modelId stringlengths 4 112 | sha stringlengths 40 40 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringclasses 29
values | private bool 1
class | author stringlengths 2 38 ⌀ | config null | id stringlengths 4 112 | downloads float64 0 36.8M ⌀ | likes float64 0 712 ⌀ | library_name stringclasses 17
values | __index_level_0__ int64 0 38.5k | readme stringlengths 0 186k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Helsinki-NLP/opus-mt-kl-en | 1a55c53e0315586661456929a8e102bfcdb90a63 | 2021-09-10T13:53:56.000Z | [
"pytorch",
"marian",
"text2text-generation",
"kl",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-kl-en | 39 | null | transformers | 6,500 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-kl-en
* source languages: kl
* target languages: en
* OPUS readme: [kl-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/kl-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Mathking/bert-base-german-cased-gnad10 | c3993046d7580335ca1c52b2e543fb449b4be00b | 2021-11-07T09:07:25.000Z | [
"pytorch",
"bert",
"text-classification",
"de",
"dataset:gnad10",
"transformers",
"german-news-classification"
] | text-classification | false | Mathking | null | Mathking/bert-base-german-cased-gnad10 | 39 | null | transformers | 6,501 | ---
language:
- de
datasets:
- gnad10
tags:
- text-classification
- german-news-classification
metrics:
- accuracy
- precision
- recall
- f1
---
# German BERT for News Classification
This a bert-base-german-cased model finetuned for text classification on german news articles
## Training data
Used the training set f... |
SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune | 26a80447a84ffc28be0bb7d562a2c6911adee9a7 | 2021-06-23T04:03:25.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune | 39 | null | transformers | 6,502 | ---
tags:
- summarization
widget:
- text: "parse the uses licence node of this package , if any , and returns the license definition if theres"
---
# CodeTrans model for api recommendation generation
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in
[th... |
WangZeJun/roformer-sim-base-chinese | 5da827822935d53285c1103f7e72cef2dae84749 | 2022-06-14T09:17:25.000Z | [
"pytorch",
"transformers"
] | null | false | WangZeJun | null | WangZeJun/roformer-sim-base-chinese | 39 | 1 | transformers | 6,503 | https://github.com/zejunwang1/bert4vec |
addy88/gptj8 | f07b3aaf67ce4053852bf45a307bd58dc2f4b39f | 2022-01-02T06:33:57.000Z | [
"pytorch",
"gptj",
"text-generation",
"arxiv:2106.09685",
"arxiv:2110.02861",
"transformers"
] | text-generation | false | addy88 | null | addy88/gptj8 | 39 | 1 | transformers | 6,504 | This Model is 8bit Version of EleutherAI/gpt-j-6B. It is converted by Facebook's bitsandbytes library. The original GPT-J takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. So for finetuning on single GPU This model is converted into 8bit.
Here's how to run it: [... |
aseifert/distilbert-casing | 1c532d673e156dba74da474add70775e44d7989a | 2020-10-29T09:44:22.000Z | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | aseifert | null | aseifert/distilbert-casing | 39 | null | transformers | 6,505 | Entry not found |
benjaminbeilharz/bart-base-empatheticdialogues | 81a073af4a4c402a5340ff6410fcd445001f8eec | 2022-01-24T11:29:02.000Z | [
"pytorch",
"tensorboard",
"bart",
"text-generation",
"transformers"
] | text-generation | false | benjaminbeilharz | null | benjaminbeilharz/bart-base-empatheticdialogues | 39 | null | transformers | 6,506 | Entry not found |
blizrys/biobert-v1.1-finetuned-pubmedqa | d36bc441473d389521c400a4814722b4a084673b | 2021-09-13T17:56:32.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | blizrys | null | blizrys/biobert-v1.1-finetuned-pubmedqa | 39 | null | transformers | 6,507 | ---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- accuracy
model-index:
- name: biobert-v1.1-finetuned-pubmedqa
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7
---
<!-- This model card has been gen... |
castorini/ance-dpr-context-multi | adb8465629826220f773030de9e06e463e486f1e | 2021-09-22T09:41:18.000Z | [
"pytorch",
"dpr",
"arxiv:2007.00808",
"transformers"
] | null | false | castorini | null | castorini/ance-dpr-context-multi | 39 | null | transformers | 6,508 | This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://ar... |
flax-community/roberta-base-thai | 3d400e88b72d7765267bd63a59eec6f34cca4f13 | 2021-07-17T09:43:54.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | flax-community | null | flax-community/roberta-base-thai | 39 | null | transformers | 6,509 | Entry not found |
flax-sentence-embeddings/all_datasets_v3_distilroberta-base | a02c9dc41679249af6ad7df1228c49026ec490be | 2021-07-23T15:43:19.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"arxiv:2104.08727",
"arxiv:1810.09305",
"arxiv:2102.07033",
"arxiv:1904.06472",
"sentence-transformers",
"feature-extraction",
"sentence-similarity"
] | sentence-similarity | false | flax-sentence-embeddings | null | flax-sentence-embeddings/all_datasets_v3_distilroberta-base | 39 | 2 | sentence-transformers | 6,510 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
---
# Model description
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained... |
huggingtweets/kaikothesharko | 76d8c4b143e655009d8bb5925611444fa46d39be | 2021-12-02T04:58:11.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/kaikothesharko | 39 | null | transformers | 6,511 | ---
language: en
thumbnail: http://www.huggingtweets.com/kaikothesharko/1638421086822/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; ... |
ismaelfaro/gpt2-poems.es | 4817ebe99130415355897998baa848fb1fd2f4ea | 2021-10-12T14:23:53.000Z | [
"pytorch",
"gpt2",
"text-generation",
"es",
"transformers",
"GPT",
"license:mit"
] | text-generation | false | ismaelfaro | null | ismaelfaro/gpt2-poems.es | 39 | 1 | transformers | 6,512 | ---
language: es
tags:
- GPT
license: mit
---
# GTP2-Poems Spanish
This model is part of the Poems+AI experiment
more info https://poems-ai.github.io/art/
# Original Dataset
- https://www.kaggle.com/andreamorgar/spanish-poetry-dataset
- Marcos de la Fuente's poems
|
it5/it5-small-wiki-summarization | 470e69837f740891d8f35e12f209acbb0caadbba | 2022-03-09T07:50:42.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"it",
"dataset:wits",
"arxiv:2203.03759",
"transformers",
"italian",
"sequence-to-sequence",
"wikipedia",
"summarization",
"wits",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible"
... | summarization | false | it5 | null | it5/it5-small-wiki-summarization | 39 | null | transformers | 6,513 | ---
language:
- it
license: apache-2.0
datasets:
- wits
tags:
- italian
- sequence-to-sequence
- wikipedia
- summarization
- wits
widget:
- text: "La 5ª Commissione ha competenza per i disegni di legge riguardanti le specifiche materie del bilancio, del personale e dei servizi del Ministero dell'economia, nonché per i ... |
izumi-lab/electra-small-japanese-discriminator | d453e1f3b45100dc246c7645b18803f2e8824126 | 2022-03-19T09:38:49.000Z | [
"pytorch",
"electra",
"pretraining",
"ja",
"dataset:wikipedia",
"arxiv:2003.10555",
"transformers",
"license:cc-by-sa-4.0"
] | null | false | izumi-lab | null | izumi-lab/electra-small-japanese-discriminator | 39 | null | transformers | 6,514 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 東京大学で[MASK]の研究をしています。
---
# ELECTRA small Japanese discriminator
This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts in the Japanese language.
The codes for the pretraining are available at [reta... |
ml6team/distilbart-tos-summarizer-tosdr | 5c4b53b6b876b4a8b861b24901c4ba2793d7b0e7 | 2022-01-20T15:21:41.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:tosdr",
"transformers",
"summarization",
"t&c",
"tos",
"distilbart",
"distilbart-6-6",
"autotrain_compatible"
] | summarization | false | ml6team | null | ml6team/distilbart-tos-summarizer-tosdr | 39 | 12 | transformers | 6,515 | ---
language:
- en
tags:
- summarization
- t&c
- tos
- distilbart
- distilbart-6-6
datasets:
- tosdr
metrics:
- rouge1
- rouge2
- rougel
inference:
parameters:
min_length: 5
max_length: 512
do_sample: False
widget:
- text: "In addition, certain portions of the Web Site may be subject to additional terms o... |
mrm8488/squeezebert-finetuned-squadv2 | a45563f9d8689d843cf8c45742e58b161e905c4f | 2020-12-11T21:55:26.000Z | [
"pytorch",
"squeezebert",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:2006.11316",
"arxiv:2004.02984",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/squeezebert-finetuned-squadv2 | 39 | null | transformers | 6,516 | ---
language: en
datasets:
- squad_v2
---
# SqueezeBERT + SQuAD v2
[squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) fine-tuned on [SQUAD v2](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of SqueezeBERT
This model, `squeezebert-unca... |
nielsr/coref-roberta-base | d193901a739fcc27cfbf1b25fd712d99bb1b69e1 | 2021-01-21T08:18:55.000Z | [
"pytorch",
"en",
"dataset:wikipedia",
"dataset:quoref",
"dataset:docred",
"dataset:fever",
"dataset:gap",
"dataset:winograd_wsc",
"dataset:winogender",
"dataset:glue",
"arxiv:2004.06870",
"transformers",
"exbert",
"license:apache-2.0"
] | null | false | nielsr | null | nielsr/coref-roberta-base | 39 | null | transformers | 6,517 | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- wikipedia
- quoref
- docred
- fever
- gap
- winograd_wsc
- winogender
- glue
---
# CorefRoBERTa base model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in
... |
panggi/t5-base-indonesian-summarization-cased | 0fb07c370837e01d71085ec681fb945ab3fed823 | 2021-06-23T13:18:18.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"id",
"dataset:indosum",
"transformers",
"pipeline:summarization",
"summarization",
"autotrain_compatible"
] | summarization | false | panggi | null | panggi/t5-base-indonesian-summarization-cased | 39 | null | transformers | 6,518 | ---
language: id
tags:
- pipeline:summarization
- summarization
- t5
datasets:
- indosum
---
# Indonesian T5 Summarization Base Model
Finetuned T5 base summarization model for Indonesian.
## Finetuning Corpus
`t5-base-indonesian-summarization-cased` model is based on `t5-base-bahasa-summarization-cased` by [husein... |
pere/norwegian-roberta-base | 08afd54fbcfe247ba66bbe5024facfafad5c633e | 2021-11-29T20:32:14.000Z | [
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | pere | null | pere/norwegian-roberta-base | 39 | null | transformers | 6,519 | Entry not found |
vslaykovsky/roberta-news-duplicates | 046b363bc6747ed13a6b176468d2700fd7acdbd0 | 2021-05-20T23:07:11.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | vslaykovsky | null | vslaykovsky/roberta-news-duplicates | 39 | null | transformers | 6,520 | Entry not found |
ghadeermobasher/BC5CDR-Disease-Modified_scibert_scivocab_uncased | a86a29368b40742102fa8e5554ed1b664408b4ab | 2022-02-25T18:11:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BC5CDR-Disease-Modified_scibert_scivocab_uncased | 39 | null | transformers | 6,521 | Entry not found |
dbmdz/bert-base-historic-multilingual-64k-td-cased | cdd9ea90daac4e436a3df382966a5da8ab6f2ff2 | 2022-06-03T09:48:41.000Z | [
"pytorch",
"bert",
"fill-mask",
"multilingual",
"arxiv:2205.15575",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/bert-base-historic-multilingual-64k-td-cased | 39 | null | transformers | 6,522 | ---
language: multilingual
license: mit
widget:
- text: "and I cannot conceive the reafon why [MASK] hath"
- text: "Täkäläinen sanomalehdistö [MASK] erit - täin"
- text: "Det vore [MASK] häller nödvändigt att be"
- text: "Comme, à cette époque [MASK] était celle de la"
- text: "In [MASK] an atmosphärischen Nahrungsmitt... |
JoofytheBloofy/T5LargeTest | 11497a056734d3b703739f53ba4cb5084cdca45c | 2022-03-27T16:26:03.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | JoofytheBloofy | null | JoofytheBloofy/T5LargeTest | 39 | null | transformers | 6,523 | ---
tags:
- summarization
--- |
NbAiLab/nb-bert-ncc-male2female | 44016b54dc71ea3b34b8d8e25a392244b9cbb518 | 2022-04-27T18:33:23.000Z | [
"pytorch",
"jax",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | NbAiLab | null | NbAiLab/nb-bert-ncc-male2female | 39 | null | transformers | 6,524 | Entry not found |
UrukHan/t5-russian-summarization | c300ff14fea8ecfbf9a7e73a4d30cceeffa01d07 | 2022-04-04T09:51:50.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | UrukHan | null | UrukHan/t5-russian-summarization | 39 | null | transformers | 6,525 | ---
tags:
- generated_from_trainer
model-index:
- name: t5-russian-summarization
results: []
widget:
- text: "Запад после начала российской специальной операции по демилитаризации Украины ввел несколько раундов новых экономических санкций. В Кремле новые ограничения назвали серьезными, но отметили, что Россия готовил... |
yihsuan/albert-base-chinese-0407-ner | 7113e82d9ecbadb3045163efcd72984059602b89 | 2022-04-07T03:20:43.000Z | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"List of ISO 639-1 code for your language",
"zh",
"transformers",
"autotrain_compatible"
] | token-classification | false | yihsuan | null | yihsuan/albert-base-chinese-0407-ner | 39 | null | transformers | 6,526 | ---
language:
- "List of ISO 639-1 code for your language"
- zh
widget:
- text: "中央疫情指揮中心臨時記者會宣布全院區為紅區，擴大隔離，但鄭文燦早在七十二小時前就主張，只要是先前在桃園醫院住院、轉院的患者與陪病家屬，都要居家隔離"
example_title: "範例ㄧ"
- text: "台東地檢署21日指揮警方前往張靜的事務所及黃姓女友所經營的按摩店進行搜索"
example_title: "範例二"
- text: "各地停電事件頻傳，即便經濟部與台電均否認「台灣缺電」，但也難消國人的疑慮。"
example_title: "... |
GPL/quora-tsdae-msmarco-distilbert-margin-mse | 873c111d4d11af7983370f15675e57d5f2f9043a | 2022-04-19T16:45:50.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"transformers"
] | feature-extraction | false | GPL | null | GPL/quora-tsdae-msmarco-distilbert-margin-mse | 39 | null | transformers | 6,527 | Entry not found |
xfbai/AMRBART-large-finetuned-AMR2.0-AMR2Text | 0268512aaefa5d26d4c4eb8dd873b3f0aca51e0a | 2022-04-26T05:57:49.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"arxiv:2203.07836",
"transformers",
"AMRBART",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | xfbai | null | xfbai/AMRBART-large-finetuned-AMR2.0-AMR2Text | 39 | null | transformers | 6,528 | ---
language: en
tags:
- AMRBART
license: mit
---
## AMRBART-large-finetuned-AMR2.0-AMR2Text
This model is a fine-tuned version of [AMRBART-large](https://huggingface.co/xfbai/AMRBART-large) on an AMR2.0 dataset. It achieves a sacre-bleu score of 45.7 on the evaluation set: More details are introduced in the paper: [... |
hustvl/yolos-small-300 | 98945dea89fc0e6396dfc3fc10d6c4de169c773d | 2022-06-27T08:38:34.000Z | [
"pytorch",
"yolos",
"object-detection",
"dataset:coco",
"arxiv:2106.00666",
"transformers",
"vision",
"license:apache-2.0"
] | object-detection | false | hustvl | null | hustvl/yolos-small-300 | 39 | 1 | transformers | 6,529 | ---
license: apache-2.0
tags:
- object-detection
- vision
datasets:
- coco
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
example_title: Savanna
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
example_title: Football Match
- s... |
BlackSamorez/ebanko-large | f6b76dd3570ef2dcc2b1a18d3fe3aef7589c5ce0 | 2022-04-28T19:19:55.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ru",
"transformers",
"PyTorch",
"Transformers",
"autotrain_compatible"
] | text2text-generation | false | BlackSamorez | null | BlackSamorez/ebanko-large | 39 | null | transformers | 6,530 | ---
language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: "https://github.com/sberbank-ai/model-zoo"
---
# ebanko-base
Model was finetuned by [black_samorez](https://github.com/BlackSamorez).
Based off [sberbank-ai/ruT5-base](https://huggingface.co/sberbank-ai/ruT5-base).
Finetuned on [Russian Language Toxic Comm... |
HiTZ/A2T_RoBERTa_SMFA_WikiEvents-arg_ACE-arg | edad04dfeba14cf700bc442318bd951f16256fc8 | 2022-05-08T23:10:17.000Z | [
"pytorch",
"roberta",
"text-classification",
"dataset:snli",
"dataset:anli",
"dataset:multi_nli",
"dataset:multi_nli_mismatch",
"dataset:fever",
"arxiv:2104.14690",
"arxiv:2203.13602",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | false | HiTZ | null | HiTZ/A2T_RoBERTa_SMFA_WikiEvents-arg_ACE-arg | 39 | null | transformers | 6,531 | ---
pipeline_tag: zero-shot-classification
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
---
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatib... |
kornosk/polibertweet-political-twitter-roberta-mlm | 7ebea6e1dab8f54f95aec14e4d3e0ffa5a407da2 | 2022-06-17T23:45:14.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"transformers",
"twitter",
"masked-token-prediction",
"bertweet",
"election2020",
"politics",
"license:gpl-3.0",
"autotrain_compatible"
] | fill-mask | false | kornosk | null | kornosk/polibertweet-political-twitter-roberta-mlm | 39 | null | transformers | 6,532 | ---
language: "en"
tags:
- twitter
- masked-token-prediction
- bertweet
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Political Election 2020
Pre-trained weights for PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter, LREC 2022.
Please see the [o... |
okho0653/Bio_ClinicalBERT-zero-shot-sentiment-model | fee1ce229a671ff13fd2f111ba21c4529ab0909a | 2022-05-06T05:57:30.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | okho0653 | null | okho0653/Bio_ClinicalBERT-zero-shot-sentiment-model | 39 | null | transformers | 6,533 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Bio_ClinicalBERT-zero-shot-sentiment-model
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. -->
#... |
Jatin-WIAI/doctor_patient_clf_en | 78115f1d9f4649339685a365649554ef018c9cb8 | 2022-05-19T09:53:05.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jatin-WIAI | null | Jatin-WIAI/doctor_patient_clf_en | 39 | null | transformers | 6,534 | Entry not found |
connectivity/feather_berts_28 | 9beae1bc72b121e5703987cc2a003eb7d1bbb3a8 | 2022-05-21T14:28:23.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_28 | 39 | null | transformers | 6,535 | Entry not found |
thunninoi/wav2vec2-japanese-vtuber | 1d9c01d13d10df74385de5b468bb692ac6ac377b | 2022-07-08T17:33:48.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | thunninoi | null | thunninoi/wav2vec2-japanese-vtuber | 39 | null | transformers | 6,536 | ---
tags:
- generated_from_trainer
model-index:
- name: checkpoints2
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. -->
# checkpoints2
This model is a fine-tuned v... |
aomar85/fine-tuned-arabert-random-negative | 44c6420e66595a90e1837aa5da74b456f99775e5 | 2022-05-29T22:24:58.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | aomar85 | null | aomar85/fine-tuned-arabert-random-negative | 39 | null | transformers | 6,537 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: fine-tuned-arabert-random-negative
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then re... |
juancavallotti/t5-base-gec | 25b3102f637927f3555e3a098577cc2b64d517d6 | 2022-06-08T15:26:04.000Z | [
"pytorch",
"tensorboard",
"onnx",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | juancavallotti | null | juancavallotti/t5-base-gec | 39 | 1 | transformers | 6,538 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-base-gec
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. -->
# t5-base-gec
This model... |
niclas/ATC_1 | 6369daa401b2761f6a1f404745a39a51fad4caec | 2022-06-09T08:52:37.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | niclas | null | niclas/ATC_1 | 39 | null | transformers | 6,539 | Entry not found |
wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics | 5889d9869dc3f3840b2aa3496b0122550233a58c | 2022-06-17T11:20:35.000Z | [
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | wvangils | null | wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics | 39 | null | transformers | 6,540 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics
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 com... |
GonzoJurezz/gpt2-horo | e26def2b2971f8c22e813de26efeb43e3d15e987 | 2022-06-18T21:30:54.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | GonzoJurezz | null | GonzoJurezz/gpt2-horo | 39 | null | transformers | 6,541 | Entry not found |
ryo0634/bert-base-zip-dependency-0 | 5436dcfe6d178aef3142a48730f41e919d8a79b5 | 2022-06-13T03:38:10.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | ryo0634 | null | ryo0634/bert-base-zip-dependency-0 | 39 | null | transformers | 6,542 | Entry not found |
Intel/distilbert-base-cased-distilled-squad-int8-static | 1a083d94199ed7c70554880354283ba9c23a4a47 | 2022-07-25T02:50:45.000Z | [
"pytorch",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"int8",
"Intel® Neural Compressor",
"PostTrainingStatic",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | Intel | null | Intel/distilbert-base-cased-distilled-squad-int8-static | 39 | null | transformers | 6,543 | ---
license: apache-2.0
tags:
- int8
- Intel® Neural Compressor
- PostTrainingStatic
datasets:
- squad
metrics:
- f1
---
# INT8 DistilBERT base cased finetuned on Squad
### Post-training static quantization
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-comp... |
mosesju/distilbert-base-uncased-finetuned-news | e52883c2776fbfc0299d3eb7ef6299970b24e602 | 2022-06-17T12:14:46.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:ag_news",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | mosesju | null | mosesju/distilbert-base-uncased-finetuned-news | 39 | null | transformers | 6,544 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-news
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
args: default
... |
emilys/BERTweet-WNUT17 | 1a748f44c714525686d30b777c36b4b5f8f40334 | 2022-06-15T22:31:22.000Z | [
"pytorch",
"roberta",
"token-classification",
"en",
"dataset:wnut_17",
"transformers",
"NER",
"autotrain_compatible"
] | token-classification | false | emilys | null | emilys/BERTweet-WNUT17 | 39 | null | transformers | 6,545 | ---
language:
- en
tags:
- NER
datasets:
- wnut_17
---
bertweet-base (https://huggingface.co/vinai/bertweet-base) finetuned on WNUT (2017), following https://github.com/huggingface/transformers/tree/main/examples/legacy/token-classification |
DingosGotMyBaby/uhn-twitch-chat | 8ac35a645e15581fdc54d1df4cca84d0a9e9daeb | 2022-06-24T05:08:58.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"license:mit"
] | text-generation | false | DingosGotMyBaby | null | DingosGotMyBaby/uhn-twitch-chat | 39 | null | transformers | 6,546 | ---
license: mit
---
# A model based on UberHaxorNova's Twitch chat
Trained on over 700 vods worth of chat and with some scuffed filtering it became a 300mb dataset.
## Dataset
The dataset was created by downloading all the available vods at the time of creation as a json file and stripping out all the chat mes... |
plncmm/mdeberta-wl-base-es | 0682a32b193e16422ff25c16ce48eb417fb737e2 | 2022-06-26T13:49:00.000Z | [
"pytorch",
"deberta-v2",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | fill-mask | false | plncmm | null | plncmm/mdeberta-wl-base-es | 39 | null | transformers | 6,547 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: mdeberta-wl-base-es
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. -->
# mdeberta-wl-base-es
T... |
hellennamulinda/eng-lug | 2ed04490c7d5bdce995951c5d3642d4e00c7aff6 | 2022-07-11T06:45:00.000Z | [
"pytorch",
"marian",
"text2text-generation",
"unk",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | hellennamulinda | null | hellennamulinda/eng-lug | 39 | null | transformers | 6,548 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
co2_eq_emissions: 0.04087910671538076
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1026034854
- CO2 Emissions (in grams): 0.04087910671538076
## Validation Metrics
- Loss: 1.0871405601501465
- Rouge1: 55.8225
- R... |
chradden/opencampus_age-detection | 7173e20896f511734cdf9aacec4e5dd9bada8d86 | 2022-07-02T12:28:02.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | chradden | null | chradden/opencampus_age-detection | 39 | null | transformers | 6,549 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: opencampus_age-detection
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5892857313156128
---
# opencampu... |
Olivia-umich/SpanKeptParaphraser | 13f08b84c05c3f67fe915f5ee461e0f86787f428 | 2022-07-04T19:45:17.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | Olivia-umich | null | Olivia-umich/SpanKeptParaphraser | 39 | null | transformers | 6,550 | ---
license: apache-2.0
---
|
turingmachine/hupd-distilroberta-base | 5bd1290dfaa958dd29ebb8641674d2c88df0176b | 2022-07-05T15:30:46.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:HUPD/hupd",
"transformers",
"hupd",
"distilroberta",
"patents",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | turingmachine | null | turingmachine/hupd-distilroberta-base | 39 | 1 | transformers | 6,551 | ---
language:
- en
thumbnail: "url to a thumbnail used in social sharing"
tags:
- hupd
- roberta
- distilroberta
- patents
license: cc-by-sa-4.0
datasets:
- HUPD/hupd
---
# HUPD DistilRoBERTa-Base Model
This HUPD DistilRoBERTa model was fine-tuned on the HUPD dataset with a masked language modeling objective. It w... |
KoichiYasuoka/deberta-large-japanese-wikipedia-luw-upos | 88ecb4c66419d6db05f57a4b600d35300129b205 | 2022-07-23T14:44:08.000Z | [
"pytorch",
"deberta-v2",
"token-classification",
"ja",
"dataset:universal_dependencies",
"transformers",
"japanese",
"wikipedia",
"pos",
"dependency-parsing",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | token-classification | false | KoichiYasuoka | null | KoichiYasuoka/deberta-large-japanese-wikipedia-luw-upos | 39 | null | transformers | 6,552 | ---
language:
- "ja"
tags:
- "japanese"
- "wikipedia"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "国境の長いトンネルを抜けると雪国であった。"
---
# deberta-large-japanese-wikipedia-luw-upos
## Model Description
... |
Team-PIXEL/pixel-base-finetuned-pos-ud-arabic-padt | d2ad296d3b6a72c6f02d6ed2ceecbc4d9b251fee | 2022-07-13T00:21:13.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-pos-ud-arabic-padt | 39 | null | transformers | 6,553 | Entry not found |
huggingtweets/angelsexytexty-janieclone | a7e5148042812eefbed5d2f88a1e6dc94966d728 | 2022-07-28T13:51:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/angelsexytexty-janieclone | 39 | null | transformers | 6,554 | ---
language: en
thumbnail: http://www.huggingtweets.com/angelsexytexty-janieclone/1659016297136/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-r... |
sam34738/bert-hindi-kabita | 5b686be729d946bee89c52f01b134530e7aae210 | 2022-07-13T19:31:57.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | sam34738 | null | sam34738/bert-hindi-kabita | 39 | null | transformers | 6,555 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-hindi-kabita
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. -->
# bert-hindi-kabita... |
Team-PIXEL/pixel-base-finetuned-masakhaner-yor | eb79d078cd4db81bdbbacdf1258af0587e633a5a | 2022-07-15T03:33:30.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-masakhaner-yor | 39 | null | transformers | 6,556 | Entry not found |
Bachstelze/Rapgenerator | 4ab4eaef76e56d6edf1efa987e1f1695976b96cd | 2022-07-20T15:39:36.000Z | [
"pytorch",
"gpt2",
"text-generation",
"de",
"dataset:genius lyrics",
"transformers",
"Text Generation",
"license:mit"
] | text-generation | false | Bachstelze | null | Bachstelze/Rapgenerator | 39 | null | transformers | 6,557 | ---
language: de
widget:
- text: "Ich mach′ ein'n Song auf mein′n Lieblings-MCs (jaja)"
tags:
- Text Generation
datasets:
- genius lyrics
license: mit
---
# GPT-Rapgenerator
The Rapgenerator is trained for [nullsechsroy](https://genius.com/artists/Nullsechsroy) on an english [GPT2](https://huggingface.co/transforme... |
shengnan/visualize-v0-pre10w-preseed1-ft2w-seed1 | 6e84747286bc734fa321720d6d917c408e87c12a | 2022-07-17T05:51:16.000Z | [
"pytorch",
"t5",
"transformers"
] | null | false | shengnan | null | shengnan/visualize-v0-pre10w-preseed1-ft2w-seed1 | 39 | null | transformers | 6,558 | Entry not found |
Anonymous1111/bert-base-emotion | d235f103f8464f7b65f1416b207d12b6797973c5 | 2022-07-18T10:32:56.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | Anonymous1111 | null | Anonymous1111/bert-base-emotion | 39 | null | transformers | 6,559 | ---
license: apache-2.0
---
|
BrunoHays/wav2vec2XLS-R-common_voice_10-fr | ac7fa7e969ef3f60fbd63f9a33947d40da38c126 | 2022-07-25T11:39:43.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | BrunoHays | null | BrunoHays/wav2vec2XLS-R-common_voice_10-fr | 39 | null | transformers | 6,560 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2XLS-R-common_voice_10-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access ... |
diwank/bartner | 50124b1f2357e726a40f5ac1aac14fabd2fd09c5 | 2022-07-27T14:24:28.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | diwank | null | diwank/bartner | 39 | null | transformers | 6,561 | ---
license: mit
---
Bart + Gartner = Bartner |
tdobrxl/ClinicBERT | 4824b6802bfd113e24551840f61ba1a1ffab9659 | 2022-07-29T22:33:11.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | tdobrxl | null | tdobrxl/ClinicBERT | 39 | null | transformers | 6,562 | ClinicBERT has the same architecture of RoBERTa model. It has been trained on clinical text and can be used for feature extraction from textual data.
## How to use
### Feature Extraction
```
from transformers import RobertaModel, RobertaTokenizer
model = RobertaModel.from_pretrained("tdobrxl/ClinicBERT")
tokenizer =... |
Emran/ClinicalBERT_description_full_ICD10_Code | 2ba30aa7e482f86279d1889507dd1664f42c2520 | 2021-10-18T20:31:13.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | Emran | null | Emran/ClinicalBERT_description_full_ICD10_Code | 38 | null | transformers | 6,563 | Entry not found |
Helsinki-NLP/opus-mt-en-hy | 2b0a113b0968e30d7c6d4eedfe58007fe50ad819 | 2021-01-18T08:09:21.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"hy",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-hy | 38 | null | transformers | 6,564 | ---
language:
- en
- hy
tags:
- translation
license: apache-2.0
---
### eng-hye
* source group: English
* target group: Armenian
* OPUS readme: [eng-hye](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hye/README.md)
* model: transformer-align
* source language(s): eng
* target langua... |
Helsinki-NLP/opus-mt-zh-fi | 02f43b28f6765bc0397c4c2da1609e8c358243e1 | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"text2text-generation",
"zh",
"fi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-zh-fi | 38 | null | transformers | 6,565 | ---
language:
- zh
- fi
tags:
- translation
license: apache-2.0
---
### zho-fin
* source group: Chinese
* target group: Finnish
* OPUS readme: [zho-fin](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-fin/README.md)
* model: transformer-align
* source language(s): cmn_Bopo cmn_Hani cm... |
IMSyPP/hate_speech_slo | e20bf5141fd7718c657493702dccaaae795147f3 | 2022-05-16T06:13:11.000Z | [
"pytorch",
"bert",
"text-classification",
"sl",
"transformers",
"license:mit"
] | text-classification | false | IMSyPP | null | IMSyPP/hate_speech_slo | 38 | null | transformers | 6,566 | ---
pipeline_tag: text-classification
inference: true
widget:
- text: "Sem Mark in živim v Ljubljani. Sem doktorski študent na Mednarodni podiplomski šoli Jožefa Stefana."
language:
- sl
license: mit
---
# Hate Speech Classifier for Social Media Content in Slovenian Language
A monolingual model for hate speech c... |
SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask_finetune | 1aa14babad086d30fd7cc14836f816a857f171d4 | 2021-06-23T05:15:56.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask_finetune | 38 | null | transformers | 6,567 | ---
tags:
- summarization
widget:
- text: "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLocalTime ... |
SaulLu/recreate-history | 858f5225017881cd075c830c329427d1bea0b001 | 2021-05-28T16:37:37.000Z | [
"pytorch",
"albert",
"token-classification",
"bn",
"dataset:xtreme",
"transformers",
"collaborative",
"bengali",
"NER",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | SaulLu | null | SaulLu/recreate-history | 38 | null | transformers | 6,568 |
---
language: bn
tags:
- collaborative
- bengali
- NER
license: apache-2.0
datasets: xtreme
metrics:
- Loss
- Accuracy
- Precision
- Recall
---
# sahajBERT Named Entity Recognition
## Model description
[sahajBERT](https://huggingface.co/neuropark/sahajBERT-NER) fine-tuned for NER using the bengali split of [WikiAN... |
XSY/albert-base-v2-fakenews-discriminator | 49230d434cd1df26a368a2284ada7aeeacd8f25b | 2021-11-16T13:11:50.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | XSY | null | XSY/albert-base-v2-fakenews-discriminator | 38 | null | transformers | 6,569 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: albert-base-v2-fakenews-discriminator
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... |
XSY/albert-base-v2-imdb-calssification | 833e0badbb0807db0f9382e61bf95f537e36cd42 | 2021-11-13T09:10:38.000Z | [
"pytorch",
"albert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | XSY | null | XSY/albert-base-v2-imdb-calssification | 38 | null | transformers | 6,570 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: albert-base-v2-imdb-calssification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
... |
aasem/wav2vec2-xls-r-300m-Urdu | 4d8badf671cc4297a2f264e61da973fb82fc78b2 | 2022-03-01T08:28:25.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | aasem | null | aasem/wav2vec2-xls-r-300m-Urdu | 38 | null | transformers | 6,571 | ---
datasets:
-
common_voice: ~
language:
-
ur: ~
library_name:
transformers: ~
license:
mit: ~
metrics:
-
wer: ~
model-index:
-
name:
wav2vec2-xls-r-300m-Urdu: ~
results:
-
task:
dataset:
args:
ur: ~
na... |
amtam0/timer-ner-en | c89664191fc6fcc9ab0755b487610121b5d171d4 | 2021-11-28T09:58:54.000Z | [
"pytorch",
"en",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | amtam0 | null | amtam0/timer-ner-en | 38 | 1 | flair | 6,572 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
widget:
- text: "12 sets of 2 minutes 38 minutes between each set"
---
#### This model is used in the [Speech Interval Timer app](https://medium.com/@amtam0/speech-interval-timer-app-using-transformers-1df8fa3821d5)
7-class NER English mod... |
asapp/sew-d-base-plus-400k | d93aa9415b25df69eba6a72df98b0bc30ad5c1de | 2021-10-28T13:55:32.000Z | [
"pytorch",
"sew-d",
"feature-extraction",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"transformers",
"speech",
"license:apache-2.0"
] | feature-extraction | false | asapp | null | asapp/sew-d-base-plus-400k | 38 | null | transformers | 6,573 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# SEW-D-base+
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this mod... |
bhavikardeshna/multilingual-bert-base-cased-vietnamese | 389f1f11625880b00408b04071e601d88936320b | 2021-12-21T11:44:14.000Z | [
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"transformers",
"autotrain_compatible"
] | question-answering | false | bhavikardeshna | null | bhavikardeshna/multilingual-bert-base-cased-vietnamese | 38 | null | transformers | 6,574 | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},... |
chinhon/pegasus-newsroom-summarizer_02 | d414b047bcbd01efa2e62a062f6fe8d4d5b5cc9e | 2021-11-06T02:55:10.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | chinhon | null | chinhon/pegasus-newsroom-summarizer_02 | 38 | 1 | transformers | 6,575 | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-newsroom-summarizer_02
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... |
danurahul/alex_gpt3_endoftext | 9472e564ff4306b8ef9387faa10dddbcb636ef8b | 2021-05-21T15:20:28.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | danurahul | null | danurahul/alex_gpt3_endoftext | 38 | null | transformers | 6,576 | Entry not found |
deepset/tinyroberta-squad2-step1 | 8289712ae4b3799ae63cdf0cdf1b321d0c9baac7 | 2022-02-15T14:29:07.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/tinyroberta-squad2-step1 | 38 | null | transformers | 6,577 | Entry not found |
educhav/Austin-DialoGPT-small | 72b5f77232ab9c085aa141971333de2372119bc9 | 2022-01-22T07:00:24.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | educhav | null | educhav/Austin-DialoGPT-small | 38 | null | transformers | 6,578 | ---
tags:
- conversational
---
# Austin Medina |
flax-community/roberta-hindi | 81f8b41477e02b631eac5fbbc6493ee57c8108ac | 2021-07-20T12:50:29.000Z | [
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | flax-community | null | flax-community/roberta-hindi | 38 | 1 | transformers | 6,579 | ---
widget:
- text: "मुझे उनसे बात करना <mask> अच्छा लगा"
- text: "हम आपके सुखद <mask> की कामना करते हैं"
- text: "सभी अच्छी चीजों का एक <mask> होता है"
---
# RoBERTa base model for Hindi language
Pretrained model on Hindi language using a masked language modeling (MLM) objective. [A more interactive & comparison dem... |
google/bert_uncased_L-10_H-256_A-4 | 652c66397cc8b8db62c1c35ab290d55ef3239c44 | 2021-05-19T17:23:44.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-10_H-256_A-4 | 38 | null | transformers | 6,580 | ---
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... |
google/tapas-medium-finetuned-wikisql-supervised | c0429384d7ca2bd8bfe64a8e1d2f00d519299b5b | 2021-11-29T13:06:28.000Z | [
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikisql",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1709.00103",
"transformers",
"license:apache-2.0"
] | table-question-answering | false | google | null | google/tapas-medium-finetuned-wikisql-supervised | 38 | null | transformers | 6,581 | ---
language: en
tags:
- tapas
license: apache-2.0
datasets:
- wikisql
---
# TAPAS medium model fine-tuned on WikiSQL (in a supervised fashion)
his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_medium_reset` checkpoint of the [original Github repository... |
gurkan08/bert-turkish-text-classification | 1a8ddc4b9c3818cb734b5c14b90141338a8e64d1 | 2021-05-19T17:50:18.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"tr",
"transformers"
] | text-classification | false | gurkan08 | null | gurkan08/bert-turkish-text-classification | 38 | 1 | transformers | 6,582 | ---
language: tr
---
# Turkish News Text Classification
Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)
# Dataset
Dataset consists of 11 classes were obtained from https://www.trthaber.com/. The model was created using the most distinctive 6 classe... |
huggingtweets/bitcoin | ef6f3c42f4645f74fca90669420836ae1b9032aa | 2021-05-21T20:44:55.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/bitcoin | 38 | null | transformers | 6,583 | ---
language: en
thumbnail: https://www.huggingtweets.com/bitcoin/1612625608055/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color:... |
huggingtweets/michaeljackson | f29d75b3690f5a345e11f376ee47e983010d8249 | 2021-05-22T14:22:13.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/michaeljackson | 38 | null | transformers | 6,584 | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
... |
iamtarun/wav2vec-osr | 74950d7c4d19662200990eba4085543a321296b1 | 2021-11-04T15:08:10.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"transformers",
"audio",
"speech to text",
"license:apache-2.0"
] | automatic-speech-recognition | false | iamtarun | null | iamtarun/wav2vec-osr | 38 | null | transformers | 6,585 | ---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
- speech to text
license: apache-2.0
widget:
- example_title: OSR sample 1
src: https://github.com/TheSoundOfAIOSR/rg_speech_to_text/blob/main/data/finetuning-dataset/audiofiles/TA-5.wav?raw=true
- example_title: OSR sample 2
... |
jordan-m-young/buzz-article-gpt-2 | 1893609bd968d9f413d0cc84c4e0859c67907024 | 2021-05-23T06:03:38.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | jordan-m-young | null | jordan-m-young/buzz-article-gpt-2 | 38 | null | transformers | 6,586 | Entry not found |
kssteven/ibert-roberta-large | 202dedcec60c0aece82a3c4d424cb7505efcb31f | 2021-05-10T05:34:01.000Z | [
"pytorch",
"ibert",
"fill-mask",
"arxiv:1907.11692",
"arxiv:2101.01321",
"transformers",
"autotrain_compatible"
] | fill-mask | false | kssteven | null | kssteven/ibert-roberta-large | 38 | null | transformers | 6,587 | # I-BERT large model
This model, `ibert-roberta-large`, is an integer-only quantized version of [RoBERTa](https://arxiv.org/abs/1907.11692), and was introduced in [this papaer](https://arxiv.org/abs/2101.01321).
I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-o... |
describeai/gemini | aa5c52e95888664ccbb83d868de3b7b26ae123c3 | 2022-05-14T00:46:52.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"Explain code",
"Code Summarization",
"Summarization",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | describeai | null | describeai/gemini | 38 | 0 | transformers | 6,588 | ---
language: en
tags:
- Explain code
- Code Summarization
- Summarization
license: mit
---
# Gemini
For in-depth understanding of our model and methods, please see our blog [here](https://www.describe-ai.com/gemini)
## Model description
Gemini is a transformer based on Google's T5 model. The model is pre-trained... |
mervenoyan/PubMedBERT-QNLI | 8bd833ad8f65c11553c0b7c9230323aed04c4df9 | 2021-08-26T10:27:15.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | mervenoyan | null | mervenoyan/PubMedBERT-QNLI | 38 | 7 | transformers | 6,589 |
# PubMedBERT Abstract + Full Text Fine-Tuned on QNLI Task
Use case: You can use it to search through a document for a given question, to see if your question is answered in that document.
LABEL0 is "not entailment" meaning your question is not answered by the context and LABEL1 is "entailment" meaning your question ... |
mrm8488/spanbert-base-finetuned-tacred | 52aefc31f92dda6435312f6c785ddb7506e6d218 | 2021-05-20T00:53:07.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"arxiv:1907.10529",
"transformers"
] | feature-extraction | false | mrm8488 | null | mrm8488/spanbert-base-finetuned-tacred | 38 | null | transformers | 6,590 | ---
language: en
thumbnail:
---
# SpanBERT base fine-tuned on TACRED
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [TACRED](https://nlp.stanford.edu/projects/tacred/) dataset by [them](https://github.com/facebookresearch/... |
mrm8488/spanish-t5-small-sqac-for-qa | a1da1f72dc52c2812517103a37ce98426d039430 | 2021-09-03T10:22:10.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"es",
"dataset:BSC-TeMU/SQAC",
"transformers",
"QA",
"Q&A",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/spanish-t5-small-sqac-for-qa | 38 | 3 | transformers | 6,591 | ---
language: es
tags:
- QA
- Q&A
datasets:
- BSC-TeMU/SQAC
widget:
- text: "question: ¿Cuál es el nombre que se le da a la unidad morfológica y funcional de los seres vivos? context: La célula (del latín cellula, diminutivo de cella, ‘celda’) es la unidad morfológica y funcional de todo ser vivo. De hecho, la célula e... |
mrm8488/t5-small-finetuned-squadv2 | 7bea82cd43074683a78620325dc0474dc97d8e85 | 2021-05-06T16:25:28.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:squad_v2",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-small-finetuned-squadv2 | 38 | 1 | transformers | 6,592 | ---
language: en
datasets:
- squad_v2
---
# T5-small fine-tuned on SQuAD v2
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [(small)](https://huggingface.co/t5-small) fine-tuned on [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
## Detail... |
ncduy/roberta-imdb-sentiment-analysis | 031415a39e5f4a2e92fff93c5637fbfb28c78674 | 2021-08-09T10:54:50.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | ncduy | null | ncduy/roberta-imdb-sentiment-analysis | 38 | null | transformers | 6,593 | Entry not found |
nguyenvulebinh/spelling-oov | 48687d18de1e89f1d0ddba0eb4e686d3a4d67264 | 2021-12-15T17:00:58.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | nguyenvulebinh | null | nguyenvulebinh/spelling-oov | 38 | null | transformers | 6,594 | ```python
from transformers import EncoderDecoderModel
from importlib.machinery import SourceFileLoader
from transformers.file_utils import cached_path, hf_bucket_url
import torch
import os
## Load model & tokenizer
cache_dir='./cache'
model_name='nguyenvulebinh/spelling-oov'
def download_tokenizer_files():
resou... |
nyu-mll/roberta-med-small-1M-3 | 1c7750b0a6258ca88958e1b9741f77d07e0fd4d3 | 2021-05-20T19:09:09.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nyu-mll | null | nyu-mll/roberta-med-small-1M-3 | 38 | null | transformers | 6,595 | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... |
pierreguillou/t5-base-qa-squad-v1.1-portuguese | 21c3e49be1ce1a083ba4278427dc7799592ab33c | 2022-01-27T14:38:28.000Z | [
"pytorch",
"t5",
"text2text-generation",
"pt",
"dataset:squad",
"dataset:squad_v1_pt",
"transformers",
"qa",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | pierreguillou | null | pierreguillou/t5-base-qa-squad-v1.1-portuguese | 38 | 4 | transformers | 6,596 | ---
language:
- pt
tags:
- text2text-generation
- t5
- pytorch
- qa
datasets:
- squad
- squad_v1_pt
metrics:
- precision
- recall
- f1
- accuracy
- squad
model-index:
- name: checkpoints
results:
- task:
name: text2text-generation
type: text2text-generation
dataset:
name: squad
type: sq... |
robkayinto/distilbert-base-uncased-finetuned-emotion | 45bd8e9904cf14c16d892e78269d5b5be9aeb6a5 | 2022-05-31T15:17:31.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | robkayinto | null | robkayinto/distilbert-base-uncased-finetuned-emotion | 38 | null | transformers | 6,597 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default... |
sentence-transformers/nli-bert-base-cls-pooling | e59f6ff65548f7fa407ddd9b4e2754454a7a36e5 | 2021-08-05T08:27:14.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/nli-bert-base-cls-pooling | 38 | null | sentence-transformers | 6,598 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... |
veronica320/TE-for-Event-Extraction | 38f55b8c833f880a487358e42e6ed5419f93a039 | 2021-07-30T23:11:05.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | veronica320 | null | veronica320/TE-for-Event-Extraction | 38 | null | transformers | 6,599 | # TE-for-Event-Extraction
## Model description
This is a TE model as part of the event extraction system in the ACL2021 paper: [Zero-shot Event Extraction via Transfer Learning: Challenges and Insights](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co... |
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