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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
hyunwoongko/ctrlsum-cnndm | 3f8f0a6caf964a79f13ba9cbb28a25757b72b4cd | 2021-03-21T15:55:50.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | hyunwoongko | null | hyunwoongko/ctrlsum-cnndm | 1,307 | 2 | transformers | 1,600 | Entry not found |
dennlinger/roberta-cls-consec | 26d06e22b97525aa959aaa5dfdaf4e3ab8bcd387 | 2021-06-14T13:07:40.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"arxiv:2012.03619",
"transformers"
] | text-classification | false | dennlinger | null | dennlinger/roberta-cls-consec | 1,304 | 1 | transformers | 1,601 | # About this model: Topical Change Detection in Documents
This network has been fine-tuned for the task described in the paper *Topical Change Detection in Documents via Embeddings of Long Sequences* and is our best-performing base-transformer model. You can find more detailed information in our GitHub page for the pap... |
pedropei/sentence-level-certainty | 57bb19e0804a77689ca02f2b1d408d162413cdc2 | 2021-09-29T05:35:19.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | pedropei | null | pedropei/sentence-level-certainty | 1,303 | null | transformers | 1,602 | Entry not found |
johngiorgi/declutr-small | d899ea3e95e6a65499184647d080379e6c477208 | 2022-03-11T14:47:48.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:2006.03659",
"transformers",
"autotrain_compatible"
] | fill-mask | false | johngiorgi | null | johngiorgi/declutr-small | 1,302 | 2 | transformers | 1,603 | # DeCLUTR-small
## Model description
The "DeCLUTR-small" 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 Univer... |
monologg/koelectra-base-discriminator | c7005c19e7e523a86c96ad67fbd49c888ebbf287 | 2021-10-20T16:55:57.000Z | [
"pytorch",
"electra",
"pretraining",
"ko",
"transformers",
"korean",
"license:apache-2.0"
] | null | false | monologg | null | monologg/koelectra-base-discriminator | 1,298 | null | transformers | 1,604 | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA (Base Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tok... |
anton-l/wav2vec2-base-ft-keyword-spotting | 30629617f4408a39489bec210f6b5127b6fbaafc | 2021-10-27T22:16:42.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"dataset:superb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | audio-classification | false | anton-l | null | anton-l/wav2vec2-base-ft-keyword-spotting | 1,294 | 1 | transformers | 1,605 | ---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-ft-keyword-spotting
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably... |
moussaKam/barthez-orangesum-abstract | 2f4969c2f16bf27aaddb87bf9b862ccead48135b | 2021-11-15T13:03:03.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"fr",
"arxiv:2010.12321",
"transformers",
"summarization",
"bart",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | moussaKam | null | moussaKam/barthez-orangesum-abstract | 1,294 | 1 | transformers | 1,606 | ---
tags:
- summarization
- bart
language:
- fr
license: apache-2.0
widget:
- text: Citant les préoccupations de ses clients dénonçant des cas de censure après la suppression du compte de Trump, un fournisseur d'accès Internet de l'État de l'Idaho a décidé de bloquer Facebook et Twitter. La mesure ne concernera cepe... |
uclanlp/plbart-python-en_XX | 48bf6e4889bdb9bafd12381a4e9a9a1e0fe224eb | 2021-11-09T17:09:27.000Z | [
"pytorch",
"plbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uclanlp | null | uclanlp/plbart-python-en_XX | 1,292 | 1 | transformers | 1,607 | Entry not found |
valhalla/gpt-neo-random-tiny | 6e358e9d007d3bf2f592832a2e1c4dce15fe409a | 2021-04-07T16:38:40.000Z | [
"pytorch",
"gpt_neo",
"feature-extraction",
"transformers"
] | feature-extraction | false | valhalla | null | valhalla/gpt-neo-random-tiny | 1,292 | null | transformers | 1,608 | **This model is uploaded for testing purpose. It's random model not trained on anything** |
Helsinki-NLP/opus-mt-ka-en | f6f4a42415aa81a926f6596654cfcbd37cefc214 | 2020-08-21T14:42:47.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ka",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ka-en | 1,288 | null | transformers | 1,609 | ---
language:
- ka
- en
tags:
- translation
license: apache-2.0
---
### kat-eng
* source group: Georgian
* target group: English
* OPUS readme: [kat-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kat-eng/README.md)
* model: transformer-align
* source language(s): kat
* target langua... |
allegro/plt5-small | 5c65ab3ab269dda279491e7e685f0adf1dadef61 | 2021-08-19T16:59:55.000Z | [
"pytorch",
"t5",
"text2text-generation",
"pl",
"dataset:ccnet",
"dataset:nkjp",
"dataset:wikipedia",
"dataset:open subtitles",
"dataset:free readings",
"transformers",
"T5",
"translation",
"summarization",
"question answering",
"reading comprehension",
"license:cc-by-4.0",
"autotrain... | translation | false | allegro | null | allegro/plt5-small | 1,281 | 2 | transformers | 1,610 | ---
language: pl
tags:
- T5
- translation
- summarization
- question answering
- reading comprehension
datasets:
- ccnet
- nkjp
- wikipedia
- open subtitles
- free readings
license: cc-by-4.0
---
# plT5 Small
**plT5** models are T5-based language models trained on Polish corpora. The models were optimized for the orig... |
facebook/wav2vec2-lv-60-espeak-cv-ft | 7718bdd728dde297e1e69d61fc782d147bac21a6 | 2021-12-08T21:03:18.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"multi-lingual",
"dataset:common_voice",
"arxiv:2109.11680",
"transformers",
"speech",
"audio",
"phoneme-recognition",
"license:apache-2.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-lv-60-espeak-cv-ft | 1,281 | 2 | transformers | 1,611 | ---
language: multi-lingual
datasets:
- common_voice
tags:
- speech
- audio
- automatic-speech-recognition
- phoneme-recognition
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co... |
pdelobelle/robBERT-dutch-books | 04eab2e04d08d4f62df7f769135bcece4f907606 | 2021-05-20T19:17:17.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | pdelobelle | null | pdelobelle/robBERT-dutch-books | 1,281 | null | transformers | 1,612 | Entry not found |
JamesStratford/Pidrow-bot-DialoGPT-Medium-v2 | 0fb0a99a49c249fdaf3335bf14ad62c71709b373 | 2022-06-29T07:02:11.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | JamesStratford | null | JamesStratford/Pidrow-bot-DialoGPT-Medium-v2 | 1,280 | null | transformers | 1,613 | ---
tags:
- conversational
---
# Pidrow bot - medium |
dandelin/vilt-b32-mlm-itm | a94469664a838bf855b40144f638ba9b3e791c89 | 2021-11-27T10:13:10.000Z | [
"pytorch",
"vilt",
"arxiv:2102.03334",
"transformers",
"license:apache-2.0"
] | null | false | dandelin | null | dandelin/vilt-b32-mlm-itm | 1,279 | 1 | transformers | 1,614 | ---
license: apache-2.0
tags:
---
# Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/... |
speechbrain/asr-crdnn-rnnlm-librispeech | d9760a0bef6c6718d30ad1271f7d05980d435677 | 2021-11-30T00:37:56.000Z | [
"en",
"dataset:librispeech",
"arxiv:2106.04624",
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"pytorch",
"license:apache-2.0"
] | automatic-speech-recognition | false | speechbrain | null | speechbrain/asr-crdnn-rnnlm-librispeech | 1,276 | 7 | speechbrain | 1,615 | ---
language: "en"
thumbnail:
tags:
- automatic-speech-recognition
- CTC
- Attention
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- librispeech
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scro... |
DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen | 38a0fbdddcb26bedfc182590a24ebc9a843832c3 | 2022-02-02T21:30:47.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | DaisyMak | null | DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen | 1,275 | null | transformers | 1,616 | Entry not found |
facebook/wav2vec2-base-100h | 9c1fef36b62a428a658e5b022ef9f21b38f47e0b | 2022-05-27T16:32:50.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"transformers",
"audio",
"license:apache-2.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-base-100h | 1,268 | 1 | transformers | 1,617 | ---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
license: apache-2.0
---
# Wav2Vec2-Base-100h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained and fine-tuned on 100 hours of Librispeech o... |
pritamdeka/S-BioBert-snli-multinli-stsb | 3ab11e57f285f37c31648373a5cb6bf0da5c7362 | 2022-03-11T12:35:08.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | pritamdeka | null | pritamdeka/S-BioBert-snli-multinli-stsb | 1,268 | 0 | sentence-transformers | 1,618 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# S-BioBert-snli-multinli-stsb
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 ta... |
google/bert_uncased_L-8_H-768_A-12 | 3f3d093c8dd66e4776c0286f0b52b8dea5865ece | 2021-05-19T17:36:32.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-8_H-768_A-12 | 1,266 | null | transformers | 1,619 | ---
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... |
hf-internal-testing/tiny-random-imagegpt | 8291cd3a0461602decb3fa68263f4ca3b278c8f9 | 2021-12-24T10:48:44.000Z | [
"pytorch",
"imagegpt",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-imagegpt | 1,266 | null | transformers | 1,620 | Entry not found |
ainize/kobart-news | 4b95cf0288646bf92bcdf7429b6f462b71db5eeb | 2021-06-29T02:51:15.000Z | [
"pytorch",
"bart",
"text2text-generation",
"ko",
"transformers",
"summarization",
"license:mit",
"autotrain_compatible"
] | summarization | false | ainize | null | ainize/kobart-news | 1,265 | 2 | transformers | 1,621 | ---
language: ko
license: mit
tags:
- summarization
- bart
---
# kobart-news
- This model is a [kobart](https://huggingface.co/hyunwoongko/kobart) fine-tuned on the [문서요약 텍스트/신문기사](https://aihub.or.kr/aidata/8054) using [Ainize Teachable-NLP](https://ainize.ai/teachable-nlp).
## Usage
### Python Code
```python
from tr... |
hf-internal-testing/tiny-random-speech_to_text | 0edd349ecdb54044ad27ca4cde3136252e3503c1 | 2021-09-17T19:26:03.000Z | [
"pytorch",
"speech_to_text",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-speech_to_text | 1,260 | null | transformers | 1,622 | Entry not found |
hf-internal-testing/tiny-random-vision-encoder-decoder | 2b34c3c71aa6c25134e293c502f172ee7368eb67 | 2021-12-15T17:14:55.000Z | [
"pytorch",
"vision-encoder-decoder",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-vision-encoder-decoder | 1,258 | null | transformers | 1,623 | Entry not found |
lgrobol/roberta-minuscule | 3ec7286af3b51b67bef74c29a8b9195205b532c4 | 2021-08-17T13:38:29.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | lgrobol | null | lgrobol/roberta-minuscule | 1,256 | 1 | transformers | 1,624 | RoBERTa-minuscule
==================
A ridiculously small model for testing purposes. |
izumi-lab/bert-small-japanese | 7472b8975446df577a1820d559197075ab05f2e1 | 2022-03-19T09:37:46.000Z | [
"pytorch",
"bert",
"fill-mask",
"ja",
"dataset:wikipedia",
"arxiv:2003.10555",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | izumi-lab | null | izumi-lab/bert-small-japanese | 1,252 | null | transformers | 1,625 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 東京大学で[MASK]の研究をしています。
---
# BERT small Japanese finance
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
The codes for the pretraining are available at [retarfi/language-p... |
castorini/afriberta_large | e74edb9488208f8a2aeb69be4c16d179ab385564 | 2022-06-10T12:05:16.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"om",
"am",
"rw",
"rn",
"ha",
"ig",
"pcm",
"so",
"sw",
"ti",
"yo",
"multilingual",
"transformers",
"autotrain_compatible"
] | fill-mask | false | castorini | null | castorini/afriberta_large | 1,251 | 2 | transformers | 1,626 | Hugging Face's logo
---
language:
- om
- am
- rw
- rn
- ha
- ig
- pcm
- so
- sw
- ti
- yo
- multilingual
---
# afriberta_large
## Model description
AfriBERTa large is a pretrained multilingual language model with around 126 million parameters.
The model has 10 layers, 6 attention heads, 768 hidden units and 3072 feed... |
KETI-AIR/ke-t5-base-ko | fda98d3a8ddad618a447c2e3043cccca5878e986 | 2021-06-23T02:46:59.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | KETI-AIR | null | KETI-AIR/ke-t5-base-ko | 1,241 | 1 | transformers | 1,627 | Entry not found |
mdhugol/indonesia-bert-sentiment-classification | 80ccb4c2817cf976534ac491020a9572e5dae54f | 2021-09-14T08:24:28.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | mdhugol | null | mdhugol/indonesia-bert-sentiment-classification | 1,241 | 1 | transformers | 1,628 | Indonesian BERT Base Sentiment Classifier is a sentiment-text-classification model. The model was originally the pre-trained [IndoBERT Base Model (phase1 - uncased)](https://huggingface.co/indobenchmark/indobert-base-p1) model using [Prosa sentiment dataset](https://github.com/indobenchmark/indonlu/tree/master/dataset/... |
staka/fugumt-ja-en | 8cb8ff81a8625a626c6f0f19cc5082c6181f223a | 2022-05-29T08:28:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"ja",
"transformers",
"translation",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | translation | false | staka | null | staka/fugumt-ja-en | 1,239 | 2 | transformers | 1,629 | ---
license: cc-by-sa-4.0
language:
- en
- ja
tags:
- translation
widget:
- text: "猫はかわいいです。"
---
# FuguMT
This is a translation model using Marian-NMT.
For more details, please see [my repository](https://github.com/s-taka/fugumt).
* source language: ja
* target language: en
### How to use
This model uses t... |
cmarkea/distilcamembert-base-qa | ea9c62f924a2464890c04979fa67ef28bb49d2ff | 2022-06-15T15:09:29.000Z | [
"pytorch",
"tf",
"camembert",
"question-answering",
"fr",
"dataset:fquad",
"dataset:piaf",
"transformers",
"license:cc-by-nc-sa-3.0",
"autotrain_compatible"
] | question-answering | false | cmarkea | null | cmarkea/distilcamembert-base-qa | 1,235 | 3 | transformers | 1,630 | ---
language: fr
license: cc-by-nc-sa-3.0
datasets:
- fquad
- piaf
widget:
- text: "Quand et où est sorti Toy Story ?"
context: "Pixar Animation Studios, ou simplement Pixar dans le langage courant, est une société américaine de production de films en images tridimensionnelles de synthèse. Elle a acquis sa notoriété ... |
elgeish/wav2vec2-large-xlsr-53-arabic | b5e6df14064b879671fd242c0366cbe2a68effc9 | 2022-06-04T23:37:05.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"dataset:arabic_speech_corpus",
"dataset:mozilla-foundation/common_voice_6_1",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | elgeish | null | elgeish/wav2vec2-large-xlsr-53-arabic | 1,230 | 6 | transformers | 1,631 | ---
language: ar
datasets:
- arabic_speech_corpus
- mozilla-foundation/common_voice_6_1
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: elgeish-wav2vec2-large-xlsr-53-arabic
results:
- task:
name: Autom... |
hetpandya/t5-small-tapaco | d9695bcb99a04766dbc41d636bf6b8646710b1e9 | 2021-06-30T06:36:41.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:tapaco",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | hetpandya | null | hetpandya/t5-small-tapaco | 1,230 | null | transformers | 1,632 | ---
language: en
datasets:
- tapaco
---
# T5-small for paraphrase generation
Google's T5 small fine-tuned on [TaPaCo](https://huggingface.co/datasets/tapaco) dataset for paraphrasing.
## Model in Action 🚀
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretra... |
monologg/koelectra-small-v2-discriminator | f2c615617707ae5e011a94c5506d0086301afe74 | 2020-12-26T16:23:57.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | monologg | null | monologg/koelectra-small-v2-discriminator | 1,230 | null | transformers | 1,633 | Entry not found |
salti/AraElectra-base-finetuned-ARCD | ba34c8067e38d6202812a3f880fd01f2cd20761e | 2021-01-29T20:39:31.000Z | [
"pytorch",
"electra",
"question-answering",
"ar",
"dataset:arcd",
"transformers",
"autotrain_compatible"
] | question-answering | false | salti | null | salti/AraElectra-base-finetuned-ARCD | 1,229 | 1 | transformers | 1,634 | ---
language:
- ar
datasets:
- arcd
widget:
- text: "أين يعيش محمد ؟"
context: "اسمي محمد وأنا أعيش في سوريا"
- text: "ما العدد الذري للهيدروجين ؟"
context: "الهيدروجين هو عنصر كيميائي عدده الذري 1 ، وهو غاز عديم الرائحة واللون وهو سريع الاشتعال"
- text: "ما خواص الهيدروجين ؟"
context: "الهيدروجين هو عنصر كيميائ... |
pierreguillou/ner-bert-large-cased-pt-lenerbr | d081b0eb833d418c68e3327fc16e956d4738b164 | 2021-12-29T19:33:17.000Z | [
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:lener_br",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | pierreguillou | null | pierreguillou/ner-bert-large-cased-pt-lenerbr | 1,227 | 2 | transformers | 1,635 | ---
language:
- pt
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: checkpoints
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lener_br
type: lener_br
metrics:
- name: F1... |
Helsinki-NLP/opus-mt-hy-en | c1f5af969aee273f845a84ad3f4b149ba5435303 | 2021-09-09T22:11:07.000Z | [
"pytorch",
"marian",
"text2text-generation",
"hy",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-hy-en | 1,226 | null | transformers | 1,636 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-hy-en
* source languages: hy
* target languages: en
* OPUS readme: [hy-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hy-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Salesforce/codegen-350M-multi | 2b61ebc2f74ace34d530e8ba9501198ee27ead82 | 2022-06-28T17:47:03.000Z | [
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"transformers",
"license:bsd-3-clause"
] | text-generation | false | Salesforce | null | Salesforce/codegen-350M-multi | 1,224 | 0 | transformers | 1,637 | ---
license: bsd-3-clause
---
# CodeGen (CodeGen-Multi 350M)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan... |
aubmindlab/bert-base-arabertv01 | 59dc633c58a7a1e9b4c1e8d4f7be94cf9dc6a2e0 | 2021-05-19T11:50:51.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B Arabic Corpus",
"arxiv:2003.00104",
"transformers",
"autotrain_compatible"
] | fill-mask | false | aubmindlab | null | aubmindlab/bert-base-arabertv01 | 1,220 | null | transformers | 1,638 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
widget:
- text: " عاصمة لبنان هي [MASK] ."
---
# !!! A newer version of this model is available !!! [AraBERTv02](https://huggingface.co/aubmindlab/bert-base-arabertv02)
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<im... |
facebook/wav2vec2-large-xlsr-53-german | 97e1c5b2b100529bbbd80d32c5b6862116beffab | 2021-07-06T02:46:28.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:common_voice",
"transformers",
"speech",
"audio",
"license:apache-2.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-large-xlsr-53-german | 1,220 | null | transformers | 1,639 | ---
language: de
datasets:
- common_voice
tags:
- speech
- audio
- automatic-speech-recognition
license: apache-2.0
---
## Evaluation on Common Voice DE Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
i... |
microsoft/cocolm-base | 2832a017dd206e3de5c043a005cb76c86b8ba83d | 2022-02-07T23:01:31.000Z | [
"pytorch",
"arxiv:2102.08473",
"transformers"
] | null | false | microsoft | null | microsoft/cocolm-base | 1,220 | 2 | transformers | 1,640 | # COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
This model card contains the COCO-LM model (**base++** version) proposed in [this paper](https://arxiv.org/abs/2102.08473). The official GitHub repository can be found [here](https://github.com/microsoft/COCO-LM).
# Citation
If you fi... |
facebook/data2vec-vision-base | 72a7bdadab41d0e9a2c8d6887b9f8a50eebb8e0f | 2022-05-03T15:52:10.000Z | [
"pytorch",
"tf",
"data2vec-vision",
"feature-extraction",
"dataset:imagenet",
"dataset:imagenet-1k",
"arxiv:2202.03555",
"arxiv:2106.08254",
"transformers",
"image-classification",
"vision",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/data2vec-vision-base | 1,220 | null | transformers | 1,641 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-1k
---
# Data2Vec-Vision (base-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data... |
cyclone/simcse-chinese-roberta-wwm-ext | 871d7039a3fccd4869d545a25b63c545341ca7f4 | 2021-09-02T03:04:17.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.08821",
"transformers"
] | feature-extraction | false | cyclone | null | cyclone/simcse-chinese-roberta-wwm-ext | 1,219 | 6 | transformers | 1,642 | ## Cyclone SIMCSE RoBERTa WWM Ext Chinese
This model provides simplified Chinese sentence embeddings encoding based on [Simple Contrastive Learning](https://arxiv.org/abs/2104.08821).
The pretrained model(Chinese RoBERTa WWM Ext) is used for token encoding.
### Usage
Please use [SentenceTransformer](https://git... |
allenai/macaw-3b | c4d1b101bcec5de649b927bb92c4e93c311c0be2 | 2021-09-21T15:59:14.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/macaw-3b | 1,216 | null | transformers | 1,643 | ---
language: en
widget:
- text: $answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?
license: apache-2.0
---
# macaw-3b
## Model description
Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of
general question answering,
showing robus... |
sentence-transformers/bert-base-wikipedia-sections-mean-tokens | bfe50e68735b7f483150fd1548ddb77e04b43fa8 | 2022-06-15T22:24:35.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/bert-base-wikipedia-sections-mean-tokens | 1,216 | null | sentence-transformers | 1,644 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... |
hf-internal-testing/tiny-random-data2vec-seq-class | 4c59e8c7dc5db8886fca7c12e9b380daefaf4aba | 2022-03-03T12:26:02.000Z | [
"pytorch",
"data2vec-audio",
"audio-classification",
"transformers"
] | audio-classification | false | hf-internal-testing | null | hf-internal-testing/tiny-random-data2vec-seq-class | 1,216 | null | transformers | 1,645 | Entry not found |
philschmid/distilbert-base-multilingual-cased-sentiment-2 | 83ff874f93aacbba79642abfe2a274a3c874232b | 2022-01-24T15:08:50.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:amazon_reviews_multi",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | philschmid | null | philschmid/distilbert-base-multilingual-cased-sentiment-2 | 1,211 | 1 | transformers | 1,646 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-multilingual-cased-sentiment-2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
ty... |
speechbrain/spkrec-xvect-voxceleb | e2cc27f853f99bd5d539432f0cba3f124c059f71 | 2022-06-25T02:56:40.000Z | [
"en",
"dataset:voxceleb",
"arxiv:2106.04624",
"speechbrain",
"embeddings",
"Speaker",
"Verification",
"Identification",
"pytorch",
"xvectors",
"TDNN",
"audio-classification",
"license:apache-2.0"
] | audio-classification | false | speechbrain | null | speechbrain/spkrec-xvect-voxceleb | 1,207 | 4 | speechbrain | 1,647 | ---
language: "en"
thumbnail:
tags:
- embeddings
- Speaker
- Verification
- Identification
- pytorch
- xvectors
- TDNN
- speechbrain
- audio-classification
license: "apache-2.0"
datasets:
- voxceleb
metrics:
- EER
- min_dct
widget:
- example_title: VoxCeleb Speaker id10003
src: https://cdn-media.huggingface.co/speech... |
clue/roberta_chinese_clue_tiny | e51239963f4ff728b1696180a9ae86ec1d3aeff4 | 2021-05-20T15:27:44.000Z | [
"pytorch",
"jax",
"roberta",
"transformers"
] | null | false | clue | null | clue/roberta_chinese_clue_tiny | 1,204 | 1 | transformers | 1,648 | Entry not found |
xlm-mlm-100-1280 | dafb8ab3a39720dcdf0687658c7fbd27e45bc071 | 2022-07-22T08:09:19.000Z | [
"pytorch",
"tf",
"xlm",
"fill-mask",
"multilingual",
"en",
"es",
"fr",
"de",
"zh",
"ru",
"pt",
"it",
"ar",
"ja",
"id",
"tr",
"nl",
"pl",
"fa",
"vi",
"sv",
"ko",
"he",
"ro",
"no",
"hi",
"uk",
"cs",
"fi",
"hu",
"th",
"da",
"ca",
"el",
"bg",
"sr",... | fill-mask | false | null | null | xlm-mlm-100-1280 | 1,201 | null | transformers | 1,649 | ---
language:
- multilingual
- en
- es
- fr
- de
- zh
- ru
- pt
- it
- ar
- ja
- id
- tr
- nl
- pl
- fa
- vi
- sv
- ko
- he
- ro
- no
- hi
- uk
- cs
- fi
- hu
- th
- da
- ca
- el
- bg
- sr
- ms
- bn
- hr
- sl
- az
- sk
- eo
- ta
- sh
- lt
- et
- ml
- la
- bs
- sq
- arz
- af
- ka
- mr
- eu
- tl
- ang
- gl
- nn
- ur... |
hf-internal-testing/tiny-detr-mobilenetsv3 | d22336251d71ba3637c29c23808b9dfeaa442eda | 2021-09-05T15:50:14.000Z | [
"pytorch",
"detr",
"object-detection",
"transformers"
] | object-detection | false | hf-internal-testing | null | hf-internal-testing/tiny-detr-mobilenetsv3 | 1,198 | null | transformers | 1,650 | Entry not found |
activebus/BERT-XD_Review | 9dbc8322c9767ac81e75e62a5a5376d948c3536f | 2021-05-19T11:38:28.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | activebus | null | activebus/BERT-XD_Review | 1,197 | null | transformers | 1,651 | # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
Please visit https://github.com/howardhsu/BERT-for-RRC-ABSA for details.
`BERT-XD_Review` is a cross-domain (beyond just `laptop` and `restaurant`) language model, where each example is from a sing... |
HooshvareLab/distilbert-fa-zwnj-base-ner | 36ccd9aa3dd64c3a83c76de0b8cc5b3f6fa3dc30 | 2021-03-21T14:32:29.000Z | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"fa",
"transformers",
"autotrain_compatible"
] | token-classification | false | HooshvareLab | null | HooshvareLab/distilbert-fa-zwnj-base-ner | 1,194 | 1 | transformers | 1,652 | ---
language: fa
---
# DistilbertNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/... |
ml6team/mt5-small-german-finetune-mlsum | c466d1eeefc34cf39b4e8411410ef1ea3bade115 | 2021-01-28T13:15:00.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"de",
"dataset:mlsum",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | ml6team | null | ml6team/mt5-small-german-finetune-mlsum | 1,193 | 9 | transformers | 1,653 | ---
language: de
tags:
- summarization
datasets:
- mlsum
---
# mT5-small fine-tuned on German MLSUM
This model was finetuned for 3 epochs with a max_len (input) of 768 tokens and target_max_len of 192 tokens.
It was fine-tuned on all German articles present in the train split of the [MLSUM dataset](https://huggingfa... |
davanstrien/deit_flyswot | 035587aa11a00f4590f87e748a359c32efe44a76 | 2022-04-03T17:45:11.000Z | [
"pytorch",
"vit",
"image-classification",
"dataset:image_folder",
"transformers",
"generated_from_trainer",
"model-index"
] | image-classification | false | davanstrien | null | davanstrien/deit_flyswot | 1,190 | null | transformers | 1,654 | ---
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- f1
model-index:
- name: deit_flyswot
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: F1
type: f1... |
RajSang/pegasus-sports-titles | 6bfbb3f6138b4b573ca80d4051b245868a1bf84e | 2022-05-09T09:26:14.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"en",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | false | RajSang | null | RajSang/pegasus-sports-titles | 1,185 | 1 | transformers | 1,655 | ---
tags:
- generated_from_trainer
widget:
- text: "Coutinho was just about to be introduced by Villa boss Gerrard midway through the second half when Bruno Fernandes slammed home
his second goal of the game off the underside of the bar. But the Brazilian proved the catalyst for a memorable response.
First he drove a... |
indobenchmark/indobart-v2 | 7192ee75ba70ca247c7abfb8e7268588145c0bde | 2022-06-21T17:52:37.000Z | [
"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-v2 | 1,183 | 3 | transformers | 1,656 | ---
language: id
tags:
- indogpt
- indobenchmark
- indonlg
license: mit
inference: false
datasets:
- Indo4B+
---
# IndoBART-v2 Model
[IndoBART-v2](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 obje... |
textattack/xlnet-base-cased-SST-2 | 9ceeb077dcd5cf5ae790572b2bd6aec755a263be | 2020-06-09T16:56:53.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/xlnet-base-cased-SST-2 | 1,183 | 2 | transformers | 1,657 | Entry not found |
facebook/mcontriever-msmarco | 9ff6abed2c2fdf32bbbd8b4e98fb10160e317375 | 2022-05-29T08:50:51.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | facebook | null | facebook/mcontriever-msmarco | 1,183 | null | transformers | 1,658 | Entry not found |
IDEA-CCNL/Erlangshen-Ubert-330M-Chinese | 13a559f940c1dec0d06812a453c9c79c1ba3c523 | 2022-07-02T13:41:32.000Z | [
"pytorch",
"bert",
"fill-mask",
"zh",
"transformers",
"NLU",
"Sentiment",
"Chinese",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | IDEA-CCNL | null | IDEA-CCNL/Erlangshen-Ubert-330M-Chinese | 1,180 | null | transformers | 1,659 | ---
language:
- zh
license: apache-2.0
tags:
- bert
- NLU
- Sentiment
- Chinese
inference: false
---
# Erlangshen-Ubert-110M, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/dev/yangping/fengshen/examples/ubert).
We collect 70+ datasets in the Chinese domain for fi... |
bakrianoo/sinai-voice-ar-stt | 2d226249edf809b01a0e11159d1201ae1704b63c | 2022-03-23T18:25:21.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | bakrianoo | null | bakrianoo/sinai-voice-ar-stt | 1,179 | 7 | transformers | 1,660 | ---
language:
- ar
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
- cer
model-index:
- name: Sinai Voice Arabic Speech Recognition Model
results:
- task:
type: automatic-speech-recognition
... |
CAMeL-Lab/bert-base-arabic-camelbert-msa | 277069fd3645fedb22b746caf38d111aadee0241 | 2021-09-14T14:33:41.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | CAMeL-Lab | null | CAMeL-Lab/bert-base-arabic-camelbert-msa | 1,178 | 3 | transformers | 1,661 | ---
language:
- ar
license: apache-2.0
widget:
- text: "الهدف من الحياة هو [MASK] ."
---
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained langu... |
hf-internal-testing/tiny-detr-mobilenetsv3-panoptic | d7cb3c9eb87c7d7de00190ea97d48da1ba07206d | 2021-09-27T19:40:12.000Z | [
"pytorch",
"detr",
"image-segmentation",
"transformers"
] | image-segmentation | false | hf-internal-testing | null | hf-internal-testing/tiny-detr-mobilenetsv3-panoptic | 1,177 | 1 | transformers | 1,662 | Entry not found |
junnyu/roformer_chinese_sim_char_ft_base | 38c5088bbdaeeecfef68696bd2c83b16baa0fb92 | 2022-04-15T03:52:49.000Z | [
"pytorch",
"roformer",
"text-generation",
"zh",
"transformers",
"tf2.0"
] | text-generation | false | junnyu | null | junnyu/roformer_chinese_sim_char_ft_base | 1,174 | 3 | transformers | 1,663 | ---
language: zh
tags:
- roformer
- pytorch
- tf2.0
inference: False
---
# 安装
- pip install roformer==0.4.3
# 使用
```python
import torch
import numpy as np
from roformer import RoFormerForCausalLM, RoFormerConfig
from transformers import BertTokenizer
device = torch.device('cuda:0' if torch.cuda.is_available() else 'c... |
Helsinki-NLP/opus-mt-de-es | d6bff091731341b977e4ca7294d2c309a2ca11e4 | 2021-09-09T21:30:58.000Z | [
"pytorch",
"marian",
"text2text-generation",
"de",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-de-es | 1,171 | null | transformers | 1,664 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-de-es
* source languages: de
* target languages: es
* OPUS readme: [de-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
facebook/wmt21-dense-24-wide-x-en | b5e35923f54293f03bd6072b93585124475829e0 | 2022-05-26T22:27:50.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"multilingual",
"ha",
"is",
"ja",
"cs",
"ru",
"zh",
"de",
"en",
"arxiv:2108.03265",
"transformers",
"translation",
"wmt21",
"license:mit",
"autotrain_compatible"
] | translation | false | facebook | null | facebook/wmt21-dense-24-wide-x-en | 1,166 | 6 | transformers | 1,665 | ---
language:
- multilingual
- ha
- is
- ja
- cs
- ru
- zh
- de
- en
license: mit
tags:
- translation
- wmt21
---
# WMT 21 X-En
WMT 21 X-En is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2108.03265) and... |
textattack/roberta-base-ag-news | 80f0a42b53970634dc15f4b59342978410585b46 | 2021-05-20T22:15:20.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/roberta-base-ag-news | 1,166 | 1 | transformers | 1,666 | ## TextAttack Model CardThis `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classi... |
CAUKiel/JavaBERT | 5028efb75040cbd2fe33e10fe5f4c232b455cee8 | 2022-07-19T18:45:37.000Z | [
"pytorch",
"bert",
"fill-mask",
"code",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | CAUKiel | null | CAUKiel/JavaBERT | 1,165 | 4 | transformers | 1,667 | ---
language:
- code
license: apache-2.0
widget:
- text: 'public [MASK] isOdd(Integer num) {if (num % 2 == 0) {return "even";} else {return "odd";}}'
---
## JavaBERT
A BERT-like model pretrained on Java software code.
### Training Data
The model was trained on 2,998,345 Java files retrieved from open source project... |
facebook/wav2vec2-large-100k-voxpopuli | ad2f1b5b6f2f0a78683b90e78ebc07af6022c6db | 2021-11-05T12:45:52.000Z | [
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"multilingual",
"arxiv:2101.00390",
"transformers",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"license:cc-by-nc-4.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-large-100k-voxpopuli | 1,163 | 2 | transformers | 1,668 | ---
language: multilingual
tags:
- audio
- automatic-speech-recognition
- voxpopuli
license: cc-by-nc-4.0
---
# Wav2Vec2-Large-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained on the 100k unlabeled subset of [VoxPopuli cor... |
codeparrot/codeparrot-small | e7e4f5d39319551a760f07c0e1035e379617c721 | 2022-07-03T19:54:59.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"code",
"dataset:codeparrot/codeparrot-clean",
"dataset:openai_humaneval",
"transformers",
"generation",
"license:apache-2.0"
] | text-generation | false | codeparrot | null | codeparrot/codeparrot-small | 1,163 | 9 | transformers | 1,669 | ---
language:
- code
license: apache-2.0
tags:
- code
- gpt2
- generation
datasets:
- "codeparrot/codeparrot-clean"
- "openai_humaneval"
metrics:
- "evaluate-metric/code_eval"
---
# CodeParrot 🦜 (small)
CodeParrot 🦜 is a GPT-2 model (110M parameters) trained to generate Python code.
## Usage
You can load the C... |
paulowoicho/t5-podcast-summarisation | 162966482402d91ce84facd36e835ad09f244a72 | 2020-11-11T10:15:57.000Z | [
"pytorch",
"t5",
"text2text-generation",
"[en]",
"dataset:Spotify Podcasts Dataset",
"arxiv:2004.04270",
"arxiv:1910.10683",
"transformers",
"summarisation",
"lm-head",
"autotrain_compatible"
] | text2text-generation | false | paulowoicho | null | paulowoicho/t5-podcast-summarisation | 1,161 | 2 | transformers | 1,670 | ---
language: "[en]"
datasets:
- Spotify Podcasts Dataset
tags:
- t5
- summarisation
- pytorch
- lm-head
metrics:
- ROUGE
pipeline:
- summarisation
---
# T5 for Automatic Podcast Summarisation
This model is the result of fine-tuning [t5-base](https://huggingface.co/t5-base) on the [Spotify Podcast Dataset](https://ar... |
MaRiOrOsSi/t5-base-finetuned-question-answering | 2c815b9dd13188d751e372a0d8cc9f3892087c9a | 2022-04-08T18:00:14.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:duorc",
"transformers",
"Generative Question Answering",
"autotrain_compatible"
] | text2text-generation | false | MaRiOrOsSi | null | MaRiOrOsSi/t5-base-finetuned-question-answering | 1,161 | null | transformers | 1,671 | ---
language: en
datasets:
- duorc
widget:
- text: "question: Is Giacomo Italian? context: Giacomo is 25 years old and he was born in Tuscany"
- text: "question: Where does Christian come from? context: Christian is a student of UNISI but he come from Caserta"
- text: "question: Is the dog coat grey? context: You ha... |
PlanTL-GOB-ES/RoBERTalex | bedf21ecb3a6beec20f1e68d88b7dbb041991dfb | 2021-11-09T09:30:02.000Z | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:legal_ES",
"dataset:temu_legal",
"arxiv:2110.12201",
"transformers",
"legal",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | PlanTL-GOB-ES | null | PlanTL-GOB-ES/RoBERTalex | 1,160 | 4 | transformers | 1,672 | ---
language:
- es
license: apache-2.0
tags:
- legal
- spanish
datasets:
- legal_ES
- temu_legal
metrics:
- ppl
widget:
- text: "La ley fue <mask> finalmente."
- text: "El Tribunal <mask> desestimó el recurso de amparo."
- text: "Hay base legal dentro del marco <mask> actual."
---
# Spanish Legal-domain RoBERTa
Th... |
peterhsu/marian-finetuned-kde4-en-to-zh_TW-accelerate | 57bd8aa1bbf04ec9234d74caabdd329a9927c942 | 2022-02-28T09:36:28.000Z | [
"pytorch",
"marian",
"text2text-generation",
"dataset:kde4",
"transformers",
"translation",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | translation | false | peterhsu | null | peterhsu/marian-finetuned-kde4-en-to-zh_TW-accelerate | 1,159 | null | transformers | 1,673 | ---
license: apache-2.0
tags:
- translation
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-zh_TW-accelerate
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
ar... |
remotejob/tweetsGPT2fi_v0 | 34abb218bb8e6f61bec9a47c0db81e776229f1a6 | 2022-05-27T22:22:53.000Z | [
"pytorch",
"rust",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | remotejob | null | remotejob/tweetsGPT2fi_v0 | 1,157 | null | transformers | 1,674 | Entry not found |
setu4993/smaller-LaBSE | abd4e324cf0850b32f1dbf4b08fad6022ab47c0b | 2021-12-05T06:13:27.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"ar",
"de",
"en",
"es",
"fr",
"it",
"ja",
"ko",
"nl",
"pl",
"pt",
"ru",
"th",
"tr",
"zh",
"dataset:CommonCrawl",
"dataset:Wikipedia",
"arxiv:2010.05609",
"arxiv:2007.01852",
"transformers",
"sentence_embedding",
... | feature-extraction | false | setu4993 | null | setu4993/smaller-LaBSE | 1,156 | 4 | transformers | 1,675 | ---
language:
- ar
- de
- en
- es
- fr
- it
- ja
- ko
- nl
- pl
- pt
- ru
- th
- tr
- zh
tags:
- bert
- sentence_embedding
- multilingual
- google
- sentence-similarity
- labse
license: apache-2.0
datasets:
- CommonCrawl
- Wikipedia
---
# LaBSE
## Model description
Small... |
Helsinki-NLP/opus-mt-az-en | d5618bb9172d2400a504d8b95baf144517ac6b48 | 2021-01-18T07:48:32.000Z | [
"pytorch",
"marian",
"text2text-generation",
"az",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-az-en | 1,155 | null | transformers | 1,676 | ---
language:
- az
- en
tags:
- translation
license: apache-2.0
---
### aze-eng
* source group: Azerbaijani
* target group: English
* OPUS readme: [aze-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aze-eng/README.md)
* model: transformer-align
* source language(s): aze_Latn
* targe... |
facebook/nllb-200-distilled-1.3B | b14baa07325b1cea23404c4d374d7eb469b1973d | 2022-07-19T15:45:28.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"bn",
"bho",
"bjn",
"bo",
"bs",
"bug"... | text2text-generation | false | facebook | null | facebook/nllb-200-distilled-1.3B | 1,155 | 2 | transformers | 1,677 | ---
language:
- ace
- acm
- acq
- aeb
- af
- ajp
- ak
- als
- am
- apc
- ar
- ars
- ary
- arz
- as
- ast
- awa
- ayr
- azb
- azj
- ba
- bm
- ban
- be
- bem
- bn
- bho
- bjn
- bo
- bs
- bug
- bg
- ca
- ceb
- cs
- cjk
- ckb
- crh
- cy
- da
- de
- dik
- dyu
- dz
- el
- en
- eo
- et
- eu
- ee
- fo
- fj
- fi
- fon
- fr
- fu... |
cambridgeltl/magic_mscoco | e0cfb935df539629d5abb2ecdc925aef3ecf35fa | 2022-04-08T14:39:58.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | cambridgeltl | null | cambridgeltl/magic_mscoco | 1,154 | null | transformers | 1,678 | Entry not found |
NlpHUST/vibert4news-base-cased | d0926f978504f72d29bea14d7315b9e3ef09f292 | 2021-08-10T03:13:56.000Z | [
"pytorch",
"fill-mask",
"vn",
"transformers",
"autotrain_compatible"
] | fill-mask | false | NlpHUST | null | NlpHUST/vibert4news-base-cased | 1,149 | 1 | transformers | 1,679 | ---
language: vn
---
# BERT for Vietnamese is trained on more 20 GB news dataset
Apply for task sentiment analysis on using [AIViVN's comments dataset](https://www.aivivn.com/contests/6)
The model achieved 0.90268 on the public leaderboard, (winner's score is 0.90087)
Bert4news is used for a toolkit Vietnames(segment... |
monologg/koelectra-base-v2-discriminator | b87e70eb7b3ea33b24fc2e7a85b2cc8321b9dd28 | 2021-10-20T16:54:30.000Z | [
"pytorch",
"electra",
"pretraining",
"ko",
"transformers",
"korean",
"license:apache-2.0"
] | null | false | monologg | null | monologg/koelectra-base-v2-discriminator | 1,149 | 1 | transformers | 1,680 | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA v2 (Base Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-v2-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model a... |
Hate-speech-CNERG/dehatebert-mono-english | 25d0e4d9122d2a5c283e07405a325e3dfd4a73b3 | 2021-09-25T13:55:16.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"arxiv:2004.06465",
"transformers",
"license:apache-2.0"
] | text-classification | false | Hate-speech-CNERG | null | Hate-speech-CNERG/dehatebert-mono-english | 1,146 | 2 | transformers | 1,681 | ---
language: en
license: apache-2.0
---
This model is used detecting **hatespeech** in **English language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rate... |
TencentGameMate/chinese-wav2vec2-base | 3991242c806928916fff4a8c0e4f76acf661b743 | 2022-06-24T01:53:18.000Z | [
"pytorch",
"wav2vec2",
"pretraining",
"transformers",
"license:mit"
] | null | false | TencentGameMate | null | TencentGameMate/chinese-wav2vec2-base | 1,145 | 3 | transformers | 1,682 | ---
license: mit
---
Pretrained on 10k hours WenetSpeech L subset. More details in [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
This model does not have a tokenizer as it was pretrained on audio alone.
In order to use this model speech recognition, a tokenizer... |
flair/ner-dutch-large | 44c285912a9d6eec4d0858580f3cb13b7b8c9959 | 2021-05-08T15:36:03.000Z | [
"pytorch",
"nl",
"dataset:conll2003",
"arxiv:2011.06993",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-dutch-large | 1,144 | 3 | flair | 1,683 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: nl
datasets:
- conll2003
widget:
- text: "George Washington ging naar Washington"
---
## Dutch NER in Flair (large model)
This is the large 4-class NER model for Dutch that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **9... |
Averium/DialoGPT-medium-TailsBot1.1 | 462a773376d390ff76c8e078388a2afde248b9de | 2022-06-17T00:29:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Averium | null | Averium/DialoGPT-medium-TailsBot1.1 | 1,136 | null | transformers | 1,684 | ---
tags:
- conversational
---
# Miles Prower DialoGPT Model |
sentence-transformers/sentence-t5-xl | e0976ba9afd18be963c22c680367a3928c44fd22 | 2022-02-09T14:02:31.000Z | [
"pytorch",
"t5",
"en",
"arxiv:2108.08877",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/sentence-t5-xl | 1,135 | 1 | sentence-transformers | 1,685 | ---
pipeline_tag: sentence-similarity
language: en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/sentence-t5-xl
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768... |
DeepPavlov/bert-base-bg-cs-pl-ru-cased | 0ab00895c22312978e0a8abd16bbec3fbf7f2bc8 | 2021-11-08T12:58:09.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
] | feature-extraction | false | DeepPavlov | null | DeepPavlov/bert-base-bg-cs-pl-ru-cased | 1,131 | null | transformers | 1,686 | ---
language:
- bg
- cs
- pl
- ru
---
# bert-base-bg-cs-pl-ru-cased
SlavicBERT\[1\] \(Slavic \(bg, cs, pl, ru\), cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on Russian News and four Wikipedias: Bulgarian, Czech, Polish, and Russian. Subtoken vocabulary was built using this data. Multilingual ... |
voidful/dpr-ctx_encoder-bert-base-multilingual | c7a3dc617754e93efe785aa88dc1f52b4f7cb688 | 2021-02-21T09:00:44.000Z | [
"pytorch",
"dpr",
"multilingual",
"dataset:NQ",
"dataset:Trivia",
"dataset:SQuAD",
"dataset:MLQA",
"dataset:DRCD",
"arxiv:2004.04906",
"transformers"
] | null | false | voidful | null | voidful/dpr-ctx_encoder-bert-base-multilingual | 1,130 | 4 | transformers | 1,687 | ---
language: multilingual
datasets:
- NQ
- Trivia
- SQuAD
- MLQA
- DRCD
---
# dpr-ctx_encoder-bert-base-multilingual
## Description
Multilingual DPR Model base on bert-base-multilingual-cased.
[DPR model](https://arxiv.org/abs/2004.04906)
[DPR repo](https://github.com/facebookresearch/DPR)
## Data
1. [NQ](https:/... |
Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit | 7853d0d3eef3dd556b99ae342e7461c61d8faed5 | 2022-06-18T20:51:30.000Z | [
"pytorch",
"gpt_neo",
"feature-extraction",
"arxiv:2202.08904",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | Muennighoff | null | Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit | 1,128 | null | sentence-transformers | 1,688 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-1.3B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval fol... |
allenai/t5-small-squad2-question-generation | 7e7d6d8a68f96223a5cdaaf063e55293d52f1aef | 2021-06-23T11:56:56.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/t5-small-squad2-question-generation | 1,128 | 12 | transformers | 1,689 | A simple question-generation model built based on SQuAD 2.0 dataset.
Example use:
```python
from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer
model_name = "allenai/t5-small-squad2-question-generation"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.fro... |
diptanu/fBERT | 7bd599f887e294a43afb6b4c3f611d66af2f94ae | 2021-09-01T19:57:23.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | diptanu | null | diptanu/fBERT | 1,128 | 3 | transformers | 1,690 | fBERT: A Neural Transformer for Identifying Offensive Content [Accepted at EMNLP 2021]
Authors: Diptanu Sarkar, Marcos Zampieri, Tharindu Ranasinghe and Alexander Ororbia
About:
Transformer-based models such as BERT, ELMO, and XLM-R have achieved state-of-the-art performance across various NLP tasks including the i... |
sentence-transformers/distilroberta-base-msmarco-v2 | f273032139d26a1e54280e0b7d2f4a2193de4feb | 2022-06-15T21:50:52.000Z | [
"pytorch",
"tf",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/distilroberta-base-msmarco-v2 | 1,128 | null | sentence-transformers | 1,691 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... |
akdeniz27/roberta-base-cuad | 94a24c27b5d8bf9c2fa89cf80729814cfb002e7b | 2021-11-14T08:42:48.000Z | [
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:cuad",
"transformers",
"autotrain_compatible"
] | question-answering | false | akdeniz27 | null | akdeniz27/roberta-base-cuad | 1,124 | null | transformers | 1,692 | ---
language: en
datasets:
- cuad
---
# RoBERTa Base Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "RoBERTa Base"
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://gith... |
tdopierre/ProtAugment-ParaphraseGenerator | d389c0e6ca11d0add1eaaecf6d8848fa76e6ab46 | 2021-07-07T14:15:07.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:Quora",
"dataset:MSR",
"dataset:Google-PAWS",
"arxiv:2105.12995",
"transformers",
"Paraphase Generation",
"Data Augmentation",
"autotrain_compatible"
] | text2text-generation | false | tdopierre | null | tdopierre/ProtAugment-ParaphraseGenerator | 1,123 | 4 | transformers | 1,693 | ---
language: "en"
tags:
- Paraphase Generation
- Data Augmentation
datasets:
- Quora
- MSR
- Google-PAWS
---
[](https://arxiv.org/abs/2105.12995)
This model is used to generate paraphrases. It has been trained on a mix of 3 different paraphrase detection datasets: M... |
valhalla/distilt5-qa-qg-hl-12-6 | f865250f90ada38bcb43602dd5254e4c166e6b8e | 2021-09-23T16:42:44.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:squad",
"transformers",
"question-generation",
"distilt5",
"distilt5-qg",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/distilt5-qa-qg-hl-12-6 | 1,119 | null | transformers | 1,694 | ---
datasets:
- squad
tags:
- question-generation
- distilt5
- distilt5-qg
widget:
- text: 'generate question: <hl> 42 <hl> is the answer to life, the universe and everything.
</s>'
- text: 'question: What is 42 context: 42 is the answer to life, the universe and
everything. </s>'
license: mit
---
## DistilT5 ... |
huggingface/CodeBERTa-language-id | 386451c69a3cb8722b742e66987d888db858b33c | 2022-06-27T15:49:04.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"code",
"dataset:code_search_net",
"arxiv:1909.09436",
"transformers"
] | text-classification | false | huggingface | null | huggingface/CodeBERTa-language-id | 1,118 | 12 | transformers | 1,695 | ---
language: code
thumbnail: https://cdn-media.huggingface.co/CodeBERTa/CodeBERTa.png
datasets:
- code_search_net
---
# CodeBERTa-language-id: The World’s fanciest programming language identification algo 🤯
To demonstrate the usefulness of our CodeBERTa pretrained model on downstream tasks beyond language modeling... |
voidful/bart-distractor-generation-both | 33dac39b96071b8fb44fe0bab40b89c2057aae27 | 2021-04-04T16:20:20.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:race",
"transformers",
"distractor",
"generation",
"seq2seq",
"autotrain_compatible"
] | text2text-generation | false | voidful | null | voidful/bart-distractor-generation-both | 1,117 | null | transformers | 1,696 | ---
language: en
tags:
- bart
- distractor
- generation
- seq2seq
datasets:
- race
metrics:
- bleu
- rouge
pipeline_tag: text2text-generation
widget:
- text: "When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes to France , you have another sp... |
snunlp/KR-SBERT-V40K-klueNLI-augSTS | f06554f8087e15a6ffc279ef812ba8fed57e81d5 | 2022-05-03T03:53:28.000Z | [
"pytorch",
"bert",
"feature-extraction",
"ko",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | snunlp | null | snunlp/KR-SBERT-V40K-klueNLI-augSTS | 1,116 | 2 | sentence-transformers | 1,697 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- ko
---
# snunlp/KR-SBERT-V40K-klueNLI-augSTS
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space ... |
facebook/xglm-1.7B | a1060a08b652653f6c0dece48f53bb785538e4d6 | 2022-02-15T01:29:52.000Z | [
"pytorch",
"xglm",
"text-generation",
"arxiv:2112.10668",
"transformers",
"license:mit"
] | text-generation | false | facebook | null | facebook/xglm-1.7B | 1,112 | null | transformers | 1,698 | ---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
inference: false
---
# XGLM-1.7B
XGLM-1.7B is a multilingual autoregressive language model (with 1.7 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the... |
svalabs/cross-electra-ms-marco-german-uncased | 34a0bc5aee354593b64f1c2cfe173356ced6e90f | 2021-06-10T07:20:46.000Z | [
"pytorch",
"electra",
"text-classification",
"arxiv:1908.10084",
"arxiv:1611.09268",
"arxiv:2104.08663",
"arxiv:2104.12741",
"arxiv:2010.02666",
"transformers"
] | text-classification | false | svalabs | null | svalabs/cross-electra-ms-marco-german-uncased | 1,112 | 3 | transformers | 1,699 | # SVALabs - German Uncased Electra Cross-Encoder
In this repository, we present our german, uncased cross-encoder for Passage Retrieval.
This model was trained on the basis of the german electra uncased model from the [german-nlp-group](https://huggingface.co/german-nlp-group/electra-base-german-uncased) and finetune... |
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