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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
huggingtweets/theeconomist | 87b63683ec35c079bdab8a77aba9982cf404aeb6 | 2021-05-23T01:35:15.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/theeconomist | 100 | null | transformers | 4,600 | ---
language: en
thumbnail: https://www.huggingtweets.com/theeconomist/1607116194498/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { c... |
m3hrdadfi/bert2bert-fa-wiki-summary | e9dc167bd34be7161f2a7e7c680c3c5cf7d53de2 | 2020-12-11T21:50:20.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"fa",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | m3hrdadfi | null | m3hrdadfi/bert2bert-fa-wiki-summary | 100 | null | transformers | 4,601 | ---
language: fa
license: apache-2.0
tags:
- summarization
---
A Bert2Bert model on the Wiki Summary dataset to summarize articles. The model achieved an 8.47 ROUGE-2 score.
For more detail, please follow the [Wiki Summary](https://github.com/m3hrdadfi/wiki-summary) repo.
## Eval results
The following table summ... |
microsoft/swin-large-patch4-window7-224-in22k | 3a03736addbe3c9ccf022e154193b8776e050135 | 2022-05-16T19:59:30.000Z | [
"pytorch",
"tf",
"swin",
"image-classification",
"dataset:imagenet-21k",
"arxiv:2103.14030",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/swin-large-patch4-window7-224-in22k | 100 | null | transformers | 4,602 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: http... |
openclimatefix/dgmr-context-conditioning-stack | ec8f1daf261a9d6919a11c8d831c48e69511ce4c | 2022-06-20T08:25:18.000Z | [
"pytorch",
"transformers"
] | null | false | openclimatefix | null | openclimatefix/dgmr-context-conditioning-stack | 100 | null | transformers | 4,603 | Entry not found |
persiannlp/mt5-base-parsinlu-qqp-query-paraphrasing | db4386cf6a360784bc3373a1debd1a046d57244f | 2021-09-23T16:20:00.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"fa",
"multilingual",
"dataset:parsinlu",
"dataset:qqp",
"transformers",
"query-paraphrasing",
"mt5",
"persian",
"farsi",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | persiannlp | null | persiannlp/mt5-base-parsinlu-qqp-query-paraphrasing | 100 | null | transformers | 4,604 | ---
language:
- fa
- multilingual
thumbnail: https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg
tags:
- query-paraphrasing
- mt5
- persian
- farsi
license: cc-by-nc-sa-4.0
datasets:
- parsinlu
- qqp
metrics:
- accuracy
---
# Detection of Paraphrased Queries (تشخصیص سوالات هممعنی)
This is a model for detec... |
philschmid/vit-base-patch16-224-in21k-image-classification-sagemaker | 45424abd0e49de81fbe657b89f9ae707be28e0ea | 2021-06-09T08:07:35.000Z | [
"pytorch",
"vit",
"image-classification",
"transformers",
"model-index"
] | image-classification | false | philschmid | null | philschmid/vit-base-patch16-224-in21k-image-classification-sagemaker | 100 | null | transformers | 4,605 | ---
tags:
- image-classification
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-image-classification-sagemaker
---
<!-- 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.... |
thu-coai/LongLM-base | fadb9378d11bb30c3f12f50ebf89bb378d313c57 | 2021-11-24T06:02:06.000Z | [
"pytorch",
"t5",
"text2text-generation",
"zh",
"arxiv:2108.12960",
"transformers",
"lm-head",
"autotrain_compatible"
] | text2text-generation | false | thu-coai | null | thu-coai/LongLM-base | 100 | 2 | transformers | 4,606 | ---
language:
- zh
thumbnail: http://coai.cs.tsinghua.edu.cn/coai/img/logo.png?v=13923
tags:
- pytorch
- lm-head
- zh
datasets:
metrics:
widget:
- text: "小咕噜对靳司寒完全是个自来熟，小家伙爬进他怀里小手搂着他的脖子，奶声奶气的要求：“靳蜀黎,你给咕噜讲故事好不好？”讲故事？童话故事吗？“我不会。”小家伙明显不信。嘟着小嘴大眼汪汪的盯着他，“哼。”小家伙轻轻哼了一声,靳司寒默了半晌，<extra_id_1>"
- text: "美女亲自打招呼，这可是破天荒第一次，之前不管他献多少次... |
uer/roberta-mini-word-chinese-cluecorpussmall | 49b23897e13966cb55c0babf66b6b7453ff1714f | 2022-02-19T15:57:45.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"arxiv:1909.05658",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uer | null | uer/roberta-mini-word-chinese-cluecorpussmall | 100 | 1 | transformers | 4,607 | \---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "最近一趟去北京的[MASK]几点发车"
---
# Chinese word-based RoBERTa Miniatures
## Model description
This is the set of 5 Chinese word-based RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org... |
Finnish-NLP/t5-small-nl24-casing-punctuation-correction | c622037d360a3ab1836607c0c4c22557b3a4843f | 2022-05-22T10:07:07.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Finnish-NLP | null | Finnish-NLP/t5-small-nl24-casing-punctuation-correction | 100 | null | transformers | 4,608 | Based on Finnish pretrained T5 model version small-nl24
Train data:
Around 300k samples from from following datasets
- [wikipedia](https://huggingface.co/datasets/wikipedia)
- [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Yle Finnish News Archive 2019-2020](http://urn.fi/urn:nbn:fi... |
nickmuchi/deberta-v3-base-finetuned-finance-text-classification | a90a4ec1eddb5d7d68afac4876fe8b65c76e10e5 | 2022-05-30T12:11:47.000Z | [
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"dataset:financial_phrasebank",
"dataset:Kaggle Self label",
"dataset:nickmuchi/financial-classification",
"transformers",
"generated_from_trainer",
"financial-sentiment-analysis",
"sentiment-analysis",
"sentence_50agree",
"finan... | text-classification | false | nickmuchi | null | nickmuchi/deberta-v3-base-finetuned-finance-text-classification | 100 | null | transformers | 4,609 | ---
license: mit
tags:
- generated_from_trainer
- financial-sentiment-analysis
- sentiment-analysis
- sentence_50agree
- financial
- stocks
- sentiment
datasets:
- financial_phrasebank
- Kaggle Self label
- nickmuchi/financial-classification
widget:
- text: "The USD rallied by 3% last night as the Fed hiked interest ra... |
adamnik/bert-entailment-detection | f5d7ce4378701cd2931790a0342fea555b3ea91c | 2022-07-21T01:30:56.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:mit"
] | text-classification | false | adamnik | null | adamnik/bert-entailment-detection | 100 | null | transformers | 4,610 | ---
license: mit
---
|
Helsinki-NLP/opus-mt-id-es | 934c40fafa6de947f8e05f05f8e3d25c30bbb744 | 2021-09-09T22:11:14.000Z | [
"pytorch",
"marian",
"text2text-generation",
"id",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-id-es | 99 | null | transformers | 4,611 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-id-es
* source languages: id
* target languages: es
* OPUS readme: [id-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/id-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Salesforce/mixqg-3b | 0db3c8a87cb87cad44a5cc1d2bf05df0a3bddfe4 | 2021-10-18T16:19:00.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:2110.08175",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Salesforce | null | Salesforce/mixqg-3b | 99 | 4 | transformers | 4,612 | ---
language: en
widget:
- text: Robert Boyle \\n In the late 17th century, Robert Boyle proved that air is necessary for combustion.
---
# MixQG (3b-sized model)
MixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper [MixQG: Neural... |
THUMT/mGPT | 92dac1dd66b77562f9a8a1fe6a24c35b9368f3f4 | 2021-10-14T05:49:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"arxiv:2110.06609",
"transformers"
] | text-generation | false | THUMT | null | THUMT/mGPT | 99 | 1 | transformers | 4,613 |
# mGPT
mGPT is pre-trained on the [mC4 dataset](https://huggingface.co/datasets/mc4) using a causal language modeling objective. It was introduced in this [paper](https://arxiv.org/abs/2110.06609) and first released on this page.
## Model description
mGPT is a Transformer-based model which pre-trained on massive mu... |
airesearch/wangchanberta-base-wiki-spm | f727663bc57f94313937dd5d469c2d4424be6e0c | 2021-09-11T09:38:49.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"th",
"arxiv:1907.11692",
"arxiv:2101.09635",
"transformers",
"autotrain_compatible"
] | fill-mask | false | airesearch | null | airesearch/wangchanberta-base-wiki-spm | 99 | null | transformers | 4,614 | ---
language: th
---
# WangchanBERTa base model: `wangchanberta-base-wiki-spm`
<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 pretra... |
ayameRushia/gpt2-small-indonesia-fine-tuning-poem | 39857971a3ec844a0f97b5ff9bdb0eee5f42398a | 2021-08-10T06:50:20.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"id",
"transformers"
] | text-generation | false | ayameRushia | null | ayameRushia/gpt2-small-indonesia-fine-tuning-poem | 99 | 1 | transformers | 4,615 | ---
language: id
widget:
- text: "Wahai rembulan yang tertutup awan hujan"
---
# Indonesian GPT-2 finetuned on Indonesian poems
This is the [Indonesian gpt2-small model](https://huggingface.co/flax-community/gpt2-small-indonesian) fine-tuned to Indonesian poems. The dataset can be found in [here](https://huggingface.co... |
ethzanalytics/GPT-J-6B-8bit-Convo-D3E | 5a9b2d5f43355d8edaa887463c7e74af6b4554df | 2022-07-20T16:50:51.000Z | [
"pytorch",
"gptj",
"text-generation",
"en",
"dataset:daily_dialog",
"transformers",
"gpt2",
"gpt",
"license:mit"
] | text-generation | false | ethzanalytics | null | ethzanalytics/GPT-J-6B-8bit-Convo-D3E | 99 | 3 | transformers | 4,616 | ---
language:
- en
tags:
- text-generation
- gpt2
- gpt
license: mit
datasets:
- daily_dialog
inference: False
---
# GPT-J 6B (8-bit edition) - Daily Dialogues 3 Epoch
> essentially, we combine the workflow presented in the huggingface documentation [here](https://huggingface.co/docs/transformers/training) with... |
flair/ner-multi-fast | 80ebda976ff428db36b39fe71ec8d06f44bbff24 | 2021-03-02T22:14:04.000Z | [
"pytorch",
"en de nl es",
"dataset:conll2003",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-multi-fast | 99 | null | flair | 4,617 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en de nl es
datasets:
- conll2003
widget:
- text: "George Washington ging nach Washington"
---
## 4-Language NER in Flair (English, German, Dutch and Spanish)
This is the fast 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](... |
kssteven/ibert-roberta-large-mnli | 5ec852d6567202390f5bcc558de70e8d23ea7d10 | 2021-05-10T05:35:32.000Z | [
"pytorch",
"ibert",
"text-classification",
"transformers"
] | text-classification | false | kssteven | null | kssteven/ibert-roberta-large-mnli | 99 | null | transformers | 4,618 | Entry not found |
nasa-impact/bert-e-base-mlm | 8afbc45239b08b6aebf6c0f44dc61cf3cf98af95 | 2022-02-24T01:08:59.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nasa-impact | null | nasa-impact/bert-e-base-mlm | 99 | 4 | transformers | 4,619 | This model is further trained on top of scibert-base using masked language modeling loss (MLM). The corpus is roughly 270,000 earth science-based publications.
The tokenizer used is AutoTokenizer, which is trained on the same corpus.
Stay tuned for further downstream task tests and updates to the model.
in the works... |
persiannlp/mt5-small-parsinlu-sentiment-analysis | 8114ac41a91370f89b03c6578963158f6451a412 | 2021-09-23T16:20:41.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"fa",
"multilingual",
"dataset:parsinlu",
"transformers",
"sentiment",
"sentiment-analysis",
"persian",
"farsi",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | persiannlp | null | persiannlp/mt5-small-parsinlu-sentiment-analysis | 99 | null | transformers | 4,620 | ---
language:
- fa
- multilingual
thumbnail: https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg
tags:
- sentiment
- sentiment-analysis
- mt5
- persian
- farsi
license: cc-by-nc-sa-4.0
datasets:
- parsinlu
metrics:
- accuracy
---
# Sentiment Analysis (آنالیز احساسات)
This is a mT5 model for sentiment analys... |
rathi/storyGenerator | f12db5032e8ac67d55a6c7cd803fc23ac73690da | 2021-05-23T12:11:32.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | rathi | null | rathi/storyGenerator | 99 | null | transformers | 4,621 | ## This is a genre-based Movie plot generator.
For best results, structure the input as follows -
1. Add a `<BOS>` tag in the start.
2. Add a `<genre>` tag (with the genre as a placeholder for lowercased genres such as `<action>`, `<romantic>`, `<thriller>`, `<comedy>` |
smmzhu/DialoGPT-small-SZ | c769886f0effb62b2b2f7ae9d346e1a38f4307a2 | 2022-02-14T20:25:36.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | smmzhu | null | smmzhu/DialoGPT-small-SZ | 99 | null | transformers | 4,622 | ---
tags:
- conversational
--- |
tals/albert-base-vitaminc | 74617d45d0e1e67c0ec806f893b5bea7dbaab394 | 2022-06-22T23:56:01.000Z | [
"pytorch",
"albert",
"text-classification",
"python",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"transformers"
] | text-classification | false | tals | null | tals/albert-base-vitaminc | 99 | null | transformers | 4,623 | ---
language: python
datasets:
- fever
- glue
- tals/vitaminc
---
# Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this m... |
yoshitomo-matsubara/bert-base-uncased-qnli | 1d297c67f59ada25497fe8b1ce802101ed8e0bdd | 2021-05-29T21:49:44.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:qnli",
"transformers",
"qnli",
"glue",
"torchdistill",
"license:apache-2.0"
] | text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-base-uncased-qnli | 99 | null | transformers | 4,624 | ---
language: en
tags:
- bert
- qnli
- glue
- torchdistill
license: apache-2.0
datasets:
- qnli
metrics:
- accuracy
---
`bert-base-uncased` fine-tuned on QNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-m... |
josu/albert-pt-br | ba4eb261c8d8f565b33192ff1ab69ee34f71b807 | 2022-03-09T03:41:04.000Z | [
"pytorch",
"albert",
"fill-mask",
"pt",
"transformers",
"portuguese",
"brazil",
"pt_BR",
"autotrain_compatible"
] | fill-mask | false | josu | null | josu/albert-pt-br | 99 | null | transformers | 4,625 | ---
language: pt
tags:
- portuguese
- brazil
- pt_BR
widget:
- text: Marte está no [MASK] solar.
---
``` python
from transformers import pipeline, AlbertTokenizer, AlbertForMaskedLM
model = AlbertForMaskedLM.from_pretrained('josu/albert-pt-br')
tokenizer = AlbertTokenizer.from_pretrained('josu/albert-pt-br')
unmasker ... |
IsaacBot/t5-small-finetuned-qa-google-en-answer_v1 | 37b218baa4c1eb237192024fef0f977d172cc9b8 | 2022-06-27T15:42:46.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | IsaacBot | null | IsaacBot/t5-small-finetuned-qa-google-en-answer_v1 | 99 | null | transformers | 4,626 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-qa-google-en_v1
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... |
flyswot/test | 0f65eb6e89b2638c1f072d6f9a98f573d9b7627b | 2022-06-15T17:22:55.000Z | [
"pytorch",
"vit",
"image-classification",
"dataset:image_folder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | flyswot | null | flyswot/test | 99 | null | transformers | 4,627 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- f1
model-index:
- name: test
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: F1
... |
Lamia/DialoGPT-small-Sundrop | 416ed82b4214213967365835640e50b68ee6e138 | 2022-07-11T17:18:30.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Lamia | null | Lamia/DialoGPT-small-Sundrop | 99 | null | transformers | 4,628 | ---
tags:
- conversational
---
# Sundrop DialoGPT Model |
nakamura196/roberta-small-hi-char-mlm | cb64e954b4991d9aa093cdcf46d5ece78b1303e6 | 2022-07-22T00:10:42.000Z | [
"pytorch",
"roberta",
"fill-mask",
"ja",
"transformers",
"japanese",
"masked-lm",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | nakamura196 | null | nakamura196/roberta-small-hi-char-mlm | 99 | 1 | transformers | 4,629 | ---
language:
- "ja"
tags:
- "japanese"
- "masked-lm"
license: "cc-by-sa-4.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
widget:
- text: "入[MASK]外無之候江戸大水又ハ大地震なと"
- text: "日[MASK]守御望之由可令披露候"
---
# roberta-small-hi-char-mlm
## Model Description
This is a RoBERTa model pre-trained on HI texts with character tokeniz... |
bigscience/distill-bloom-1b3-10x | 4a8f3997eda7b62f6ab43808954b98861f1b83a9 | 2022-07-18T08:58:46.000Z | [
"pytorch",
"bloom",
"feature-extraction",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
... | text-generation | false | bigscience | null | bigscience/distill-bloom-1b3-10x | 99 | null | transformers | 4,630 | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-gener... |
DeepChem/ChemBERTa-10M-MLM | a5cfe173103cff3149e7322130342a4880010cba | 2022-01-20T18:01:08.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | DeepChem | null | DeepChem/ChemBERTa-10M-MLM | 98 | null | transformers | 4,631 | Entry not found |
Helsinki-NLP/opus-mt-es-ru | 332aa8549e185c507579275be71156665765de5e | 2021-09-09T21:44:27.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"ru",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-ru | 98 | null | transformers | 4,632 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-es-ru
* source languages: es
* target languages: ru
* OPUS readme: [es-ru](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-ru/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-ru-ar | ab04b35320965fbf22f7d9a9f40c9d96677f978a | 2020-08-21T14:42:49.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ru",
"ar",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ru-ar | 98 | null | transformers | 4,633 | ---
language:
- ru
- ar
tags:
- translation
license: apache-2.0
---
### rus-ara
* source group: Russian
* target group: Arabic
* OPUS readme: [rus-ara](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-ara/README.md)
* model: transformer
* source language(s): rus
* target language(s): a... |
KoboldAI/GPT-Neo-125M-AID | e110966ae0510c56e863ce76f45526b0791b4394 | 2022-04-29T14:48:16.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-Neo-125M-AID | 98 | 1 | transformers | 4,634 | # GPT-Neo-125M-AID
This model was finetuned by Henk717 on Google Colab, it contains text adventure tuning and its the smallest 'Adventure' model of its size.
Because of its limited size the behavior is mostly suitable for testing text adventure gamemodes at fast speeds, for a coherent adventure you are better off using... |
ahmedrachid/FinancialBERT | 1ab60da3548fe8c9a5a5dd4af0d3ee490cfd3191 | 2022-02-07T15:00:03.000Z | [
"pytorch",
"bert",
"fill-mask",
"en",
"transformers",
"autotrain_compatible"
] | fill-mask | false | ahmedrachid | null | ahmedrachid/FinancialBERT | 98 | 3 | transformers | 4,635 | ---
language: en
widget:
- text: Tesla remains one of the highest [MASK] stocks on the market. Meanwhile, Aurora Innovation is a pre-revenue upstart that shows promise.
- text: Asian stocks [MASK] from a one-year low on Wednesday as U.S. share futures and oil recovered from the previous day's selloff, but uncertainty o... |
allenai/ivila-block-layoutlm-finetuned-grotoap2 | e28b0964d21162676949e10aaaf6c50f0b861398 | 2021-09-27T23:32:43.000Z | [
"pytorch",
"layoutlm",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | allenai | null | allenai/ivila-block-layoutlm-finetuned-grotoap2 | 98 | null | transformers | 4,636 | Entry not found |
ceyda/wav2vec2-base-760-turkish | 59cd959c640f72ca15dcdda760ceb30cf1ee1304 | 2021-07-06T00:16:04.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | ceyda | null | ceyda/wav2vec2-base-760-turkish | 98 | 2 | transformers | 4,637 | ---
language: tr
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Wav2Vec2-Base Turkish by Ceyda Cinarel
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset... |
dbmdz/flair-distilbert-ner-germeval14 | fc9ea2e8b6f76aa33997be09df8ab5b6fe89a73a | 2021-03-02T18:32:30.000Z | [
"pytorch",
"de",
"dataset:germeval_14",
"flair",
"token-classification",
"sequence-tagger-model",
"license:mit"
] | token-classification | false | dbmdz | null | dbmdz/flair-distilbert-ner-germeval14 | 98 | 1 | flair | 4,638 | ---
datasets:
- germeval_14
tags:
- flair
- token-classification
- sequence-tagger-model
language: de
widget:
- text: "Hugging Face ist eine französische Firma mit Sitz in New York."
license: mit
---
# Flair NER model trained on GermEval14 dataset
This model was trained on the official [GermEval14](https://sites.goog... |
facebook/xglm-4.5B | 19523cf39b8f6f61232e9aa4191fa9473b398bff | 2022-02-15T01:32:08.000Z | [
"pytorch",
"xglm",
"text-generation",
"arxiv:2112.10668",
"transformers",
"license:mit"
] | text-generation | false | facebook | null | facebook/xglm-4.5B | 98 | 2 | transformers | 4,639 | ---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
inference: false
---
# XGLM-4.5B
XGLM-4.5B is a multilingual autoregressive language model (with 4.5 billion parameters) trained on a balanced corpus of a diverse set of 134 languages. It was introduced in the paper [Few-shot Learning wi... |
nielsr/beit-large-patch16-224-pt22k-ft22k | 6ae4dfaec1a310c4d0d69b1accf747854b1f9632 | 2021-08-03T15:49:41.000Z | [
"pytorch",
"beit",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"transformers",
"image-classification",
"license:apache-2.0"
] | image-classification | false | nielsr | null | nielsr/beit-large-patch16-224-pt22k-ft22k | 98 | null | transformers | 4,640 | ---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet
- imagenet-21k
---
# BEiT (large-sized model, fine-tuned on ImageNet-22k)
BEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and ... |
snunlp/KR-ELECTRA-generator | 9b3389a4aabcb1abb4fa72b60ad5239e5e68bed3 | 2022-05-04T06:24:04.000Z | [
"pytorch",
"electra",
"fill-mask",
"ko",
"transformers",
"autotrain_compatible"
] | fill-mask | false | snunlp | null | snunlp/KR-ELECTRA-generator | 98 | null | transformers | 4,641 | ---
language:
- "ko"
---
## KoRean based ELECTRA (KR-ELECTRA)
This is a release of a Korean-specific ELECTRA model with comparable or better performances developed by the Computational Linguistics Lab at Seoul National University. Our model shows remarkable performances on tasks related to informal texts such as r... |
speechbrain/sepformer-wham-enhancement | 2f9717e80979502f27ef8f542e54f0e84fa17a90 | 2022-06-30T23:14:06.000Z | [
"en",
"dataset:WHAM!",
"arxiv:2010.13154",
"arxiv:2106.04624",
"speechbrain",
"audio-to-audio",
"Speech Enhancement",
"WHAM!",
"SepFormer",
"Transformer",
"pytorch",
"license:apache-2.0"
] | audio-to-audio | false | speechbrain | null | speechbrain/sepformer-wham-enhancement | 98 | 1 | speechbrain | 4,642 | ---
language: "en"
thumbnail:
tags:
- audio-to-audio
- Speech Enhancement
- WHAM!
- SepFormer
- Transformer
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- WHAM!
metrics:
- SI-SNR
- PESQ
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v... |
thinhda/chatbot | 4106752f7334a91863d710c11e43257145b48caf | 2021-09-19T07:07:09.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | thinhda | null | thinhda/chatbot | 98 | 1 | transformers | 4,643 | ---
tags:
- conversational
---
# Joey from Friends |
vuiseng9/bert-base-uncased-squad | 067490e92a98e7cbdd5761e842572fcd78189763 | 2022-01-08T18:08:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | vuiseng9 | null | vuiseng9/bert-base-uncased-squad | 98 | null | transformers | 4,644 | This model is developed with transformers v4.10.3.
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=bert-base-uncased-squad
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR
nohup python run_qa.py \
--model_name_or_path bert-base-uncased \
--dataset_name squad \
... |
wukevin/tcr-bert-mlm-only | 8518235d6f14b462d78f15369e0bb65dc4449026 | 2021-11-22T08:32:41.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | wukevin | null | wukevin/tcr-bert-mlm-only | 98 | null | transformers | 4,645 | Pretrained on:
* Masked amino acid modeling
Please see our [main model](https://huggingface.co/wukevin/tcr-bert) for additional details. |
hf-internal-testing/test-opus-tatoeba-fi-en-v2 | 96a4d4666ebcb5b4f03173c1a80b253d7df5ec6f | 2022-03-10T17:25:39.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | hf-internal-testing | null | hf-internal-testing/test-opus-tatoeba-fi-en-v2 | 98 | null | transformers | 4,646 | Entry not found |
IIC/mt5-spanish-mlsum | 6d40c985bdba270bac92242106e6f6c884e3ad2c | 2022-04-02T15:09:23.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"es",
"dataset:mlsum",
"transformers",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | IIC | null | IIC/mt5-spanish-mlsum | 98 | 2 | transformers | 4,647 | ---
language:
- es
tags:
- summarization
license: apache-2.0
datasets:
- mlsum
metrics:
- rouge1
- rouge2
- rougeL
- rougeLsum
model-index:
- name: xprophetnet-spanish-mlsum
results:
- task:
type: summarization
name: abstractive summarization
dataset:
type: mlsum
name: mlsum-es
... |
castorini/monot5-3b-msmarco-10k | e12dd6847a8e26c0e9e85b204acfee20365455dd | 2022-03-28T15:17:29.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | castorini | null | castorini/monot5-3b-msmarco-10k | 98 | null | transformers | 4,648 | This model is a T5-3B reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
For more details on how to use it, check [pygaggle.ai](pygaggle.ai)
Paper describing the model: [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://www.aclweb.org/anthology/2020.findings-emnlp.63/... |
kamalkraj/bert-base-cased-ner-conll2003 | 9e08afd817857207cdc8a0e740a60ec471665952 | 2022-04-24T14:51:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | kamalkraj | null | kamalkraj/bert-base-cased-ner-conll2003 | 98 | null | transformers | 4,649 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-ner-conll2003
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
... |
DedsecurityAI/dpt-125mb | c0f6720dc015b3b0746816537bd51026ae323633 | 2022-05-28T04:30:09.000Z | [
"pytorch",
"opt",
"text-generation",
"transformers",
"license:mit"
] | text-generation | false | DedsecurityAI | null | DedsecurityAI/dpt-125mb | 98 | null | transformers | 4,650 | ---
license: mit
---
# How to use
```python
from transformers import pipeline
generator = pipeline('text-generation', model="DedsecurityAI/dpt-125mb")
generator("Hello Simon")
[{'generated_text': 'Hello Simon :) Welcome aboard aboard :) :) :) :) :) :) :) :) :) :) :) :) :) :)'}]
``` |
CenIA/albert-tiny-spanish | 6af62f921e5c06d542f24fc0353aa3a766395dbe | 2022-04-28T19:54:10.000Z | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null | false | CenIA | null | CenIA/albert-tiny-spanish | 97 | 1 | transformers | 4,651 | ---
language:
- es
tags:
- albert
- spanish
- OpenCENIA
datasets:
- large_spanish_corpus
---
# ALBERT Tiny Spanish
This is an [ALBERT](https://github.com/google-research/albert) model trained on a [big spanish corpora](https://github.com/josecannete/spanish-corpora).
The model was trained on a single TPU v3-8 with th... |
Helsinki-NLP/opus-mt-ar-it | e191529799738628f0f16cf657a456205bebde18 | 2021-01-18T07:47:30.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ar",
"it",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ar-it | 97 | null | transformers | 4,652 | ---
language:
- ar
- it
tags:
- translation
license: apache-2.0
---
### ara-ita
* source group: Arabic
* target group: Italian
* OPUS readme: [ara-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-ita/README.md)
* model: transformer
* source language(s): ara
* target language(s): i... |
KETI-AIR/ke-t5-large-ko | f1d299906d0a2e9106316d89861c13918936fe82 | 2021-06-23T02:54:27.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | KETI-AIR | null | KETI-AIR/ke-t5-large-ko | 97 | null | transformers | 4,653 | Entry not found |
LeoCordoba/mt5-small-cc-news-es-titles | 515533bdc69e8acaf6bf8b45057ae9f3f5c6ca6e | 2021-09-08T17:03:30.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"es",
"dataset:LeoCordoba/CC-NEWS-ES-titles",
"transformers",
"summarization",
"spanish",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | LeoCordoba | null | LeoCordoba/mt5-small-cc-news-es-titles | 97 | null | transformers | 4,654 | ---
language: es
tags:
- summarization
- mt5
- spanish
license: apache-2.0
datasets:
- LeoCordoba/CC-NEWS-ES-titles
model-index:
- name: mt5-small-ccnews-titles-es
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "CCNEWS-ES-ti... |
Mary222/GPT2_RU_GAME | ed7c7df0e3032b7a4b184397a301bceba6da6927 | 2021-11-04T15:58:24.000Z | [
"pytorch",
"gpt2",
"text-generation",
"ru",
"transformers"
] | text-generation | false | Mary222 | null | Mary222/GPT2_RU_GAME | 97 | null | transformers | 4,655 | ---
language: ru
tags:
- text-generation
---
# GPT2 - RUS |
Qishuai/distilbert_punctuator_zh | 0bed7e402b9d9936697d34ac198c69fc8a41f163 | 2021-12-13T15:05:45.000Z | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Qishuai | null | Qishuai/distilbert_punctuator_zh | 97 | 2 | transformers | 4,656 | # Punctuator for Simplified Chinese
The model is fine-tuned based on `DistilBertForTokenClassification` for adding punctuations to plain text (simplified Chinese). The model is fine-tuned based on distilled model `bert-base-chinese`.
## Usage
```python
from transformers import DistilBertForTokenClassification, Disti... |
blanchefort/rubert-base-cased-sentiment-rurewiews | fa298cae2c62473004c347cabd9a44379d795383 | 2021-05-19T13:02:26.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"ru",
"dataset:RuReviews",
"transformers",
"sentiment"
] | text-classification | false | blanchefort | null | blanchefort/rubert-base-cased-sentiment-rurewiews | 97 | null | transformers | 4,657 | ---
language:
- ru
tags:
- sentiment
- text-classification
datasets:
- RuReviews
---
# RuBERT for Sentiment Analysis of Product Reviews
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuReviews](https://github.com/sismetanin... |
flax-community/gpt-neo-125M-code-clippy-dedup | b4ba7c8b7a505b17b27e9740d128244b184ac07e | 2021-07-26T14:07:29.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"arxiv:2107.03374",
"transformers"
] | text-generation | false | flax-community | null | flax-community/gpt-neo-125M-code-clippy-dedup | 97 | null | transformers | 4,658 | # GPT-Neo-125M-Code-Clippy-Dedup
> **Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot**
## Model Description
PT-Neo-125M-Code-Clippy-Dedup is a [GPT-Neo-125M model](https://huggingface.co... |
laxya007/gpt2_till10 | d22959e62a589876dbeab822fe3a9c895788e0d4 | 2021-05-23T08:21:38.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | laxya007 | null | laxya007/gpt2_till10 | 97 | null | transformers | 4,659 | Entry not found |
manandey/wav2vec2-large-xlsr-punjabi | 31bea48ebd8a57713e0bd55bf5863bc5d30341c2 | 2022-03-25T16:54:20.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"pa-IN",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | manandey | null | manandey/wav2vec2-large-xlsr-punjabi | 97 | null | transformers | 4,660 | ---
language: pa-IN
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Punjabi by Manan Dey
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
da... |
pucpr/clinicalnerpt-procedure | 2bd68cf3aa4f49fb4bd3e88771ac9a7f30ed44fa | 2021-10-13T09:32:04.000Z | [
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"transformers",
"autotrain_compatible"
] | token-classification | false | pucpr | null | pucpr/clinicalnerpt-procedure | 97 | 4 | transformers | 4,661 | ---
language: "pt"
widget:
- text: "Dispneia venoso central em subclavia D duplolumen recebendo solução salina e glicosada em BI."
- text: "FOI REALIZADO CURSO DE ATB COM LEVOFLOXACINA POR 7 DIAS."
datasets:
- SemClinBr
thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1... |
sebastian-hofstaetter/idcm-distilbert-msmarco_doc | 4d1a03b94f38099cc927aa4ce4a1d3c40ea4e1b4 | 2021-05-26T14:14:50.000Z | [
"pytorch",
"IDCM",
"en",
"dataset:ms_marco",
"arxiv:2105.09816",
"transformers",
"document-retrieval",
"knowledge-distillation"
] | null | false | sebastian-hofstaetter | null | sebastian-hofstaetter/idcm-distilbert-msmarco_doc | 97 | 1 | transformers | 4,662 | ---
language: "en"
tags:
- document-retrieval
- knowledge-distillation
datasets:
- ms_marco
---
# Intra-Document Cascading (IDCM)
We provide a retrieval trained IDCM model. Our model is trained on MSMARCO-Document with up to 2000 tokens.
This instance can be used to **re-rank a candidate set** of long d... |
sentence-transformers/msmarco-bert-co-condensor | 153347cd1e647921de84615b73c2d50788dd72df | 2021-09-24T10:57:05.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2108.05540",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-bert-co-condensor | 97 | null | sentence-transformers | 4,663 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-bert-co-condensor
This is a port of the [Luyu/co-condenser-marco-retriever](https://huggingface.co/Luyu/co-condenser-marco-retriever) mo... |
tdklab/hebert-finetuned-hebrew-squad | abf5f9b3a7509b8b104eed54d3f6441fb0ae2238 | 2022-04-07T09:36:33.000Z | [
"pytorch",
"bert",
"question-answering",
"Hebrew",
"dataset:tdklab/Hebrew_Squad_v1",
"transformers",
"generated_from_trainer",
"avichr/heBERT",
"he",
"model-index",
"autotrain_compatible"
] | question-answering | false | tdklab | null | tdklab/hebert-finetuned-hebrew-squad | 97 | 1 | transformers | 4,664 | ---
language: Hebrew
datasets:
- tdklab/Hebrew_Squad_v1
tags:
- generated_from_trainer
- avichr/heBERT
- he
model-index:
- name: hebert-finetuned-hebrew-squad
results: []
widget:
- text: "מתי הוקמה הכרמלית ?"
context: "כרמלית היא כלי תחבורה ציבורית תת-קרקעי, היחיד בישראל. הכרמלית מחברת בין שלושה אזורים מרכזיים... |
ccdv/lsg-distilroberta-base-4096 | 3224af86e9d209a1d9e41275b9e103ec423e8ea6 | 2022-07-25T05:36:22.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"transformers",
"long context",
"autotrain_compatible"
] | fill-mask | false | ccdv | null | ccdv/lsg-distilroberta-base-4096 | 97 | null | transformers | 4,665 | ---
language: en
tags:
- long context
pipeline_tag: fill-mask
---
# LSG model
**Transformers >= 4.18.0**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
* [Usage](#usage)
* [Parameters](#parameters)
* [... |
Intel/xlnet-base-cased-mrpc | 686aebce620f3a8944a3faaafefe031aad4ebc6c | 2022-04-21T07:46:07.000Z | [
"pytorch",
"xlnet",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | Intel | null | Intel/xlnet-base-cased-mrpc | 97 | null | transformers | 4,666 | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: xlnet-base-cased-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
-... |
mrm8488/gpt-neo-1.3B-8bit | ae21d4aaa623fb0e8a23ae75bbcfafb6ec17b949 | 2022-06-01T14:51:41.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"license:wtfpl"
] | text-generation | false | mrm8488 | null | mrm8488/gpt-neo-1.3B-8bit | 97 | null | transformers | 4,667 | ---
license: wtfpl
---
|
knkarthick/TOPIC-DIALOGSUM | 744efa4bc5bb1653445d6d254274e2b2a199d8fe | 2022-07-07T06:19:40.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | knkarthick | null | knkarthick/TOPIC-DIALOGSUM | 97 | null | transformers | 4,668 | Entry not found |
Cameron/BERT-SBIC-targetcategory | dcb394be5d011eb3a67c06cea07ab7ef40daf264 | 2021-05-18T17:23:42.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Cameron | null | Cameron/BERT-SBIC-targetcategory | 96 | null | transformers | 4,669 | Entry not found |
Geotrend/bert-base-en-fr-cased | 30b1dd5115bb2441a8c098ff08aa67048e70c71d | 2021-05-18T19:15:20.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Geotrend | null | Geotrend/bert-base-en-fr-cased | 96 | 1 | transformers | 4,670 | ---
language: multilingual
datasets: wikipedia
license: apache-2.0
widget:
- text: "Google generated 46 billion [MASK] in revenue."
- text: "Paris is the capital of [MASK]."
- text: "Algiers is the largest city in [MASK]."
- text: "Paris est la [MASK] de la France."
- text: "Paris est la capitale de la [MASK]."
- te... |
Helsinki-NLP/opus-mt-en-sla | 64585957c615474bfad967cc8f526ee7961f7769 | 2021-01-18T08:16:05.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"be",
"hr",
"mk",
"cs",
"ru",
"pl",
"bg",
"uk",
"sl",
"sla",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-sla | 96 | null | transformers | 4,671 | ---
language:
- en
- be
- hr
- mk
- cs
- ru
- pl
- bg
- uk
- sl
- sla
tags:
- translation
license: apache-2.0
---
### eng-sla
* source group: English
* target group: Slavic languages
* OPUS readme: [eng-sla](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-sla/README.md)
* model: trans... |
Helsinki-NLP/opus-mt-es-id | e22dc8508f8b092f6b45b3c8495b35b2bc7c2c68 | 2021-09-09T21:43:02.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"id",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-id | 96 | null | transformers | 4,672 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-es-id
* source languages: es
* target languages: id
* OPUS readme: [es-id](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-id/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-tr-es | 25c7e6495932403c656eecab8729bdf49ee483c8 | 2021-09-11T10:49:38.000Z | [
"pytorch",
"marian",
"text2text-generation",
"tr",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tr-es | 96 | null | transformers | 4,673 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-tr-es
* source languages: tr
* target languages: es
* OPUS readme: [tr-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tr-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
UBC-NLP/AraT5-msa-base | 11f4f6e8367594d53f180d458babbd6e7046d240 | 2022-05-26T18:26:35.000Z | [
"pytorch",
"tf",
"t5",
"ar",
"transformers",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation"
] | null | false | UBC-NLP | null | UBC-NLP/AraT5-msa-base | 96 | 2 | transformers | 4,674 | ---
language:
- ar
tags:
- Arabic T5
- MSA
- Twitter
- Arabic Dialect
- Arabic Machine Translation
- Arabic Text Summarization
- Arabic News Title and Question Generation
- Arabic Paraphrasing and Transliteration
- Arabic Code-Switched Translation
---
# AraT5-msa-base
# AraT5: Text-to-Text Transfor... |
allenai/news_roberta_base | 1739782c98519dae4a4d79dd180d3f75b0a33a27 | 2021-05-20T13:35:01.000Z | [
"pytorch",
"jax",
"roberta",
"transformers"
] | null | false | allenai | null | allenai/news_roberta_base | 96 | null | transformers | 4,675 | Entry not found |
deepset/roberta-large-squad2-hp | 3d4caa9066ebb825f1aa054406e9a2f873368c20 | 2021-05-20T16:05:04.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/roberta-large-squad2-hp | 96 | 3 | transformers | 4,676 | Entry not found |
flax-community/clip-rsicd-v2 | fbe163da0609a2f185c22bb3af7b54ebad5a1800 | 2022-04-24T21:03:53.000Z | [
"pytorch",
"jax",
"clip",
"feature-extraction",
"transformers",
"vision"
] | feature-extraction | false | flax-community | null | flax-community/clip-rsicd-v2 | 96 | 5 | transformers | 4,677 | ---
tags:
- vision
---
# Model Card: clip-rsicd
## Model Details
This model is a fine-tuned [CLIP by OpenAI](https://huggingface.co/openai/clip-vit-base-patch32). It is designed with an aim to improve zero-shot image classification, text-to-image and image-to-image retrieval specifically on remote sensing images.
#... |
google/vit-large-patch32-224-in21k | aca4f3f0f317ae94659cbb186e8534ff1d3e25d1 | 2022-01-28T10:21:30.000Z | [
"pytorch",
"tf",
"jax",
"vit",
"feature-extraction",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"transformers",
"vision",
"license:apache-2.0"
] | feature-extraction | false | google | null | google/vit-large-patch32-224-in21k | 96 | null | transformers | 4,678 | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet-21k
inference: false
---
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transforme... |
huggingtweets/porns_xx | c4fb2674fde92bc314caa33c0f8b03b589fe97c5 | 2021-08-07T13:34:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/porns_xx | 96 | null | transformers | 4,679 | ---
language: en
thumbnail: https://www.huggingtweets.com/porns_xx/1628343064919/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width... |
mental/mental-roberta-base | 8166c3cb64a03c50b21c98ec1d6e4ab2c9617b07 | 2022-04-05T17:41:15.000Z | [
"pytorch",
"roberta",
"fill-mask",
"arxiv:2110.15621",
"transformers",
"autotrain_compatible"
] | fill-mask | false | mental | null | mental/mental-roberta-base | 96 | 3 | transformers | 4,680 | # MentalRoBERTa
[MentalRoBERTa](https://arxiv.org/abs/2110.15621) is a model initialized with RoBERTa-Base (`cased_L-12_H-768_A-12`) and trained with mental health-related posts collected from Reddit.
We follow the standard pretraining protocols of BERT and RoBERTa with [Huggingface’s Transformers library](https://g... |
pierreguillou/bert-base-cased-pt-lenerbr | 7b39cc6efc62a98450cd1257832760ca64b2d92f | 2022-01-04T08:51:23.000Z | [
"pytorch",
"bert",
"fill-mask",
"pt",
"dataset:pierreguillou/lener_br_finetuning_language_model",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | pierreguillou | null | pierreguillou/bert-base-cased-pt-lenerbr | 96 | 3 | transformers | 4,681 | ---
language:
- pt
tags:
- generated_from_trainer
datasets:
- pierreguillou/lener_br_finetuning_language_model
model-index:
- name: checkpoints
results:
- task:
name: Fill Mask
type: fill-mask
dataset:
name: pierreguillou/lener_br_finetuning_language_model
type: pierreguillou/lener_br_f... |
readerbench/RoGPT2-medium | f0587ebf9f0be6c25a222b4026ca7f893031d94b | 2021-07-22T11:18:49.000Z | [
"pytorch",
"tf",
"gpt2",
"text-generation",
"ro",
"transformers"
] | text-generation | false | readerbench | null | readerbench/RoGPT2-medium | 96 | null | transformers | 4,682 | Model card for RoGPT2-medium
---
language:
- ro
---
# RoGPT2: Romanian GPT2 for text generation
All models are available:
* [RoBERT-base](https://huggingface.co/readerbench/RoGPT2-base)
* [RoBERT-medium](https://huggingface.co/readerbench/RoGPT2-medium)
* [RoBERT-large](https://huggingface.co/readerbench/RoGPT2-larg... |
vyang/plc2proc | c17288bf03cf8269231c7870e32543c403f54d2e | 2022-02-23T15:43:40.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | vyang | null | vyang/plc2proc | 96 | null | transformers | 4,683 | ---
license: apache-2.0
---
|
xhyi/PT_GPTNEO350_ATG | 56ab08aaa6802d0f830d42c352d5d536be72811d | 2022-07-27T19:23:11.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | xhyi | null | xhyi/PT_GPTNEO350_ATG | 96 | 7 | transformers | 4,684 |
# GPT NEO 350M
This hosts the pulled 350M that Eleuther removed. I am keeping it 😎 |
amandakonet/climatebert-fact-checking | a5675d3444ed1a3113a2ac9a4a565ae2f2b6c237 | 2022-04-16T22:39:10.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:climate_fever",
"transformers",
"fact-checking",
"climate",
"text entailment",
"license:mit"
] | text-classification | false | amandakonet | null | amandakonet/climatebert-fact-checking | 96 | null | transformers | 4,685 | ---
license: mit
language:
- en
datasets: climate_fever
tags:
- fact-checking
- climate
- text entailment
---
This model fine-tuned [ClimateBert](https://huggingface.co/climatebert/distilroberta-base-climate-f) on the textual entailment task using Climate FEVER data. Given (claim, evidence) pairs, the model predi... |
anshr/distilgpt2_reward_model_02 | 49952bbcbe46d9d68e6d61bc15dbb99b6755bab2 | 2022-04-24T00:53:14.000Z | [
"pytorch",
"gpt2",
"text-classification",
"transformers"
] | text-classification | false | anshr | null | anshr/distilgpt2_reward_model_02 | 96 | null | transformers | 4,686 | Entry not found |
florentgbelidji/clip-text-feature-extraction | 64005001f2f133c67d81ba48c73381b843181e1f | 2022-07-06T08:28:24.000Z | [
"pytorch",
"feature-extraction",
"sentence_embedding"
] | feature-extraction | false | florentgbelidji | null | florentgbelidji/clip-text-feature-extraction | 96 | null | null | 4,687 | ---
tags:
- feature-extraction
- sentence_embedding
--- |
lgessler/coptic-bert-small-uncased | 0dd4abdc3a94b6d3a5648082b83c5f01152e1364 | 2022-07-21T19:38:13.000Z | [
"pytorch",
"bert",
"feature-extraction",
"cop",
"transformers"
] | feature-extraction | false | lgessler | null | lgessler/coptic-bert-small-uncased | 96 | null | transformers | 4,688 | ---
language: cop
widget:
- text: "ⲁⲩⲱ ⲉⲓⲥ ⲡⲉⲧⲙⲙⲁⲩ ⲁϥⲉⲓ ⲉϥⲣⲓⲙⲉ."
---
A small `BertModel` for Coptic. |
Finnish-NLP/convbert-base-finnish | 7ca436faf91f685e3a8137bec726012cf88fcbcf | 2022-06-13T16:15:25.000Z | [
"pytorch",
"tf",
"tensorboard",
"convbert",
"feature-extraction",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:2008.02496",
"transformers",
"finnish",
"license:apache-2.0"
] | feature-extraction | false | Finnish-NLP | null | Finnish-NLP/convbert-base-finnish | 95 | 1 | transformers | 4,689 | ---
language:
- fi
license: apache-2.0
tags:
- finnish
- convbert
datasets:
- Finnish-NLP/mc4_fi_cleaned
- wikipedia
---
# ConvBERT for Finnish
Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective. ConvBERT was introduced in
[this paper](https://arxiv.org/abs/2008.02496)
and... |
Helsinki-NLP/opus-mt-es-pl | a1514156efe6b61a49e19e67d93628c482f63f9a | 2021-09-09T21:44:13.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"pl",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-pl | 95 | null | transformers | 4,690 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-es-pl
* source languages: es
* target languages: pl
* OPUS readme: [es-pl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-pl/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-vi-es | d6f74862cce929649b728270c7337a97881bda23 | 2020-08-21T14:42:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"vi",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-vi-es | 95 | null | transformers | 4,691 | ---
language:
- vi
- es
tags:
- translation
license: apache-2.0
---
### vie-spa
* source group: Vietnamese
* target group: Spanish
* OPUS readme: [vie-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-spa/README.md)
* model: transformer-align
* source language(s): vie
* target lang... |
HooshvareLab/bert-fa-base-uncased-ner-arman | 889a2b8c1d7d4c8bca305365069ad3045a00c224 | 2021-05-18T20:52:21.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | HooshvareLab | null | HooshvareLab/bert-fa-base-uncased-ner-arman | 95 | null | transformers | 4,692 | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](... |
IDEA-CCNL/Zhouwenwang-Unified-1.3B | 9cfe461021dbc10c2fc8657b6794d07f643b4a79 | 2022-04-12T02:03:58.000Z | [
"pytorch",
"megatron-bert",
"zh",
"transformers",
"license:apache-2.0"
] | null | false | IDEA-CCNL | null | IDEA-CCNL/Zhouwenwang-Unified-1.3B | 95 | null | transformers | 4,693 | ---
language:
- zh
license: apache-2.0
widget:
- text: "生活的真谛是[MASK]。"
---
# Zhouwenwang-Unified-1.3B model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
Zhouwenwang-Unified-1.3B apply a new unified structure, and jointly developed by the IDEA-CCNL and Zhuiyi Technology. In ... |
akahana/vit-base-cats-vs-dogs | b7e917ca8728ad2138712aa863a87303c453b0e6 | 2021-12-09T04:36:57.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"dataset:cats_vs_dogs",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | akahana | null | akahana/vit-base-cats-vs-dogs | 95 | null | transformers | 4,694 | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- cats_vs_dogs
metrics:
- accuracy
model-index:
- name: vit-base-cats-vs-dogs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cats_vs_dogs
type: cats_vs_dogs
... |
dbmdz/electra-base-italian-xxl-cased-generator | 900e1c97be444c42439b796c16e5b62d899b6e5f | 2020-12-11T21:37:22.000Z | [
"pytorch",
"electra",
"fill-mask",
"it",
"dataset:wikipedia",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/electra-base-italian-xxl-cased-generator | 95 | null | transformers | 4,695 | ---
language: it
license: mit
datasets:
- wikipedia
---
# 🤗 + 📚 dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models 🎉
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikiped... |
ethanyt/guwenbert-large | 183588933ec4f07a29c05c2ef116c2074233c078 | 2021-06-02T03:24:26.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"zh",
"transformers",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | ethanyt | null | ethanyt/guwenbert-large | 95 | 1 | transformers | 4,696 | ---
language:
- "zh"
thumbnail: "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "pytorch"
license: "apache-2.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
widget:
- text... |
google/t5-efficient-small-dm768 | 447413aaa95f9c43eaf49f6202763dbd560e6f7e | 2022-02-15T10:56:46.000Z | [
"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-dm768 | 95 | null | transformers | 4,697 | ---
language:
- en
datasets:
- c4
tags:
- deep-narrow
inference: false
license: apache-2.0
---
# T5-Efficient-SMALL-DM768 (Deep-Narrow version)
T5-Efficient-SMALL-DM768 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model archite... |
microsoft/unilm-base-cased | 9e2691e6ff9711fbd922f0b18dc0a6b77c7cc530 | 2020-04-28T21:22:52.000Z | [
"pytorch",
"transformers"
] | null | false | microsoft | null | microsoft/unilm-base-cased | 95 | null | transformers | 4,698 | Entry not found |
nateraw/huggingpics-package-demo | 99bd2b30940d28bfc7ff7bfdd667d1b8c8784b51 | 2021-11-09T20:44:45.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | nateraw | null | nateraw/huggingpics-package-demo | 95 | null | transformers | 4,699 | ---
license: apache-2.0
tags:
- image-classification
- huggingpics
- generated_from_trainer
model-index:
- name: huggingpics-package-demo
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 r... |
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