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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sileod/roberta-base-mnli | 86d5eb9545d2276806ce7290e670134a65e95e84 | 2022-05-31T10:08:10.000Z | [
"pytorch",
"roberta",
"text-classification",
"dataset:multi_nli",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | sileod | null | sileod/roberta-base-mnli | 516 | 1 | transformers | 2,300 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- multi_nli
metrics:
- accuracy
model-index:
- name: roberta-base-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: multi_nli
type: multi_nli
args: default
metrics:
- name: Accu... |
nvidia/mit-b2 | 44acc700d01cdfdac6f5c236e69da847985eaac3 | 2022-07-29T13:15:51.000Z | [
"pytorch",
"tf",
"segformer",
"image-classification",
"dataset:imagenet_1k",
"arxiv:2105.15203",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | nvidia | null | nvidia/mit-b2 | 515 | null | transformers | 2,301 | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet_1k
widget:
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
example_title: House
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg
exampl... |
prajjwal1/bert-mini-mnli | 2793a188a2d6f995f9e6a5f73d9dd8b7a3a3aaa6 | 2021-10-05T17:57:20.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1908.08962",
"arxiv:2110.01518",
"transformers"
] | text-classification | false | prajjwal1 | null | prajjwal1/bert-mini-mnli | 515 | null | transformers | 2,302 | The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](... |
Biasface/DDDC | 4481ffe566e96900e4b4e4df6ebc815524295bbf | 2021-11-30T17:30:53.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Biasface | null | Biasface/DDDC | 513 | null | transformers | 2,303 | ---
tags:
- conversational
---
#hi |
studio-ousia/mluke-base | 0f3c9dc42873eaf0e807bd2736bc4cfbe73de3b2 | 2022-03-11T02:58:43.000Z | [
"pytorch",
"luke",
"fill-mask",
"multilingual",
"transformers",
"named entity recognition",
"relation classification",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | studio-ousia | null | studio-ousia/mluke-base | 513 | 3 | transformers | 2,304 | ---
language: multilingual
thumbnail: https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png
tags:
- luke
- named entity recognition
- relation classification
- question answering
license: apache-2.0
---
## mLUKE
**mLUKE** (multilingual LUKE) is a multilingual extension of LUKE.
Please check... |
DeepPavlov/distilrubert-small-cased-conversational | e348066b4a7279b97138038299bddc6580a9169a | 2022-06-28T17:19:09.000Z | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | false | DeepPavlov | null | DeepPavlov/distilrubert-small-cased-conversational | 513 | null | transformers | 2,305 | ---
language:
- ru
---
# distilrubert-small-cased-conversational
Conversational DistilRuBERT-small \(Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversatio... |
tner/xlm-roberta-large-uncased-wnut2017 | d2f13491ebb59b477fa61dc0224d88daf851513f | 2021-02-13T00:12:33.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/xlm-roberta-large-uncased-wnut2017 | 512 | null | transformers | 2,306 | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-wnut2017")
mo... |
huggingface/distilbert-base-uncased-finetuned-mnli | 0fadb1fe60cd119b3af82e2bf9cb98a59336d7bc | 2021-02-25T20:27:07.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | huggingface | null | huggingface/distilbert-base-uncased-finetuned-mnli | 512 | null | transformers | 2,307 | Entry not found |
SIKU-BERT/sikuroberta | bb25260d5c321924fe4fb353c09191c0aaf5c5c6 | 2021-09-22T00:22:36.000Z | [
"pytorch",
"bert",
"fill-mask",
"zh",
"transformers",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"roberta",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | SIKU-BERT | null | SIKU-BERT/sikuroberta | 511 | 2 | transformers | 2,308 | ---
language:
- "zh"
thumbnail: "https://raw.githubusercontent.com/SIKU-BERT/SikuBERT/main/appendix/sikubert.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "roberta"
- "pytorch"
inference: false
license: "apache-2.0"
---
# SikuBERT
## Model description
![SikuBERT](htt... |
Helsinki-NLP/opus-mt-ru-fr | 55c73236818495c7a6dd5a98e3529de3481bc3ae | 2021-09-10T14:02:31.000Z | [
"pytorch",
"jax",
"marian",
"text2text-generation",
"ru",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ru-fr | 510 | null | transformers | 2,309 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ru-fr
* source languages: ru
* target languages: fr
* OPUS readme: [ru-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ru-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
KoichiYasuoka/chinese-roberta-base-upos | 2fcc4e89732370e30451b65e5a7227c78811f0d4 | 2022-02-11T06:28:59.000Z | [
"pytorch",
"bert",
"token-classification",
"zh",
"dataset:universal_dependencies",
"transformers",
"chinese",
"pos",
"wikipedia",
"dependency-parsing",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | KoichiYasuoka | null | KoichiYasuoka/chinese-roberta-base-upos | 510 | 2 | transformers | 2,310 | ---
language:
- "zh"
tags:
- "chinese"
- "token-classification"
- "pos"
- "wikipedia"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "token-classification"
---
# chinese-roberta-base-upos
## Model Description
This is a BERT model pre-trained on Chinese Wikipedia texts... |
anton-l/wav2vec2-base-superb-sv | 0a1a74d00d5e44dbd7344b65c9847a1eb625c73b | 2021-12-14T12:49:10.000Z | [
"pytorch",
"wav2vec2",
"audio-xvector",
"transformers"
] | null | false | anton-l | null | anton-l/wav2vec2-base-superb-sv | 510 | null | transformers | 2,311 | Entry not found |
Davlan/bert-base-multilingual-cased-finetuned-yoruba | 000f80b4509f73bca9a33f9db0573d6f67396a12 | 2022-06-27T11:50:30.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"yo",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Davlan | null | Davlan/bert-base-multilingual-cased-finetuned-yoruba | 509 | null | transformers | 2,312 | Hugging Face's logo
---
language: yo
datasets:
---
# bert-base-multilingual-cased-finetuned-yoruba
## Model description
**bert-base-multilingual-cased-finetuned-yoruba** is a **Yoruba BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Yorùbá language texts. It provides **better performance... |
facebook/wav2vec2-xls-r-1b | 6d8fad78d7d9c252adfdf48da029590b21f47414 | 2021-11-18T16:32:35.000Z | [
"pytorch",
"wav2vec2",
"pretraining",
"multilingual",
"dataset:common_voice",
"dataset:multilingual_librispeech",
"arxiv:2111.09296",
"transformers",
"speech",
"xls_r",
"xls_r_pretrained",
"license:apache-2.0"
] | null | false | facebook | null | facebook/wav2vec2-xls-r-1b | 509 | 10 | transformers | 2,313 | ---
language: multilingual
datasets:
- common_voice
- multilingual_librispeech
tags:
- speech
- xls_r
- xls_r_pretrained
license: apache-2.0
---
# Wav2Vec2-XLS-R-1B
[Facebook's Wav2Vec2 XLS-R](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) counting **1 billion** parameters.
!... |
oliverguhr/spelling-correction-english-base | a30d76e2e7de0b0b350304c8e17cef99da8eb8e7 | 2022-06-13T12:09:01.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"en",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | oliverguhr | null | oliverguhr/spelling-correction-english-base | 509 | 2 | transformers | 2,314 | ---
language:
- en
license: mit
widget:
- text: "lets do a comparsion"
example_title: "1"
- text: "Their going to be here so0n"
example_title: "2"
- text: "ze shop is cloed due to covid 19"
example_title: "3"
metrics:
- cer
---
This is an experimental model that should fix your typos and punctuation.
If you like... |
SEBIS/code_trans_t5_base_code_documentation_generation_python | f42aaecddfc35f12575e9c887ee79cf3d6cdb97d | 2021-06-23T04:43:22.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_base_code_documentation_generation_python | 508 | null | transformers | 2,315 | ---
tags:
- summarization
widget:
- text: "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"
---
# CodeTrans model for code documentation generation python
Pretrained model on programming language python using the t5 base model architect... |
indobenchmark/indobert-large-p2 | 4b280c3bfcc1ed2d6b4589be5c876076b7d73568 | 2021-05-19T20:28:22.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"transformers",
"indobert",
"indobenchmark",
"indonlu",
"license:mit"
] | feature-extraction | false | indobenchmark | null | indobenchmark/indobert-large-p2 | 508 | null | transformers | 2,316 | ---
language: id
tags:
- indobert
- indobenchmark
- indonlu
license: mit
inference: false
datasets:
- Indo4B
---
# IndoBERT Large Model (phase2 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a m... |
kamalkraj/bioelectra-base-discriminator-pubmed-pmc-lt | d807405696fdace62f42841dc06289d2354e1158 | 2021-06-10T14:22:08.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | kamalkraj | null | kamalkraj/bioelectra-base-discriminator-pubmed-pmc-lt | 508 | 2 | transformers | 2,317 | ## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model that ada... |
facebook/regnet-y-040 | 40577f588ce4b8b3a306e59b93b117047e0a6625 | 2022-06-30T18:56:14.000Z | [
"pytorch",
"tf",
"regnet",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/regnet-y-040 | 508 | null | transformers | 2,318 | ---
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: htt... |
cross-encoder/quora-roberta-base | 195493c8767e7155c449e9ff7e64890d116d432d | 2021-08-05T08:41:36.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/quora-roberta-base | 507 | 1 | transformers | 2,319 | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questi... |
valurank/distilroberta-news-small | dad826d1ce6732850428d4673ff50835c8f7f59b | 2022-06-08T20:45:50.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:valurank/news-small",
"transformers",
"license:other"
] | text-classification | false | valurank | null | valurank/distilroberta-news-small | 507 | null | transformers | 2,320 | ---
license: other
language: en
datasets:
- valurank/news-small
---
# DistilROBERTA fine-tuned for news classification
This model is based on [distilroberta-base](https://huggingface.co/distilroberta-base) pretrained weights, with a classification head fine-tuned to classify news articles into 3 categories (bad, medi... |
Milos/slovak-gpt-j-1.4B | 1ca9a664fba18d050377579e43b92897efca62d4 | 2022-02-17T14:29:47.000Z | [
"pytorch",
"gptj",
"text-generation",
"sk",
"arxiv:2104.09864",
"transformers",
"Slovak GPT-J",
"causal-lm",
"license:gpl-3.0"
] | text-generation | false | Milos | null | Milos/slovak-gpt-j-1.4B | 506 | null | transformers | 2,321 | ---
language:
- sk
tags:
- Slovak GPT-J
- pytorch
- causal-lm
license: gpl-3.0
---
# Slovak GPT-J-1.4B
Slovak GPT-J-1.4B with the whopping `1,415,283,792` parameters is the latest and the largest model released in Slovak GPT-J series. Smaller variants, [Slovak GPT-J-405M](https://huggingface.co/Milos/slovak-gpt-j-405M... |
SIKU-BERT/sikubert | fc656de2d6bde33919102dd3abe31c843f42226a | 2021-09-13T13:34:40.000Z | [
"pytorch",
"bert",
"fill-mask",
"zh",
"transformers",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"roberta",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | SIKU-BERT | null | SIKU-BERT/sikubert | 506 | 2 | transformers | 2,322 | ---
language:
- "zh"
thumbnail: "https://raw.githubusercontent.com/SIKU-BERT/SikuBERT/main/appendix/sikubert.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "roberta"
- "pytorch"
inference: false
license: "apache-2.0"
---
# SikuBERT
## Model description
![SikuBERT](htt... |
google/owlvit-base-patch32 | 4641e344cbbe8e25e0f2ab4e7e53372091ef9cfd | 2022-07-21T10:49:01.000Z | [
"pytorch",
"owlvit",
"transformers"
] | null | false | google | null | google/owlvit-base-patch32 | 506 | null | transformers | 2,323 | Entry not found |
boychaboy/SNLI_distilbert-base-cased | fabefe1f7390d5aecf5d152e13da5998eee2e84d | 2021-05-10T17:08:47.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | boychaboy | null | boychaboy/SNLI_distilbert-base-cased | 505 | null | transformers | 2,324 | Entry not found |
microsoft/unispeech-sat-base-plus-sv | a492b4bf41b1bd2fa6e6d07c6eae573b3f711b66 | 2021-12-17T13:56:17.000Z | [
"pytorch",
"unispeech-sat",
"audio-xvector",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.05752",
"transformers",
"speech"
] | null | false | microsoft | null | microsoft/unispeech-sat-base-plus-sv | 505 | null | transformers | 2,325 | ---
language:
- en
tags:
- speech
---
# UniSpeech-SAT-Base for Speaker Verification
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled speech audio with utterance and... |
nkoh01/MSRoberta | 3ff20e811ea95572470d3538cad29e816f05d7f4 | 2021-05-20T18:51:20.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nkoh01 | null | nkoh01/MSRoberta | 505 | null | transformers | 2,326 | # MSRoBERTa
Fine-tuned RoBERTa MLM model for [`Miscrosoft Sentence Completion Challenge`](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR_SCCD.pdf). This model case-sensitive following the `Roberta-base` model.
# Model description (taken from: [here](https://huggingface.co/roberta-base))
RoBE... |
fmikaelian/camembert-base-fquad | 341bf4683d9388a0a4022ce4062283255dc9246c | 2020-12-11T21:40:08.000Z | [
"pytorch",
"camembert",
"question-answering",
"fr",
"transformers",
"autotrain_compatible"
] | question-answering | false | fmikaelian | null | fmikaelian/camembert-base-fquad | 504 | 1 | transformers | 2,327 | ---
language: fr
---
# camembert-base-fquad
## Description
A baseline model for question-answering in french ([CamemBERT](https://camembert-model.fr/) model fine-tuned on [FQuAD](https://fquad.illuin.tech/))
## Training hyperparameters
```shell
python3 ./examples/question-answering/run_squad.py \
--m... |
lanwuwei/GigaBERT-v3-Arabic-and-English | ee5c781756946364d989e0102b91b4a15390f6ac | 2021-05-19T00:17:42.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"ar",
"dataset:gigaword",
"dataset:oscar",
"dataset:wikipedia",
"transformers"
] | feature-extraction | false | lanwuwei | null | lanwuwei/GigaBERT-v3-Arabic-and-English | 504 | null | transformers | 2,328 | ---
language:
- en
- ar
datasets:
- gigaword
- oscar
- wikipedia
---
## GigaBERT-v3
GigaBERT-v3 is a customized bilingual BERT for English and Arabic. It was pre-trained in a large-scale corpus (Gigaword+Oscar+Wikipedia) with ~10B tokens, showing state-of-the-art zero-shot transfer performance from English to Arabic o... |
orai-nlp/ElhBERTeu | 8d4de0a5d8c49f260010d5ea239afe77de31cfe2 | 2022-07-06T10:21:53.000Z | [
"pytorch",
"bert",
"feature-extraction",
"eu",
"transformers",
"basque",
"euskara",
"license:cc-by-4.0"
] | feature-extraction | false | orai-nlp | null | orai-nlp/ElhBERTeu | 502 | 0 | transformers | 2,329 | ---
license: cc-by-4.0
language: eu
tags:
- bert
- basque
- euskara
---
# ElhBERTeu
This is a BERT model for Basque introduced in [BasqueGLUE: A Natural Language Understanding Benchmark for Basque]().
To train ElhBERTeu, we collected different corpora sources from several domains: updated (2021) national and local ... |
dhtocks/Named-Entity-Recognition | c9eb2cb284b0b69709132d19eeac3816ceb89c5b | 2022-01-15T11:22:33.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | dhtocks | null | dhtocks/Named-Entity-Recognition | 500 | null | transformers | 2,330 | Entry not found |
anas-awadalla/splinter-large-finetuned-squad | 36015d000da8055edcfbbf0a14c6f5d31a2e837c | 2022-05-15T10:51:43.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-finetuned-squad | 500 | null | transformers | 2,331 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-finetuned-squad
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 co... |
STAM/agricore | b6dfd05bfdcb097a78e563599517f8441452b404 | 2022-06-01T14:24:16.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | STAM | null | STAM/agricore | 500 | null | transformers | 2,332 | ---
license: mit
---
|
TofuBoy/DialoGPT-medium-boon | cd59807e12d63621addb6c915273fe8621ba6145 | 2022-01-23T05:46:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | TofuBoy | null | TofuBoy/DialoGPT-medium-boon | 499 | null | transformers | 2,333 | ---
tags:
- conversational
---
# Boon Bot DialoGPT Model |
Recognai/zeroshot_selectra_medium | 6c3ff31c3c1acb96375d7913f90a19707af33b9a | 2022-03-27T09:30:04.000Z | [
"pytorch",
"electra",
"text-classification",
"es",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"nli",
"license:apache-2.0"
] | zero-shot-classification | false | Recognai | null | Recognai/zeroshot_selectra_medium | 498 | 3 | transformers | 2,334 | ---
language: es
tags:
- zero-shot-classification
- nli
- pytorch
datasets:
- xnli
pipeline_tag: zero-shot-classification
license: apache-2.0
widget:
- text: "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo"
candidate_labels: "cultura, sociedad, economia, salud, deportes"
---
# Z... |
castorini/doc2query-t5-large-msmarco | e607227b4d07161391f3a61a7ccd9efcf875ea14 | 2021-11-24T19:16:08.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | castorini | null | castorini/doc2query-t5-large-msmarco | 497 | null | transformers | 2,335 | For more information, check [doc2query.ai](http://doc2query.ai) |
j-hartmann/purchase-intention-english-roberta-large | e26a7d11ced410a78f1fe9a710e61ca14a2a0014 | 2022-02-06T12:22:55.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"transformers",
"sentiment",
"twitter"
] | text-classification | false | j-hartmann | null | j-hartmann/purchase-intention-english-roberta-large | 497 | 1 | transformers | 2,336 | ---
language: "en"
tags:
- roberta
- sentiment
- twitter
widget:
- text: "This looks tasty. Where can I buy it??"
- text: "Now I want this, too."
- text: "You look great today!"
- text: "I just love spring and sunshine!"
---
This RoBERTa-based model can classify *expressed purchase intentions* in English language te... |
naver/efficient-splade-V-large-query | eb23fdf72c344e26d37d63a86cf536b3a6e11118 | 2022-07-08T13:12:08.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:ms_marco",
"transformers",
"splade",
"query-expansion",
"document-expansion",
"bag-of-words",
"passage-retrieval",
"knowledge-distillation",
"document encoder",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | naver | null | naver/efficient-splade-V-large-query | 497 | null | transformers | 2,337 | ---
license: cc-by-nc-sa-4.0
language: "en"
tags:
- splade
- query-expansion
- document-expansion
- bag-of-words
- passage-retrieval
- knowledge-distillation
- document encoder
datasets:
- ms_marco
---
## Efficient SPLADE
Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for quer... |
canwenxu/BERT-of-Theseus-MNLI | ee82a9e7c3fec19661f93a2291295ea62e8acee1 | 2021-05-19T13:58:30.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"dataset:multi_nli",
"arxiv:2002.02925",
"arxiv:2005.00628",
"transformers"
] | feature-extraction | false | canwenxu | null | canwenxu/BERT-of-Theseus-MNLI | 496 | null | transformers | 2,338 | ---
thumbnail: https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png
datasets:
- multi_nli
---
# BERT-of-Theseus
See our paper ["BERT-of-Theseus: Compressing BERT by Progressive Module Replacing"](http://arxiv.org/abs/2002.02925).
BERT-of-Theseus is a new compressed BERT by progressiv... |
readerbench/RoBERT-base | 42fa3f7ca1731b66401081554a36ef072279402a | 2021-05-20T04:05:43.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"ro",
"transformers"
] | null | false | readerbench | null | readerbench/RoBERT-base | 496 | null | transformers | 2,339 | Model card for RoBERT-base
---
language:
- ro
---
# RoBERT-base
## Pretrained BERT model for Romanian
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this [paper](https://www.aclweb.org/anthology/2020.coling-main.581... |
GroNLP/gpt2-small-dutch | a4d770e17c7b3b2aa3ff29c6e52c7c8284974fb9 | 2021-05-21T09:55:47.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"nl",
"arxiv:2012.05628",
"transformers",
"adaption",
"recycled",
"gpt2-small"
] | text-generation | false | GroNLP | null | GroNLP/gpt2-small-dutch | 495 | null | transformers | 2,340 | ---
language: nl
tags:
- adaption
- recycled
- gpt2-small
pipeline_tag: text-generation
---
# GPT-2 recycled for Dutch (small)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This mod... |
textattack/roberta-base-MRPC | c8e94968c57c5d825bf0476261d3fb0602c1e0ac | 2021-05-20T22:07:47.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/roberta-base-MRPC | 495 | null | transformers | 2,341 | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 256.
Since this was a classifi... |
thanathorn/mt5-cpe-kmutt-thai-sentence-sum | cc479312c558c62618d794a961d994be2a12d0fc | 2022-05-13T18:20:03.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"th",
"transformers",
"summarization",
"mT5",
"autotrain_compatible"
] | summarization | false | thanathorn | null | thanathorn/mt5-cpe-kmutt-thai-sentence-sum | 495 | 1 | transformers | 2,342 | ---
tags:
- summarization
- mT5
language:
- th
widget:
- text: "simplify: ถ้าพูดถึงขนมหวานในตำนานที่ชื่นใจที่สุดแล้วละก็ต้องไม่พ้น น้ำแข็งใส แน่เพราะว่าเป็นอะไรที่ชื่นใจสุด"
---
# mt5-cpe-kmutt-thai-sentence-sum
This repository contains the finetuned mT5-base model for Thai sentence summarization. The architecture of... |
Harshveer/autonlp-formality_scoring_2-32597818 | 0683aa8fe9feb6b9824e38a256f6258aaaf79f34 | 2021-11-14T06:46:39.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:Harshveer/autonlp-data-formality_scoring_2",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | false | Harshveer | null | Harshveer/autonlp-formality_scoring_2-32597818 | 494 | null | transformers | 2,343 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Harshveer/autonlp-data-formality_scoring_2
co2_eq_emissions: 8.655894631203154
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 32597818
- CO2 Emissions (in grams): 8.655894631203154
## Validation Met... |
bayartsogt/albert-mongolian | 33be497e1f7f561b0b1d58880d523be723830771 | 2021-03-17T19:01:07.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"mn",
"arxiv:1904.00962",
"transformers",
"autotrain_compatible"
] | fill-mask | false | bayartsogt | null | bayartsogt/albert-mongolian | 494 | 2 | transformers | 2,344 | ---
language: mn
---
# ALBERT-Mongolian
[pretraining repo link](https://github.com/bayartsogt-ya/albert-mongolian)
## Model description
Here we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Training data is the Mongolian wikipedia corpus from Wikipedia Downloads and Mongolian News ... |
bigjoedata/rockbot355M | c43da88f2a0221ca19bdc99d81cbcc05d65474eb | 2021-05-21T14:17:25.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | bigjoedata | null | bigjoedata/rockbot355M | 494 | null | transformers | 2,345 |
# 🎸 🥁 Rockbot 🎤 🎧
A [GPT-2](https://openai.com/blog/better-language-models/) based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).
**Instructions:** Type in a fake song title, pick an artist, click "Generate".
Most language models are imprecise... |
superb/wav2vec2-base-superb-sid | 73365f1ed139a3d88fb8a72b98ecac3a38a1fa0e | 2021-11-04T16:03:40.000Z | [
"pytorch",
"wav2vec2",
"audio-classification",
"en",
"dataset:superb",
"arxiv:2105.01051",
"transformers",
"speech",
"audio",
"license:apache-2.0"
] | audio-classification | false | superb | null | superb/wav2vec2-base-superb-sid | 494 | null | transformers | 2,346 | ---
language: en
datasets:
- superb
tags:
- speech
- audio
- wav2vec2
- audio-classification
widget:
- example_title: VoxCeleb Speaker id10003
src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav
- example_title: VoxCeleb Speaker id10004
src: https://cdn-media.huggingface.co/speech_samples/VoxCe... |
crystallyzing/DialoGPT-small-nishikiyama | e2268eaff68c5ac9dc1e475d7b3362f22c5f67ff | 2022-06-21T00:05:00.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | crystallyzing | null | crystallyzing/DialoGPT-small-nishikiyama | 494 | null | transformers | 2,347 | ---
tags:
- conversational
---
# Nishiki Chatbot Model |
Norod78/hebrew-gpt_neo-tiny | 61d3dddbbf95e3096e6a4249dc5d7fe396de529a | 2022-07-04T07:27:46.000Z | [
"pytorch",
"jax",
"gpt_neo",
"text-generation",
"he",
"transformers",
"license:mit"
] | text-generation | false | Norod78 | null | Norod78/hebrew-gpt_neo-tiny | 493 | null | transformers | 2,348 | ---
language: he
thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg
widget:
- text: "עוד בימי קדם"
- text: "קוראים לי דורון ואני מעוניין ל"
- text: "קוראים לי איציק ואני חושב ש"
- text: "החתול שלך מאוד חמוד ו"
license: mit
---
# hebrew-gpt_neo-tiny
Hebrew text generation model based on [EleutherA... |
asafaya/bert-large-arabic | 980a2eb3a4b8b3eb156b82ae30cc9768ef3794de | 2021-05-19T00:07:46.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"dataset:oscar",
"dataset:wikipedia",
"transformers",
"autotrain_compatible"
] | fill-mask | false | asafaya | null | asafaya/bert-large-arabic | 492 | null | transformers | 2,349 | ---
language: ar
datasets:
- oscar
- wikipedia
---
# Arabic BERT Large Model
Pretrained BERT Large language model for Arabic
_If you use this model in your work, please cite this paper:_
```
@inproceedings{safaya-etal-2020-kuisail,
title = "{KUISAIL} at {S}em{E}val-2020 Task 12: {BERT}-{CNN} for Offensive Spe... |
facebook/hubert-xlarge-ls960-ft | 8b565fd5c194610f72ff01f4fecf7ccde17f9638 | 2022-05-24T10:44:12.000Z | [
"pytorch",
"tf",
"hubert",
"automatic-speech-recognition",
"en",
"dataset:libri-light",
"dataset:librispeech_asr",
"arxiv:2106.07447",
"transformers",
"speech",
"audio",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | facebook | null | facebook/hubert-xlarge-ls960-ft | 492 | 7 | transformers | 2,350 | ---
language: en
datasets:
- libri-light
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: hubert-large-ls960-ft
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
n... |
DMetaSoul/sbert-chinese-general-v2-distill | 7f91a6d64ffa5a0031587f9738dd603219abf8c3 | 2022-04-02T09:58:33.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese"
] | sentence-similarity | false | DMetaSoul | null | DMetaSoul/sbert-chinese-general-v2-distill | 492 | null | sentence-transformers | 2,351 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-general-v2-distill
此模型是之前[开源通用语义匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2)的蒸馏版本（仅4层 BERT），适用于**通用语义匹配**场景，从效果来看该... |
tscholak/1wnr382e | 44847d47b5b59789aadc86c7f88d2574cf1f284c | 2022-01-10T21:50:25.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:spider",
"arxiv:2109.05093",
"transformers",
"text2sql",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | tscholak | null | tscholak/1wnr382e | 490 | null | transformers | 2,352 | ---
language:
- en
thumbnail: "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab"
tags:
- text2sql
widget:
- "How many singers do we have? | concert_singer | stadium : stadium_id, location, name, capacity, highest, lowest, average | singer : singer_id, name, country, song... |
codeparrot/codeparrot-small-multi | 7753edbe82562bf23c6ff15ad46ce6f0f2307139 | 2022-07-15T10:56:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"code",
"dataset:codeparrot/github-code-clean",
"dataset:openai_humaneval",
"transformers",
"generation",
"license:apache-2.0"
] | text-generation | false | codeparrot | null | codeparrot/codeparrot-small-multi | 490 | null | transformers | 2,353 | ---
language:
- code
license: apache-2.0
tags:
- code
- gpt2
- generation
datasets:
- "codeparrot/github-code-clean"
- "openai_humaneval"
metrics:
- "evaluate-metric/code_eval"
---
# CodeParrot-Multi 🦜 (small)
CodeParrot-Multi 🦜 is a GPT-2 model (110M parameters) trained to generate code in 9 programming languag... |
HooshvareLab/gpt2-fa | 9c1fa5edb93f30ca93df0d1f1abcc44bcc73e5d1 | 2021-05-21T10:51:23.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fa",
"transformers",
"license:apache-2.0"
] | text-generation | false | HooshvareLab | null | HooshvareLab/gpt2-fa | 489 | null | transformers | 2,354 | ---
language: fa
license: apache-2.0
widget:
- text: "در یک اتفاق شگفت انگیز، پژوهشگران"
- text: "گرفتگی بینی در کودکان و بهخصوص نوزادان باعث میشود"
- text: "امیدواریم نوروز امسال سالی"
---
# ParsGPT2
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ParsGPT2,
... |
IMSyPP/hate_speech_en | ffe54334b9df65e704492d2d660610dd848658d6 | 2022-05-16T06:13:38.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"license:mit"
] | text-classification | false | IMSyPP | null | IMSyPP/hate_speech_en | 489 | 1 | transformers | 2,355 | ---
widget:
- text: "My name is Mark and I live in London. I am a postgraduate student at Queen Mary University."
language:
- en
license: mit
---
# Hate Speech Classifier for Social Media Content in English Language
A monolingual model for hate speech classification of social media content in English language. Th... |
dbmdz/bert-base-german-europeana-uncased | f703f5a27791d5c8e083eab510563083fb7ed18d | 2021-05-19T14:55:07.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"de",
"transformers",
"historic german",
"license:mit"
] | null | false | dbmdz | null | dbmdz/bert-base-german-europeana-uncased | 489 | null | transformers | 2,356 | ---
language: de
license: mit
tags:
- "historic german"
---
# 🤗 + 📚 dbmdz BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources German Europeana BERT models 🎉
# German Europeana BERT
We use the open source [Europeana newspapers](http://www.europeana-news... |
naver-clova-ocr/bros-large-uncased | a644113dc6c2b6dd53f99f94feb7ed4a5e3fdf71 | 2022-04-05T13:57:07.000Z | [
"pytorch",
"bros",
"arxiv:2108.04539",
"transformers"
] | null | false | naver-clova-ocr | null | naver-clova-ocr/bros-large-uncased | 489 | 1 | transformers | 2,357 | # BROS
GitHub: https://github.com/clovaai/bros
## Introduction
BROS (BERT Relying On Spatiality) is a pre-trained language model focusing on text and layout for better key information extraction from documents.<br>
Given the OCR results of the document image, which are text and bounding box pairs, it can perform var... |
oigele/Fb_improved_zeroshot | d68aaffe80f68f2a820944c59a92b2e285741725 | 2021-11-29T11:51:49.000Z | [
"pytorch",
"bart",
"text-classification",
"dataset:multi_nli",
"arxiv:1909.00161",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | false | oigele | null | oigele/Fb_improved_zeroshot | 488 | 4 | transformers | 2,358 | ---
pipeline_tag: zero-shot-classification
datasets:
- multi_nli
widget:
- text: "natural language processing"
candidate_labels: "Location & Address, Employment, Organizational, Name, Service, Studies, Science"
hypothesis_template: "This is {}."
---
# Fb_improved_zeroshot
Zero-Shot Model designed to classify aca... |
imxly/sentence_roberta_wwm_ext | 28b1082b623326456cdec17ee4b521e21e823434 | 2021-05-19T20:20:32.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | imxly | null | imxly/sentence_roberta_wwm_ext | 487 | null | transformers | 2,359 | Entry not found |
KBLab/bert-base-swedish-cased-pos | eae7acf6c32812794b8edd93a944c6b1bd1e402a | 2021-05-18T21:20:59.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | KBLab | null | KBLab/bert-base-swedish-cased-pos | 486 | 2 | transformers | 2,360 | Entry not found |
lirondos/anglicisms-spanish-mbert | 11e819e8161f1162b2b09d253dde4a927a9dc3e0 | 2022-05-16T14:03:29.000Z | [
"pytorch",
"bert",
"token-classification",
"es",
"dataset:coalas",
"transformers",
"anglicisms",
"loanwords",
"borrowing",
"codeswitching",
"arxiv:2203.16169",
"license:cc-by-4.0",
"autotrain_compatible"
] | token-classification | false | lirondos | null | lirondos/anglicisms-spanish-mbert | 486 | null | transformers | 2,361 | ---
language:
- es
license: cc-by-4.0
tags:
- anglicisms # Example: audio
- loanwords # Example: automatic-speech-recognition
- borrowing # Example: speech
- codeswitching # Example to specify a library: allennlp
- arxiv:2203.16169
datasets:
- coalas # Example: common_voice. Use dataset id from https://hf.co/datas... |
FangLee/DialoGPT-small-Kirito | b367d8ac8cbfabbaeb96bfd98a3f4550687daa99 | 2021-09-04T14:25:26.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | FangLee | null | FangLee/DialoGPT-small-Kirito | 485 | null | transformers | 2,362 | ---
tags:
- conversational
---
@Kirito DialoGPT Small Model |
filco306/gpt2-base-style-paraphraser | e320d414ae5ef9a893c4a6bc3604117f9e436c53 | 2021-08-28T19:27:41.000Z | [
"pytorch",
"text-generation",
"arxiv:2010.05700",
"transformers"
] | text-generation | false | filco306 | null | filco306/gpt2-base-style-paraphraser | 485 | 2 | transformers | 2,363 | # GPT2 base style transfer paraphraser
This is the trained base-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 th... |
Helsinki-NLP/opus-mt-th-fr | b9e7a1b2d0a2aa9c1cc4123c37dcef4b13d41c15 | 2021-09-11T10:48:01.000Z | [
"pytorch",
"marian",
"text2text-generation",
"th",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-th-fr | 484 | null | transformers | 2,364 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-th-fr
* source languages: th
* target languages: fr
* OPUS readme: [th-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/th-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
deepset/bert-base-german-cased-hatespeech-GermEval18Coarse | 9423036452a34960b227e787d8fd86063c6b87ad | 2021-05-19T15:25:01.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers",
"license:cc-by-4.0"
] | text-classification | false | deepset | null | deepset/bert-base-german-cased-hatespeech-GermEval18Coarse | 484 | 6 | transformers | 2,365 | ---
license: cc-by-4.0
---
This is a German BERT v1 (https://deepset.ai/german-bert) trained to do hate speech detection on the GermEval18Coarse dataset |
ELiRF/mbart-large-cc25-dacsa-es | c0f9e6d88fc2f865327cb63898186036944d204e | 2022-07-11T17:34:09.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"es",
"arxiv:2001.08210",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | ELiRF | null | ELiRF/mbart-large-cc25-dacsa-es | 484 | null | transformers | 2,366 | ---
language: es
tags:
- summarization
widget:
- text: "La Universitat Politècnica de València (UPV), a través del proyecto Atenea “plataforma de mujeres, arte y tecnología” y en colaboración con las compañías tecnológicas Metric Salad y Zetalab, ha digitalizado y modelado en 3D para la 35ª edición del Festival Dansa... |
ckiplab/bert-tiny-chinese-ner | a18df36c7f73ae3329877506be48a86c09599e8d | 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-ner | 483 | null | transformers | 2,367 | ---
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... |
navteca/roberta-base-squad2 | 6c7bec0e5e05d24070d598661767d8004c097553 | 2021-04-06T16:27:48.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:mit",
"autotrain_compatible"
] | question-answering | false | navteca | null | navteca/roberta-base-squad2 | 482 | null | transformers | 2,368 | ---
datasets:
- squad_v2
language: en
license: mit
pipeline_tag: question-answering
tags:
- roberta
- question-answering
---
# Roberta base model for QA (SQuAD 2.0)
This model uses [roberta-base](https://huggingface.co/roberta-base).
## Training Data
The models have been trained on the [SQuAD 2.0](https://rajpurkar.g... |
Helsinki-NLP/opus-mt-en-he | 6b58caddd6ee489cafb8dd45d0e76a9c9b61de4c | 2021-09-09T21:35:50.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"en",
"he",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-he | 481 | 1 | transformers | 2,369 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-he
* source languages: en
* target languages: he
* OPUS readme: [en-he](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-he/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
speechbrain/asr-crdnn-transformerlm-librispeech | 6c7c0a922755a083805630e0c1bfc2258da3fe4c | 2021-11-30T00:38:21.000Z | [
"en",
"dataset:librispeech",
"arxiv:2106.04624",
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"Tranformer",
"pytorch",
"license:apache-2.0"
] | automatic-speech-recognition | false | speechbrain | null | speechbrain/asr-crdnn-transformerlm-librispeech | 481 | null | speechbrain | 2,370 | ---
language: "en"
thumbnail:
tags:
- automatic-speech-recognition
- CTC
- Attention
- Tranformer
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- librispeech
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" framebo... |
IIC/dpr-spanish-passage_encoder-squades-base | fa963e0a2626fa6ea5553894d5685cd262cc6382 | 2022-04-02T15:08:22.000Z | [
"pytorch",
"bert",
"fill-mask",
"es",
"dataset:squad_es",
"arxiv:2004.04906",
"transformers",
"sentence similarity",
"passage retrieval",
"model-index",
"autotrain_compatible"
] | fill-mask | false | IIC | null | IIC/dpr-spanish-passage_encoder-squades-base | 481 | 3 | transformers | 2,371 | ---
language:
- es
tags:
- sentence similarity # Example: audio
- passage retrieval # Example: automatic-speech-recognition
datasets:
- squad_es
metrics:
- eval_loss: 0.08608942725107592
- eval_accuracy: 0.9925325215819639
- eval_f1: 0.8805402320715237
- average_rank: 0.27430093209054596
model-index:
- name: dpr-spa... |
luyaojie/uie-base-en | 966f8b1fc4c74e94ab552081605913ad5133cc41 | 2022-04-15T13:09:21.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | luyaojie | null | luyaojie/uie-base-en | 481 | null | transformers | 2,372 | ---
license: cc-by-nc-sa-4.0
---
|
zuu/grammar-error-correcter | e6b6507ef6e9308d0e344845c2e7486eaaecca5d | 2022-06-02T18:10:59.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | zuu | null | zuu/grammar-error-correcter | 481 | 0 | transformers | 2,373 | ```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
GED_TOKENIZER = AutoTokenizer.from_pretrained("zuu/grammar-error-correcter")
GED_MODEL = AutoModelForSeq2SeqLM.from_pretrained("zuu/grammar-error-correcter")
# Incorrect text
incorrect_text = 'young children should avoid exposure to contageous di... |
cambridgeltl/simctg_lccc_dialogue | 45b51e1c98f8dc6f0b65a2ade9bdff6d9a128b79 | 2022-06-25T19:21:55.000Z | [
"pytorch",
"gpt2",
"text-generation",
"arxiv:2008.03946",
"arxiv:2202.06417",
"transformers"
] | text-generation | false | cambridgeltl | null | cambridgeltl/simctg_lccc_dialogue | 480 | null | transformers | 2,374 | This model provides a Chinese GPT-2 language model trained with SimCTG on the LCCC benchmark [(Wang et al., 2020)](https://arxiv.org/pdf/2008.03946v2.pdf) based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply SimCTG a... |
j-hartmann/emotion-english-roberta-large | ab319b8cfc7ca91478e74bce639ed8b8e0927d0c | 2021-08-29T11:48:09.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"transformers",
"sentiment",
"emotion",
"twitter",
"reddit"
] | text-classification | false | j-hartmann | null | j-hartmann/emotion-english-roberta-large | 480 | 1 | transformers | 2,375 | ---
language: "en"
tags:
- roberta
- sentiment
- emotion
- twitter
- reddit
widget:
- text: "Oh wow. I didn't know that."
- text: "This movie always makes me cry.."
- text: "Oh Happy Day"
---
## Description ℹ
With this model, you can classify emotions in English text data. The model was trained on 6 diverse datase... |
Helsinki-NLP/opus-mt-tc-big-fr-en | df6dfc5e22be93169ad457196ad8472ad749f886 | 2022-06-01T13:01:21.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"fr",
"transformers",
"translation",
"opus-mt-tc",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tc-big-fr-en | 480 | 1 | transformers | 2,376 | ---
language:
- en
- fr
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-fr-en
results:
- task:
name: Translation fra-eng
type: translation
args: fra-eng
dataset:
name: flores101-devtest
type: flores_101
args: fra eng devtest
metrics... |
ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive2 | 4e7b2dda5588080784ac6f7482060026296d5cea | 2022-06-22T01:36:28.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ZipperXYZ | null | ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive2 | 480 | null | transformers | 2,377 | ---
tags:
- conversational
---
# The world machine DialoGPT model |
facebook/hubert-xlarge-ll60k | b0cef767123fe004883915a053f538f1737a1e47 | 2021-10-20T10:20:44.000Z | [
"pytorch",
"tf",
"hubert",
"feature-extraction",
"en",
"dataset:libri-light",
"arxiv:2106.07447",
"transformers",
"speech",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/hubert-xlarge-ll60k | 479 | 3 | transformers | 2,378 | ---
language: en
datasets:
- libri-light
tags:
- speech
license: apache-2.0
---
# Hubert-Extra-Large
[Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression)
The extra large model pretrained on 16kHz sampled speech audio. When... |
mrm8488/roberta-med-small2roberta-med-small-finetuned-cnn_daily_mail-summarization | 3df1c9e04581ca196e80b9ce1e4c22db6431bec7 | 2021-04-06T09:22:39.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | mrm8488 | null | mrm8488/roberta-med-small2roberta-med-small-finetuned-cnn_daily_mail-summarization | 479 | null | transformers | 2,379 | ---
language: en
license: apache-2.0
datasets:
- cnn_dailymail
tags:
- summarization
---
Shared [RoBERTa2RoBERTa (med-small)](https://huggingface.co/nyu-mll/roberta-med-small-1M-1) Summarization with 🤗EncoderDecoder Framework
This model is a warm-started *RoBERTaShared* (med-small) model fine-tuned on the *cn... |
facebook/xglm-7.5B | b4f0ef7d74603a0e63a05695cd38d08260961e3a | 2022-02-14T22:54:52.000Z | [
"pytorch",
"xglm",
"text-generation",
"arxiv:2112.10668",
"transformers",
"license:mit"
] | text-generation | false | facebook | null | facebook/xglm-7.5B | 478 | 5 | transformers | 2,380 | ---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
inference: false
---
# XGLM-7.5B
XGLM-7.5B is a multilingual autoregressive language model (with 7.5 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the... |
sadakmed/distiluse-base-multilingual-cased-v2 | d4e9bba5ac7e7bb5a86e3b97e8150e8fc1fbd931 | 2021-09-22T09:37:21.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"multilingual",
"sentence-transformers",
"DistilBert",
"Universal Sentence Encoder",
"sentence-embeddings",
"sentence-similarity",
"license:apache-2.0"
] | feature-extraction | false | sadakmed | null | sadakmed/distiluse-base-multilingual-cased-v2 | 478 | null | sentence-transformers | 2,381 | ---
language: multilingual
tags:
- DistilBert
- Universal Sentence Encoder
- sentence-embeddings
- sentence-transformers
- sentence-similarity
license: apache-2.0
---
While v1 model supports 15 languages, this version supports 50+ languages. However, performance on the 15 languages mentioned in v1 are reported to be a... |
ethanyt/guwen-quote | a5a28406ac0e3ab13727a3295c15f84f425ac9e8 | 2021-06-17T08:22:56.000Z | [
"pytorch",
"roberta",
"token-classification",
"zh",
"transformers",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"quotation detection",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | ethanyt | null | ethanyt/guwen-quote | 477 | null | transformers | 2,382 | ---
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"
- "quotation detection"
license: "apache-2.0"
pipeline_tag: "token-classification"
... |
google/pegasus-arxiv | 8d68b512ac8f83bd6ecfb651a793a35e71fdc402 | 2020-10-22T16:33:20.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | google | null | google/pegasus-arxiv | 477 | 1 | transformers | 2,383 | ---
language: en
tags:
- summarization
---
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@... |
gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner | aeb8f1a4908c7f21676dd7c1572e303a685056e1 | 2022-05-25T08:55:03.000Z | [
"pytorch",
"distilbert",
"token-classification",
"en",
"de",
"nl",
"es",
"multilingual",
"dataset:conll2003",
"transformers",
"model-index",
"autotrain_compatible"
] | token-classification | false | gunghio | null | gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner | 477 | null | transformers | 2,384 | ---
metrics:
- precision: 0.936
- recall: 0.9458
- f1: 0.9409
- accuracy: 0.9902
datasets:
- conll2003
language:
- en
- de
- nl
- es
- multilingual
model-index:
- name: gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
results:
- task:
type: ner
name: Named Entity Reco... |
nsi319/legal-pegasus | 54ef2872d33bbff28eb09544bdecbf6699f5b0b8 | 2021-03-11T08:50:52.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"transformers",
"summarization",
"license:mit",
"autotrain_compatible"
] | summarization | false | nsi319 | null | nsi319/legal-pegasus | 477 | null | transformers | 2,385 | ---
language: en
tags: summarization
metrics:
- rouge
- precision
inference: false
license: mit
---
## PEGASUS for legal document summarization
**legal-pegasus** is a finetuned version of ([**google/pegasus-cnn_dailymail**](https://huggingface.co/google/pegasus-cnn_dailymail)) for the **legal domain**, trained to perf... |
hfl/chinese-electra-180g-large-discriminator | d017e219578df8e4885484edbc8969dbdea9cbe0 | 2021-03-03T01:29:12.000Z | [
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0"
] | null | false | hfl | null | hfl/chinese-electra-180g-large-discriminator | 476 | 3 | transformers | 2,386 | ---
language:
- zh
license: "apache-2.0"
---
# 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 competitive performance compa... |
Visual-Attention-Network/van-base | 569d1d8e1323ad5baefa8c00b11d82de0e42cfad | 2022-03-31T12:45:44.000Z | [
"pytorch",
"van",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2202.09741",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | Visual-Attention-Network | null | Visual-Attention-Network/van-base | 476 | null | transformers | 2,387 | ---
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: htt... |
DTAI-KULeuven/robbert-v2-dutch-sentiment | bb4e1466d94f15534e792fc6870040e024000432 | 2022-06-29T13:11:28.000Z | [
"pytorch",
"roberta",
"text-classification",
"nl",
"dataset:dbrd",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"license:mit",
"model-index"
] | text-classification | false | DTAI-KULeuven | null | DTAI-KULeuven/robbert-v2-dutch-sentiment | 476 | null | transformers | 2,388 | ---
language: nl
license: mit
datasets:
- dbrd
model-index:
- name: robbert-v2-dutch-sentiment
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: dbrd
type: sentiment-analysis
split: test
metrics:
- name: Accuracy
type: accuracy
... |
facebook/wmt21-dense-24-wide-en-x | ee254716c52331df63a08ac929da96c59e68b057 | 2022-05-26T22:23:33.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"multilingual",
"ha",
"is",
"ja",
"cs",
"ru",
"zh",
"de",
"en",
"arxiv:2108.03265",
"transformers",
"translation",
"wmt21",
"license:mit",
"autotrain_compatible"
] | translation | false | facebook | null | facebook/wmt21-dense-24-wide-en-x | 475 | 9 | transformers | 2,389 | ---
language:
- multilingual
- ha
- is
- ja
- cs
- ru
- zh
- de
- en
license: mit
tags:
- translation
- wmt21
---
# WMT 21 En-X
WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2108.03265) an... |
alistair7/bbt-diagpt2-model | 2539b4c94eccb5f0ee1d9d86b191f492c70d4fa8 | 2021-06-06T21:49:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | alistair7 | null | alistair7/bbt-diagpt2-model | 474 | null | transformers | 2,390 | ---
tags:
- conversational
---
# A conversational model based on the character of Sheldon Cooper from Big Bang Theory. |
impyadav/GPT2-FineTuned-Hinglish-Song-Generation | 7c5694e0b1ec8dab4f17a857b3778911af56609a | 2022-01-03T11:33:54.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | impyadav | null | impyadav/GPT2-FineTuned-Hinglish-Song-Generation | 474 | 1 | transformers | 2,391 | GPT-2 model fine-tuned on Custom old Hindi songs (Hinglish) for text-generation task (AI Lyricist)
language:
- Hindi
- Hinglish
|
JorisCos/DPRNNTasNet-ks2_Libri1Mix_enhsingle_16k | e37a839cfaa3ce1e0c04d93a0e242d8ec8a694ed | 2021-09-23T15:49:18.000Z | [
"pytorch",
"dataset:Libri1Mix",
"dataset:enh_single",
"asteroid",
"audio",
"DPRNNTasNet",
"audio-to-audio",
"license:cc-by-sa-4.0"
] | audio-to-audio | false | JorisCos | null | JorisCos/DPRNNTasNet-ks2_Libri1Mix_enhsingle_16k | 471 | null | asteroid | 2,392 | ---
tags:
- asteroid
- audio
- DPRNNTasNet
- audio-to-audio
datasets:
- Libri1Mix
- enh_single
license: cc-by-sa-4.0
---
## Asteroid model `JorisCos/DPRNNTasNet_Libri1Mix_enhsignle_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/ast... |
TransQuest/monotransquest-da-en_zh-wiki | fefd083a71d9be578d7d98191b880d4578898619 | 2021-06-03T19:04:32.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"en-zh",
"transformers",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0"
] | text-classification | false | TransQuest | null | TransQuest/monotransquest-da-en_zh-wiki | 471 | null | transformers | 2,393 | ---
language: en-zh
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE t... |
neuralspace-reverie/indic-transformers-bn-distilbert | 4662cb6d6dd900f8dff05896cd1494a8ed0e1ecf | 2020-12-11T21:57:07.000Z | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"bn",
"transformers",
"MaskedLM",
"Bengali",
"DistilBERT",
"Question-Answering",
"Token Classification",
"Text Classification",
"autotrain_compatible"
] | fill-mask | false | neuralspace-reverie | null | neuralspace-reverie/indic-transformers-bn-distilbert | 471 | null | transformers | 2,394 | ---
language:
- bn
tags:
- MaskedLM
- Bengali
- DistilBERT
- Question-Answering
- Token Classification
- Text Classification
---
# Indic-Transformers Bengali DistilBERT
## Model description
This is a DistilBERT language model pre-trained on ~6 GB of monolingual training corpus. The pre-training data was majorly taken... |
HooshvareLab/bert-fa-zwnj-base-ner | 17d4928f28c36fd74864c221a27134da8b6bf9bc | 2021-05-18T21:04:35.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"transformers",
"autotrain_compatible"
] | token-classification | false | HooshvareLab | null | HooshvareLab/bert-fa-zwnj-base-ner | 470 | 3 | transformers | 2,395 | ---
language: fa
---
# BertNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wikian... |
KoboldAI/GPT-Neo-2.7B-Janeway | 56b0950204eafb4673c78595669cf8b04e413ab4 | 2022-03-20T12:57:50.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"en",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-Neo-2.7B-Janeway | 469 | 2 | transformers | 2,396 | ---
language: en
license: mit
---
# GPT-Neo 2.7B - Janeway
## Model Description
GPT-Neo 2.7B-Janeway is a finetune created using EleutherAI's GPT-Neo 2.7B model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used... |
nvidia/segformer-b3-finetuned-cityscapes-1024-1024 | 74ff1cf1357f4bfa962660c491282dfc3e7c72c2 | 2022-07-20T09:53:50.000Z | [
"pytorch",
"tf",
"segformer",
"dataset:cityscapes",
"arxiv:2105.15203",
"transformers",
"vision",
"image-segmentation",
"license:apache-2.0"
] | image-segmentation | false | nvidia | null | nvidia/segformer-b3-finetuned-cityscapes-1024-1024 | 469 | null | transformers | 2,397 | ---
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... |
cardiffnlp/bertweet-base-emotion | 89c1f1de95e4ae3979c82155d9a8f00be45c1668 | 2021-05-20T14:45:11.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/bertweet-base-emotion | 468 | null | transformers | 2,398 | |
ricardo-filho/bert-portuguese-cased-nli-assin-assin-2 | 17efd936dc233255fe5c95474813a51e9c3be9f8 | 2021-08-04T13:24:42.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | ricardo-filho | null | ricardo-filho/bert-portuguese-cased-nli-assin-assin-2 | 468 | 3 | sentence-transformers | 2,399 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.