modelId stringlengths 4 112 | sha stringlengths 40 40 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringclasses 29
values | private bool 1
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sudo-s/exper_batch_16_e8 | b8556977f61df28e344812e2cb7917909fbb20fa | 2022-06-26T22:18:36.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | sudo-s | null | sudo-s/exper_batch_16_e8 | 151 | null | transformers | 4,000 | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_16_e8
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... |
SetFit/distilbert-base-uncased__enron_spam__all-train | becd769a06f34c608663d0e04bda5fc3a8e04268 | 2022-01-26T21:35:28.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | SetFit | null | SetFit/distilbert-base-uncased__enron_spam__all-train | 150 | null | transformers | 4,001 | Entry not found |
beomi/kcgpt2 | 5554362307183649f72b31624203308a96392c56 | 2021-11-22T16:02:10.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | beomi | null | beomi/kcgpt2 | 150 | 1 | transformers | 4,002 | Entry not found |
deepset/gbert-large-sts | df9bf7c1d1f0482e9109c07d82c3ce7ab6895046 | 2021-10-21T12:16:36.000Z | [
"pytorch",
"bert",
"text-classification",
"de",
"transformers",
"exbert",
"license:mit"
] | text-classification | false | deepset | null | deepset/gbert-large-sts | 150 | 5 | transformers | 4,003 | ---
language: de
license: mit
tags:
- exbert
---
## Overview
**Language model:** gbert-large-sts
**Language:** German
**Training data:** German STS benchmark train and dev set
**Eval data:** German STS benchmark test set
**Infrastructure**: 1x V100 GPU
**Published**: August 12th, 2021
## Details
- We traine... |
julien-c/reactiongif-roberta | 0d81232b9f158b051a4caee860ad287b08940d48 | 2021-06-11T15:59:26.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:julien-c/reactiongif",
"transformers",
"generated-from-trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | julien-c | null | julien-c/reactiongif-roberta | 150 | 1 | transformers | 4,004 | ---
license: apache-2.0
tags:
- generated-from-trainer
datasets:
- julien-c/reactiongif
metrics:
- accuracy
model-index:
- name: model
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.2662102282047272
---
<... |
mrm8488/umberto-wikipedia-uncased-v1-finetuned-squadv1-it | f25016c4813e79ba2f3f663a93524c2a68b785a6 | 2020-12-11T21:56:44.000Z | [
"pytorch",
"camembert",
"question-answering",
"it",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/umberto-wikipedia-uncased-v1-finetuned-squadv1-it | 150 | null | transformers | 4,005 | ---
language: it
---
# UmBERTo Wikipedia Uncased + italian SQuAD v1 📚 🧐 ❓
[UmBERTo-Wikipedia-Uncased](https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1) fine-tuned on [Italian SQUAD v1 dataset](https://github.com/crux82/squad-it) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - ... |
uer/roberta-tiny-word-chinese-cluecorpussmall | 17eeb8ac048438331c97281b3142afc7827af294 | 2022-02-19T15:57:24.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uer | null | uer/roberta-tiny-word-chinese-cluecorpussmall | 150 | 1 | transformers | 4,006 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "最近一趟去北京的[MASK]几点发车"
---
# Chinese word-based RoBERTa Miniatures
## Model description
This is the set of 5 Chinese word-based RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/... |
zhufy/xquad-th-mbert-base | 48bcc21b1821b1d36d00d1eeecc1e6732a2d4538 | 2022-04-23T05:07:59.000Z | [
"pytorch",
"bert",
"question-answering",
"Thai",
"dataset:xquad.th",
"transformers",
"bert-base",
"autotrain_compatible"
] | question-answering | false | zhufy | null | zhufy/xquad-th-mbert-base | 150 | null | transformers | 4,007 | ---
language: Thai
task: extractive question answering
datasets: xquad.th
tags:
- bert-base
---
# Model Description
This model is for Thai extractive question answering. It is based on the multilingual BERT [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model, and it is case-sen... |
eslamxm/mbart-finetune-en-cnn | 2b086abad9a7c4f45730188d2b7056580721b628 | 2022-06-17T16:48:32.000Z | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"summarization",
"en",
"seq2seq",
"Abstractive Summarization",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | summarization | false | eslamxm | null | eslamxm/mbart-finetune-en-cnn | 150 | null | transformers | 4,008 | ---
tags:
- summarization
- en
- seq2seq
- mbart
- Abstractive Summarization
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: mbert-finetune-en-cnn
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably ... |
ricardo-filho/bert_base_tcm_0.7 | 6f6251c0533dd90f79f7a587ff5c9972d719e8dc | 2022-06-17T13:40:22.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | ricardo-filho | null | ricardo-filho/bert_base_tcm_0.7 | 150 | null | transformers | 4,009 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bert_base_tcm_0.7
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_base_tcm_0.7
This ... |
zhifei/autotrain-autotrain-chinese-title-summarization-9-1101340178 | dc42d4057c06576b99763c108124aecea13b0b56 | 2022-07-07T10:49:19.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"unk",
"dataset:zhifei/autotrain-data-autotrain-chinese-title-summarization-9",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | zhifei | null | zhifei/autotrain-autotrain-chinese-title-summarization-9-1101340178 | 150 | null | transformers | 4,010 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- zhifei/autotrain-data-autotrain-chinese-title-summarization-9
co2_eq_emissions: 1.565396518204961
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1101340178
- CO2 Emissions (in grams): 1.565396518204961
... |
pszemraj/grammar-synthesis-base | 4b24c4281298966b47251d88feada34754849af6 | 2022-07-22T08:30:42.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:jfleg",
"arxiv:2107.06751",
"transformers",
"grammar",
"spelling",
"punctuation",
"error-correction",
"grammar synthesis",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | pszemraj | null | pszemraj/grammar-synthesis-base | 150 | null | transformers | 4,011 | ---
license: cc-by-nc-sa-4.0
tags:
- grammar
- spelling
- punctuation
- error-correction
- grammar synthesis
datasets:
- jfleg
widget:
- text: "i can has cheezburger"
example_title: "cheezburger"
- text: "There car broke down so their hitching a ride to they're class."
example_title: "compound-1"
- text: "so em if ... |
ThomasNLG/t5-qg_webnlg_synth-en | 10d5367deb88263e2fcb3a25ae6eb8bd93aedebf | 2021-07-09T07:45:44.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:squad_v2",
"arxiv:2104.07555",
"transformers",
"qa",
"question",
"generation",
"SQuAD",
"data2text",
"metric",
"nlg",
"t5-small",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | ThomasNLG | null | ThomasNLG/t5-qg_webnlg_synth-en | 149 | 2 | transformers | 4,012 | ---
language: en
tags:
- qa
- question
- generation
- SQuAD
- data2text
- metric
- nlg
- t5-small
license: mit
datasets:
- squad_v2
model-index:
- name: t5-qg_webnlg_synth-en
results:
- task:
name: Data Question Generation
type: Text To Text Generation
widget:
- text: "The Eagle </s> name [ The Eagle... |
asapp/sew-mid-100k | 8ea839f6cd7d4258bca9c76c0790e2041081f728 | 2021-10-26T19:38:18.000Z | [
"pytorch",
"sew",
"feature-extraction",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"transformers",
"speech",
"license:apache-2.0"
] | feature-extraction | false | asapp | null | asapp/sew-mid-100k | 149 | null | transformers | 4,013 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# SEW-mid
[SEW 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 model sho... |
bespin-global/klue-roberta-small-3i4k-intent-classification | cd3371a10fc8d6da5be80d66cc212a07d3e6aaed | 2021-12-20T05:56:59.000Z | [
"pytorch",
"tf",
"roberta",
"text-classification",
"ko",
"dataset:kor_3i4k",
"transformers",
"intent-classification",
"license:cc-by-nc-4.0"
] | text-classification | false | bespin-global | null | bespin-global/klue-roberta-small-3i4k-intent-classification | 149 | 2 | transformers | 4,014 | ---
language: ko
tags:
- intent-classification
datasets:
- kor_3i4k
license: cc-by-nc-4.0
---
## Finetuning
- Pretrain Model : [klue/roberta-small](https://github.com/KLUE-benchmark/KLUE)
- Dataset for fine-tuning : [3i4k](https://github.com/warnikchow/3i4k)
- Train : 46,863
- Validation : 8,271 (15% of Train)
... |
ml6team/mbart-large-cc25-cnn-dailymail-nl-finetune | 695b2d48d52ff60ee5cae7395d02f7347e40e94d | 2022-05-16T11:41:05.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"nl",
"dataset:ml6team/cnn_dailymail_nl",
"transformers",
"bart",
"summarization",
"autotrain_compatible"
] | summarization | false | ml6team | null | ml6team/mbart-large-cc25-cnn-dailymail-nl-finetune | 149 | 9 | transformers | 4,015 | ---
language:
- nl
tags:
- mbart
- bart
- summarization
datasets:
- ml6team/cnn_dailymail_nl
pipeline_tag: summarization
widget:
- text: 'Het jongetje werd eind april met zwaar letsel naar het ziekenhuis gebracht in Maastricht. Drie weken later overleed het kindje als gevolg van het letsel. Onderzoek moet nog uitwijze... |
mrm8488/distilroberta-finetuned-tweets-hate-speech | 9ddfe248ddc5e5e916c43ef64bfdf8406dbfe266 | 2021-05-20T18:25:15.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"dataset:tweets_hate_speech_detection",
"transformers",
"twitter",
"hate",
"speech"
] | text-classification | false | mrm8488 | null | mrm8488/distilroberta-finetuned-tweets-hate-speech | 149 | 3 | transformers | 4,016 | ---
language: en
tags:
- twitter
- hate
- speech
datasets:
- tweets_hate_speech_detection
widget:
- text: "the fuck done with #mansplaining and other bullshit."
---
# distilroberta-base fine-tuned on tweets_hate_speech_detection dataset for hate speech detection
Validation accuray: 0.98 |
ydshieh/tiny-random-gptj-for-sequence-classification | 265b0629231ade56e81aa62e4a8c4623970a7cf9 | 2022-04-08T10:21:26.000Z | [
"pytorch",
"tf",
"gptj",
"text-classification",
"transformers"
] | text-classification | false | ydshieh | null | ydshieh/tiny-random-gptj-for-sequence-classification | 149 | null | transformers | 4,017 | Entry not found |
lightonai/RITA_l | b38b5daa35b416f710216305c7258e3138503b62 | 2022-05-19T08:23:12.000Z | [
"pytorch",
"rita",
"text-generation",
"protein",
"dataset:uniref-100",
"arxiv:2205.05789",
"transformers"
] | text-generation | false | lightonai | null | lightonai/RITA_l | 149 | null | transformers | 4,018 | ---
language: protein
tags:
- protein
datasets:
- uniref-100
---
# RITA-L
RITA is a family of autoregressive protein models, developed by a collaboration of [Lighton](https://lighton.ai/), the [OATML group](https://oatml.cs.ox.ac.uk/) at Oxford, and the [Debbie Marks Lab](https://www.deboramarkslab.com/) at Harvard.
... |
rowidabelaal/wordpred_arabert | 236fb04271eeaeb770c9e18938622f34d3d8f8ad | 2022-05-12T19:41:21.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | rowidabelaal | null | rowidabelaal/wordpred_arabert | 149 | null | transformers | 4,019 | Entry not found |
pszemraj/gpt2-medium-email-generation | ae52296cb2964425706ec826cf12595b5a7295fe | 2022-07-27T10:46:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"dataset:aeslc",
"transformers",
"generated_from_trainer",
"email generation",
"email",
"license:mit"
] | text-generation | false | pszemraj | null | pszemraj/gpt2-medium-email-generation | 149 | 1 | transformers | 4,020 | ---
license:
- mit
tags:
- generated_from_trainer
- email generation
- email
datasets:
- aeslc
widget:
- text: "Hey <NAME>,\n\nThank you for signing up for my weekly newsletter. Before we get started, you'll have to confirm your email address."
example_title: "newsletter"
- text: "Hi <NAME>,\n\nI hope this email fi... |
albert-xlarge-v1 | a74dd20bb0c3d36e36938da0b86adc5d2ec60315 | 2021-01-13T15:30:39.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | albert-xlarge-v1 | 148 | null | transformers | 4,021 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT XLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-re... |
Geotrend/distilbert-base-en-cased | 7c8066c5447b0aeee182db36d1e1cecf38b38860 | 2021-08-16T13:16:37.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Geotrend | null | Geotrend/distilbert-base-en-cased | 148 | null | transformers | 4,022 | ---
language: en
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations pro... |
Helsinki-NLP/opus-mt-pl-es | 46860c7251776618d4decd484f405ff0593fe858 | 2021-09-10T14:01:19.000Z | [
"pytorch",
"marian",
"text2text-generation",
"pl",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-pl-es | 148 | null | transformers | 4,023 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-pl-es
* source languages: pl
* target languages: es
* OPUS readme: [pl-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pl-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Muennighoff/SGPT-125M-weightedmean-nli-bitfit | f0dd127410940d2c0b30cc1f02a81a0ad28d3cc7 | 2022-06-18T12:55:07.000Z | [
"pytorch",
"gpt_neo",
"feature-extraction",
"arxiv:2202.08904",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | Muennighoff | null | Muennighoff/SGPT-125M-weightedmean-nli-bitfit | 148 | null | sentence-transformers | 4,024 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-125M-weightedmean-nli-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our... |
airesearch/wangchanberta-base-wiki-20210520-spm-finetune-qa | 81c21061d2ab35f9d60597be8273679e1954f437 | 2021-09-11T09:28:19.000Z | [
"pytorch",
"camembert",
"question-answering",
"th",
"transformers",
"autotrain_compatible"
] | question-answering | false | airesearch | null | airesearch/wangchanberta-base-wiki-20210520-spm-finetune-qa | 148 | null | transformers | 4,025 | ---
language: th
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) ... |
clue/roberta_chinese_base | 9328ef0a5bea8a3a0085a190af7c7148417dc66e | 2021-05-20T15:23:58.000Z | [
"pytorch",
"jax",
"roberta",
"zh",
"transformers"
] | null | false | clue | null | clue/roberta_chinese_base | 148 | 2 | transformers | 4,026 | ---
language: zh
---
## roberta_chinese_base
### Overview
**Language model:** roberta-base
**Model size:** 392M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on... |
flyhero/gpt-j-6B | c7aaf3b7617a255aee737e38d44a18da649a07e7 | 2021-08-19T05:47:39.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | flyhero | null | flyhero/gpt-j-6B | 148 | 7 | transformers | 4,027 | ### Model Description
GPT-J 6B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-J refers to the class of models, while 6B represents the number of parameters of this particular pre-trained model.
The original GPT-J-6B model is trained with TPUs, which is not easy to use for... |
hyesunyun/update-summarization-bart-large-longformer | b7ebb57f52314c93149cc1efa26621dadca43b63 | 2022-04-21T15:46:55.000Z | [
"pytorch",
"led",
"text2text-generation",
"en",
"transformers",
"update summarization",
"longformer",
"BART",
"autotrain_compatible"
] | text2text-generation | false | hyesunyun | null | hyesunyun/update-summarization-bart-large-longformer | 148 | null | transformers | 4,028 | ---
language:
- en
tags:
- update summarization
- longformer
- transformers
- BART
metrics:
- edit distance
- ROUGE
- BertScore
---
# Update Summarization with BART Large and Longformer Encoder Decoder
## Model description
This model is a Transformer-based model that supports long document generative sequence-to-se... |
lgris/wav2vec2-large-xlsr-open-brazilian-portuguese-v2 | 1019e06ef5b5d61bf5d56bed7b8e7557084f20ec | 2022-04-01T20:35:26.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"arxiv:2012.03411",
"transformers",
"audio",
"speech",
"portuguese-speech-corpus",
"PyTorch",
"hf-asr-leaderboard",
"license:apac... | automatic-speech-recognition | false | lgris | null | lgris/wav2vec2-large-xlsr-open-brazilian-portuguese-v2 | 148 | 4 | transformers | 4,029 | ---
language: pt
datasets:
- common_voice
- mls
- cetuc
- lapsbm
- voxforge
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
- hf-asr-leaderboard
model-index:
- name: wav2vec2-large-xlsr-open-brazilian-portuguese-v2
results:
- task:
... |
Matej/bert-base-buddhist-sanskrit | 0cc5dbdae27fec90217e4c5f82b1af86450b4b52 | 2022-04-15T08:54:45.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Matej | null | Matej/bert-base-buddhist-sanskrit | 148 | null | transformers | 4,030 | ---
tags:
- Buddhist Sanskrit
- BERT
- name: bert-base-buddhist-sanskrit
---
<!-- 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-base-buddhist-sanskrit
The best performing mod... |
has-abi/distilBERT-finetuned-resumes-sections | 1e3cc903614b8398834ec8d9da059b92015ff3fc | 2022-07-22T09:47:33.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | has-abi | null | has-abi/distilBERT-finetuned-resumes-sections | 148 | null | transformers | 4,031 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: distilBERT-finetuned-resumes-sections
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 ... |
VictorSanh/roberta-base-finetuned-yelp-polarity | b16b941c51bcec0298e9ee5eab2c1e2602e1a141 | 2021-05-20T12:30:20.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"dataset:yelp_polarity",
"transformers"
] | text-classification | false | VictorSanh | null | VictorSanh/roberta-base-finetuned-yelp-polarity | 147 | 1 | transformers | 4,032 | ---
language: en
datasets:
- yelp_polarity
---
# RoBERTa-base-finetuned-yelp-polarity
This is a [RoBERTa-base](https://huggingface.co/roberta-base) checkpoint fine-tuned on binary sentiment classifcation from [Yelp polarity](https://huggingface.co/nlp/viewer/?dataset=yelp_polarity).
It gets **98.08%** accuracy on the... |
akhooli/personachat-arabic | b5f47486bdda2d38da9f27c9e10df2c716268ce8 | 2021-05-21T12:39:24.000Z | [
"pytorch",
"gpt2",
"ar",
"transformers",
"conversational",
"license:mit"
] | conversational | false | akhooli | null | akhooli/personachat-arabic | 147 | null | transformers | 4,033 | ---
tags:
- conversational
language:
- ar
license: mit
---
## personachat-arabic (conversational AI)
This is personachat-arabic, using a subset from the persona-chat validation dataset, machine translated to Arabic (from English)
and fine-tuned from [akhooli/gpt2-small-arabic](https://huggingface.co/akhooli/gpt2-small... |
google/bert_uncased_L-10_H-512_A-8 | 386dd3983f52f94116d5add2abc7bb5f1ed167ba | 2021-05-19T17:24:16.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-10_H-512_A-8 | 147 | null | transformers | 4,034 | ---
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... |
kleinay/nominalization-candidate-classifier | 45e4bddfa88473e390d89cab5de5868e7a8c2050 | 2022-01-11T04:12:39.000Z | [
"pytorch",
"bert",
"token-classification",
"en",
"dataset:kleinay/qanom",
"transformers",
"nominalizations",
"autotrain_compatible"
] | token-classification | false | kleinay | null | kleinay/nominalization-candidate-classifier | 147 | null | transformers | 4,035 | ---
language:
- en
tags:
- pytorch
- token-classification
- nominalizations
datasets:
- kleinay/qanom
---
# Nominalization Detector
This model identifies "predicative nominalizations", that is, nominalizations that carry an eventive (or "verbal") meaning in context. It is a `bert-base-cased` pretrained model, fine-tu... |
mpoyraz/wav2vec2-xls-r-300m-cv6-turkish | 787df48a382597a0112afe7830a6c0258f4d8044 | 2022-03-23T18:26:27.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | mpoyraz | null | mpoyraz/wav2vec2-xls-r-300m-cv6-turkish | 147 | 1 | transformers | 4,036 | ---
license: apache-2.0
language: tr
tags:
- automatic-speech-recognition
- common_voice
- hf-asr-leaderboard
- robust-speech-event
- tr
datasets:
- common_voice
model-index:
- name: mpoyraz/wav2vec2-xls-r-300m-cv6-turkish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recogn... |
mrm8488/bert-base-german-finetuned-ler | 025ad559e4cefe898079e3cbaf89f6a54b00636d | 2021-05-20T00:20:06.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"de",
"transformers",
"autotrain_compatible"
] | token-classification | false | mrm8488 | null | mrm8488/bert-base-german-finetuned-ler | 147 | null | transformers | 4,037 | ---
language: de
---
# German BERT + LER (Legal Entity Recognition) ⚖️
German BERT ([BERT-base-german-cased](https://huggingface.co/bert-base-german-cased)) fine-tuned on [Legal-Entity-Recognition](https://github.com/elenanereiss/Legal-Entity-Recognition) dataset for **LER** (NER) downstream task.
## Details of the ... |
patrickvonplaten/wav2vec2-2-bart-base | 109c6165bef1351c39403fa008c5e72c89530167 | 2021-12-29T15:53:10.000Z | [
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"transformers",
"librispeech_asr",
"generated_from_trainer",
"asr_seq2esq",
"model-index"
] | automatic-speech-recognition | false | patrickvonplaten | null | patrickvonplaten/wav2vec2-2-bart-base | 147 | 2 | transformers | 4,038 | ---
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
- asr_seq2esq
model-index:
- name: wav2vec2-2-bart-base
results: []
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https:/... |
tals/albert-base-vitaminc-fever | 15de029c9f4e929c8f6cc56bc39fcac22f6bdbc3 | 2022-06-22T23:57:17.000Z | [
"pytorch",
"albert",
"text-classification",
"python",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"transformers"
] | text-classification | false | tals | null | tals/albert-base-vitaminc-fever | 147 | null | transformers | 4,039 | ---
language: python
datasets:
- fever
- glue
- tals/vitaminc
---
# Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this m... |
vblagoje/dpr-ctx_encoder-single-lfqa-base | 57dcaa1817f316e6c284bf9db697914225c607b7 | 2022-01-17T15:34:10.000Z | [
"pytorch",
"dpr",
"en",
"dataset:vblagoje/lfqa",
"transformers",
"license:mit"
] | null | false | vblagoje | null | vblagoje/dpr-ctx_encoder-single-lfqa-base | 147 | null | transformers | 4,040 | ---
language: en
datasets:
- vblagoje/lfqa
license: mit
---
## Introduction
The context/passage encoder model based on [DPRContextEncoder](https://huggingface.co/docs/transformers/master/en/model_doc/dpr#transformers.DPRContextEncoder) architecture. It uses the transformer's pooler outputs as context/passage represent... |
silencesys/paraphrase-xlm-r-multilingual-v1-fine-tuned-for-latin | 1fca272cc64dc4dda8b456dc8bbc3b82d489b397 | 2022-04-12T17:08:30.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | silencesys | null | silencesys/paraphrase-xlm-r-multilingual-v1-fine-tuned-for-latin | 147 | null | sentence-transformers | 4,041 | ---
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... |
nbroad/bigbird-base-health-fact | 21b2deb28b7fd1bfa1b7df9e40a3dd86040dbcf4 | 2022-06-29T18:29:17.000Z | [
"pytorch",
"big_bird",
"text-classification",
"en",
"dataset:health_fact",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | nbroad | null | nbroad/bigbird-base-health-fact | 147 | null | transformers | 4,042 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- health_fact
model-index:
- name: bigbird-base-health-fact
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: health_fact
type: health_fact
split: test
metrics:
... |
vitouphy/wav2vec2-xls-r-300m-timit-phoneme | bef918530717a49a4c5cf20bc4e364e885cb6a5b | 2022-06-29T12:37:23.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | vitouphy | null | vitouphy/wav2vec2-xls-r-300m-timit-phoneme | 147 | null | transformers | 4,043 | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- pytorch
- transformers
- en
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-300m-phoneme
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: DARPA TIMIT
type... |
juridics/jurisbert-base-portuguese-uncased | 29c1d9c8a4444029c2583a23ff8d9395f3733112 | 2022-07-02T13:44:21.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | juridics | null | juridics/jurisbert-base-portuguese-uncased | 147 | null | transformers | 4,044 | ---
tags:
- generated_from_trainer
model-index:
- name: bertlawbr
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. -->
# bertlawbr
This model is a fine-tuned version... |
NbAiLab/nb-wav2vec2-300m-bokmaal | ce53dd19f2b45a32c6e150496db480b53c5aa7e6 | 2022-06-13T10:54:49.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"nb-NO",
"dataset:NbAiLab/NPSC",
"transformers",
"NbAiLab/NPSC",
"no",
"nb",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | NbAiLab | null | NbAiLab/nb-wav2vec2-300m-bokmaal | 146 | null | transformers | 4,045 | ---
license: apache-2.0
tags:
- automatic-speech-recognition
- NbAiLab/NPSC
- no
- nb
- nb-NO
datasets:
- NbAiLab/NPSC
language:
- nb-NO
model-index:
- name: nb-wav2vec2-300m-bokmaal
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
... |
ThomasNLG/t5-weighter_cnndm-en | bf39bc60189bedbbf4980588a32b27ffbe4b85f1 | 2021-07-09T07:45:02.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:squad",
"dataset:cnndm",
"arxiv:2103.12693",
"transformers",
"qa",
"classification",
"question",
"answering",
"SQuAD",
"metric",
"nlg",
"t5-small",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | ThomasNLG | null | ThomasNLG/t5-weighter_cnndm-en | 146 | null | transformers | 4,046 | ---
language: en
tags:
- qa
- classification
- question
- answering
- SQuAD
- metric
- nlg
- t5-small
license: mit
datasets:
- squad
- cnndm
model-index:
- name: t5-weighter_cnndm-en
results:
- task:
name: Classification
type: Question Weighter
widget:
- text: "a Buckingham Palace guard </s> Who felt... |
airKlizz/t5-base-multi-fr-wiki-news | 3cedcec1afc1e62ccc2d0f701b34805645deff1e | 2021-10-17T20:09:42.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"fr",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | airKlizz | null | airKlizz/t5-base-multi-fr-wiki-news | 146 | null | transformers | 4,047 | ---
language: fr
license: mit
---
|
alienspaceman/rus_dreamgen_fulltext_medium | c4e352a97929f34f8a5d9db3801a1bc17fb6d9c0 | 2021-05-21T13:06:00.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | alienspaceman | null | alienspaceman/rus_dreamgen_fulltext_medium | 146 | null | transformers | 4,048 | Entry not found |
m3hrdadfi/icelandic-ner-roberta | b361605cd6f50451d831bd8c8ef12b0cec9c9255 | 2021-05-27T17:13:07.000Z | [
"pytorch",
"tf",
"roberta",
"token-classification",
"is",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | m3hrdadfi | null | m3hrdadfi/icelandic-ner-roberta | 146 | null | transformers | 4,049 | ---
language: is
license: apache-2.0
widget:
- text: "Kristin manneskja getur ekki lagt frásagnir af Jesú Kristi á hilluna vegna þess að hún sé búin að lesa þær ."
- text: "Til hvers að kjósa flokk , sem þykist vera Jafnaðarmannaflokkur rétt fyrir kosningar , þegar að það er hægt að kjósa sannnan jafnaðarmannaflokk ,... |
razent/cotext-1-cc | 2def0f774019e02e1f26570b3f1c6689459dc397 | 2022-03-15T03:02:50.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"feature-extraction",
"code",
"dataset:code_search_net",
"transformers"
] | feature-extraction | false | razent | null | razent/cotext-1-cc | 146 | null | transformers | 4,050 | ---
language: code
datasets:
- code_search_net
---
# CoText (1-CC)
## Introduction
Paper: [CoTexT: Multi-task Learning with Code-Text Transformer](https://aclanthology.org/2021.nlp4prog-1.5.pdf)
Authors: _Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye_
## How to use
Support... |
ml6team/keyphrase-generation-keybart-inspec | efdb4ed322cf3d95cfaeb8d59486e80263346ac8 | 2022-06-16T18:02:45.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:midas/inspec",
"arxiv:2112.08547",
"transformers",
"keyphrase-generation",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | ml6team | null | ml6team/keyphrase-generation-keybart-inspec | 146 | null | transformers | 4,051 | ---
language: en
license: mit
tags:
- keyphrase-generation
datasets:
- midas/inspec
widget:
- text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document.
Thanks to these keyphrases humans can understand the content of a text very quickly and easily without re... |
oliverguhr/wav2vec2-large-xlsr-53-german-cv9 | 15d44405d5bda6bf702f4194727ac51067ba5942 | 2022-07-27T07:27:32.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_9_0",
"transformers",
"mozilla-foundation/common_voice_9_0",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | oliverguhr | null | oliverguhr/wav2vec2-large-xlsr-53-german-cv9 | 146 | null | transformers | 4,052 | ---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
model-index:
- name: wav2vec2-large-xlsr-53-german-cv9
results:
- task:
name: Automatic Speech Recognition
type: a... |
NlpHUST/t5-vi-en-base | 67987bafd5a35ece746cd7528d3a64f323e49ce3 | 2021-06-23T03:40:44.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | NlpHUST | null | NlpHUST/t5-vi-en-base | 145 | null | transformers | 4,053 | ---
language:
- vi
tags:
- t5
- seq2seq
# Machine translation for vietnamese
## Model Description
T5-vi-en-base is a transformer model for vietnamese machine translation designed using T5 architecture.
## Training data
T5-vi-en-base was trained on 4M sentence pairs (english,vietnamese)
### How to use
```py
from tran... |
cardiffnlp/bertweet-base-irony | 49963c2b5ddec67a8f0337345a6aaccea1d52505 | 2021-05-20T14:48:25.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/bertweet-base-irony | 145 | null | transformers | 4,054 | |
google/pegasus-aeslc | 0bc6bad1a4385c7d99237faf84d66e035dfb57ed | 2020-08-25T18:50:01.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | google | null | google/pegasus-aeslc | 145 | null | transformers | 4,055 | ---
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: [@... |
imvladikon/charbert-bert-wiki | efb4ab028d25eef2b0af50f327037c990229d831 | 2022-01-30T11:35:48.000Z | [
"pytorch",
"en",
"dataset:wikipedia",
"arxiv:2011.01513",
"transformers",
"language model"
] | null | false | imvladikon | null | imvladikon/charbert-bert-wiki | 145 | null | transformers | 4,056 | ---
language:
- en
tags:
- language model
datasets:
- wikipedia
---
pre-trained model from [CharBERT: Character-aware Pre-trained Language Model](https://github.com/wtma/CharBERT)
```
@misc{ma2020charbert,
title={CharBERT: Character-aware Pre-trained Language Model},
author={Wentao Ma and Yiming Cui a... |
mrm8488/t5-small-finetuned-wikiSQL | 056271dcea0ef8323a836f4f836a1632b0579234 | 2021-06-23T13:09:51.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:wikisql",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-small-finetuned-wikiSQL | 145 | 2 | transformers | 4,057 | ---
language: en
datasets:
- wikisql
---
# T5-small fine-tuned on WikiSQL
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [small](https://huggingface.co/t5-small) fine-tuned on [WikiSQL](https://github.com/salesforce/WikiSQL) for **English** to **SQL** **translation**.
## De... |
raynardj/wenyanwen-chinese-translate-to-ancient | a5d4494b86f434a71fcde04d09e8e8dd73687663 | 2021-11-29T14:42:25.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"zh",
"transformers",
"translation",
"文言文",
"ancient",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | raynardj | null | raynardj/wenyanwen-chinese-translate-to-ancient | 145 | 2 | transformers | 4,058 | ---
language:
- zh
- zh
tags:
- translation
- 文言文
- ancient
license: apache-2.0
widget:
- text: "轻轻的我走了，正如我轻轻的来。我轻轻的招手，作别西天的云彩。"
example_title: "再别康桥"
- text: "当恐惧逝去，我会打开心眼，看清它的轨迹。"
example_title: "沙丘"
- text: "暴力是无能者的最后手段"
example_title: "基地"
---
# From modern Chinese to Ancient Chinese
> This model translate ... |
rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment | efeb9edba760887149556619b6b81586027795f1 | 2021-05-19T00:35:16.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"hi",
"en",
"dataset:SAIL 2017",
"transformers",
"codemix",
"license:apache-2.0"
] | text-classification | false | rohanrajpal | null | rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment | 145 | null | transformers | 4,059 | ---
language:
- hi
- en
tags:
- hi
- en
- codemix
license: "apache-2.0"
datasets:
- SAIL 2017
metrics:
- fscore
- accuracy
---
# BERT codemixed base model for hinglish (cased)
## Model description
Input for the model: Any codemixed hinglish text
Output for the model: Sentiment. (0 - Negative, 1 - Neutral, 2 - Positi... |
zhufy/squad-en-bert-base | 413398c40a09b607b5bcc5dff592f456112dda89 | 2022-04-23T05:09:27.000Z | [
"pytorch",
"bert",
"question-answering",
"English",
"dataset:SQuAD 2.0",
"transformers",
"bert-base",
"autotrain_compatible"
] | question-answering | false | zhufy | null | zhufy/squad-en-bert-base | 145 | null | transformers | 4,060 | ---
language: English
task: extractive question answering
datasets: SQuAD 2.0
tags:
- bert-base
---
# Model Description
This model is for English extractive question answering. It is based on the [bert-base-cased](https://huggingface.co/bert-base-uncased) model, and it is case-sensitive: it makes a difference betwee... |
kakife3586/Null | b7de9ff384a3a30d568109cb59c392b3aba5261d | 2022-07-10T03:27:04.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | kakife3586 | null | kakife3586/Null | 145 | null | transformers | 4,061 | Entry not found |
autoevaluate/roberta-base-squad2 | 55f272a1e6e3c73e8797e7d38630dff8c37de8d9 | 2022-07-20T13:11:11.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | question-answering | false | autoevaluate | null | autoevaluate/roberta-base-squad2 | 145 | null | transformers | 4,062 | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
---
# roberta-base for QA
> Note: this is a clone of [`roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2) for internal testing.
This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https... |
allenai/t5-small-next-word-generator-qoogle | f97b573aaec7ba6a86bfce5777d0711ccb829628 | 2021-06-23T11:14:19.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/t5-small-next-word-generator-qoogle | 144 | 1 | transformers | 4,063 | Next word generator trained on questions. Receives partial questions and tries to predict the next word.
Example use:
```python
from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer
model_name = "allenai/t5-small-next-word-generator-qoogle"
tokenizer = T5Tokenizer.from_pretrained(model_name)
mod... |
allenai/unifiedqa-v2-t5-small-1251000 | 46255a0defae614c9e7057e461ae5276432c3212 | 2022-02-21T23:11:26.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/unifiedqa-v2-t5-small-1251000 | 144 | null | transformers | 4,064 | # Further details: https://github.com/allenai/unifiedqa |
textattack/roberta-base-rotten-tomatoes | 3fd77299e29adc4fe8c5f181810a6a1e7498dbd4 | 2021-05-20T22:17:29.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/roberta-base-rotten-tomatoes | 144 | null | transformers | 4,065 | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of ... |
moussaKam/AraBART | cb67d617b84f52755c0cbf34684a41fe0f01cdb1 | 2022-05-05T13:17:29.000Z | [
"pytorch",
"mbart",
"feature-extraction",
"ar",
"transformers",
"summarization",
"bart",
"license:apache-2.0",
"fill-mask"
] | fill-mask | false | moussaKam | null | moussaKam/AraBART | 144 | 3 | transformers | 4,066 | ---
tags:
- summarization
- bart
language:
- ar
widget:
- text: بيروت هي عاصمة <mask>.
license: apache-2.0
pipeline_tag: "fill-mask"
---
AraBART is the first Arabic model in which the encoder and the decoder are pretrained end-to-end, based on BART. AraBART follows the architecture of BART-Base
which has 6 encoder ... |
Helsinki-NLP/opus-mt-tc-big-he-en | d92e718946e9085ef6607711ccdc90ec1abd77c8 | 2022-06-01T12:59:59.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"he",
"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-he-en | 144 | null | transformers | 4,067 | ---
language:
- en
- he
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-he-en
results:
- task:
name: Translation heb-eng
type: translation
args: heb-eng
dataset:
name: flores101-devtest
type: flores_101
args: heb eng devtest
metrics... |
mriggs/wikisource_lemmatized_epoch1 | 7e024a1b9d63ba7a72dcaeb30d0ff5de4cc7729c | 2022-05-16T08:30:44.000Z | [
"pytorch",
"flaubert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | mriggs | null | mriggs/wikisource_lemmatized_epoch1 | 144 | null | transformers | 4,068 | Entry not found |
ahmeddbahaa/xlmroberta2xlmroberta-finetune-summarization-ur | 25c716a9b2682c8b3fd5d1d27854b44f375d3d24 | 2022-06-16T10:27:20.000Z | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xlsum",
"transformers",
"summarization",
"ur",
"xlm-roberta",
"Abstractive Summarization",
"roberta",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | summarization | false | ahmeddbahaa | null | ahmeddbahaa/xlmroberta2xlmroberta-finetune-summarization-ur | 144 | null | transformers | 4,069 | ---
tags:
- summarization
- ur
- encoder-decoder
- xlm-roberta
- Abstractive Summarization
- roberta
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: xlmroberta2xlmroberta-finetune-summarization-ur
results: []
---
<!-- This model card has been generated automatically according to the information the T... |
samroni/puisi_gpt2 | 97c5062e5b99efbcf1d542e7e8b0546e55770e7c | 2022-07-11T20:37:15.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | samroni | null | samroni/puisi_gpt2 | 144 | null | transformers | 4,070 | Entry not found |
codeparrot/codeparrot-small-text-to-code | 5d1a6204d944095ea923facac39081b8ba092b72 | 2022-07-19T15:46:48.000Z | [
"pytorch",
"gpt2",
"text-generation",
"code",
"dataset:codeparrot/codeparrot-clean",
"dataset:codeparrot/github-jupyter-text-to-code",
"transformers",
"generation",
"license:apache-2.0"
] | text-generation | false | codeparrot | null | codeparrot/codeparrot-small-text-to-code | 144 | null | transformers | 4,071 | ---
language:
- code
license: apache-2.0
tags:
- code
- gpt2
- generation
datasets:
- "codeparrot/codeparrot-clean"
- "codeparrot/github-jupyter-text-to-code"
---
# CodeParrot 🦜 small for text-t-code generation
This model is [CodeParrot-small](https://huggingface.co/codeparrot/codeparrot-small) (from `branch mega... |
Connorvr/BrightBot-small | c74836e9e613e57b8a934a6c240644db62d27972 | 2021-11-11T07:33:58.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Connorvr | null | Connorvr/BrightBot-small | 143 | null | transformers | 4,072 | ---
tags:
- conversational
---
#enlightened GPT model |
Emanuel/bertweet-emotion-base | 7a6581aad00369a89a2eb9e2ca9aec2ee4792f4e | 2022-07-13T12:37:16.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Emanuel | null | Emanuel/bertweet-emotion-base | 143 | 1 | transformers | 4,073 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bertweet-emotion-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name:... |
cambridgeltl/trans-encoder-cross-simcse-roberta-large | 6cc9a073d62636bc9fac1730c13e6736c2b7c98e | 2021-11-26T18:30:02.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | cambridgeltl | null | cambridgeltl/trans-encoder-cross-simcse-roberta-large | 143 | 1 | transformers | 4,074 | Entry not found |
ceostroff/harry-potter-gpt2-fanfiction | 868cd2c6bbd57898211b69baab308f775d4e5c4c | 2021-05-21T14:51:47.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"harry-potter",
"license:mit"
] | text-generation | false | ceostroff | null | ceostroff/harry-potter-gpt2-fanfiction | 143 | null | transformers | 4,075 | ---
language:
- en
tags:
- harry-potter
license: mit
---
# Harry Potter Fanfiction Generator
This is a pre-trained GPT-2 generative text model that allows you to generate your own Harry Potter fanfiction, trained off of the top 100 rated fanficition stories. We intend for this to be used for individual fun and exper... |
deepset/bert-base-german-cased-sentiment-Germeval17 | ece355d4692fd2233094b5b491d0a585526ae5a7 | 2021-05-19T15:27:03.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | deepset | null | deepset/bert-base-german-cased-sentiment-Germeval17 | 143 | 3 | transformers | 4,076 | Entry not found |
gargam/roberta-base-crest | 1fabf3dd2c00917840c3dbf5222659f1c8cd3842 | 2021-05-20T16:31:49.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | gargam | null | gargam/roberta-base-crest | 143 | null | transformers | 4,077 | Entry not found |
sachin/vit2distilgpt2 | 51be2b2bcadf5f37a4b2da6466f64e764c85add7 | 2022-01-27T12:15:27.000Z | [
"pytorch",
"vision-encoder-decoder",
"en",
"dataset:coco2017",
"transformers",
"image-to-text",
"license:mit"
] | image-to-text | false | sachin | null | sachin/vit2distilgpt2 | 143 | 6 | transformers | 4,078 | ---
language:
- en
tags:
- image-to-text
license: mit
datasets:
- coco2017
---
# Vit2-DistilGPT2
This model takes in an image and outputs a caption. It was trained using the Coco dataset and the full training script can be found in [this kaggle kernel](https://www.kaggle.com/sachin/visionencoderdecoder-model-training)... |
seyonec/BPE_SELFIES_PubChem_shard00_150k | 4f2cf5d53ee98bf947bc3874243ddc5a7fdedbc2 | 2021-05-20T20:44:59.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/BPE_SELFIES_PubChem_shard00_150k | 143 | null | transformers | 4,079 | Entry not found |
PoloHuggingface/French_grammar_error_corrector | b46c8948a54491fb6d1448c2cba868cb0d01a644 | 2022-05-03T13:32:40.000Z | [
"pytorch",
"t5",
"text2text-generation",
"fr",
"transformers",
"text2text generation",
"autotrain_compatible"
] | text2text-generation | false | PoloHuggingface | null | PoloHuggingface/French_grammar_error_corrector | 143 | 2 | transformers | 4,080 | ---
content:
language:
- fr
tags:
- text2text generation
widget:
- text: "improve grammar: Elle ne peux jamais aller au cinéma avec son amis"
example_title: "Grammar correction"
---
# Finetuned T5 on the french part of Lang-8 to automatically correct sentences.
Since the Lang-8 dataset contains really short sen... |
oliverguhr/fullstop-punctuation-multilingual-sonar-base | a4b84ab2a2de103ddc13f5ccf8b60d11dd10368f | 2022-05-17T08:15:02.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"en",
"de",
"fr",
"it",
"nl",
"dataset:wmt/europarl",
"transformers",
"punctuation prediction",
"punctuation",
"license:mit",
"autotrain_compatible"
] | token-classification | false | oliverguhr | null | oliverguhr/fullstop-punctuation-multilingual-sonar-base | 143 | null | transformers | 4,081 | ---
language:
- en
- de
- fr
- it
- nl
tags:
- punctuation prediction
- punctuation
datasets: wmt/europarl
license: mit
widget:
- text: "Ho sentito che ti sei laureata il che mi fa molto piacere"
example_title: "Italian"
- text: "Tous les matins vers quatre heures mon père ouvrait la porte de ma chambre"
example_ti... |
djagatiya/ner-bert-base-cased-ontonotesv5-englishv4 | eefd7c2a76d27e8ed21b549ccf6d1641b4c6d520 | 2022-07-03T11:34:55.000Z | [
"pytorch",
"bert",
"token-classification",
"dataset:djagatiya/ner-ontonotes-v5-eng-v4",
"transformers",
"autotrain_compatible"
] | token-classification | false | djagatiya | null | djagatiya/ner-bert-base-cased-ontonotesv5-englishv4 | 143 | null | transformers | 4,082 | ---
tags:
- token-classification
task_ids:
- named-entity-recognition
datasets:
- djagatiya/ner-ontonotes-v5-eng-v4
widget:
- text: "On September 1st George won 1 dollar while watching Game of Thrones."
---
# (NER) bert-base-cased : conll2012_ontonotesv5-english-v4
This `bert-base-cased` NER model was finetuned on `... |
deepset/deberta-v3-large-squad2 | 54d10aab0793cc5eb1a3403bdd2282020cedbdb9 | 2022-07-25T13:29:54.000Z | [
"pytorch",
"deberta-v2",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"deberta",
"deberta-v3",
"deberta-v3-large",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/deberta-v3-large-squad2 | 143 | 4 | transformers | 4,083 | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
tags:
- deberta
- deberta-v3
- deberta-v3-large
model-index:
- name: deepset/deberta-v3-large-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
... |
IMSyPP/hate_speech_it | 3bf99526e30770a40cd4656fd87a0c95a8a050cb | 2022-05-16T06:13:29.000Z | [
"pytorch",
"bert",
"text-classification",
"it",
"transformers",
"license:mit"
] | text-classification | false | IMSyPP | null | IMSyPP/hate_speech_it | 142 | null | transformers | 4,084 | ---
widget:
- text: "Ciao, mi chiamo Marcantonio, sono di Roma. Studio informatica all'Università di Roma."
language:
- it
license: mit
---
# Hate Speech Classifier for Social Media Content in Italian Language
A monolingual model for hate speech classification of social media content in Italian language. The model... |
Narrativaai/fake-news-detection-spanish | 0eab6d956dbfc035dfd4ef2d0d40700876401ad3 | 2021-10-28T11:03:28.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"es",
"dataset:fakedes",
"transformers",
"generated_from_trainer",
"fake",
"news",
"competition",
"model-index"
] | text-classification | false | Narrativaai | null | Narrativaai/fake-news-detection-spanish | 142 | 3 | transformers | 4,085 | ---
language: es
tags:
- generated_from_trainer
- fake
- news
- competition
datasets:
- fakedes
widget:
- text: 'La palabra "haiga", aceptada por la RAE [SEP] La palabra "haiga", aceptada por la RAE La Real Academia de la Lengua (RAE), ha aceptado el uso de "HAIGA", para su utilización en las tres personas del singul... |
Sahajtomar/NER_legal_de | 4db2b2c5b19c24ff4c0064b07bdf5f97359d5951 | 2021-05-18T22:27:00.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"de",
"dataset:legal entity recognition",
"transformers",
"NER",
"autotrain_compatible"
] | token-classification | false | Sahajtomar | null | Sahajtomar/NER_legal_de | 142 | null | transformers | 4,086 | ---
language: de
tags:
- pytorch
- tf
- bert
- NER
datasets:
- legal entity recognition
---
### NER model trained on BERT
MODEL used for fine tuning is GBERT Large by deepset.ai
## Test
Accuracy: 98 \
F1: 84.1 \
Precision: 82.7 \
Recall: 85.5
## Model inferencing:
```python
!pip install -q transformers
from tra... |
cambridgeltl/trans-encoder-bi-simcse-bert-large | 81194d51c150bd001ec10bab083fdcd04d315aef | 2021-11-26T18:26:08.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2109.13059",
"transformers"
] | feature-extraction | false | cambridgeltl | null | cambridgeltl/trans-encoder-bi-simcse-bert-large | 142 | null | transformers | 4,087 | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-bert-large
An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). The model is trained with unlabelled sentence pairs sampled from ST... |
cointegrated/rubert-base-cased-nli-twoway | bb84213f0b1b56fedaf025f8cbc54583b0393774 | 2021-10-10T11:08:15.000Z | [
"pytorch",
"bert",
"text-classification",
"ru",
"transformers",
"rubert",
"russian",
"nli",
"rte",
"zero-shot-classification"
] | zero-shot-classification | false | cointegrated | null | cointegrated/rubert-base-cased-nli-twoway | 142 | null | transformers | 4,088 | ---
language: ru
pipeline_tag: zero-shot-classification
tags:
- rubert
- russian
- nli
- rte
- zero-shot-classification
widget:
- text: "Я хочу поехать в Австралию"
candidate_labels: "спорт,путешествия,музыка,кино,книги,наука,политика"
hypothesis_template: "Тема текста - {}."
---
# RuBERT for NLI (natural language... |
junnyu/roformer_chinese_small | ff58affc8d7472c3b792fad18f68baa8490672b5 | 2022-01-03T15:44:37.000Z | [
"pytorch",
"tf",
"jax",
"roformer",
"fill-mask",
"zh",
"arxiv:2104.09864",
"transformers",
"tf2.0",
"autotrain_compatible"
] | fill-mask | false | junnyu | null | junnyu/roformer_chinese_small | 142 | 2 | transformers | 4,089 | ---
language: zh
tags:
- roformer
- pytorch
- tf2.0
widget:
- text: "今天[MASK]很好，我想去公园玩！"
---
## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
## pytorch使用
```python
import torch
from transformers import RoFormerForMaskedLM, RoFormerTokenizer... |
salesken/paraphrase_diversity_ranker | f1547a5d7f3c7fab96b346ec682f9049ff52999e | 2021-05-20T20:05:19.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers",
"salesken",
"license:apache-2.0"
] | text-classification | false | salesken | null | salesken/paraphrase_diversity_ranker | 142 | 1 | transformers | 4,090 | ---
tags: salesken
license: apache-2.0
inference: false
---
We have trained a model to evaluate if a paraphrase is a semantic variation to the input query or just a surface level variation. Data augmentation by adding Surface level variations does not add much value to the NLP model training. if the approach to parap... |
snunlp/KR-Medium | 2816fd37f5338827a265243a9abb59b2ac815099 | 2021-11-22T06:19:42.000Z | [
"pytorch",
"jax",
"bert",
"ko",
"transformers"
] | null | false | snunlp | null | snunlp/KR-Medium | 142 | 3 | transformers | 4,091 | ---
language:
- ko
---
# KR-BERT-MEDIUM
A pretrained Korean-specific BERT model developed by Computational Linguistics Lab at Seoul National University.
It is based on our character-level [KR-BERT](https://github.com/snunlp/KR-BERT) model which utilize WordPiece tokenizer.
Here, the model name has a suffix 'MEDIUM... |
thu-coai/CDial-GPT_LCCC-large | d0d1614dbd3982c715c672998f5f498246cb041f | 2020-12-23T05:56:25.000Z | [
"pytorch",
"transformers"
] | null | false | thu-coai | null | thu-coai/CDial-GPT_LCCC-large | 142 | 3 | transformers | 4,092 | # CDial-GPT_LCCC-large
https://github.com/thu-coai/CDial-GPT |
typeform/distilroberta-base-v2 | d3a8651147e18a6145d67d445c4b5229682dcae2 | 2021-05-20T22:46:35.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"en",
"dataset:openwebtext",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | typeform | null | typeform/distilroberta-base-v2 | 142 | null | transformers | 4,093 | ---
language: en
license: apache-2.0
datasets:
- openwebtext
---
# DistilRoBERTa base model
Forked from https://huggingface.co/distilroberta-base
|
ydshieh/tiny-random-gptj-for-question-answering | c804c9baa450871dcafe5ce20b54a066f3b889f1 | 2022-04-08T10:21:07.000Z | [
"pytorch",
"tf",
"gptj",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | ydshieh | null | ydshieh/tiny-random-gptj-for-question-answering | 142 | 1 | transformers | 4,094 | Entry not found |
sarakolding/daT5-summariser | 190e1505acc55da54bdf32016bcb06387a526a2f | 2022-07-05T09:45:46.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"da",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | sarakolding | null | sarakolding/daT5-summariser | 142 | 6 | transformers | 4,095 | ---
language:
- da
tags:
- summarization
widget:
- text: "Det nye studie Cognitive Science på Aarhus Universitet, som i år havde Østjyllands højeste adgangskrav på 11,7 i karaktergennemsnit, udklækker det første hold bachelorer til sommer.
Men når de skal læse videre på kandidaten må de til udlandet, hvis ikke de v... |
psyche/KoT5-paraphrase-generation | 929af1dcc025667315a3a56196b92feb3d24ccdf | 2022-06-19T15:39:42.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | psyche | null | psyche/KoT5-paraphrase-generation | 142 | null | transformers | 4,096 | ---
languages:
- ko
license: apache-2.0
---
More Information of KoT5 -> https://github.com/wisenut-research/KoT5 |
lucataco/DialogGPT-med-Rick | f753e6506a9d52f3609307ffa77dbc7a15494ebd | 2022-06-28T23:17:11.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | lucataco | null | lucataco/DialogGPT-med-Rick | 142 | null | transformers | 4,097 | ---
tags:
- conversational
---
# Rick Dialog GPT Model Medium 12
# Trained on:
# kaggle rick n morty Tv transcript |
shatabdi/twisent_twisent | ab00a8dbe09f44216edba796318345482c52a91f | 2022-06-24T00:40:53.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | shatabdi | null | shatabdi/twisent_twisent | 142 | null | transformers | 4,098 | ---
tags:
- generated_from_trainer
model-index:
- name: twisent_twisent
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. -->
# twisent_twisent
This model is a fine-t... |
trickstters/evbot2 | bb110a8e747a6db9b4a47ec82a26e94fa0057270 | 2022-07-28T08:04:46.000Z | [
"pytorch",
"conversational"
] | conversational | false | trickstters | null | trickstters/evbot2 | 142 | null | null | 4,099 | ---
tags:
- conversational
---
# a |
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