compatible"
] | fill-mask | false | ltgoslo | null | ltgoslo/norbert | 1,956 | 6 | transformers | 1,377 | ---
language: no
license: cc-by-4.0
pipeline_tag: fill-mask
tags:
- norwegian
- bert
thumbnail: https://raw.githubusercontent.com/ltgoslo/NorBERT/main/Norbert.png
---
## Quickstart
**Release 1.1** (February 13, 2021)
Please check also our newer model: [NorBERT 2](https://huggingface.co/ltgoslo/norbert2), trained on ... |
hf-internal-testing/test_dynamic_model_with_util | b731e5fae6d80a4a775461251c4388886fb7a249 | 2022-01-26T17:54:17.000Z | [
"pytorch",
"new-model",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/test_dynamic_model_with_util | 1,953 | null | transformers | 1,378 | Entry not found |
facebook/wav2vec2-large-960h-lv60 | 8e7d14742e8f98c6bbb24e5231406af321a8f9ce | 2022-04-05T16:42:07.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"transformers",
"speech",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-large-960h-lv60 | 1,947 | 5 | transformers | 1,379 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
model-index:
- name: wav2vec2-large-960h-lv60
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Librispeech (clean)
type: librispeech_asr
args: en
... |
nikokons/gpt2-greek | b2bb85c722742ce6ea0b9e025d50425e061181c8 | 2022-07-20T09:59:03.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"el",
"transformers"
] | text-generation | false | nikokons | null | nikokons/gpt2-greek | 1,944 | null | transformers | 1,380 | ---
language: el
---
## gpt2-greek
## Dataset:
The model is trained on a collection of almost 5GB Greek texts, with the main source to be from Greek Wikipedia. The content is extracted using the Wikiextractor tool (Attardi, 2012). The dataset is constructed as 5 sentences per sample (about 3.7 millions of samples) an... |
nghuyong/ernie-gram-zh | 257fee0915f1cba8dbea92c976493dcdd0491174 | 2022-04-04T06:00:26.000Z | [
"pytorch",
"bert",
"feature-extraction",
"zh",
"arxiv:2010.12148",
"transformers"
] | feature-extraction | false | nghuyong | null | nghuyong/ernie-gram-zh | 1,943 | null | transformers | 1,381 | ---
language: zh
---
# ERNIE-Gram-zh
## Introduction
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding
More detail: https://arxiv.org/abs/2010.12148
## Released Model Info
|Model Name|Language|Model Structure|
|:---:|:---:|:---:|
|ernie-gram-zh| Chinese |L... |
monologg/distilkobert | cfbce1328041f68781414250c9013128e77e82d2 | 2020-05-13T03:37:29.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | monologg | null | monologg/distilkobert | 1,941 | 2 | transformers | 1,382 | Entry not found |
sentence-transformers/msmarco-distilbert-base-dot-prod-v3 | 0bafe057815532ca7ee37f002d9d1413d78b6d67 | 2022-06-15T22:20:51.000Z | [
"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-dot-prod-v3 | 1,936 | 1 | sentence-transformers | 1,383 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-distilbert-base-dot-prod-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dime... |
cahya/bert-base-indonesian-522M | 7baa8f5fa385e6eff31184f11876d0d19bf5eb6c | 2021-05-19T13:38:45.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"id",
"dataset:wikipedia",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | cahya | null | cahya/bert-base-indonesian-522M | 1,934 | 3 | transformers | 1,384 | ---
language: "id"
license: "mit"
datasets:
- wikipedia
widget:
- text: "Ibu ku sedang bekerja [MASK] sawah."
---
# Indonesian BERT base model (uncased)
## Model description
It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This
model is uncased: it does n... |
phiyodr/bert-base-finetuned-squad2 | c73e3f22381ce4c230b49844ea7b8c703887385c | 2021-05-20T02:34:19.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"dataset:squad2",
"arxiv:1810.04805",
"arxiv:1806.03822",
"transformers",
"autotrain_compatible"
] | question-answering | false | phiyodr | null | phiyodr/bert-base-finetuned-squad2 | 1,934 | null | transformers | 1,385 | ---
language: en
tags:
- pytorch
- question-answering
datasets:
- squad2
metrics:
- exact
- f1
widget:
- text: "What discipline did Winkelmann create?"
context: "Johann Joachim Winckelmann was a German art historian and archaeologist. He was a pioneering Hellenist who first articulated the difference between Greek, G... |
microsoft/xlm-align-base | 3e2a40ea5f9c75353ad2769bd74f7cb425fce671 | 2021-08-04T15:23:10.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/xlm-align-base | 1,932 | 3 | transformers | 1,386 | # XLM-Align
**XLM-Align** (ACL 2021, [paper](https://aclanthology.org/2021.acl-long.265/), [repo](https://github.com/CZWin32768/XLM-Align), [model](https://huggingface.co/microsoft/xlm-align-base)) Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment
XLM-Align is a pretrained cross-lingu... |
Alireza1044/albert-base-v2-sst2 | e406771b99e1913921a68fbb95d121b582d1ecb7 | 2021-07-26T14:02:35.000Z | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | false | Alireza1044 | null | Alireza1044/albert-base-v2-sst2 | 1,930 | null | transformers | 1,387 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metric:
name: Accuracy
... |
google/bert_uncased_L-2_H-256_A-4 | 4e937a8675e5afd9a4836735c186ec01695bc3ea | 2021-05-19T17:28:46.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-2_H-256_A-4 | 1,928 | 1 | transformers | 1,388 | ---
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... |
indobenchmark/indobart | 73bead20e4a67f578f6f3b3f7038040304dc7065 | 2022-06-21T17:52:16.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"id",
"dataset:Indo4B+",
"arxiv:2104.08200",
"transformers",
"indogpt",
"indobenchmark",
"indonlg",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | indobenchmark | null | indobenchmark/indobart | 1,923 | 1 | transformers | 1,389 | ---
language: id
tags:
- indogpt
- indobenchmark
- indonlg
license: mit
inference: false
datasets:
- Indo4B+
---
# IndoBART Model
[IndoBART](https://arxiv.org/abs/2104.08200) is a state-of-the-art language model for Indonesian based on the BART model. The pretrained model is trained using the BART training objective.... |
microsoft/CodeGPT-small-py | 97ebaaa7103f64e3085e88f0ecd28d1ffeb01bea | 2021-05-23T09:01:50.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | microsoft | null | microsoft/CodeGPT-small-py | 1,922 | 1 | transformers | 1,390 | Entry not found |
saattrupdan/nbailab-base-ner-scandi | 8635b40703c27f868a29a36d99e264facddc6610 | 2022-02-09T15:21:05.000Z | [
"pytorch",
"bert",
"token-classification",
"da",
"no",
"nb",
"nn",
"sv",
"fo",
"is",
"dataset:dane",
"dataset:norne",
"dataset:wikiann",
"dataset:suc3.0",
"arxiv:1911.12146",
"transformers",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | saattrupdan | null | saattrupdan/nbailab-base-ner-scandi | 1,921 | 8 | transformers | 1,391 | ---
language:
- da
- no
- nb
- nn
- sv
- fo
- is
license: mit
datasets:
- dane
- norne
- wikiann
- suc3.0
model-index:
- name: nbailab-base-ner-scandi
results: []
widget:
- "Hans er en professor på Københavns Universitetet i København, og han er en rigtig københavner. Hans kat, altså Hans' kat, Lisa, er supersød. Han... |
studio-ousia/luke-large-finetuned-conll-2003 | 2508abe6e591d7a9142d5ee9ab2eb5dccd7741fd | 2021-04-26T16:09:42.000Z | [
"pytorch",
"luke",
"transformers"
] | null | false | studio-ousia | null | studio-ousia/luke-large-finetuned-conll-2003 | 1,920 | null | transformers | 1,392 | Entry not found |
m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 | 4454cfbc82952da79729e33e81c37a72dc095b4b | 2021-05-19T22:20:54.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | m-polignano-uniba | null | m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 | 1,917 | 3 | transformers | 1,393 | Entry not found |
klue/roberta-small | f360e3d753b17f3b7508154fefdb042c706db147 | 2021-10-20T16:13:01.000Z | [
"pytorch",
"roberta",
"fill-mask",
"ko",
"arxiv:2105.09680",
"transformers",
"korean",
"klue",
"autotrain_compatible"
] | fill-mask | false | klue | null | klue/roberta-small | 1,911 | null | transformers | 1,394 | ---
language: ko
tags:
- korean
- klue
mask_token: "[MASK]"
widget:
- text: 대한민국의 수도는 [MASK] 입니다.
---
# KLUE RoBERTa small
Pretrained RoBERTa 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
_NOTE:_ Use... |
monologg/koelectra-base-v3-naver-ner | 7fe2d3297113e0753716d7f2c85d4880d288542d | 2020-11-30T11:55:35.000Z | [
"pytorch",
"electra",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | monologg | null | monologg/koelectra-base-v3-naver-ner | 1,911 | null | transformers | 1,395 | Entry not found |
hf-internal-testing/tiny-random-rembert | 917e1c9f997b17fc81d9ed84713f5de8abe57c1b | 2022-03-08T13:50:53.000Z | [
"pytorch",
"tf",
"rembert",
"feature-extraction",
"transformers",
"generated_from_keras_callback",
"model-index"
] | feature-extraction | false | hf-internal-testing | null | hf-internal-testing/tiny-random-rembert | 1,909 | null | transformers | 1,396 | ---
tags:
- generated_from_keras_callback
model-index:
- name: tiny-random-rembert
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tiny-random-rembert
This model wa... |
textattack/bert-base-uncased-MNLI | 3a97f689528cbd91bcc71ab29ea6c20c089d8f28 | 2021-05-20T07:31:58.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/bert-base-uncased-MNLI | 1,908 | null | transformers | 1,397 | Entry not found |
google/mt5-xxl | d4ac5e6d5125f8d30cba8763cd0ad71e5d34c17b | 2022-05-27T15:06:56.000Z | [
"pytorch",
"tf",
"mt5",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
... | text2text-generation | false | google | null | google/mt5-xxl | 1,906 | 8 | transformers | 1,398 | ---
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
-... |
ml6team/keyphrase-extraction-kbir-inspec | 70c7250d0cb932f4ee3332c50a73583b7cd7995d | 2022-06-16T14:51:11.000Z | [
"pytorch",
"roberta",
"token-classification",
"en",
"dataset:midas/inspec",
"arxiv:2112.08547",
"transformers",
"keyphrase-extraction",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | ml6team | null | ml6team/keyphrase-extraction-kbir-inspec | 1,906 | 2 | transformers | 1,399 | ---
language: en
license: mit
tags:
- keyphrase-extraction
datasets:
- midas/inspec
metrics:
- seqeval
widget:
- text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document.
Thanks to these keyphrases humans can understand the content of a text very quickly a... |
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