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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Helsinki-NLP/opus-mt-guw-en | 84fb2fa0624cd832855fb196770f4e294c2df8d5 | 2021-09-09T21:59:32.000Z | [
"pytorch",
"marian",
"text2text-generation",
"guw",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-guw-en | 22 | null | transformers | 8,000 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-guw-en
* source languages: guw
* target languages: en
* OPUS readme: [guw-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/guw-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* d... |
Helsinki-NLP/opus-mt-lu-en | e5a8fbfce6798964395091f07a440a5b568229c7 | 2021-09-10T13:55:45.000Z | [
"pytorch",
"marian",
"text2text-generation",
"lu",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-lu-en | 22 | null | transformers | 8,001 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-lu-en
* source languages: lu
* target languages: en
* OPUS readme: [lu-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lu-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-ng-en | 57bfb4a1922ad1f807a8e951ee46145b9dc45dce | 2021-09-10T13:58:41.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ng",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ng-en | 22 | null | transformers | 8,002 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ng-en
* source languages: ng
* target languages: en
* OPUS readme: [ng-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ng-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-rn-en | 209441b0eb367684cdbb2b4e852571afbbaee771 | 2020-08-21T14:42:49.000Z | [
"pytorch",
"marian",
"text2text-generation",
"rn",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-rn-en | 22 | null | transformers | 8,003 | ---
language:
- rn
- en
tags:
- translation
license: apache-2.0
---
### run-eng
* source group: Rundi
* target group: English
* OPUS readme: [run-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/run-eng/README.md)
* model: transformer-align
* source language(s): run
* target language(... |
Helsinki-NLP/opus-mt-to-en | 00a0ae6a795fc4cc7da526bc63212fc1a763513c | 2021-09-11T10:48:53.000Z | [
"pytorch",
"marian",
"text2text-generation",
"to",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-to-en | 22 | null | transformers | 8,004 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-to-en
* source languages: to
* target languages: en
* OPUS readme: [to-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/to-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-umb-en | 4baaf48bdbc9f45e0abdbae7490e5ccaa39c12e7 | 2021-09-11T10:51:33.000Z | [
"pytorch",
"marian",
"text2text-generation",
"umb",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-umb-en | 22 | null | transformers | 8,005 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-umb-en
* source languages: umb
* target languages: en
* OPUS readme: [umb-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/umb-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* d... |
Helsinki-NLP/opus-mt-urj-urj | 7a66b71ec9428eb22fe49e189929c64466a45f43 | 2020-08-21T14:42:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"se",
"fi",
"hu",
"et",
"urj",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-urj-urj | 22 | null | transformers | 8,006 | ---
language:
- se
- fi
- hu
- et
- urj
tags:
- translation
license: apache-2.0
---
### urj-urj
* source group: Uralic languages
* target group: Uralic languages
* OPUS readme: [urj-urj](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/urj-urj/README.md)
* model: transformer
* source langu... |
RecordedFuture/Swedish-Sentiment-Violence | 881e0f742307fa2740b47a7d96750d28cf8ff99f | 2021-05-18T22:02:50.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"sv",
"transformers",
"license:mit"
] | text-classification | false | RecordedFuture | null | RecordedFuture/Swedish-Sentiment-Violence | 22 | null | transformers | 8,007 | ---
language: sv
license: mit
---
## Swedish BERT models for sentiment analysis
[Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases two language models for sentiment analysis in Swedish. The two models are based on the [KB\/bert-base-swedish-cased](https://huggi... |
SEBIS/code_trans_t5_base_code_documentation_generation_javascript_multitask | 2572188f11a56254b981bac5ea59275b3d769550 | 2021-06-23T04:29:50.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_base_code_documentation_generation_javascript_multitask | 22 | null | transformers | 8,008 | ---
tags:
- summarization
widget:
- text: "function isStandardBrowserEnv ( ) { if ( typeof navigator !== 'undefined' && ( navigator . product === 'ReactNative' || navigator . product === 'NativeScript' || navigator . product === 'NS' ) ) { return false ; } return ( typeof window !== 'undefined' && typeof document !== '... |
aditeyabaral/sentencetransformer-bert-hinglish-small | 494792d8ff0aa558e2d2bc825814b536d766732e | 2021-10-20T06:28:16.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | aditeyabaral | null | aditeyabaral/sentencetransformer-bert-hinglish-small | 22 | null | sentence-transformers | 8,009 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# aditeyabaral/sentencetransformer-bert-hinglish-small
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector spac... |
airesearch/wangchanberta-base-wiki-sefr | 9c7f7cbe9fdf6ec51696769591e11eb8c1b99e76 | 2021-09-11T09:39:05.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"th",
"arxiv:1907.11692",
"arxiv:2101.09635",
"transformers",
"autotrain_compatible"
] | fill-mask | false | airesearch | null | airesearch/wangchanberta-base-wiki-sefr | 22 | null | transformers | 8,010 | ---
language: th
---
# WangchanBERTa base model: `wangchanberta-base-wiki-sefr`
<br>
Pretrained RoBERTa BASE model on Thai Wikipedia corpus.
The script and documentation can be found at [this reposiryory](https://github.com/vistec-AI/thai2transformers).
<br>
## Model description
<br>
The architecture of the pretr... |
alexanderfalk/danbert-small-cased | 2f63f543a11ad95cff4149288d04714da11167c4 | 2021-09-21T15:57:39.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"da",
"en",
"dataset:custom danish dataset",
"transformers",
"named entity recognition",
"token criticality",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | alexanderfalk | null | alexanderfalk/danbert-small-cased | 22 | null | transformers | 8,011 | ---
language:
- da
- en
thumbnail:
tags:
- named entity recognition
- token criticality
license: apache-2.0
datasets:
- custom danish dataset
inference: false
metrics:
- array of metric identifiers
---
# DanBERT
## Model description
DanBERT is a danish pre-trained model based on BERT-Base. The pre-trained model has ... |
beomi/kobert | 372ec671481c751af771f28c6f191d420d1a1d86 | 2021-06-08T08:36:08.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | beomi | null | beomi/kobert | 22 | null | transformers | 8,012 | Entry not found |
birgermoell/roberta-swedish | 8b52b9fc6d6589e3dff4bee44c3e811315c1e071 | 2021-07-17T07:52:59.000Z | [
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | birgermoell | null | birgermoell/roberta-swedish | 22 | null | transformers | 8,013 | ---
widget:
- text: "Var kan jag hitta någon <mask> talar engelska?"
---
Swedish RoBERTa
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
https://huggingface.co/birgermoell/swedish-gpt/
## Swedish gpt wiki
ht... |
bochaowei/t5-small-finetuned-cnn-wei1 | 5996b8c932368e7d0b076ff93d6fe5825ac0039c | 2021-10-28T20:24:24.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | bochaowei | null | bochaowei/t5-small-finetuned-cnn-wei1 | 22 | null | transformers | 8,014 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cnn-wei1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
... |
bowipawan/bert-sentimental | 2f9b7e4ef23b55b98cdb38153c67548da073cee5 | 2021-11-12T13:47:43.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | bowipawan | null | bowipawan/bert-sentimental | 22 | null | transformers | 8,015 | For studying only |
cactode/gpt2_urbandict_textgen_torch | 32706d845387d1a05aaa58ad87a9b7e36f6957ae | 2021-11-05T03:53:10.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | cactode | null | cactode/gpt2_urbandict_textgen_torch | 22 | null | transformers | 8,016 | # GPT2 Fine Tuned on UrbanDictionary
Honestly a little horrifying, but still funny.
## Usage
Use with GPT2Tokenizer. Pad token should be set to the EOS token.
Inputs should be of the form "define <your word>: ".
## Training Data
All training data was obtained from [Urban Dictionary Words And Definitions on Kaggle](ht... |
cahya/wav2vec2-luganda | ad1c5c036b67f488c416912d0aa3f6c0b65c1fa2 | 2022-03-23T18:27:18.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"lg",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"audio",
"common_voice",
"hf-asr-leaderboard",
"robust-speech-event",
"speech",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | cahya | null | cahya/wav2vec2-luganda | 22 | null | transformers | 8,017 | ---
language: lg
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- common_voice
- hf-asr-leaderboard
- lg
- robust-speech-event
- speech
license: apache-2.0
model-index:
- name: Wav2Vec2 Luganda by Indonesian-NLP
results:
- task:
name: Speech Recogni... |
castorini/monot5-large-msmarco | 48cfad1d8dd587670393f27ee8ec41fde63e3d98 | 2021-10-17T11:20:56.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers"
] | feature-extraction | false | castorini | null | castorini/monot5-large-msmarco | 22 | null | transformers | 8,018 | This model is a T5-large reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 10 epochs).
For more details on how to use it, check the following links:
- [A simple reranking example](https://github.com/castorini/pygaggle#a-simple-reranking-example)
- [Rerank MS MARCO passages](https://github.com/cast... |
dbmdz/electra-base-french-europeana-cased-discriminator | 685c31965459d92093facd8b2b31ee164ffc031e | 2021-09-13T21:05:37.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"fr",
"transformers",
"historic french",
"license:mit"
] | null | false | dbmdz | null | dbmdz/electra-base-french-europeana-cased-discriminator | 22 | 1 | transformers | 8,019 | ---
language: fr
license: mit
tags:
- "historic french"
---
# 🤗 + 📚 dbmdz ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana ELECTRA models 🎉
# French Europeana ELECTRA
We extracted all French texts using the `language` metadata att... |
dbmdz/electra-base-turkish-cased-v0-generator | 1369328c4a81db81cfa654ceabb4eafb19ab1df1 | 2020-04-24T15:57:22.000Z | [
"pytorch",
"electra",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/electra-base-turkish-cased-v0-generator | 22 | null | transformers | 8,020 | Entry not found |
dbragdon/noam-masked-lm | 4cc42aa0b51d2907f1f690098e32336205081fda | 2021-06-10T17:21:44.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | dbragdon | null | dbragdon/noam-masked-lm | 22 | null | transformers | 8,021 | Masked Language Model trained on the articles and talks of Noam Chomsky. |
ddobokki/klue-roberta-small-nli-sts | e2a0bafb78d6395e6f9bc8fc35338a998eaa9eb0 | 2022-04-14T08:08:55.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers",
"ko"
] | sentence-similarity | false | ddobokki | null | ddobokki/klue-roberta-small-nli-sts | 22 | 1 | sentence-transformers | 8,022 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- ko
---
# ddobokki/klue-roberta-small-nli-sts
한국어 Sentence Transformer 모델입니다.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
[sentence-transformers](https://www.SBERT.... |
dhikri/question_answering_glue | 598dde6797de2e74ec4f04bed3584fd3ea202e0b | 2021-02-22T08:49:56.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | dhikri | null | dhikri/question_answering_glue | 22 | null | transformers | 8,023 | "hello"
|
diiogo/electra-base | f8eb03cbba5bbf76c8657231115810459a0ba933 | 2021-12-17T17:33:23.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | diiogo | null | diiogo/electra-base | 22 | null | transformers | 8,024 | Entry not found |
dsilin/detok-deberta-xl | 40bc550b16f9c35b2c93d5c9d4fe90a320c86c3c | 2021-05-10T23:15:59.000Z | [
"pytorch",
"deberta-v2",
"token-classification",
"english",
"transformers",
"autotrain_compatible"
] | token-classification | false | dsilin | null | dsilin/detok-deberta-xl | 22 | null | transformers | 8,025 | ---
language: english
widget:
- text: "They 're a young team . they have great players and amazing freshmen coming in , so think they 'll grow into themselves next year ,"
- text: "\" We 'll talk go by now ; \" says Shucksmith ;"
- text: "\" Warren Gatland is a professional person and it wasn 't a case of 's I 'll phon... |
facebook/s2t-small-covost2-de-en-st | 2d3a4e9f1046e3fecedf7b6c710aae8f9ec00f78 | 2022-02-07T15:13:02.000Z | [
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"de",
"en",
"dataset:covost2",
"arxiv:2010.05171",
"arxiv:1912.06670",
"arxiv:1904.08779",
"transformers",
"audio",
"speech-translation",
"license:mit"
] | automatic-speech-recognition | false | facebook | null | facebook/s2t-small-covost2-de-en-st | 22 | null | transformers | 8,026 | ---
language:
- de
- en
datasets:
- covost2
tags:
- audio
- speech-translation
- automatic-speech-recognition
license: mit
pipeline_tag: automatic-speech-recognition
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
... |
finiteautomata/betonews-tweetcontext | d72a1918e1c1d64b8e193d3694d4f42c284b05ac | 2021-10-03T15:44:57.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | finiteautomata | null | finiteautomata/betonews-tweetcontext | 22 | null | transformers | 8,027 | Entry not found |
flax-community/bertin-roberta-large-spanish | 1bec2392a37173d35ce6e5dfa1b408b14b39d168 | 2021-09-23T13:53:03.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"es",
"transformers",
"spanish",
"license:cc-by-4.0",
"autotrain_compatible"
] | fill-mask | false | flax-community | null | flax-community/bertin-roberta-large-spanish | 22 | null | transformers | 8,028 | ---
language: es
license: cc-by-4.0
tags:
- spanish
- roberta
pipeline_tag: fill-mask
widget:
- text: Fui a la librería a comprar un <mask>.
---
# NOTE: This repository is now superseded by https://huggingface.co/bertin-project/bertin-roberta-base-spanish. This model corresponds to the `beta` version of the model usin... |
flax-community/gpt2-medium-indonesian | 23930cb6645dcc5208fea615079ff919e0900516 | 2021-09-02T12:22:45.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"id",
"transformers"
] | text-generation | false | flax-community | null | flax-community/gpt2-medium-indonesian | 22 | 1 | transformers | 8,029 | ---
language: id
widget:
- text: "Sewindu sudah kita tak berjumpa, rinduku padamu sudah tak terkira."
---
# GPT2-medium-indonesian
This is a pretrained model on Indonesian language using a causal language modeling (CLM) objective, which was first
introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-... |
flax-sentence-embeddings/multi-qa_v1-distilbert-mean_cos | 26ec1992576fb6821e1e66ada936860b0cbaa4fa | 2021-07-26T01:34:46.000Z | [
"pytorch",
"distilbert",
"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-distilbert-mean_cos | 22 | null | sentence-transformers | 8,030 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-qa_v1-distilbert-mean_cos
## Model Description
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sente... |
flax-sentence-embeddings/reddit_single-context_mpnet-base | 33651e090d1a29fc0e66e87603716ff5e15bb759 | 2021-07-26T01:36:18.000Z | [
"pytorch",
"mpnet",
"fill-mask",
"en",
"arxiv:1904.06472",
"sentence-transformers",
"feature-extraction",
"sentence-similarity"
] | sentence-similarity | false | flax-sentence-embeddings | null | flax-sentence-embeddings/reddit_single-context_mpnet-base | 22 | 1 | sentence-transformers | 8,031 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
---
# Model description
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained ... |
fractalego/fewrel-zero-shot | 509e315f9894bf6df8624296cbc5c9e13bbea000 | 2021-08-13T11:22:55.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | fractalego | null | fractalego/fewrel-zero-shot | 22 | 4 | transformers | 8,032 | ## Introduction
This is a zero-shot relation extractor based on the paper [Exploring the zero-shot limit of FewRel](https://www.aclweb.org/anthology/2020.coling-main.124).
## Installation
```bash
$ pip install zero-shot-re
```
## Run the Extractor
```python
from transformers import AutoTokenizer
from zero_shot_re im... |
gilparmentier/pokemon_gptj_model | 03a8ea980eae1b67105a61cc11028d6f0ad55021 | 2022-01-31T21:19:06.000Z | [
"pytorch",
"gptj",
"text-generation",
"en",
"dataset:The Pile",
"arxiv:2104.09864",
"arxiv:2101.00027",
"transformers",
"causal-lm",
"license:apache-2.0"
] | text-generation | false | gilparmentier | null | gilparmentier/pokemon_gptj_model | 22 | null | transformers | 8,033 | ---
language:
- en
tags:
- pytorch
- causal-lm
license: apache-2.0
datasets:
- The Pile
---
# GPT-J 6B
## Model Description
GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represent... |
google/t5-efficient-small | 661ddbe1d7b609351dbc14a1e49acbb595a21baa | 2022-02-15T10:51:02.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2109.10686",
"transformers",
"deep-narrow",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-efficient-small | 22 | 1 | transformers | 8,034 | ---
language:
- en
datasets:
- c4
tags:
- deep-narrow
inference: false
license: apache-2.0
---
# T5-Efficient-SMALL (Deep-Narrow version)
T5-Efficient-SMALL is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https... |
huggingtweets/asmallfiction | 2ef5093253a88dc5860c806a24d4dab5cbc0a9e2 | 2021-05-21T19:33:11.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/asmallfiction | 22 | null | transformers | 8,035 | ---
language: en
thumbnail: https://www.huggingtweets.com/asmallfiction/1616770285259/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/87539445444... |
it5/mt5-base-question-answering | 2220b747c65156a3569f940cf6249441b93ca4ad | 2022-03-09T07:57:29.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"mt5",
"text2text-generation",
"it",
"dataset:squad_it",
"arxiv:2203.03759",
"transformers",
"italian",
"sequence-to-sequence",
"squad_it",
"text2text-question-answering",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_com... | text2text-generation | false | it5 | null | it5/mt5-base-question-answering | 22 | null | transformers | 8,036 | ---
language:
- it
license: apache-2.0
datasets:
- squad_it
tags:
- italian
- sequence-to-sequence
- squad_it
- text2text-question-answering
- text2text-generation
widget:
- text: "In seguito all' evento di estinzione del Cretaceo-Paleogene, l' estinzione dei dinosauri e il clima umido possono aver permesso alla forest... |
josephgatto/paint_doctor_speaker_identification | 24eb623bb8b3870efeed315e157d44f6d72e43f2 | 2021-11-01T23:29:14.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | josephgatto | null | josephgatto/paint_doctor_speaker_identification | 22 | null | transformers | 8,037 | This model is a bert for sequence classification model fine-tuned on the MedDialogue dataset. Basically, the task is just to predict if a given sentence in the corpus was spoken by the patient or doctor. |
malay-huggingface/t5-tiny-bahasa-cased | 223aa52af6551946c0a9e7bb637c776e8d6a64fd | 2021-09-05T13:02:47.000Z | [
"pytorch",
"t5",
"feature-extraction",
"ms",
"transformers"
] | feature-extraction | false | malay-huggingface | null | malay-huggingface/t5-tiny-bahasa-cased | 22 | null | transformers | 8,038 | ---
language: ms
---
# t5-tiny-bahasa-cased
Pretrained T5 tiny language model for Malay.
## Pretraining Corpus
`t5-tiny-bahasa-cased` model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, bahasa Academia.edu, bahasa parliament and ... |
morenolq/distilbart-bbc | c919ddbd924938e1abd2432552237b123321d631 | 2021-12-29T14:43:04.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | morenolq | null | morenolq/distilbart-bbc | 22 | 2 | transformers | 8,039 | This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the BBC News Summary dataset (https://www.kaggle.com/pariza/bbc-news-summary).
The model has been generated as part of the in-lab practice of **Deep NLP course** currently held at Politecnico ... |
mrm8488/bert-medium-wrslb-finetuned-squadv1 | 8a5696507a219939816688fa40c8ced8c1413889 | 2021-05-20T00:25:31.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/bert-medium-wrslb-finetuned-squadv1 | 22 | null | transformers | 8,040 | Entry not found |
mrm8488/bert-spanish-cased-finetuned-pos-syntax | a6adb2b94c4120043a6bdc3493a711b6795b57ae | 2021-05-20T00:37:26.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"es",
"transformers",
"autotrain_compatible"
] | token-classification | false | mrm8488 | null | mrm8488/bert-spanish-cased-finetuned-pos-syntax | 22 | null | transformers | 8,041 | ---
language: es
thumbnail:
---
# Spanish BERT (BETO) + Syntax POS tagging ✍🏷
This model is a fine-tuned version of the Spanish BERT [(BETO)](https://github.com/dccuchile/beto) on Spanish **syntax** annotations in [CONLL CORPORA](https://www.kaggle.com/nltkdata/conll-corpora) dataset for **syntax POS** (Part of Spee... |
mrm8488/codebert2codebert-finetuned-code-defect-detection | 565d44b32c550aca63efcf980231c4af5f6e673c | 2021-06-14T17:17:29.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | mrm8488 | null | mrm8488/codebert2codebert-finetuned-code-defect-detection | 22 | 1 | transformers | 8,042 | Entry not found |
mudes/multilingual-base | 61aeae16e83b822cb7695f6d5696246cd1b5fe47 | 2021-05-07T16:27:58.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"multilingual",
"arxiv:2102.09665",
"arxiv:2104.04630",
"transformers",
"mudes",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | mudes | null | mudes/multilingual-base | 22 | null | transformers | 8,043 | ---
language: multilingual
tags:
- mudes
license: apache-2.0
---
# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in [this paper](https://arxiv.org/abs/2102.09665). We have evaluated our models on Toxic... |
navteca/all-mpnet-base-v2 | d8c0f0aa479ac7e550a1d16cfda17fb23bcbad91 | 2022-03-18T11:28:42.000Z | [
"pytorch",
"mpnet",
"fill-mask",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:mit"
] | sentence-similarity | false | navteca | null | navteca/all-mpnet-base-v2 | 22 | null | sentence-transformers | 8,044 | ---
language: en
license: mit
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
- sentence-transformers
---
# All MPNet base model (v2) for Semantic Search
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector ... |
ncduy/bert-base-uncased-finetuned-swag | 7a0c8f25efad921b664e72893afbc997ca3f4bd6 | 2021-08-07T05:28:53.000Z | [
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"dataset:swag",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | multiple-choice | false | ncduy | null | ncduy/bert-base-uncased-finetuned-swag | 22 | null | transformers | 8,045 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- swag
model_index:
- name: bert-base-uncased-finetuned-swag
results:
- dataset:
name: swag
type: swag
args: regular
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. Y... |
openai/imagegpt-large | 6d413391888f5d7a1d7d903a46a19ad25ba7b9f2 | 2022-06-30T06:46:29.000Z | [
"pytorch",
"imagegpt",
"dataset:imagenet-21k",
"transformers",
"vision",
"license:apache-2.0"
] | null | false | openai | null | openai/imagegpt-large | 22 | 1 | transformers | 8,046 | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet-21k
---
# ImageGPT (large-sized model)
ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper [Generative Pretraining from Pixels](https://cdn.openai.com/papers/Gener... |
pere/norwegian-gptneo-red | 7f3e1a6252793027ce697e6d8282826669550058 | 2021-09-25T18:43:08.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | pere | null | pere/norwegian-gptneo-red | 22 | null | transformers | 8,047 | Entry not found |
safsaf/poemAR | cd7d54b8e381c538a54bf1c31d138fb1c094db79 | 2021-05-23T12:19:44.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | safsaf | null | safsaf/poemAR | 22 | null | transformers | 8,048 | Entry not found |
secometo/mt5-base-turkish-question-paraphrase-generator | 354ff4650759c412bfd70c7c01af7919a79b8e5a | 2021-09-11T17:14:52.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | secometo | null | secometo/mt5-base-turkish-question-paraphrase-generator | 22 | 2 | transformers | 8,049 | # Turkish-question-paraphrase-generator
mT5 based pre-trained model to generate question paraphrases in Turkish language.
## Acknowledgement
In this project, which we undertook as an BLM3010 Computer Project of Yildiz Technical University, our goal was to conduct research on Turkish in area that has not been studied ... |
seduerr/pai_wikisplit | 4f1e5d28043a52c5c9d78b876003b849d7360d64 | 2021-07-21T12:19:41.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | seduerr | null | seduerr/pai_wikisplit | 22 | null | transformers | 8,050 | Entry not found |
tartuNLP/gpt-4-est-base | 8608e761cb925a38e0976d640c3c2a07f65af504 | 2022-03-10T10:06:15.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"model-index"
] | text-generation | false | tartuNLP | null | tartuNLP/gpt-4-est-base | 22 | null | transformers | 8,051 | ---
tags:
- generated_from_trainer
model-index:
- name: gpt-4-est-base
results: []
widget:
- text: ">wiki< mis on GPT? Vastus:"
---
---
# gpt-4-est-base
This is GPT for Estonian. Not GPT-4 :-) This is the base-size [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2) model, trained from scrat... |
textattack/xlnet-base-cased-imdb | 694be93d2ad4c287b622b9bccf8cd240bb1be92a | 2020-07-06T16:35:25.000Z | [
"pytorch",
"xlnet",
"text-generation",
"transformers"
] | text-generation | false | textattack | null | textattack/xlnet-base-cased-imdb | 22 | null | transformers | 8,052 | ## TextAttack Model Card
This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 512.
Since this was a clas... |
w11wo/sundanese-roberta-base | 14f10ed3b408ad3ab1abddee70c9734e9a86d07f | 2022-02-26T13:14:48.000Z | [
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"su",
"dataset:mc4",
"dataset:cc100",
"dataset:oscar",
"dataset:wikipedia",
"arxiv:1907.11692",
"transformers",
"sundanese-roberta-base",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | w11wo | null | w11wo/sundanese-roberta-base | 22 | 2 | transformers | 8,053 | ---
language: su
tags:
- sundanese-roberta-base
license: mit
datasets:
- mc4
- cc100
- oscar
- wikipedia
widget:
- text: "Budi nuju <mask> di sakola."
---
## Sundanese RoBERTa Base
Sundanese RoBERTa Base is a masked language model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. It was trai... |
warwickai/fin-perceiver | 1ee8539cb5cee43c188ddaf94a854e73e4c6e75c | 2022-01-07T11:36:13.000Z | [
"pytorch",
"perceiver",
"text-classification",
"en",
"dataset:financial_phrasebank",
"transformers",
"financial-sentiment-analysis",
"sentiment-analysis",
"language-perceiver",
"license:apache-2.0",
"model-index"
] | text-classification | false | warwickai | null | warwickai/fin-perceiver | 22 | 3 | transformers | 8,054 | ---
language: "en"
license: apache-2.0
tags:
- financial-sentiment-analysis
- sentiment-analysis
- language-perceiver
datasets:
- financial_phrasebank
widget:
- text: "INDEX100 fell sharply today."
- text: "ImaginaryJetCo bookings hit by Omicron variant as losses total £1bn."
- text: "Q1 ImaginaryGame's earnings beat e... |
yangheng/deberta-v3-base-absa | 84dd631427e6762fb6ce8ae194483195648f088b | 2022-03-19T01:06:27.000Z | [
"pytorch",
"deberta-v2",
"en",
"dataset:laptop14 (w/ augmentation)",
"dataset:restaurant14 (w/ augmentation)",
"dataset:restaurant16 (w/ augmentation)",
"dataset:ACL-Twitter (w/ augmentation)",
"dataset:MAMS (w/ augmentation)",
"dataset:Television (w/ augmentation)",
"dataset:TShirt (w/ augmentati... | null | false | yangheng | null | yangheng/deberta-v3-base-absa | 22 | 1 | transformers | 8,055 | ---
language:
- en
tags:
- aspect-based-sentiment-analysis
- lcf-bert
license: mit
datasets:
- laptop14 (w/ augmentation)
- restaurant14 (w/ augmentation)
- restaurant16 (w/ augmentation)
- ACL-Twitter (w/ augmentation)
- MAMS (w/ augmentation)
- Television (w/ augmentation)
- TShirt (w/ augmentation)
- ... |
aaraki/bert-base-uncased-finetuned-swag | 55d09ad21446d90cd4d4b82973a9635bede29c5a | 2022-03-18T08:16:58.000Z | [
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"dataset:swag",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | multiple-choice | false | aaraki | null | aaraki/bert-base-uncased-finetuned-swag | 22 | null | transformers | 8,056 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-swag
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it... |
Aleksandar1932/gpt-neo-125M-spanish-classics | e6b8298ae82de343453705931a6715bec914abe1 | 2022-03-19T19:59:48.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | Aleksandar1932 | null | Aleksandar1932/gpt-neo-125M-spanish-classics | 22 | null | transformers | 8,057 | Entry not found |
dhlee347/distilbert-imdb | 9a3bad591668f8a8a85aa86d2d8259075011e699 | 2022-03-28T14:07:15.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | dhlee347 | null | dhlee347/distilbert-imdb | 22 | null | transformers | 8,058 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
... |
samayash/finetuning-financial-news-sentiment | 33f36ac9eefe95b141724a1b254956f4dc7df030 | 2022-03-30T03:36:40.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | samayash | null | samayash/finetuning-financial-news-sentiment | 22 | null | transformers | 8,059 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-financial-news-sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then re... |
arampacha/gpt-neo-therapist-small | 29f3c56461d5e17428e7e665b8fb1f05dd4f1942 | 2022-03-31T20:34:26.000Z | [
"pytorch",
"tensorboard",
"onnx",
"gpt_neo",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | arampacha | null | arampacha/gpt-neo-therapist-small | 22 | 1 | transformers | 8,060 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: gpt-neo-therapist-small
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -... |
manu/lilt-camembert-dit-base | 74b9e14ae4a055c3c5ca7d5f5e3ee9772b87fd80 | 2022-04-02T16:48:05.000Z | [
"pytorch",
"liltrobertalike",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | manu | null | manu/lilt-camembert-dit-base | 22 | null | transformers | 8,061 | Entry not found |
facebook/regnet-y-10b-seer-in1k | 2e2409ed91657ca8a34fff921b16c8893d64153e | 2022-06-30T10:24:15.000Z | [
"pytorch",
"tf",
"regnet",
"image-classification",
"dataset:imagenet1k",
"arxiv:2003.13678",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/regnet-y-10b-seer-in1k | 22 | 1 | transformers | 8,062 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet1k
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... |
ELiRF/mbart-large-cc25-dacsa-ca | d83ad1450ce1ea3c7c98f1e4691b9ede773dc9e7 | 2022-07-11T17:33:48.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"ca",
"arxiv:2001.08210",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | ELiRF | null | ELiRF/mbart-large-cc25-dacsa-ca | 22 | null | transformers | 8,063 | ---
language: ca
tags:
- summarization
widget:
- text: "La Universitat Politècnica de València (UPV), a través del projecte Atenea “plataforma de dones, art i tecnologia” i en col·laboració amb les companyies tecnològiques Metric Salad i Zetalab, ha digitalitzat i modelat en 3D per a la 35a edició del Festival Dansa ... |
ELiRF/mt5-base-dacsa-es | 5536eaf4eb1f6dd2347dffa9693599c5a25052cf | 2022-07-11T17:33:03.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"es",
"arxiv:2010.11934",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | ELiRF | null | ELiRF/mt5-base-dacsa-es | 22 | null | transformers | 8,064 | ---
language: es
tags:
- summarization
widget:
- text: "La Universitat Politècnica de València (UPV), a través del proyecto Atenea “plataforma de mujeres, arte y tecnología” y en colaboración con las compañías tecnológicas Metric Salad y Zetalab, ha digitalizado y modelado en 3D para la 35ª edición del Festival Dansa... |
domenicrosati/t5-small-finetuned-contradiction | c50fd83ddf2a8e80962a24d556b5a8a79b943f70 | 2022-04-28T03:07:30.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"dataset:snli",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | domenicrosati | null | domenicrosati/t5-small-finetuned-contradiction | 22 | 1 | transformers | 8,065 | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- snli
metrics:
- rouge
model-index:
- name: t5-small-finetuned-contradiction
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: snli
type: snli
arg... |
manueltonneau/bert-twitter-pt-job-offer | daff02cbaa19b04abb1b13fcd21051d0808e4195 | 2022-04-27T10:17:18.000Z | [
"pytorch",
"bert",
"text-classification",
"pt",
"arxiv:2203.09178",
"transformers"
] | text-classification | false | manueltonneau | null | manueltonneau/bert-twitter-pt-job-offer | 22 | null | transformers | 8,066 | ---
language: pt # <-- my language
widget:
- text: "VAGA - Assistente Comercial - São Paulo; Interessados mandar currículo"
---
# Detection of employment status disclosures on Twitter
## Model main characteristics:
- class: Job Offer (1), else (0)
- country: BR
- language: Portuguese
- architecture: BERT ba... |
gunghio/xlm-roberta-base-finetuned-panx-ner | 79ae76eb12734fb288abc879a31a2187c28cc4c6 | 2022-05-17T21:41:03.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"it",
"en",
"de",
"fr",
"es",
"dataset:xtreme",
"transformers",
"license:mit",
"autotrain_compatible"
] | token-classification | false | gunghio | null | gunghio/xlm-roberta-base-finetuned-panx-ner | 22 | null | transformers | 8,067 | ---
license:
- mit
datasets:
- xtreme
language:
- it
- en
- de
- fr
- es
metrics:
- precision: 0.874
- recall: 0.880
- f1: 0.877
- accuracy: 0.943
inference:
parameters:
aggregation_strategy: "first"
---
# gunghio/xlm-roberta-base-finetuned-panx-ner
This model was trained starting from xlm-r... |
doc2query/msmarco-spanish-mt5-base-v1 | 5ae42776dd7e22705003b3c89f0b0f21011c218b | 2022-04-29T12:11:59.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"es",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | doc2query | null | doc2query/msmarco-spanish-mt5-base-v1 | 22 | 1 | transformers | 8,068 | ---
language: es
datasets:
- unicamp-dl/mmarco
widget:
- text: "Python es un lenguaje de alto nivel de programación interpretado cuya filosofía hace hincapié en la legibilidad de su código, se utiliza para desarrollar aplicaciones de todo tipo, ejemplos: Instagram, Netflix, Panda 3D, entre otros.2 Se trata de un ... |
patrickvonplaten/wav2vec2-conformer-rel-pos-large-960h-ft-4-gram | 931a3bcd042a3fcb047ab260c2e998d0ec9fb3a6 | 2022-05-24T11:10:15.000Z | [
"pytorch",
"wav2vec2-conformer",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"transformers",
"speech",
"audio",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | patrickvonplaten | null | patrickvonplaten/wav2vec2-conformer-rel-pos-large-960h-ft-4-gram | 22 | null | transformers | 8,069 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: wav2vec2-conformer-rel-pos-large-960h-ft-4-gram
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
data... |
HiTZ/A2T_RoBERTa_SMFA_TACRED-re | 923cbb4e9d82065e828f15667464974c56214f5f | 2022-05-08T23:09:49.000Z | [
"pytorch",
"roberta",
"text-classification",
"dataset:snli",
"dataset:anli",
"dataset:multi_nli",
"dataset:multi_nli_mismatch",
"dataset:fever",
"arxiv:2104.14690",
"arxiv:2203.13602",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | false | HiTZ | null | HiTZ/A2T_RoBERTa_SMFA_TACRED-re | 22 | null | transformers | 8,070 | ---
pipeline_tag: zero-shot-classification
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
---
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatib... |
laituan245/molt5-large-caption2smiles | 9e82786751c0ea421b9252f632e763934dfbe8c2 | 2022-05-03T18:08:19.000Z | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | laituan245 | null | laituan245/molt5-large-caption2smiles | 22 | null | transformers | 8,071 | ---
license: apache-2.0
---
This model can be used to generate a SMILES string from an input caption.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-large-caption2smiles", model_max_length=512)
model = T5ForConditi... |
ml4pubmed/xtremedistil-l12-h384-uncased_pub_section | db9cc5d15345bfc80b95effe5a2b6e3a1b58f41d | 2022-06-22T12:29:07.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:pubmed",
"transformers",
"document sections",
"sentence classification",
"document classification",
"medical",
"health",
"biomedical"
] | text-classification | false | ml4pubmed | null | ml4pubmed/xtremedistil-l12-h384-uncased_pub_section | 22 | null | transformers | 8,072 | ---
language:
- en
datasets:
- pubmed
metrics:
- f1
tags:
- text-classification
- document sections
- sentence classification
- document classification
- medical
- health
- biomedical
pipeline_tag: text-classification
widget:
- text: "many pathogenic processes and diseases are the result of an erroneous activation of t... |
fabiochiu/t5-base-medium-title-generation | 384c8158ed22e75bf7ceff0a8b43f8ae01e124b6 | 2022-05-23T13:39:22.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | fabiochiu | null | fabiochiu/t5-base-medium-title-generation | 22 | 1 | transformers | 8,073 | ---
tags:
- generated_from_keras_callback
model-index:
- name: t5-base-medium-title-generation
results: []
widget:
- text: "summarize: Many financial institutions started building conversational AI, prior to the Covid19 pandemic, as part of a digital transformation initiative. These initial solutions were high profil... |
AlekseyKorshuk/opt-1.3b | 5b21e55674fbd694bf081c46e25d5ba4bb729ae5 | 2022-05-05T18:32:57.000Z | [
"pytorch",
"opt",
"text-generation",
"transformers",
"license:apache-2.0"
] | text-generation | false | AlekseyKorshuk | null | AlekseyKorshuk/opt-1.3b | 22 | null | transformers | 8,074 | ---
license: apache-2.0
---
|
charlieoneill/distilbert-base-uncased-gradient-clinic | 0697c8d2dd06d799d262e214ec44b6c851a9a533 | 2022-07-10T19:52:13.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | charlieoneill | null | charlieoneill/distilbert-base-uncased-gradient-clinic | 22 | 1 | transformers | 8,075 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-gradient-clinic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. --... |
ccdv/lsg-bart-base-4096-arxiv | 2d42dd7455cf480313f1a90639d33a619e8d15b9 | 2022-07-25T05:30:44.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:scientific_papers",
"transformers",
"summarization",
"model-index",
"autotrain_compatible"
] | summarization | false | ccdv | null | ccdv/lsg-bart-base-4096-arxiv | 22 | null | transformers | 8,076 | ---
language:
- en
tags:
- summarization
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: ccdv/lsg-bart-base-4096-arxiv
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then ... |
lucifermorninstar011/autotrain-lucifer_job_title_comb-858027260 | 83b2a074f52b32ac673f8c271d3ef3ff402df5b2 | 2022-05-12T08:58:09.000Z | [
"pytorch",
"bert",
"token-classification",
"en",
"dataset:lucifermorninstar011/autotrain-data-lucifer_job_title_comb",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | token-classification | false | lucifermorninstar011 | null | lucifermorninstar011/autotrain-lucifer_job_title_comb-858027260 | 22 | null | transformers | 8,077 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lucifermorninstar011/autotrain-data-lucifer_job_title_comb
co2_eq_emissions: 66.24310525675156
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 858027260
- CO2 Emissions (in grams): 66.24310525675156
#... |
charsiu/g2p_multilingual_byT5_tiny_8_layers | 0f580d3f3948030c59fcdfcdc14afbba2ef78616 | 2022-05-19T05:03:28.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | charsiu | null | charsiu/g2p_multilingual_byT5_tiny_8_layers | 22 | null | transformers | 8,078 | Entry not found |
ENM/scibert_scivocab_cased-new-finetuned-breastcancer | 5d180369ef6636b6225bba9031beeed047ed39e8 | 2022-05-26T02:28:12.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-generation",
"transformers",
"generated_from_trainer",
"model-index"
] | text-generation | false | ENM | null | ENM/scibert_scivocab_cased-new-finetuned-breastcancer | 22 | null | transformers | 8,079 | ---
tags:
- generated_from_trainer
model-index:
- name: scibert_scivocab_cased-new-finetuned-breastcancer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scibe... |
Abderrahim2/bert-finetuned-Location | 44a97b66a5f90853edf7a494e415cb1467297d77 | 2022-06-01T20:18:34.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | Abderrahim2 | null | Abderrahim2/bert-finetuned-Location | 22 | null | transformers | 8,080 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-finetuned-Location
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. ... |
wawaup/MengziT5-Comment | 764e561e5d89d715b391f2c5df261834edb950f1 | 2022-06-19T16:59:37.000Z | [
"pytorch",
"t5",
"text2text-generation",
"zh",
"dataset:TencentKuaibao",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | wawaup | null | wawaup/MengziT5-Comment | 22 | null | transformers | 8,081 | ---
language:
- zh
license: apache-2.0
datasets:
- TencentKuaibao
metrics:
- bleu
- rouge
---
## 模型
- 基于中文[MengziT5](https://huggingface.co/Langboat/mengzi-t5-base)的新闻评论生成模型
- 数据集来源于论文[《Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model》](https://github.com/lancopku/Graph-to-seq-comment-... |
yanekyuk/berturk-keyword-extractor | 0af06543592681df8c7ca889fd99ed85b3d55d45 | 2022-06-04T01:57:03.000Z | [
"pytorch",
"bert",
"token-classification",
"tr",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | yanekyuk | null | yanekyuk/berturk-keyword-extractor | 22 | null | transformers | 8,082 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- tr
widget:
- text: "İngiltere'de düzenlenen Avrupa Tekvando ve Para Tekvando Şampiyonası’nda millî tekvandocular 5 altın, 2 gümüş ve 4 bronz olmak üzere 11, millî para tekvandocular ise 4 altın, 3 gümüş ve 1 bronz ... |
SalamaThanks/SalamaThanksTransformer_fil2en_v3 | 11ba7a1ed0a678682b42b5990d5722ba93cad4b1 | 2022-06-06T11:39:23.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | SalamaThanks | null | SalamaThanks/SalamaThanksTransformer_fil2en_v3 | 22 | null | transformers | 8,083 | Entry not found |
ghadeermobasher/BC4CHEMD-Chem-Modified-BioBERT-512 | c5f649d58c915803a5aa4dcea32ead7e5b98f8ef | 2022-06-16T06:52:32.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BC4CHEMD-Chem-Modified-BioBERT-512 | 22 | null | transformers | 8,084 | Entry not found |
Matthijs/mobilenet_v1_1.0_224 | c173be55f634ff127231454809e51ed145debed4 | 2022-06-22T12:49:25.000Z | [
"pytorch",
"mobilenet_v1",
"dataset:imagenet-1k",
"arxiv:1704.04861",
"transformers",
"vision",
"image-classification",
"license:other"
] | image-classification | false | Matthijs | null | Matthijs/mobilenet_v1_1.0_224 | 22 | null | transformers | 8,085 | ---
license: other
tags:
- vision
- image-classification
datasets:
- imagenet-1k
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: https://hu... |
RogerKam/roberta_RCADE_fine_tuned_sentiment_covid_news | 316f4824833b3134278171a287b713644dc2e9f8 | 2022-06-22T19:15:59.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | RogerKam | null | RogerKam/roberta_RCADE_fine_tuned_sentiment_covid_news | 22 | null | transformers | 8,086 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_RCADE_fine_tuned_sentiment_covid_news
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remo... |
kktoto/4L_weight_decay | ee4399240df6faf18c4d96f791c03e42cda69396 | 2022-06-23T04:49:26.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | kktoto | null | kktoto/4L_weight_decay | 22 | null | transformers | 8,087 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 4L_weight_decay
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. ... |
inigopm/beto-base-spanish-squades2 | d62b153b4371b57695293e3298662e5197833652 | 2022-06-26T14:48:20.000Z | [
"pytorch",
"bert",
"question-answering",
"es",
"dataset:squad_es",
"transformers",
"model-index",
"autotrain_compatible"
] | question-answering | false | inigopm | null | inigopm/beto-base-spanish-squades2 | 22 | 1 | transformers | 8,088 | ---
language:
- es
tags:
- question-answering
datasets:
- squad_es
metrics:
- f1
- em
# Optional. Add this if you want to encode your eval results in a structured way.
model-index:
- name: beto-base-spanish-squades2
results:
- task:
type: question-answering # Required. Example: automatic-speech-recognition
... |
Maha/xlmtwtroberta_label2 | 818090795586a3ac5cfd76222f22efdbe8af8e7c | 2022-06-27T10:06:47.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | false | Maha | null | Maha/xlmtwtroberta_label2 | 22 | null | transformers | 8,089 | Entry not found |
jvanz/querido_diario_autoencoder | e0f8d41da93f0a50708257138b9cecbb4c949eb0 | 2022-07-01T11:53:59.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"pt",
"dataset:jvanz/querido_diario",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | jvanz | null | jvanz/querido_diario_autoencoder | 22 | null | transformers | 8,090 | ---
language:
- pt
datasets:
- jvanz/querido_diario
---
# Querido Diario Autoencoder
Autoencoder based on portuguese BERT using the Querido Diario dataset |
Matthijs/mobilenet_v2_1.4_224 | bff5e2944e1ea36bb715cc34e69b1fb837cb5aad | 2022-06-28T12:50:41.000Z | [
"pytorch",
"coreml",
"mobilenet_v2",
"dataset:imagenet-1k",
"arxiv:1801.04381",
"transformers",
"vision",
"image-classification",
"license:other"
] | image-classification | false | Matthijs | null | Matthijs/mobilenet_v2_1.4_224 | 22 | null | transformers | 8,091 | ---
license: other
tags:
- vision
- image-classification
datasets:
- imagenet-1k
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: https://hu... |
fxtentacle/tevr-token-entropy-predictor-de | dbeb0d0ae7b05690576cb2efd9b5f1e609e1ef90 | 2022-06-28T16:19:45.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | fxtentacle | null | fxtentacle/tevr-token-entropy-predictor-de | 22 | null | transformers | 8,092 | This repo contains the fully trained ByT5 that was used to estimate per-character entropies. Using it, you can also recreate the illustration in the paper.
## Generate TEVR Tokenizer from Text corpus
(copy of `Generate TEVR Tokenizer.ipynb`)
```python
# TODO: load large text dataset like OSCAR
all_sentences_de = ["Üb... |
bayartsogt/dlub-2022-mlm | cf4f9a07c20bc9474e125e935276999a8a4f43e3 | 2022-06-30T04:04:54.000Z | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | bayartsogt | null | bayartsogt/dlub-2022-mlm | 22 | null | transformers | 8,093 | ---
tags:
- generated_from_trainer
model-index:
- name: dlub-2022-mlm
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dlub-2022-mlm
This model is a fine-tuned... |
projecte-aina/roberta-base-ca-v2-cased-te | a6b25e5c5227a873c41b4ff602f54748c1abed38 | 2022-07-25T06:51:49.000Z | [
"pytorch",
"roberta",
"text-classification",
"ca",
"dataset:projecte-aina/teca",
"arxiv:1907.11692",
"transformers",
"catalan",
"textual entailment",
"teca",
"CaText",
"Catalan Textual Corpus",
"license:apache-2.0",
"model-index"
] | text-classification | false | projecte-aina | null | projecte-aina/roberta-base-ca-v2-cased-te | 22 | null | transformers | 8,094 | ---
language:
- ca
license: apache-2.0
tags:
- "catalan"
- "textual entailment"
- "teca"
- "CaText"
- "Catalan Textual Corpus"
datasets:
- "projecte-aina/teca"
metrics:
- "accuracy"
model-index:
- name: roberta-base-ca-v2-cased-te
results:
- task:
type: text-classification # Required. Example... |
gaunernst/bert-mini-uncased | e9056139b012a62b6f859412b8f60bc38f0055bc | 2022-07-02T03:09:03.000Z | [
"pytorch",
"bert",
"transformers",
"license:apache-2.0"
] | null | false | gaunernst | null | gaunernst/bert-mini-uncased | 22 | null | transformers | 8,095 | ---
license: apache-2.0
---
|
AdShenoy/fineTuneRoberta | a29987d788aedd85d5f8311515f5636c353fa4ce | 2022-07-08T16:46:33.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | AdShenoy | null | AdShenoy/fineTuneRoberta | 22 | null | transformers | 8,096 | Entry not found |
jakka/t5-small-finetuned-xsum | f3e59f5c176a4d84957b2ff1663ba8179c73bc5b | 2022-07-11T14:00:49.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | jakka | null | jakka/t5-small-finetuned-xsum | 22 | null | transformers | 8,097 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
... |
juridics/bertimbau-base-portuguese-sts | 0c02aa449ff7806dfa3daed9e754fd6b32c21b88 | 2022-07-04T15:51:01.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | juridics | null | juridics/bertimbau-base-portuguese-sts | 22 | null | sentence-transformers | 8,098 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# juridics/bertimbau-base-portuguese-sts-scale
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and c... |
uaritm/multilingual_en_ru_uk | 078a388e3a6e6c4cc6bc3e7c78dc4eb032608d74 | 2022-07-04T17:54:13.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | uaritm | null | uaritm/multilingual_en_ru_uk | 22 | null | sentence-transformers | 8,099 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# uaritm/multilingual_en_ru_uk
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for t... |
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