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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
monsoon-nlp/bangla-electra | 6b1473a54f66add692a6e106e91e1212a9ccb145 | 2020-07-29T07:58:53.000Z | [
"pytorch",
"tf",
"electra",
"bn",
"arxiv:2004.07807",
"transformers"
] | null | false | monsoon-nlp | null | monsoon-nlp/bangla-electra | 62 | 1 | transformers | 5,600 | ---
language: bn
---
# Bangla-Electra
This is a second attempt at a Bangla/Bengali language model trained with
Google Research's [ELECTRA](https://github.com/google-research/electra).
Tokenization and pre-training CoLab: https://colab.research.google.com/drive/1gpwHvXAnNQaqcu-YNx1kafEVxz07g2jL
V1 - 120,000 steps; V... |
nvidia/segformer-b0-finetuned-cityscapes-640-1280 | 335a691b445d6cf38370a42546c037d8ff978685 | 2022-07-20T09:54:18.000Z | [
"pytorch",
"tf",
"segformer",
"dataset:cityscapes",
"arxiv:2105.15203",
"transformers",
"vision",
"image-segmentation",
"license:apache-2.0"
] | image-segmentation | false | nvidia | null | nvidia/segformer-b0-finetuned-cityscapes-640-1280 | 62 | null | transformers | 5,601 | ---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- cityscapes
widget:
- src: https://www.researchgate.net/profile/Anurag-Arnab/publication/315881952/figure/fig5/AS:667673876779033@1536197265755/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.jpg
example_ti... |
pszemraj/pegasus-large-summary-explain | 5b28618565079b7701b586f01e31f3d294067c18 | 2022-07-15T21:00:36.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:kmfoda/booksum",
"transformers",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | pszemraj | null | pszemraj/pegasus-large-summary-explain | 62 | 1 | transformers | 5,602 | ---
language:
- en
tags:
- summarization
- pegasus
license: apache-2.0
datasets:
- kmfoda/booksum
metrics:
- rouge
widget:
- text: large earthquakes along a given fault segment do not occur at random intervals
because it takes time to accumulate the strain energy for the rupture. The rates
at which tectonic pla... |
textattack/facebook-bart-large-MNLI | a9bbf281f3c2ea56317e31551b7f3161412906c7 | 2020-06-09T16:49:34.000Z | [
"pytorch",
"bart",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/facebook-bart-large-MNLI | 62 | null | transformers | 5,603 | Entry not found |
timo/timo-BART-german | 168cf21134bb84888174af675f26de45b4803d3d | 2020-10-28T19:09:26.000Z | [
"pytorch",
"fsmt",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | timo | null | timo/timo-BART-german | 62 | null | transformers | 5,604 | Entry not found |
clips/contact | c87cdcee36a7de0bb572b5201b9ec1795b8f7925 | 2022-03-15T12:57:53.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2203.07362",
"transformers"
] | feature-extraction | false | clips | null | clips/contact | 62 | null | transformers | 5,605 | # CoNTACT
### Model description
<u>Co</u>ntextual <u>N</u>eural <u>T</u>ransformer <u>A</u>dapted to <u>C</u>OVID-19 <u>T</u>weets or **CoNTACT** is a Dutch RobBERT model (```pdelobelle/robbert-v2-dutch-base```) adapted to the domain of COVID-19 tweets. The model was developed at [CLiPS](https://www.uantwerpen.be/en/... |
NbAiLab/nb-gpt-j-6B | 3c6134eda729fba015f91e8a1776e9b4592a2b55 | 2022-06-17T12:04:34.000Z | [
"pytorch",
"gptj",
"text-generation",
"no",
"nb",
"nn",
"dataset:NbAiLab/NCC",
"dataset:mc4",
"dataset:oscar",
"arxiv:2104.09864",
"arxiv:2101.00027",
"transformers",
"causal-lm",
"license:apache-2.0"
] | text-generation | false | NbAiLab | null | NbAiLab/nb-gpt-j-6B | 62 | 4 | transformers | 5,606 | ---
language:
- no
- nb
- nn
tags:
- pytorch
- causal-lm
license: apache-2.0
datasets:
- NbAiLab/NCC
- mc4
- oscar
---
- **Release v1beta2** (June 18th, 2022) *[Full-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta2), [sharded](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/sharded), and [half-preci... |
KBLab/megatron-bert-large-swedish-cased-165k | 459c4eeb687d7c5cd526139195c00c6f160a2f5a | 2022-04-05T09:01:40.000Z | [
"pytorch",
"megatron-bert",
"fill-mask",
"sv",
"transformers",
"autotrain_compatible"
] | fill-mask | false | KBLab | null | KBLab/megatron-bert-large-swedish-cased-165k | 62 | null | transformers | 5,607 | ---
language:
- sv
---
# Megatron-BERT-large Swedish 165k
This BERT model was trained using the Megatron-LM library.
The size of the model is a regular BERT-large with 340M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library o... |
itsfuckingdenis/diplomauno | 84ca726b2390ec2e14c57686f7f726707cdbc464 | 2022-03-22T15:29:46.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers",
"license:wtfpl"
] | feature-extraction | false | itsfuckingdenis | null | itsfuckingdenis/diplomauno | 62 | null | transformers | 5,608 | ---
license: wtfpl
---
|
doc2query/msmarco-russian-mt5-base-v1 | a7d5ede00d679dee611a7bd700275fb4f4b8667e | 2022-04-29T12:10:29.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"ru",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | doc2query | null | doc2query/msmarco-russian-mt5-base-v1 | 62 | null | transformers | 5,609 | ---
language: ru
datasets:
- unicamp-dl/mmarco
widget:
- text: "Python (МФА: [ˈpʌɪθ(ə)n]; в русском языке встречаются названия пито́н или па́йтон) — высокоуровневый язык программирования общего назначения с динамической строгой типизацией и автоматическим управлением памятью, ориентированный на повышение производи... |
paust/pko-t5-base | 6d16c5c154114f24d837f8d01e92d49e828bf543 | 2022-07-13T07:12:46.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ko",
"arxiv:2105.09680",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | text2text-generation | false | paust | null | paust/pko-t5-base | 62 | 1 | transformers | 5,610 | ---
language: ko
license: cc-by-4.0
---
# pko-t5-base
[Source Code](https://github.com/paust-team/pko-t5)
pko-t5 는 한국어 전용 데이터로 학습한 [t5 v1.1 모델](https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/released_checkpoints.md)입니다.
한국어를 tokenize 하기 위해서 sentenc... |
autoevaluate/entity-extraction | 654993b2d1862c85f67e10c0e23b0b7131446bd9 | 2022-05-28T11:16:55.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | autoevaluate | null | autoevaluate/entity-extraction | 62 | 1 | transformers | 5,611 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: entity-extraction
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: co... |
svenstahlmann/finetuned-distilbert-needmining | 092176dd5c42ae0db65f437b942e1323d96ae5fa | 2022-07-18T13:15:23.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"needmining",
"license:apache-2.0"
] | text-classification | false | svenstahlmann | null | svenstahlmann/finetuned-distilbert-needmining | 62 | null | transformers | 5,612 | ---
language: en
tags:
- distilbert
- needmining
license: apache-2.0
metric:
- f1
---
# Finetuned-Distilbert-needmining (uncased)
This model is a finetuned version of the [Distilbert base model](https://huggingface.co/distilbert-base-uncased). It was
trained to predict need-containing sentences from amazon product re... |
kapuska/dialogue-summarizationv1 | 91c770ac8f6549e0384e3e2db15434de6e3867e4 | 2022-07-20T10:57:35.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"dataset:samsum",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | kapuska | null | kapuska/dialogue-summarizationv1 | 62 | null | transformers | 5,613 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: dialogue-summarizationv1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
args: samsum
metr... |
Dev-DGT/food-dbert-multiling | b648054d802b2ac2a99f57954e8b8df1cf14320f | 2021-06-18T21:55:58.000Z | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Dev-DGT | null | Dev-DGT/food-dbert-multiling | 61 | null | transformers | 5,614 | ---
widget:
- text: "El paciente se alimenta de pan, sopa de calabaza y coca-cola"
---
# Token classification for FOODs.
Detects foods in sentences.
Currently, only supports spanish. Multiple words foods are detected as one entity.
## To-do
- English support.
- Negation support.
- Quantity tags.
- Psychosocial ta... |
ErykWdowiak/GPTalian | 93ca5ca1b6bc26868d825135d5c0f7773af4e606 | 2021-05-21T09:42:05.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"it",
"scn",
"nap",
"transformers",
"exbert",
"license:apache-2.0"
] | text-generation | false | ErykWdowiak | null | ErykWdowiak/GPTalian | 61 | null | transformers | 5,615 | ---
language:
- en
- it
- scn
- nap
tags:
- exbert
- gpt2
license: apache-2.0
---
# GPTalian
This is a GPT2 model of Italian regional languages trained on [collections of Italian "dialect poetry"](http://dialectpoetry.com) by Luigi Bonaffini.
This is a multilingual model. Italians use the word "dialect" to desc... |
Helsinki-NLP/opus-mt-fi-de | 306b92594304d4c34107a4bba34e01a24a27efc4 | 2021-09-09T21:47:10.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"de",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-de | 61 | null | transformers | 5,616 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fi-de
* source languages: fi
* target languages: de
* OPUS readme: [fi-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
MYX4567/distilgpt2-finetuned-wikitext2 | 7e486eedcd270a40efe669de9349d659b9eadfc7 | 2021-07-28T06:37:12.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-generation | false | MYX4567 | null | MYX4567/distilgpt2-finetuned-wikitext2 | 61 | null | transformers | 5,617 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: distilgpt2-finetuned-wikitext2
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access... |
SkolkovoInstitute/t5-paranmt-detox | a60d9b9d7fa44211621e52156d59dbee54e49b0b | 2021-11-03T08:40:36.000Z | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:1711.05732",
"arxiv:1911.00536",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | SkolkovoInstitute | null | SkolkovoInstitute/t5-paranmt-detox | 61 | 1 | transformers | 5,618 | This is a paraphraser based on [ceshine/t5-paraphrase-paws-msrp-opinosis](https://huggingface.co/ceshine/t5-paraphrase-paws-msrp-opinosis)
and additionally fine-tuned on [ParaNMT](https://arxiv.org/abs/1711.05732) filtered for the task of detoxification.
The model was trained for the paper [Text Detoxification using L... |
chinhon/pegasus-multi_news-headline | bf2a96a29df0b4bb16d9d70e1bd21d87454c5947 | 2021-10-31T01:30:14.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | chinhon | null | chinhon/pegasus-multi_news-headline | 61 | 3 | transformers | 5,619 | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-multi_news-headline
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-mu... |
facebook/s2t-small-covost2-en-fa-st | db4d49793a8778016bcea34c789c5ad6a9d18a83 | 2022-02-07T15:24:59.000Z | [
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"en",
"fa",
"dataset:covost2",
"arxiv:2010.05171",
"arxiv:1912.06670",
"arxiv:1904.08779",
"transformers",
"audio",
"speech-translation",
"license:mit"
] | automatic-speech-recognition | false | facebook | null | facebook/s2t-small-covost2-en-fa-st | 61 | 1 | transformers | 5,620 | ---
language:
- en
- fa
datasets:
- covost2
tags:
- audio
- speech-translation
- automatic-speech-recognition
license: mit
pipeline_tag: automatic-speech-recognition
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
... |
hfl/chinese-electra-180g-base-generator | c7713846e35a5a43635508ed64b648b737ff92fa | 2021-03-03T01:26:40.000Z | [
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0",
"fill-mask"
] | fill-mask | false | hfl | null | hfl/chinese-electra-180g-base-generator | 61 | null | transformers | 5,621 | ---
language:
- zh
license: "apache-2.0"
pipeline_tag: "fill-mask"
---
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively com... |
huggingtweets/billgates | 87a40ef9ff2217f17c666202db4b84cf3bf094ca | 2022-06-19T05:06:00.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/billgates | 61 | null | transformers | 5,622 | ---
language: en
thumbnail: http://www.huggingtweets.com/billgates/1655615155620/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... |
ielab/TILDE | de04dcba18612539a64bc2c26aa6234064eeaa31 | 2021-06-24T05:46:57.000Z | [
"pytorch",
"bert",
"text-generation",
"transformers"
] | text-generation | false | ielab | null | ielab/TILDE | 61 | 1 | transformers | 5,623 | Please treat TILDE as a BertLMHeadModel model:
```
from transformers import BertLMHeadModel, BertTokenizerFast
model = BertLMHeadModel.from_pretrained("ielab/TILDE")
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
```
Github: https://github.com/ielab/TILDE |
indobenchmark/indogpt | 625917f730659d16068897c6d4e497f915af9737 | 2022-06-21T17:51:47.000Z | [
"pytorch",
"gpt2",
"text-generation",
"id",
"dataset:Indo4B+",
"arxiv:2104.08200",
"transformers",
"indogpt",
"indobenchmark",
"indonlg",
"license:mit"
] | text-generation | false | indobenchmark | null | indobenchmark/indogpt | 61 | 2 | transformers | 5,624 | ---
language: id
tags:
- indogpt
- indobenchmark
- indonlg
license: mit
inference: false
datasets:
- Indo4B+
---
# IndoGPT Model
[IndoGPT](https://arxiv.org/abs/2104.08200) is a state-of-the-art language model for Indonesian based on the GPT model. The pretrained model is trained using the GPT training objective.
##... |
kco4776/kogpt-chat | 1494648519c992c2cc904188cee1ff2633c8e53d | 2021-12-10T06:24:09.000Z | [
"pytorch",
"gpt2",
"transformers"
] | null | false | kco4776 | null | kco4776/kogpt-chat | 61 | null | transformers | 5,625 | ## References
- [koGPT2](https://github.com/SKT-AI/KoGPT2)
- [koGPT2-chatbot](https://github.com/haven-jeon/KoGPT2-chatbot) |
liaad/srl-en_mbert-base | 0c963b783ca3a81aafce1c53c0bb17715a7f0903 | 2021-09-22T08:56:08.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"multilingual",
"pt",
"en",
"dataset:PropBank.Br",
"dataset:CoNLL-2012",
"arxiv:2101.01213",
"transformers",
"bert-base-multilingual-cased",
"semantic role labeling",
"finetuned",
"license:apache-2.0"
] | feature-extraction | false | liaad | null | liaad/srl-en_mbert-base | 61 | null | transformers | 5,626 | ---
language:
- multilingual
- pt
- en
tags:
- bert-base-multilingual-cased
- semantic role labeling
- finetuned
license: apache-2.0
datasets:
- PropBank.Br
- CoNLL-2012
metrics:
- F1 Measure
---
# mBERT fine-tuned on English semantic role labeling
## Model description
This model is the [`bert-base-multilingual-case... |
m3hrdadfi/bert-fa-base-uncased-farstail-mean-tokens | f80ddc8f90470a4b267e83a69c07dd56fbcff3e8 | 2021-05-28T06:03:42.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"fa",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | m3hrdadfi | null | m3hrdadfi/bert-fa-base-uncased-farstail-mean-tokens | 61 | null | transformers | 5,627 | ---
language: fa
license: apache-2.0
---
# FarsTail + ParsBERT
Please follow the [FarsTail](https://github.com/dml-qom/FarsTail) repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check out the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transfo... |
mitra-mir/BERT-Persian-Poetry | 7e9db6fe8d4a404fdc120228f4f6546aa44c23c4 | 2021-05-19T23:34:26.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | mitra-mir | null | mitra-mir/BERT-Persian-Poetry | 61 | null | transformers | 5,628 | BERT Language Model Further Pre-trained on Persian Poetry |
mrm8488/t5-base-e2e-question-generation | 61fe7d47f2e5a26a6e2523dd8b78460d259930cd | 2021-08-24T15:37:55.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-e2e-question-generation | 61 | 1 | transformers | 5,629 | Entry not found |
sagteam/pharm-relation-extraction | c4bb64feddb2d1229f9b457330a502828f0289dd | 2021-11-24T17:12:12.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"arxiv:2105.00059",
"arxiv:1911.02116",
"transformers"
] | text-classification | false | sagteam | null | sagteam/pharm-relation-extraction | 61 | 2 | transformers | 5,630 | pharm-relation-extraction
===
Model trained to recognize 4 types of relationships between significant pharmacological entities in russian-language reviews: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. The input of the model is a review text and a pair of entities, between which i... |
textattack/distilbert-base-uncased-QNLI | 429c2124a83a32d4dc9b7fd2bb0141f298cac5e9 | 2020-06-09T16:47:34.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/distilbert-base-uncased-QNLI | 61 | null | transformers | 5,631 | Entry not found |
uer/chinese_roberta_L-12_H-512 | 6675ef56dca044a81fe27ba071e7159f1a62d10d | 2022-07-15T08:15:58.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:1908.08962",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uer | null | uer/chinese_roberta_L-12_H-512 | 61 | null | transformers | 5,632 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "北京是[MASK]国的首都。"
---
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658).
[Turc e... |
wanyu/IteraTeR-ROBERTA-Intention-Classifier | fcb6e9c52a4ee6eb9dcf6985cb1a2f6796babb81 | 2022-04-04T20:13:42.000Z | [
"pytorch",
"roberta",
"text-classification",
"dataset:IteraTeR_full_sent",
"arxiv:2203.03802",
"transformers"
] | text-classification | false | wanyu | null | wanyu/IteraTeR-ROBERTA-Intention-Classifier | 61 | null | transformers | 5,633 | ---
datasets:
- IteraTeR_full_sent
---
# IteraTeR RoBERTa model
This model was obtained by fine-tuning [roberta-large](https://huggingface.co/roberta-large) on [IteraTeR-human-sent](https://huggingface.co/datasets/wanyu/IteraTeR_human_sent) dataset.
Paper: [Understanding Iterative Revision from Human-Written Text](ht... |
brad1141/gpt2-finetuned-comp2 | fcb91eaea65865b9df8753ebf6f359bd9ba31230 | 2022-03-18T08:47:38.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | brad1141 | null | brad1141/gpt2-finetuned-comp2 | 61 | null | transformers | 5,634 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gpt2-finetuned-comp2
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 rem... |
ckiplab/bert-tiny-chinese-pos | 5459b8666bf3aa754cefd6d7501b623dfd6a5af2 | 2022-05-10T03:28:12.000Z | [
"pytorch",
"bert",
"token-classification",
"zh",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | token-classification | false | ckiplab | null | ckiplab/bert-tiny-chinese-pos | 61 | null | transformers | 5,635 | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- bert
- zh
license: gpl-3.0
---
# CKIP BERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segment... |
Amalq/stress-roberta-large | 58e8b6d0707b5432016b3fd5b974226bbbc18259 | 2022-06-08T15:20:24.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:Dreaddit",
"transformers",
"Transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Amalq | null | Amalq/stress-roberta-large | 61 | null | transformers | 5,636 | ---
language: en
tags:
- Transformers
license: apache-2.0
datasets:
- Dreaddit
---
# StressRoberta model
is a model initialized with roberta-large (https://huggingface.co/roberta-large )and trained with
[Dreaddit: A Reddit Dataset for Stress Analysis in Social Media] ( http://www.cs.columbia.edu/eturcan/data/dreaddi... |
anahitapld/dbd_bert_da_simple | 465b6d91cf4af79218866df8e11ee5e664f2627c | 2022-07-18T07:59:45.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | anahitapld | null | anahitapld/dbd_bert_da_simple | 61 | null | transformers | 5,637 | ---
license: apache-2.0
---
|
loubnabnl/codeparrot-small-multi-small-near-dedup | 939eb5b96b33d233d5bb482a83488b6eceb51b22 | 2022-07-18T09:20:36.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"license:apache-2.0"
] | text-generation | false | loubnabnl | null | loubnabnl/codeparrot-small-multi-small-near-dedup | 61 | null | transformers | 5,638 | ---
license: apache-2.0
---
|
Billwzl/20split_dataset_version2 | 007338f42769490b47a9a2ef1ffed5dc58c74df9 | 2022-07-27T08:07:06.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | Billwzl | null | Billwzl/20split_dataset_version2 | 61 | null | transformers | 5,639 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: 20split_dataset_version2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 20split_da... |
uaritm/ukrt5-base | 7f7487864ff0d9e9da47e80b4325bf1f6e52388f | 2022-07-28T11:02:32.000Z | [
"pytorch",
"t5",
"text2text-generation",
"uk",
"en",
"transformers",
"ukrainian",
"english",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | uaritm | null | uaritm/ukrt5-base | 61 | null | transformers | 5,640 | ---
language: ["uk", "en"]
tags:
- ukrainian
- english
license: mit
---
This is a variant of the [google/mt5-base](https://huggingface.co/google/mt5-base) model, in which Ukrainian and 9% English words remain.
This model has 252M parameters - 43% of the original size.
Special thanks for the practical example and inspi... |
trickstters/evanbot-gpt | 9f2cdd0f874e430e8bf5222c335971e970ff6323 | 2022-07-27T15:15:16.000Z | [
"pytorch",
"conversational"
] | conversational | false | trickstters | null | trickstters/evanbot-gpt | 61 | null | null | 5,641 | ---
tags:
- conversational
---
# bot |
AykeeSalazar/vc-bantai-vit-withoutAMBI-adunest-v1 | fd6dec2ddc4912e9e294c0d17a1148cb99f1c0b2 | 2022-07-28T02:45:09.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"dataset:imagefolder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | AykeeSalazar | null | AykeeSalazar/vc-bantai-vit-withoutAMBI-adunest-v1 | 61 | null | transformers | 5,642 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vc-bantai-vit-withoutAMBI-adunest-v1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: Vio... |
Rifky/FND | c3f33a822223f3a9ad991f743360978537c9b0fd | 2022-07-28T18:57:57.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | Rifky | null | Rifky/FND | 61 | null | transformers | 5,643 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: FND
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# FND
This model is... |
Helsinki-NLP/opus-mt-dra-en | add52153b130e3e38db8497798b1276320b6c971 | 2021-01-18T08:02:54.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ta",
"kn",
"ml",
"te",
"dra",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-dra-en | 60 | null | transformers | 5,644 | ---
language:
- ta
- kn
- ml
- te
- dra
- en
tags:
- translation
license: apache-2.0
---
### dra-eng
* source group: Dravidian languages
* target group: English
* OPUS readme: [dra-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/dra-eng/README.md)
* model: transformer
* source langua... |
Helsinki-NLP/opus-mt-es-es | dc18d22d76133106d65d46e1ffa43b2cc7b8a416 | 2021-09-09T21:42:12.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-es | 60 | null | transformers | 5,645 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-es-es
* source languages: es
* target languages: es
* OPUS readme: [es-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Irina/trans_GPT3Medium | bc135e10ddcf6b098cc55b661b8e02e4b2f89edf | 2021-11-13T16:37:50.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Irina | null | Irina/trans_GPT3Medium | 60 | null | transformers | 5,646 | Entry not found |
NYTK/sentiment-hts5-xlm-roberta-hungarian | c38cbf7336574a02ebd53c106d43c2a336e2ceba | 2022-02-14T13:33:04.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"hu",
"transformers",
"license:gpl"
] | text-classification | false | NYTK | null | NYTK/sentiment-hts5-xlm-roberta-hungarian | 60 | null | transformers | 5,647 | ---
language:
- hu
tags:
- text-classification
license: gpl
metrics:
- accuracy
widget:
- text: "Jó reggelt! majd küldöm az élményhozókat :)."
---
# Hungarian Sentence-level Sentiment Analysis model with XLM-RoBERTa
For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-... |
alireza7/PEGASUS-persian-base-perkey-summary | 7e76c778545dd20894cd7d08723bcda1ed806ce5 | 2021-09-29T19:25:45.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | alireza7 | null | alireza7/PEGASUS-persian-base-perkey-summary | 60 | null | transformers | 5,648 | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). |
arianpasquali/twitter-xlm-roberta-base-sentiment-finetunned | c777d22dc6c1d5a6d2c0ad6408a5f108140b8075 | 2022-01-25T23:34:13.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | false | arianpasquali | null | arianpasquali/twitter-xlm-roberta-base-sentiment-finetunned | 60 | null | transformers | 5,649 | Entry not found |
cahya/wav2vec2-large-xlsr-indonesian-artificial | 92576c679e8cbbe21481f7ec7b734b0e69c5b29a | 2021-07-05T23:51:17.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"id",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | cahya | null | cahya/wav2vec2-large-xlsr-indonesian-artificial | 60 | null | transformers | 5,650 | ---
language: id
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Indonesian with Artificial Voice by Cahya
results:
- task:
name: Speech Recognition
type: automatic-speech-recog... |
castorini/monot5-3b-msmarco | c8432e9220adb0c59fc360db20274a1729b64802 | 2021-04-03T13:48:44.000Z | [
"pytorch",
"t5",
"feature-extraction",
"transformers"
] | feature-extraction | false | castorini | null | castorini/monot5-3b-msmarco | 60 | null | transformers | 5,651 | This model is a T5-3B reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 10 epochs).
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.... |
dandelin/vilt-b32-finetuned-flickr30k | 494e36e6ea1edd9e295e0eea3d6cc7264efe39e1 | 2022-01-23T09:46:32.000Z | [
"pytorch",
"vilt",
"arxiv:1505.04870",
"arxiv:2102.03334",
"transformers",
"license:apache-2.0"
] | null | false | dandelin | null | dandelin/vilt-b32-finetuned-flickr30k | 60 | 1 | transformers | 5,652 | ---
license: apache-2.0
---
# Vision-and-Language Transformer (ViLT), fine-tuned on Flickr30k
Vision-and-Language Transformer (ViLT) model fine-tuned on [Flickr30k](https://arxiv.org/abs/1505.04870#:~:text=The%20Flickr30k%20dataset%20has%20become,for%20sentence%2Dbased%20image%20description.&text=Such%20annotations%2... |
facebook/convnext-base-384 | 493ade9a30c4c8ce13d85da7fb0fdbe3e0e066b1 | 2022-02-26T12:16:12.000Z | [
"pytorch",
"tf",
"convnext",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/convnext-base-384 | 60 | null | transformers | 5,653 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https... |
facebook/convnext-large-224 | 0dedb40e29ccd026c79cabd94ef6c3c2a4bcdd9a | 2022-03-02T19:04:49.000Z | [
"pytorch",
"tf",
"convnext",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/convnext-large-224 | 60 | null | transformers | 5,654 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https... |
facebook/wav2vec2-base-10k-voxpopuli-ft-pl | fd1af7cf77bcb00ecf62c91771c3771a3e863bdc | 2021-07-06T01:52:01.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pl",
"arxiv:2101.00390",
"transformers",
"audio",
"voxpopuli",
"license:cc-by-nc-4.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-base-10k-voxpopuli-ft-pl | 60 | 1 | transformers | 5,655 | ---
language: pl
tags:
- audio
- automatic-speech-recognition
- voxpopuli
license: cc-by-nc-4.0
---
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus... |
filco306/gpt2-tweet-paraphraser | 55baaeb80e7057bc9641d0711956b0b5e6cdb108 | 2021-08-28T23:34:31.000Z | [
"pytorch",
"text-generation",
"arxiv:2010.05700",
"transformers"
] | text-generation | false | filco306 | null | filco306/gpt2-tweet-paraphraser | 60 | null | transformers | 5,656 | # GPT2 Tweet style transfer paraphraser
This is the trained Tweet-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by ... |
flax-community/gpt-neo-125M-code-search-py | f8e55c5a2348e00e286318f870ad162c68b1a152 | 2021-07-26T14:06:51.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | flax-community | null | flax-community/gpt-neo-125M-code-search-py | 60 | null | transformers | 5,657 | # GPT-Code-Clippy-125M-Code-Search-Py
> **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
GPT-CC-125M-Code-Search is a [GPT-Neo-125M model](https://huggingface.co/... |
fspanda/electra-medical-small-discriminator | 237898f5ee8bac6197ad0c2860caea3b38f123dc | 2020-10-29T00:30:38.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | fspanda | null | fspanda/electra-medical-small-discriminator | 60 | null | transformers | 5,658 | Entry not found |
huggingtweets/bestmusiclyric-bygpt3 | 6b65f651bea6e5c09207516e0860529232335fb7 | 2021-05-21T20:28:19.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/bestmusiclyric-bygpt3 | 60 | null | transformers | 5,659 | ---
language: en
thumbnail: https://www.huggingtweets.com/bestmusiclyric-bygpt3/1621260459372/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-righ... |
huggingtweets/funnyordie | 02da8d24a1be3958820d34839b944f9c15fc4ecc | 2022-01-04T19:39:10.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/funnyordie | 60 | null | transformers | 5,660 | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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: 4... |
huggingtweets/sentienter | 808b0296edbe288c92c8211d6601ad876db2d006 | 2021-05-22T22:28:11.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/sentienter | 60 | null | transformers | 5,661 | ---
language: en
thumbnail: https://www.huggingtweets.com/sentienter/1616642835417/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/12748735087119... |
jonatasgrosman/wav2vec2-xls-r-1b-dutch | 3caa6c25336dfa23f6585773cebf4a0ccc8765be | 2022-07-27T23:38:48.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-xls-r-1b-dutch | 60 | 1 | transformers | 5,662 | ---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- nl
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R Wav2Vec2 Dutch by Jonatas Grosman
results:
- task:
name: Automatic Speech... |
kco4776/soongsil-bert-wellness | 040ac5d8c56c6512f2d3aa1732f7b7c84a168057 | 2021-12-19T15:23:09.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | kco4776 | null | kco4776/soongsil-bert-wellness | 60 | null | transformers | 5,663 | ## References
- [Soongsil-BERT](https://github.com/jason9693/Soongsil-BERT) |
lewtun/xlm-roberta-base-finetuned-marc | 207ce3118b631d5f792f20f20b0fa9c3775ea503 | 2021-10-15T21:10:49.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"dataset:amazon_reviews_multi",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | lewtun | null | lewtun/xlm-roberta-base-finetuned-marc | 60 | 1 | transformers | 5,664 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc
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 remov... |
malay-huggingface/xlnet-tiny-bahasa-cased | 1cff829bede4df4bf3438e5310e71df6d9348f9f | 2021-09-18T13:50:09.000Z | [
"pytorch",
"xlnet",
"ms",
"transformers"
] | null | false | malay-huggingface | null | malay-huggingface/xlnet-tiny-bahasa-cased | 60 | null | transformers | 5,665 | ---
language: ms
---
# xlnet-tiny-bahasa-cased
Pretrained XLNET tiny language model for Malay.
## Pretraining Corpus
`xlnet-tiny-bahasa-cased` model was pretrained on ~1.4 Billion words. Below is list of data we trained on,
1. [cleaned local texts](https://github.com/huseinzol05/malay-dataset/tree/master/dumping/c... |
nlp-waseda/gpt2-small-japanese-wikipedia | 5fb982276618b4e8bd97c483b041fecdbe245e25 | 2021-12-28T06:31:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"ja",
"dataset:wikipedia",
"transformers",
"license:cc-by-sa-4.0"
] | text-generation | false | nlp-waseda | null | nlp-waseda/gpt2-small-japanese-wikipedia | 60 | 1 | transformers | 5,666 | ---
language:
- ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: "早稲田 大学 で 自然 言語 処理 を"
---
# nlp-waseda/gpt2-small-japanese-wikipedia
This model is Japanese GPT-2 pretrained on Japanese Wikipedia.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstr... |
speechbrain/asr-conformer-transformerlm-ksponspeech | 5928b5da43df4df102c4c2885f74b45707cc291d | 2022-06-25T03:07:00.000Z | [
"kr",
"dataset:ksponspeech",
"arxiv:2106.04624",
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"Conformer",
"pytorch",
"license:apache-2.0"
] | automatic-speech-recognition | false | speechbrain | null | speechbrain/asr-conformer-transformerlm-ksponspeech | 60 | 3 | speechbrain | 5,667 | ---
language: "kr"
thumbnail:
tags:
- automatic-speech-recognition
- CTC
- Attention
- Conformer
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- ksponspeech
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" framebor... |
textattack/distilbert-base-cased-snli | 313497ea96b4d773ac95b665a56a3d5d5ef0d3ca | 2020-07-06T16:37:00.000Z | [
"pytorch",
"distilbert",
"transformers"
] | null | false | textattack | null | textattack/distilbert-base-cased-snli | 60 | null | transformers | 5,668 | ## TextAttack Model Card
This `distilbert-base-cased` model was fine-tuned for sequence classificationusing TextAttack
and the snli dataset loaded using the `nlp` library. The model was fine-tuned
for 3 epochs with a batch size of 256, a learning
rate of 2e-05, and a maximum sequence length of 12... |
vaughnw128/DialoGPT-medium-sexybabeycord | 824ce2be568da70429a376cc6402c70504cc4ebb | 2022-01-27T22:10:31.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | vaughnw128 | null | vaughnw128/DialoGPT-medium-sexybabeycord | 60 | null | transformers | 5,669 | ---
tags:
- conversational
---
We love owen |
inovex/multi2convai-corona-de-bert | 053d067e5a87d90516c8b56ecd2d9eadd6f03454 | 2022-03-01T09:18:20.000Z | [
"pytorch",
"bert",
"text-classification",
"de",
"transformers",
"license:mit"
] | text-classification | false | inovex | null | inovex/multi2convai-corona-de-bert | 60 | 1 | transformers | 5,670 | ---
tags:
- text-classification
- pytorch
- transformers
widget:
- text: "Muss ich eine Maske tragen?"
license: mit
language: de
---
# Multi2ConvAI-Corona: finetuned Bert for German
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Corona (more details about our u... |
allenai/aspire-biencoder-compsci-spec | 00f38c1584ec14f372c22acc4b6e9b11efb35d77 | 2022-04-24T19:39:24.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2111.08366",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | allenai | null | allenai/aspire-biencoder-compsci-spec | 60 | null | transformers | 5,671 | ---
license: apache-2.0
---
## Overview
Model included in a paper for modeling fine grained similarity between documents:
**Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
**Authors**: Sheshera Mysore, Arman Cohan, Tom Hope
**Paper**: https://arxiv.org/abs/2111.... |
smeoni/nbme-deberta-large | 418cca0a10d998784b929c867f5f44352b9c6062 | 2022-04-23T18:29:48.000Z | [
"pytorch",
"tensorboard",
"deberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | fill-mask | false | smeoni | null | smeoni/nbme-deberta-large | 60 | null | transformers | 5,672 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: nbme-deberta-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nbme-deberta-large
Thi... |
allenai/mtk-instruct-11b-def-pos | ff993b4a27cc7cea557ecf3ccbebc96711b6f97c | 2022-05-27T22:20:08.000Z | [
"pytorch",
"t5",
"text2text-generation",
"multilingual",
"dataset:natural instructions v2.0",
"arxiv:1910.10683",
"arxiv:2204.07705",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/mtk-instruct-11b-def-pos | 60 | 1 | transformers | 5,673 | ---
language: multilingual
license: apache-2.0
datasets:
- natural instructions v2.0
---
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, e... |
RogerKam/roberta_fine_tuned_sentiment_newsmtsc | a34ebe62615db87dedf1fd7f24d881ac45e12fc8 | 2022-06-09T14:27:18.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | RogerKam | null | RogerKam/roberta_fine_tuned_sentiment_newsmtsc | 60 | null | transformers | 5,674 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_fine_tuned_sentiment_newsmtsc
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 ... |
plncmm/beto-clinical-wl-es | 05f09fd37d7d25dbb3321507b57057769929c646 | 2022-06-07T23:06:51.000Z | [
"pytorch",
"bert",
"fill-mask",
"es",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | plncmm | null | plncmm/beto-clinical-wl-es | 60 | null | transformers | 5,675 | ---
language:
- es
widget:
- text: "Periodontitis [MASK] generalizada severa."
- text: "Caries dentinaria [MASK]."
- text: "Movilidad aumentada en pza [MASK]."
- text: "Pcte con dm en tto con [MASK]."
- text: "Pcte con erc en tto con [MASK]."
tags:
- generated_from_trainer
model-index:
- name: bio-bert-base-spanish-wwm... |
respect5716/koenbert-base | b92f1c3b06024a5c280886766c69560e33c98616 | 2022-07-17T04:52:33.000Z | [
"pytorch",
"bert",
"feature-extraction",
"ko",
"transformers"
] | feature-extraction | false | respect5716 | null | respect5716/koenbert-base | 60 | null | transformers | 5,676 | ---
language: ko
---
# koenbert-base
최근 다양한 한국어 언어 모델들이 개발 및 공유되고 있습니다. 하지만 이러한 모델들은 한국어만 지원하기 때문에 Dialog system, Information retrieval 등 다양한 도메인에서 제작되는 영어 데이터를 활용하기 어렵다는 한계점이 있습니다. Multilingual 모델의 경우 지원하는 언어의 수가 많아 모델 크기가 크고 한국어 성능이 떨어진다는 단점이 있습니다. 이러한 한계점을 해소하고 한국어 모델의 활용도를 높이기 위해 한국어 언어 모델에 영어를 학습하는 프로젝트를 진행하고 있습니... |
ij5/kobart | 8e4c5ae44bbe6a155c6d898874300a6c1eb58ffc | 2022-07-19T11:54:49.000Z | [
"pytorch",
"bart",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | ij5 | null | ij5/kobart | 60 | null | transformers | 5,677 | ---
license: mit
---
|
SIMAS-UN/blaming_geopolitics | 8821e15051d319514d66e2a7650c6dc1a2f0257a | 2022-07-24T04:02:06.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | SIMAS-UN | null | SIMAS-UN/blaming_geopolitics | 60 | null | transformers | 5,678 | Entry not found |
anzorq/ru-kbd_lat-t5-small | 0873e058cf8d5f47a8cda38185cba306e78b8613 | 2022-07-27T10:14:33.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ru",
"kbd",
"dataset:anzorq/kbd_lat-ru",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | anzorq | null | anzorq/ru-kbd_lat-t5-small | 60 | null | transformers | 5,679 | ---
language:
- ru
- kbd
license: mit
tags:
- generated_from_trainer
datasets:
- anzorq/kbd_lat-ru
metrics:
- bleu
model-index:
- name: tst-translation
results:
- task:
name: translation
type: translation
dataset:
name: anzorq/kbd_lat-ru anzorq--kbd-ru
type: anzorq/kbd_lat-ru
args... |
derwahnsinn/gpt2-mediumBIGBANG | dbe9db8f7e319854cac777a0cfcc064d2d382139 | 2022-07-28T03:29:24.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | derwahnsinn | null | derwahnsinn/gpt2-mediumBIGBANG | 60 | null | transformers | 5,680 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-mediumBIGBANG
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-mediumBIGBANG
Thi... |
Forest/gpt2-fanfic | b908209a8b686492239e2ff3773fb2333c8a1477 | 2021-05-21T09:44:04.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Forest | null | Forest/gpt2-fanfic | 59 | null | transformers | 5,681 | Entry not found |
Greg1901/BertSummaDev_summariser | 94f60550472a7d98fd2c369b4ea785adfa0fd1e6 | 2021-07-24T15:23:23.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Greg1901 | null | Greg1901/BertSummaDev_summariser | 59 | null | transformers | 5,682 | Entry not found |
Harveenchadha/vakyansh-wav2vec2-tamil-tam-250 | 0bd7c7d87da18a71b246ce3e543244bdba983e36 | 2021-09-22T07:55:33.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ta",
"arxiv:2107.07402",
"transformers",
"audio",
"speech",
"license:mit",
"model-index"
] | automatic-speech-recognition | false | Harveenchadha | null | Harveenchadha/vakyansh-wav2vec2-tamil-tam-250 | 59 | null | transformers | 5,683 | ---
language: ta
#datasets:
#- Interspeech 2021
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
license: mit
model-index:
- name: Wav2Vec2 Vakyansh Tamil Model by Harveen Chadha
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Com... |
Helsinki-NLP/opus-mt-en-afa | 9ce92c06934cc12f849cc9139a1174f8ef5fde7e | 2021-01-18T08:04:43.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"so",
"ti",
"am",
"he",
"mt",
"ar",
"afa",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-afa | 59 | null | transformers | 5,684 | ---
language:
- en
- so
- ti
- am
- he
- mt
- ar
- afa
tags:
- translation
license: apache-2.0
---
### eng-afa
* source group: English
* target group: Afro-Asiatic languages
* OPUS readme: [eng-afa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-afa/README.md)
* model: transformer
* ... |
Helsinki-NLP/opus-mt-es-he | 4cfa5a4ded55d0cf1160feaa4ccf78c23f3561b5 | 2021-01-18T08:24:50.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"he",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-he | 59 | null | transformers | 5,685 | ---
language:
- es
- he
tags:
- translation
license: apache-2.0
---
### es-he
* source group: Spanish
* target group: Hebrew
* OPUS readme: [spa-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-heb/README.md)
* model: transformer
* source language(s): spa
* target language(s): heb
*... |
Vasanth/bert-base-uncased-qa-squad2 | 6a516b9d7cec1aa88159e125f50de159f693892a | 2022-02-08T13:54:11.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | Vasanth | null | Vasanth/bert-base-uncased-qa-squad2 | 59 | null | transformers | 5,686 | "hello"
|
WikinewsSum/bert2bert-multi-en-wiki-news | b47941ade29374c94cc0f197a545c6a1e616ab89 | 2020-08-11T09:05:49.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | WikinewsSum | null | WikinewsSum/bert2bert-multi-en-wiki-news | 59 | null | transformers | 5,687 | Entry not found |
ainize/gpt2-mcu-script-large | ba9555ee702589283d84b5a0a4dfe009c4c096cb | 2021-05-21T12:03:49.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | ainize | null | ainize/gpt2-mcu-script-large | 59 | 1 | transformers | 5,688 | Entry not found |
flax-community/gpt-code-clippy-125M-1024-f | 5ad3368b5018423a62b5aafe7fff8a20221338a0 | 2021-07-18T03:40:23.000Z | [
"pytorch",
"jax",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | flax-community | null | flax-community/gpt-code-clippy-125M-1024-f | 59 | 1 | transformers | 5,689 | Entry not found |
google/bert_uncased_L-12_H-256_A-4 | 2ab9a0f41435a4d23d4cbc11cc3e7c922545f13d | 2021-05-19T17:26:24.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-12_H-256_A-4 | 59 | null | transformers | 5,690 | ---
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... |
m3hrdadfi/wav2vec2-large-xlsr-persian-shemo | f9aa526bb0408f48543d0359dca089555adefc05 | 2021-07-06T10:48:23.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"fa",
"dataset:shemo",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | m3hrdadfi | null | m3hrdadfi/wav2vec2-large-xlsr-persian-shemo | 59 | 1 | transformers | 5,691 | ---
language: fa
datasets:
- shemo
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
widget:
- label: ShEMO sample 250
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-shemo/resolve/main/sample250.flac
- label: ShEMO sample 52
src: https://huggingface... |
macedonizer/mk-gpt2 | e0038f08ff5b5f0d932e0a6f511e1b3925f832d6 | 2021-09-22T08:58:46.000Z | [
"pytorch",
"gpt2",
"text-generation",
"mk",
"dataset:wiki-mk",
"dataset:time-mk-news-2010-2015",
"transformers",
"license:apache-2.0"
] | text-generation | false | macedonizer | null | macedonizer/mk-gpt2 | 59 | null | transformers | 5,692 | ---
language:
- mk
thumbnail: https://huggingface.co/macedonizer/mk-roberta-base/blaze-koneski.jpg
license: apache-2.0
datasets:
- wiki-mk
- time-mk-news-2010-2015
---
# mk-gpt2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a c... |
microsoft/deberta-xlarge-v2 | 314bc0db16b9890225cd5c531ae7f0dabfc0cc74 | 2021-02-11T02:04:50.000Z | [
"pytorch",
"deberta-v2",
"en",
"transformers",
"deberta",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-xlarge-v2 | 59 | null | transformers | 5,693 | ---
language: en
tags: deberta
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)
|
monologg/koelectra-base-generator | fe6a7147be11ae58af0f78206f558c8e31e8c5c9 | 2021-10-20T16:55:00.000Z | [
"pytorch",
"electra",
"fill-mask",
"ko",
"transformers",
"korean",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | monologg | null | monologg/koelectra-base-generator | 59 | null | transformers | 5,694 | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA (Base Generator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-generator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
... |
monsoon-nlp/muril-adapted-local | 9f79abba6e9e39a2fefc3dc7ecdcb4354f60b1a3 | 2021-05-20T00:11:39.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"hi",
"bn",
"ta",
"as",
"gu",
"kn",
"ks",
"ml",
"mr",
"ne",
"or",
"pa",
"sa",
"sd",
"te",
"ur",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | monsoon-nlp | null | monsoon-nlp/muril-adapted-local | 59 | 2 | transformers | 5,695 | ---
language:
- en
- hi
- bn
- ta
- as
- gu
- kn
- ks
- ml
- mr
- ne
- or
- pa
- sa
- sd
- te
- ur
license: apache-2.0
---
## MuRIL - Unofficial
Multilingual Representations for Indian Languages : Google open sourced
this BERT model pre-trained on 17 Indian languages, and their transliterated
counterparts.
The model... |
pearsonkyle/gpt2-exomachina | f4d75d137497bebdd3984a26418de58b1c324218 | 2021-05-23T10:57:32.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | pearsonkyle | null | pearsonkyle/gpt2-exomachina | 59 | null | transformers | 5,696 | # Exo-Machina
A deep language model, GPT-2, is trained on scientific manuscripts from NASA's Astrophysical Data System pertaining to extrasolar planets and the references therein. This pilot study uses the abstracts of each article as training data in order to explore correlations in scientific literature from a langu... |
stanlochten/t5-KGQgen | a9132bf5135433431a8e014afa1572c59a29d209 | 2021-07-09T15:53:30.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | stanlochten | null | stanlochten/t5-KGQgen | 59 | 1 | transformers | 5,697 | T5-base model fine-tuned for question generation from knowledge graphs. Can be used to generate questions from linearized knowledge graphs, meaning graphs in the form of its all its triples listed in the following format:
`<A> answer node(s) <H> head <R> relation <T> tail <H> head <R> relation <T> tail ... etc ...`,
w... |
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_test_set_05_03_2022-05_51_01 | a37fd7d061d8395c51026c577233da84358d404b | 2022-03-05T04:53:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ali2066 | null | ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_webDiscourse_TEST_test_set_05_03_2022-05_51_01 | 59 | null | transformers | 5,698 | Entry not found |
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_essays_TEST_test_set_05_03_2022-05_58_31 | fd11a9a77cd60ff90bb7f040908262a63f58bb46 | 2022-03-05T05:00:58.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ali2066 | null | ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_essays_TEST_test_set_05_03_2022-05_58_31 | 59 | null | transformers | 5,699 | Entry not found |
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