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values | private bool 1
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
gchhablani/bert-base-cased-finetuned-mrpc | cdece3698f342cc94478a61128f719df7229580b | 2021-09-20T09:07:44.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"en",
"dataset:glue",
"arxiv:2105.03824",
"transformers",
"generated_from_trainer",
"fnet-bert-base-comparison",
"license:apache-2.0",
"model-index"
] | text-classification | false | gchhablani | null | gchhablani/bert-base-cased-finetuned-mrpc | 575 | null | transformers | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-cased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
ty... | [
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0.0634... |
voidful/bart-distractor-generation-pm | ab4250ce4b6d339654263b3c4625ed69bbc38173 | 2021-04-04T16:20:25.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:race",
"transformers",
"distractor",
"generation",
"seq2seq",
"autotrain_compatible"
] | text2text-generation | false | voidful | null | voidful/bart-distractor-generation-pm | 575 | null | transformers | ---
language: en
tags:
- bart
- distractor
- generation
- seq2seq
datasets:
- race
metrics:
- bleu
- rouge
pipeline_tag: text2text-generation
widget:
- text: "When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes to France , you have another sp... | [
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0.028811732307076454,
-0.0214... |
abhishek/autonlp-bbc-news-classification-37229289 | 7feb5c325c92f2425f7c338160cdb5afc117aaed | 2021-11-30T12:56:59.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:abhishek/autonlp-data-bbc-news-classification",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | false | abhishek | null | abhishek/autonlp-bbc-news-classification-37229289 | 574 | 1 | transformers | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-bbc-news-classification
co2_eq_emissions: 5.448567309047846
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 37229289
- CO2 Emissions (in grams): 5.448567309047846
## Validatio... | [
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0.0200410... |
recobo/agriculture-bert-uncased | 641de86d01ed5f0e22f2301e85a3da518173dcad | 2021-10-08T13:50:49.000Z | [
"pytorch",
"bert",
"fill-mask",
"en",
"transformers",
"agriculture-domain",
"agriculture",
"autotrain_compatible"
] | fill-mask | false | recobo | null | recobo/agriculture-bert-uncased | 574 | 2 | transformers | ---
language: "en"
tags:
- agriculture-domain
- agriculture
- fill-mask
widget:
- text: "[MASK] agriculture provides one of the most promising areas for innovation in green and blue infrastructure in cities."
---
# BERT for Agriculture Domain
A BERT-based language model further pre-trained from the checkpoint of [SciBE... | [
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0.017487844452261925,
-0.01... |
IDEA-CCNL/Wenzhong2.0-GPT2-3.5B-chinese | 67838ddfa79c4b7bbd3ab88006e7e38d70b24f19 | 2022-07-29T08:56:23.000Z | [
"pytorch",
"gpt2",
"text-generation",
"zh",
"transformers",
"license:apache-2.0"
] | text-generation | false | IDEA-CCNL | null | IDEA-CCNL/Wenzhong2.0-GPT2-3.5B-chinese | 574 | 2 | transformers | ---
language:
- zh
inference:
parameters:
max_new_tokens: 250
repetition_penalty: 1.1
top_p: 0.9
do_sample: True
license: apache-2.0
---
# Wenzhong2.0-GPT2-3.5B model (chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
As we all know, the single direc... | [
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jkgrad/xlnet-base-squadv2 | 36d0bd03fc05331bae053db4fa35865ba74dd2a2 | 2021-01-17T11:52:34.000Z | [
"pytorch",
"xlnet",
"question-answering",
"arxiv:1906.08237",
"transformers",
"autotrain_compatible"
] | question-answering | false | jkgrad | null | jkgrad/xlnet-base-squadv2 | 571 | 1 | transformers | # XLNet Fine-tuned on SQuAD 2.0 Dataset
[XLNet](https://arxiv.org/abs/1906.08237) jointly developed by Google and CMU and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for question answering down-stream task.
## Training Results (Metrics)
```
{
"HasAns_exact": 74.7132253711201
"HasAns_... | [
-0.06258906424045563,
-0.06100110337138176,
-0.046813301742076874,
-0.03700926899909973,
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-0.023712119087576866,
-0.018322836607694626,
0.01367949042469263,
-0.033574242144823074,
0.0033010186161845922,
-0.019478250294923782,
-0.06998512148857117,
-0.018532583490014076,
... |
studio-ousia/mluke-large-lite-finetuned-kbp37 | cc425b55c0eb00bcaa951287d2bce238a7d86687 | 2022-03-28T07:38:46.000Z | [
"pytorch",
"luke",
"transformers",
"license:apache-2.0"
] | null | false | studio-ousia | null | studio-ousia/mluke-large-lite-finetuned-kbp37 | 571 | null | transformers | ---
license: apache-2.0
---
| [
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0.014524046331644058,
0.06244279816746712,
-0.03306293115019798,
-0.057087719440460205,
-0.02... |
malteos/gpt2-xl-wechsel-german | 40465eb67657fe7e9176b014a7b6b8322032d706 | 2022-06-24T10:49:20.000Z | [
"pytorch",
"gpt2",
"text-generation",
"de",
"arxiv:2112.06598",
"transformers",
"license:mit"
] | text-generation | false | malteos | null | malteos/gpt2-xl-wechsel-german | 569 | 4 | transformers | ---
license: mit
language: de
widget:
- text: "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einhörner, die in "
---
# German GPT2-XL (1.5B)
- trained with [BigScience's DeepSpeed-Megatron-LM code base](https://github.com/bigscience-workshop/Megatron-DeepSpeed)
- word embedding initialized with... | [
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0.0018683295929804444,
-0.0... |
IIC/dpr-spanish-question_encoder-allqa-base | a4cc70e53295779c0cf761af8fc49a267ee56099 | 2022-04-02T15:07:32.000Z | [
"pytorch",
"bert",
"fill-mask",
"es",
"dataset:squad_es",
"dataset:PlanTL-GOB-ES/SQAC",
"dataset:IIC/bioasq22_es",
"arxiv:2004.04906",
"transformers",
"sentence similarity",
"passage retrieval",
"model-index",
"autotrain_compatible"
] | fill-mask | false | IIC | null | IIC/dpr-spanish-question_encoder-allqa-base | 568 | 1 | transformers | ---
language:
- es
tags:
- sentence similarity # Example: audio
- passage retrieval # Example: automatic-speech-recognition
datasets:
- squad_es
- PlanTL-GOB-ES/SQAC
- IIC/bioasq22_es
metrics:
- eval_loss: 0.010779764448327261
- eval_accuracy: 0.9982682224158297
- eval_f1: 0.9446059155411182
- average_rank: 0.117285... | [
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0.0020136984530836344,
-0.09394446015357971,
0.03575698658823967,
-0.13919955492019653,
-0.01928822509944439,
0.041... |
winegarj/distilbert-base-uncased-finetuned-sst2 | dffa754606014596c0bcedd7920d45671102fc90 | 2022-04-10T02:09:16.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | winegarj | null | winegarj/distilbert-base-uncased-finetuned-sst2 | 568 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- ... | [
-0.09979649633169174,
-0.02432732842862606,
-0.009284965693950653,
0.06053381413221359,
0.02478518895804882,
0.0017582856817170978,
-0.011138261295855045,
0.029302086681127548,
-0.06154419109225273,
-0.15025530755519867,
0.04108990728855133,
-0.06274823099374771,
0.018025433644652367,
-0.0... |
google/multiberts-seed_2 | 6ca96336eb9c5571b274ec67ca5d3d88980a57eb | 2021-11-05T22:10:49.000Z | [
"pytorch",
"tf",
"bert",
"pretraining",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"transformers",
"multiberts",
"multiberts-seed_2",
"license:apache-2.0"
] | null | false | google | null | google/multiberts-seed_2 | 567 | null | transformers | ---
language: en
tags:
- multiberts
- multiberts-seed_2
license: apache-2.0
---
# MultiBERTs - Seed 2
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://gi... | [
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-0.030634379014372826,
-0.014365735463798046,
0.00613204762339592,
0.0691845491528511,
-0.02216... |
google/multiberts-seed_3 | e7349d42b02a2c42b87f6a01046f3f4278361e37 | 2021-11-05T22:12:27.000Z | [
"pytorch",
"tf",
"bert",
"pretraining",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"transformers",
"multiberts",
"multiberts-seed_3",
"license:apache-2.0"
] | null | false | google | null | google/multiberts-seed_3 | 567 | null | transformers | ---
language: en
tags:
- multiberts
- multiberts-seed_3
license: apache-2.0
---
# MultiBERTs - Seed 3
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://gi... | [
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0.020982742309570312,
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0.005305802449584007,
0.06798917055130005,
-0.0... |
sshleifer/tiny-distilbert-base-uncased-finetuned-sst-2-english | 40f5ccbce1646c98ea0fabb02f96182a08a5a9d9 | 2020-05-12T01:51:10.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | sshleifer | null | sshleifer/tiny-distilbert-base-uncased-finetuned-sst-2-english | 567 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
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0.0019101888174191117,
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-0.015212527476251125,
0.017284274101257324,
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0.03817418962717056,
-0.... |
Gunulhona/tbstmodel_v2 | deb515d43c4985e3318a0ea172cd563bcde230fa | 2022-07-20T10:32:43.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Gunulhona | null | Gunulhona/tbstmodel_v2 | 566 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
cross-encoder/ms-marco-TinyBERT-L-4 | 12a9f222056982640d3735ab94d865761c8fdd16 | 2021-08-05T08:39:59.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/ms-marco-TinyBERT-L-4 | 565 | null | transformers | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | [
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hfl/chinese-pert-large | 2e523595cb3d0d157f847cd0ec1b3914c8740fe1 | 2022-02-25T04:09:23.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"zh",
"transformers",
"license:cc-by-nc-sa-4.0"
] | feature-extraction | false | hfl | null | hfl/chinese-pert-large | 565 | 2 | transformers | ---
language:
- zh
license: "cc-by-nc-sa-4.0"
---
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT | [
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... |
w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0 | 0fce6ebcb814fa0f624e3a8ba83f682a222c60f6 | 2021-10-06T04:15:40.000Z | [
"pytorch",
"roberta",
"text-classification",
"id",
"dataset:indolem",
"arxiv:1907.11692",
"transformers",
"indonesian-roberta-base-indolem-sentiment-classifier-fold-0",
"license:mit"
] | text-classification | false | w11wo | null | w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0 | 563 | null | transformers | ---
language: id
tags:
- indonesian-roberta-base-indolem-sentiment-classifier-fold-0
license: mit
datasets:
- indolem
widget:
- text: "Pelayanan hotel ini sangat baik."
---
## Indonesian RoBERTa Base IndoLEM Sentiment Classifier
Indonesian RoBERTa Base IndoLEM Sentiment Classifier is a sentiment-text-classifica... | [
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Gunulhona/tbbcmodel | 97497c151100a30da0d19771d3dbc7c457befaac | 2022-01-06T07:01:22.000Z | [
"pytorch",
"bart",
"text-classification",
"transformers"
] | text-classification | false | Gunulhona | null | Gunulhona/tbbcmodel | 562 | null | transformers | Entry not found | [
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0.03817418962717056,
-0.... |
fav-kky/FERNET-C5 | ff5399d8222bce8d7356c7face6d0d0263f9cb8c | 2021-07-26T21:05:31.000Z | [
"pytorch",
"tf",
"bert",
"fill-mask",
"cs",
"arxiv:2107.10042",
"transformers",
"Czech",
"KKY",
"FAV",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | fav-kky | null | fav-kky/FERNET-C5 | 562 | null | transformers | ---
language: "cs"
tags:
- Czech
- KKY
- FAV
license: "cc-by-nc-sa-4.0"
---
# FERNET-C5
FERNET-C5 is a monolingual Czech BERT-base model pre-trained from 93GB of filtered Czech Common Crawl dataset (C5).
Preprint of our paper is available at https://arxiv.org/abs/2107.10042. | [
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0.02889750339090824,
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0.027293534949421883,
-0.010749994777143002,
-0.07568371295928955,
-0.045298732817173004,
0... |
junnyu/roformer_chinese_sim_char_base | 6e0353805a82525679b0d5d9e97c51fdbf8378eb | 2022-04-15T03:52:35.000Z | [
"pytorch",
"roformer",
"text-generation",
"zh",
"transformers",
"tf2.0"
] | text-generation | false | junnyu | null | junnyu/roformer_chinese_sim_char_base | 562 | 1 | transformers | ---
language: zh
tags:
- roformer
- pytorch
- tf2.0
inference: False
---
# 安装
- pip install roformer==0.4.3
# 使用
```python
import torch
import numpy as np
from roformer import RoFormerForCausalLM, RoFormerConfig
from transformers import BertTokenizer
device = torch.device('cuda:0' if torch.cuda.is_available() else 'c... | [
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flair/chunk-english-fast | f6040da676441b1c13f702119e8cc12e0a533350 | 2021-03-02T21:59:23.000Z | [
"pytorch",
"en",
"dataset:conll2000",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/chunk-english-fast | 561 | 2 | flair | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- conll2000
widget:
- text: "The happy man has been eating at the diner"
---
## English Chunking in Flair (fast model)
This is the fast phrase chunking model for English that ships with [Flair](https://github.com/flairNLP/flair/).... | [
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snunlp/KR-FinBert-SC | f8586286cc3161fb648e9fee09a456069fd846d0 | 2022-04-28T05:07:18.000Z | [
"pytorch",
"bert",
"text-classification",
"ko",
"transformers"
] | text-classification | false | snunlp | null | snunlp/KR-FinBert-SC | 561 | 2 | transformers | ---
language:
- ko
---
# KR-FinBert & KR-FinBert-SC
Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning with labeled data is effective for overall performance improvement.
we proposed KR-FinBert for... | [
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Helsinki-NLP/opus-mt-mr-en | 040060aef28d3ace6070a967dc4d22bce13fe98d | 2021-09-10T13:58:19.000Z | [
"pytorch",
"marian",
"text2text-generation",
"mr",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-mr-en | 560 | null | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-mr-en
* source languages: mr
* target languages: en
* OPUS readme: [mr-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/mr-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... | [
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yoshitomo-matsubara/bert-base-uncased-sst2 | 7d0cf617c3efaeb57e0cf15962c0fb3c0174d9bb | 2021-05-29T21:57:09.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:sst2",
"transformers",
"sst2",
"glue",
"torchdistill",
"license:apache-2.0"
] | text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-base-uncased-sst2 | 560 | null | transformers | ---
language: en
tags:
- bert
- sst2
- glue
- torchdistill
license: apache-2.0
datasets:
- sst2
metrics:
- accuracy
---
`bert-base-uncased` fine-tuned on SST-2 dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-... | [
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0.0... |
ShiroNeko/DialoGPT-small-rick | 3dd3341e6bd09f9905dcc3d38a4ad897504bbdc7 | 2021-09-20T08:46:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ShiroNeko | null | ShiroNeko/DialoGPT-small-rick | 558 | null | transformers | ---
tags:
- conversational
---
# Rick DialoGPT Model | [
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0.011... |
abhishek/autonlp-japanese-sentiment-59363 | 0d765f697ed9076d536ca72fa44a7666400e1ae3 | 2021-05-18T22:56:15.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"ja",
"dataset:abhishek/autonlp-data-japanese-sentiment",
"transformers",
"autonlp"
] | text-classification | false | abhishek | null | abhishek/autonlp-japanese-sentiment-59363 | 558 | null | transformers | ---
tags: autonlp
language: ja
widget:
- text: "🤗AutoNLPが大好きです"
datasets:
- abhishek/autonlp-data-japanese-sentiment
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 59363
## Validation Metrics
- Loss: 0.12651239335536957
- Accuracy: 0.9532079853817648
- Precision: 0.972968827882... | [
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0.025... |
nielsr/layoutlmv3-finetuned-funsd | 99e76f3c6200c43c300cd597d86bb519cbb91d25 | 2022-05-02T16:57:40.000Z | [
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"dataset:nielsr/funsd-layoutlmv3",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | nielsr | null | nielsr/layoutlmv3-finetuned-funsd | 558 | 2 | transformers | ---
tags:
- generated_from_trainer
datasets:
- nielsr/funsd-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-funsd
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: nielsr/funsd-layoutlmv3
type: nielsr/... | [
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0.00... |
Gunulhona/tbnymodel | 4607ed2430c42ccdc6054e7a51c1965dfd2ca70c | 2022-04-04T04:46:06.000Z | [
"pytorch",
"bart",
"text-classification",
"transformers"
] | text-classification | false | Gunulhona | null | Gunulhona/tbnymodel | 557 | null | transformers | Entry not found | [
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-0.... |
cahya/gpt2-small-indonesian-522M | 6d53094a6ca11236e62c54916c486e2b41d0b9aa | 2021-05-21T14:41:35.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"id",
"dataset:Indonesian Wikipedia",
"transformers",
"license:mit"
] | text-generation | false | cahya | null | cahya/gpt2-small-indonesian-522M | 557 | 1 | transformers | ---
language: "id"
license: "mit"
datasets:
- Indonesian Wikipedia
widget:
- text: "Pulau Dewata sering dikunjungi"
---
# Indonesian GPT2 small model
## Model description
It is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This
model is uncased: it does not... | [
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nvidia/segformer-b0-finetuned-cityscapes-1024-1024 | bca5b3ecf06ad6e3d732b277420a05e59e248d35 | 2022-07-20T09:53:38.000Z | [
"pytorch",
"tf",
"segformer",
"dataset:cityscapes",
"arxiv:2105.15203",
"transformers",
"vision",
"image-segmentation",
"license:apache-2.0"
] | image-segmentation | false | nvidia | null | nvidia/segformer-b0-finetuned-cityscapes-1024-1024 | 557 | null | transformers | ---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- cityscapes
widget:
- src: https://www.researchgate.net/profile/Anurag-Arnab/publication/315881952/figure/fig5/AS:667673876779033@1536197265755/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.jpg
example_ti... | [
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0.011753183789551258,
0.0327... |
Gunulhona/tbecmodel | 6f555e96bafdb845b2affa4586ab339db5516144 | 2022-01-25T06:37:13.000Z | [
"pytorch",
"bart",
"text-classification",
"transformers"
] | text-classification | false | Gunulhona | null | Gunulhona/tbecmodel | 555 | null | transformers | Entry not found | [
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0.011261860840022564,
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0.017284274101257324,
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0.03817418962717056,
-0.... |
google/long-t5-local-large | a4b28551322d14828192722fff4576100a9e18be | 2022-06-22T09:06:02.000Z | [
"pytorch",
"jax",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/long-t5-local-large | 555 | 1 | transformers | ---
license: apache-2.0
language: en
---
# LongT5 (local attention, large-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the Long... | [
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0.02... |
akhooli/gpt2-small-arabic | c123d61dfde7003e1e250016152f28c5b71a5dc3 | 2021-05-21T12:38:38.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"ar",
"dataset:Arabic Wikipedia",
"transformers"
] | text-generation | false | akhooli | null | akhooli/gpt2-small-arabic | 554 | 3 | transformers | ---
language: "ar"
datasets:
- Arabic Wikipedia
metrics:
- none
---
# GPT2-Small-Arabic
## Model description
GPT2 model from Arabic Wikipedia dataset based on gpt2-small (using Fastai2).
## Intended uses & limitations
#### How to use
An example is provided in this [colab notebook](https://colab.research.google.co... | [
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Wikidepia/IndoT5-base-paraphrase | 5d591dc3aeae0aade0f327a5ebdd0f071c83f567 | 2021-09-04T02:49:33.000Z | [
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"id",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Wikidepia | null | Wikidepia/IndoT5-base-paraphrase | 553 | null | transformers | ---
language:
- id
---
# Paraphrase Generation with IndoT5 Base
IndoT5-base trained on translated PAWS.
## Model in action
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Wikidepia/IndoT5-base-paraphrase")
model = AutoModelForSeq2SeqLM.from_pretra... | [
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ttop324/wav2vec2-live-japanese | 3db54ebbfc3e19ca24aab50f14e13a3c411726cf | 2021-10-31T15:34:55.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | ttop324 | null | ttop324/wav2vec2-live-japanese | 553 | 1 | transformers | ---
language: ja
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: wav2vec2-live-japanese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Com... | [
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... |
jonatasgrosman/wav2vec2-large-fr-voxpopuli-french | 9a18cf72882b27fe13d8df75b761c000c2e726dd | 2022-07-27T23:33:59.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-large-fr-voxpopuli-french | 552 | 1 | transformers | ---
language: fr
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Voxpopuli Wav2Vec2 French by Jonatas Grosman
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition... | [
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... |
Helsinki-NLP/opus-mt-en-vi | 2d731274713eb04ca01788dff46beee9b894766d | 2021-01-18T08:19:11.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"vi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-vi | 551 | 1 | transformers | ---
language:
- en
- vi
tags:
- translation
license: apache-2.0
---
### eng-vie
* source group: English
* target group: Vietnamese
* OPUS readme: [eng-vie](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-vie/README.md)
* model: transformer-align
* source language(s): eng
* target lang... | [
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Invincible/Chat_bot-Harrypotter-medium | 0c7709b101432363343da0f414cd0fbbb67aefa5 | 2021-09-02T04:14:39.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Invincible | null | Invincible/Chat_bot-Harrypotter-medium | 551 | null | transformers | ---
tags:
- conversational
---
#harry potter | [
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-0.03159607574343681,
0.003793015144765377,
-0.04896255210042,
-0.00... |
sberbank-ai/ruclip-vit-large-patch14-336 | c6746c7408550b773bf8d620ef96069b8f005849 | 2022-01-09T22:25:33.000Z | [
"pytorch",
"transformers"
] | null | false | sberbank-ai | null | sberbank-ai/ruclip-vit-large-patch14-336 | 551 | null | transformers | # ruclip-vit-large-patch14-336
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language process... | [
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0.006042092572897673,
0.028338143602013588,
0.0735... |
assemblyai/distilbert-base-uncased-sst2 | b22ecd1ae8fe1a941bf478fec027bdc996ba190f | 2021-06-14T22:04:03.000Z | [
"pytorch",
"distilbert",
"text-classification",
"arxiv:1910.01108",
"transformers"
] | text-classification | false | assemblyai | null | assemblyai/distilbert-base-uncased-sst2 | 550 | null | transformers | # DistilBERT-Base-Uncased for Sentiment Analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) originally released in ["DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"](https://arxiv.org/abs/1910.01108) and trained on the [... | [
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0.0... |
readerbench/RoGPT2-base | b5e625fea1f4040b224799e6cbd9d52324432320 | 2021-07-22T11:24:47.000Z | [
"pytorch",
"tf",
"gpt2",
"text-generation",
"ro",
"transformers"
] | text-generation | false | readerbench | null | readerbench/RoGPT2-base | 550 | null | transformers | Model card for RoGPT2-base
---
language:
- ro
---
# RoGPT2: Romanian GPT2 for text generation
All models are available:
* [RoBERT-base](https://huggingface.co/readerbench/RoGPT2-base)
* [RoBERT-medium](https://huggingface.co/readerbench/RoGPT2-medium)
* [RoBERT-large](https://huggingface.co/readerbench/RoGPT2-large)... | [
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0.04... |
Yale-LILY/brio-xsum-cased | 78ce8a35c6e73b2220b5a8b99c4b690f9bb22e01 | 2022-03-31T03:13:07.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Yale-LILY | null | Yale-LILY/brio-xsum-cased | 550 | 1 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
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0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
Rostlab/prot_xlnet | 7fe4d7b13695ccf8fb14a257986976bd7f704782 | 2020-08-20T14:57:30.000Z | [
"pytorch",
"xlnet",
"transformers"
] | null | false | Rostlab | null | Rostlab/prot_xlnet | 549 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
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0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
ionite/DialoGPT-medium-MarkAI | 14fa33a0e25a302e5e309439c09c2231afd75bec | 2022-05-21T20:37:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ionite | null | ionite/DialoGPT-medium-MarkAI | 549 | null | transformers | ---
tags:
- conversational
---
# MarkAI DialoGPT Model | [
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0.... |
HomerChatbot/DialoGPT-small-homersimpsonbot | 77c5f374e7a7c760352818155c2266ed8ebdb06a | 2022-05-25T23:05:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | HomerChatbot | null | HomerChatbot/DialoGPT-small-homersimpsonbot | 549 | null | transformers | ---
tags:
- conversational
---
# Homer Simpson DialogGPT Model | [
-0.028337828814983368,
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0.03712169826030731,
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-... |
NeuML/bert-small-cord19qa | 88e15ee7018bdf3cd98d5c55b975cb87f48d6686 | 2021-05-18T21:53:32.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | NeuML | null | NeuML/bert-small-cord19qa | 548 | 1 | transformers | # BERT-Small fine-tuned on CORD-19 QA dataset
[bert-small-cord19-squad model](https://huggingface.co/NeuML/bert-small-cord19-squad2) fine-tuned on the [CORD-19 QA dataset](https://www.kaggle.com/davidmezzetti/cord19-qa?select=cord19-qa.json).
## CORD-19 QA dataset
The CORD-19 QA dataset is a SQuAD 2.0 formatted list ... | [
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0... |
sentence-transformers/stsb-bert-large | 5e8a2c75ee8ccaa42c89aa0e26ec75e4bc9e1ba8 | 2022-06-15T22:57:44.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/stsb-bert-large | 547 | 1 | sentence-transformers | ---
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... | [
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0.085365... |
RohanVB/umlsbert_ner | 843c67105aba9b0edc6547a1c14092bc9578475a | 2022-05-09T13:46:36.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"license:mit",
"autotrain_compatible"
] | token-classification | false | RohanVB | null | RohanVB/umlsbert_ner | 546 | null | transformers | ---
license: mit
---
| [
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-0... |
Gunulhona/tbqgmodel | d1bbd7307a70d4d7d4cef8849156c248a58a655e | 2021-12-29T01:18:56.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Gunulhona | null | Gunulhona/tbqgmodel | 544 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
Seethal/sentiment_analysis_generic_dataset | 26edc13094473094204e42245018bee141699c8c | 2022-04-19T06:26:33.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | Seethal | null | Seethal/sentiment_analysis_generic_dataset | 544 | null | transformers | ## BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.
## Model description
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashi... | [
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0.02264021337032318,
0.047... |
nvidia/stt_en_conformer_transducer_xlarge | b7f7fceb0c6a4009e2d4d7492eee68405df36837 | 2022-06-30T02:25:13.000Z | [
"nemo",
"en",
"dataset:librispeech_asr",
"dataset:fisher_corpus",
"dataset:Switchboard-1",
"dataset:WSJ-0",
"dataset:WSJ-1",
"dataset:National Singapore Corpus Part 1",
"dataset:National Singapore Corpus Part 6",
"dataset:vctk",
"dataset:VoxPopuli (EN)",
"dataset:Europarl-ASR (EN)",
"dataset... | automatic-speech-recognition | false | nvidia | null | nvidia/stt_en_conformer_transducer_xlarge | 544 | 17 | nemo | ---
language:
- en
library_name: nemo
datasets:
- librispeech_asr
- fisher_corpus
- Switchboard-1
- WSJ-0
- WSJ-1
- National Singapore Corpus Part 1
- National Singapore Corpus Part 6
- vctk
- VoxPopuli (EN)
- Europarl-ASR (EN)
- Multilingual LibriSpeech (2000 hours)
- mozilla-foundation/common_voice_8_0
- MLCommons/pe... | [
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-0.07786072045564651,
-0.0082... |
cardiffnlp/twitter-roberta-base-emoji | e7efb0d4f929fce6b1477405d6f59c526e4272ac | 2021-05-20T14:59:33.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-emoji | 543 | 3 | transformers | # Twitter-roBERTa-base for Emoji prediction
This is a roBERTa-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.... | [
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0.007... |
zanelim/singbert-lite-sg | 74e501f6729ad50522c3d5f4a5793b770ab21f30 | 2020-12-11T22:05:08.000Z | [
"pytorch",
"tf",
"albert",
"pretraining",
"en",
"dataset:reddit singapore, malaysia",
"dataset:hardwarezone",
"transformers",
"singapore",
"sg",
"singlish",
"malaysia",
"ms",
"manglish",
"albert-base-v2",
"license:mit"
] | null | false | zanelim | null | zanelim/singbert-lite-sg | 542 | null | transformers | ---
language: en
tags:
- singapore
- sg
- singlish
- malaysia
- ms
- manglish
- albert-base-v2
license: mit
datasets:
- reddit singapore, malaysia
- hardwarezone
widget:
- text: "dont play [MASK] leh"
- text: "die [MASK] must try"
---
# Model name
SingBert Lite - Bert for Singlish (SG) and Manglish (MY).
## Model de... | [
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anuragshas/wav2vec2-large-xlsr-53-telugu | 35b88df6c2e57a5514caec962ea87222ead7bce7 | 2021-07-05T21:31:14.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"te",
"dataset:openslr",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | anuragshas | null | anuragshas/wav2vec2-large-xlsr-53-telugu | 541 | null | transformers | ---
language: te
datasets:
- openslr
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Anurag Singh XLSR Wav2Vec2 Large 53 Telugu
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:... | [
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-0... |
Helsinki-NLP/opus-mt-tc-big-fi-en | 484fdf7bee8ff967301c3dfe0fbece1d37e257df | 2022-06-01T13:10:36.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"fi",
"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-fi-en | 541 | null | transformers | ---
language:
- en
- fi
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-fi-en
results:
- task:
name: Translation fin-eng
type: translation
args: fin-eng
dataset:
name: flores101-devtest
type: flores_101
args: fin eng devtest
metrics... | [
-0.027217337861657143,
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0.013647185638546944,
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0.03756999969482422,
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0.006499972194433212,
-0.17066241800785065,
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... |
dbmdz/bert-base-multilingual-cased-finetuned-conll03-dutch | 30f2f8872dd7fda80a723d1073b7f40dd905371c | 2021-05-19T15:05:18.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | dbmdz | null | dbmdz/bert-base-multilingual-cased-finetuned-conll03-dutch | 539 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
gsarti/scibert-nli | 4e77bab8a7a4b3dde3dfaa3ac86a180b4680213f | 2021-05-19T17:49:18.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | gsarti | null | gsarti/scibert-nli | 539 | 1 | transformers | # SciBERT-NLI
This is the model [SciBERT](https://github.com/allenai/scibert) [1] fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to... | [
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0.03511892631649971,
-0.07475011795759201,
0.0552370510995388,
0.010851346887648106,
-0.07766249030828476,
-0.003041794989258051,
-0.043962180614471436,
0.03623071685433388,
0.020... |
textattack/bert-base-uncased-snli | d4ef8a69a50bc95cc074514f4b798c67f572163a | 2021-05-20T07:48:06.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/bert-base-uncased-snli | 538 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
Geotrend/bert-base-en-es-pt-cased | dc6371e38b2e1179ad8ae78fa6c1e3bcd3046bf3 | 2021-05-18T19:11:40.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Geotrend | null | Geotrend/bert-base-en-es-pt-cased | 537 | null | transformers | ---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# bert-base-en-es-pt-cased
We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://hugg... | [
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0.069... |
cristian-popa/bart-tl-ng | f78305bba5742afa17380997f569659ebea7f7eb | 2021-09-22T08:18:06.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"transformers",
"topic labeling",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | cristian-popa | null | cristian-popa/bart-tl-ng | 537 | null | transformers | ---
language:
- en
<!-- thumbnail: https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png
-->
tags:
- topic labeling
license: apache-2.0
metrics:
- ndcg
---
# MyModel
## Model description
This is the `BART-TL-ng` model from the paper [BART-TL: Weakly-Supervised Topic Label Generatio... | [
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0.076461... |
m3hrdadfi/wav2vec2-large-xlsr-persian | a6fc7cdc898c6ec218e7f337a4835c3cd1ab8fab | 2021-11-04T15:22:12.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"fa",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | m3hrdadfi | null | m3hrdadfi/wav2vec2-large-xlsr-persian | 537 | 3 | transformers | ---
language: fa
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
widget:
- example_title: Common Voice sample 687
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian/resolve/main/sample687.flac
- example_title: Common Voice sampl... | [
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0.01... |
valhalla/t5-base-squad | d19773e0f8a0d4cf6f087e425674dffba44b4b42 | 2020-12-11T22:03:51.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/t5-base-squad | 537 | null | transformers | # T5 for question-answering
This is T5-base model fine-tuned on SQuAD1.1 for QA using text-to-text approach
## Model training
This model was trained on colab TPU with 35GB RAM for 4 epochs
## Results:
| Metric | #Value |
|-------------|---------|
| Exact Match | 81.5610 |
| F1 | 89.9601 |
## Model in ... | [
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0.018167665228247643,
0.0097... |
KoboldAI/GPT-J-6B-Janeway | 036bb03496d648ddc8cf932ad91df8ef1287116c | 2022-03-20T12:59:44.000Z | [
"pytorch",
"gptj",
"text-generation",
"en",
"arxiv:2101.00027",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-J-6B-Janeway | 537 | null | transformers | ---
language: en
license: mit
---
# GPT-J 6B - Janeway
## Model Description
GPT-J 6B-Janeway is a finetune created using EleutherAI's GPT-J 6B model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used by GPT-Neo-... | [
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0.001690241857431829,
-0.0... |
sultan/BioM-ELECTRA-Large-SQuAD2-BioASQ8B | 1dfc7f810497d9752fbc9a82fc7e376546242e46 | 2021-07-24T20:18:22.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | sultan | null | sultan/BioM-ELECTRA-Large-SQuAD2-BioASQ8B | 536 | null | transformers | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... | [
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... |
KETI-AIR/ke-t5-large | bb55bafacd3e35feca548c9bcffdf799236203d2 | 2021-06-23T03:09:21.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | KETI-AIR | null | KETI-AIR/ke-t5-large | 534 | 1 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
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0.0019101888174191117,
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-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
microsoft/DialogRPT-depth | 0fa8a8770e267801a5779788ccfd921192ae7f40 | 2021-05-23T09:15:24.000Z | [
"pytorch",
"gpt2",
"text-classification",
"arxiv:2009.06978",
"transformers"
] | text-classification | false | microsoft | null | microsoft/DialogRPT-depth | 534 | 2 | transformers | # Demo
Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
| Context | Response | `depth` score |
| :------ | :------- | :------------: |
| I love NLP! | Can anyone recommend a nice review paper? | 0.724 |
| I love NLP! | ... | [
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0.0176310483366251,
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0.06550810486078262,
-0.01040644757449627,
-0.06973608583211899,
-0.031880926340818405,
0.0030234719160944223,
0.02070852... |
microsoft/DialogRPT-width | bd3aad6082f8b725d27bb29cb4f5001e58b03fd0 | 2021-05-23T09:20:20.000Z | [
"pytorch",
"gpt2",
"text-classification",
"arxiv:2009.06978",
"transformers"
] | text-classification | false | microsoft | null | microsoft/DialogRPT-width | 534 | 1 | transformers | # Demo
Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
| Context | Response | `width` score |
| :------ | :------- | :------------: |
| I love NLP! | Can anyone recommend a nice review paper? | 0.701 |
| I love NLP! | ... | [
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0.03836798667907715,
0.06802711635828018,
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0.0202912837266922,
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-0.05497121810913086,
-0.01695149950683117,
-0.005482820328325033,
0.0409507... |
succinctly/text2image-prompt-generator | 5b096e9aa15e37f5193b669013ddaae7d77b4984 | 2022-07-22T18:26:53.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"dataset:succinctly/midjourney-prompts",
"transformers",
"text2image",
"prompting",
"license:apache-2.0"
] | text-generation | false | succinctly | null | succinctly/text2image-prompt-generator | 534 | 0 | transformers | ---
language:
- "en"
thumbnail: "https://drive.google.com/uc?export=view&id=1JWwrxQbr1s5vYpIhPna_p2IG1pE5rNiV"
tags:
- text2image
- prompting
license: "apache-2.0"
datasets:
- "succinctly/midjourney-prompts"
---
This is a GPT-2 model fine-tuned on the [succinctly/midjourney-prompts](https://huggingface.co/datasets/... | [
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0.08874409645795822,
-0.02... |
asahi417/tner-xlm-roberta-large-ontonotes5 | 08326a195be4564f4ce6b057033c65d0e47de3cc | 2021-02-13T00:10:15.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | asahi417 | null | asahi417/tner-xlm-roberta-large-ontonotes5 | 533 | null | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-ontonotes5")
model = ... | [
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0.039427872747182846,
0.05... |
Helsinki-NLP/opus-mt-tc-big-tr-en | 168ae5336d311e146eb774c5d85698548aa0da11 | 2022-06-01T12:58:47.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"tr",
"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-tr-en | 533 | 1 | transformers | ---
language:
- en
- tr
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-tr-en
results:
- task:
name: Translation tur-eng
type: translation
args: tur-eng
dataset:
name: flores101-devtest
type: flores_101
args: tur eng devtest
metrics... | [
-0.029634026810526848,
-0.03620944544672966,
-0.004852271638810635,
0.0013485606759786606,
0.02866600826382637,
-0.021275779232382774,
0.02726624347269535,
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0.02372397668659687,
-0.01821732148528099,
0.004427328705787659,
-0.16659551858901978,
-0.03527969866991043,
-0... |
dmis-lab/biobert-large-cased-v1.1-mnli | a68c447b532ba6af6b2050036a05057dd036ef94 | 2021-05-19T15:58:34.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | dmis-lab | null | dmis-lab/biobert-large-cased-v1.1-mnli | 532 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
kiri-ai/t5-base-qa-summary-emotion | 66b7d2e5273b4fd4fff90c366702b71a857e5801 | 2021-09-22T08:55:00.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:coqa",
"dataset:squad_v2",
"dataset:go_emotions",
"dataset:cnn_dailymail",
"transformers",
"question-answering",
"emotion-detection",
"summarisation",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | kiri-ai | null | kiri-ai/t5-base-qa-summary-emotion | 532 | null | transformers | ---
language:
- en
tags:
- question-answering
- emotion-detection
- summarisation
license: apache-2.0
datasets:
- coqa
- squad_v2
- go_emotions
- cnn_dailymail
metrics:
- f1
pipeline_tag: text2text-generation
widget:
- text: 'q: Who is Elon Musk? a: an entrepreneur q: When was he born? c: Elon Musk
is an entreprene... | [
-0.05713238939642906,
0.05485402047634125,
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0.001694298698566854,
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0.021803569048643112,
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0.04280996695160866,
0.02158... |
Helsinki-NLP/opus-mt-tc-big-en-fr | 4bc9bda0d1631919558705df71a6471c6eb0e1c5 | 2022-06-01T13:04:06.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"fr",
"transformers",
"translation",
"opus-mt-tc",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tc-big-en-fr | 532 | null | transformers | ---
language:
- en
- fr
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-fr
results:
- task:
name: Translation eng-fra
type: translation
args: eng-fra
dataset:
name: flores101-devtest
type: flores_101
args: eng fra devtest
metrics... | [
-0.05028076469898224,
-0.04785999655723572,
0.006304119247943163,
0.000012468545719457325,
0.05550124868750572,
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0.021110737696290016,
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0.020192330703139305,
-0.014134705066680908,
0.012471972964704037,
-0.16953982412815094,
-0.014374732039868832,
... |
Qiaozhen/fake-news-detector | 00a0a890197ddc3c67a5e3521d06558bd7cd8b2f | 2021-12-01T01:26:57.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | Qiaozhen | null | Qiaozhen/fake-news-detector | 531 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
chriskhanhtran/spanberta | cf241bbd36ff5868eb9e0b603f6d6d4c52b6bc56 | 2021-05-20T15:20:16.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | chriskhanhtran | null | chriskhanhtran/spanberta | 530 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
Lowin/chinese-bigbird-base-4096 | 1914e13c124f37e28fa1b4960e7676e49f515564 | 2022-07-05T08:36:12.000Z | [
"pytorch",
"big_bird",
"fill-mask",
"zh",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Lowin | null | Lowin/chinese-bigbird-base-4096 | 529 | 1 | transformers | ---
language:
- zh
license:
- apache-2.0
---
```python
import jieba_fast
from transformers import BertTokenizer
from transformers import BigBirdModel
class JiebaTokenizer(BertTokenizer):
def __init__(
self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs
):
super().__init__... | [
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0.058794889599084854,
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-... |
tomh/toxigen_hatebert | c260d78a7867bb9a9748184afaf454d6ccf28129 | 2022-05-02T12:42:51.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:2203.09509",
"transformers"
] | text-classification | false | tomh | null | tomh/toxigen_hatebert | 529 | null | transformers | ---
language:
- en
tags:
- text-classification
---
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar.
This model comes from the paper [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://arxiv.org/abs/2203.09509) and can... | [
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microsoft/swin-small-patch4-window7-224 | 1e8f54cc3d19ded9cda11db863080c79b1096289 | 2022-05-16T18:11:23.000Z | [
"pytorch",
"tf",
"swin",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/swin-small-patch4-window7-224 | 528 | null | transformers | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https... | [
-0.08790218085050583,
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alger-ia/dziribert | f48769d3b3c07e40de8e1649bd1e3de4c4e15b2e | 2021-09-28T13:13:56.000Z | [
"pytorch",
"tf",
"bert",
"fill-mask",
"ar",
"dz",
"arxiv:2109.12346",
"transformers",
"multilingual",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | alger-ia | null | alger-ia/dziribert | 527 | 5 | transformers | ---
language:
- ar
- dz
tags:
- pytorch
- bert
- multilingual
- ar
- dz
license: apache-2.0
widget:
- text: " أنا من الجزائر من ولاية [MASK] "
- text: "rabi [MASK] khouya sami"
- text: " ربي [MASK] خويا لعزيز"
- text: "tahya el [MASK]."
- text: "rouhi ya dzayer [MASK]"
inference: true
---
... | [
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0.0017370398854836822,
0.0... |
optimum/distilbert-base-uncased-finetuned-banking77 | caf9d9f17154f487c6f968641ce0d2dc8f592165 | 2022-06-24T14:30:32.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:banking77",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | optimum | null | optimum/distilbert-base-uncased-finetuned-banking77 | 527 | 1 | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- accuracy
- f1
widget:
- text: Could you assist me in finding my lost card?
example_title: Example 1
- text: I found my lost card. Am I still able to use it?
example_title: Example 2
- text: "Hey, I thought my topup was all done ... | [
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0.01937422901391983,
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-0.02165096253156662,
-0.07... |
neulab/gpt2-finetuned-wikitext103 | f042c5d9d998c564e49cddb98ddec90148e5aa43 | 2022-07-14T15:38:21.000Z | [
"pytorch",
"gpt2",
"text-generation",
"arxiv:2201.12431",
"transformers"
] | text-generation | false | neulab | null | neulab/gpt2-finetuned-wikitext103 | 527 | null | transformers | This is a `gpt2` model, finetuned on the Wikitext-103 dataset.
It achieves a perplexity of **14.84** using a "sliding window" context, using the `run_clm.py` script at [https://github.com/neulab/knn-transformers](https://github.com/neulab/knn-transformers).
| Base LM: | `distilgpt2` | `gpt2` |
| :--- ... | [
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0.... |
stefan-it/german-gpt2-larger | aa2138bb716507181c1bbd288a1076837ed0ca3b | 2021-09-17T09:48:43.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"de",
"transformers",
"license:mit"
] | text-generation | false | stefan-it | null | stefan-it/german-gpt2-larger | 526 | 2 | transformers | ---
language: de
widget:
- text: "Heute ist sehr schönes Wetter in"
license: mit
---
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on ~90 GB from the ["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4).
The ... | [
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0.01604210026562214,
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-0.... |
hustvl/yolos-base | 54e810c3e4165d3e2cdc5888fd8da2b30172a596 | 2022-06-27T08:37:10.000Z | [
"pytorch",
"yolos",
"object-detection",
"dataset:coco",
"arxiv:2106.00666",
"transformers",
"vision",
"license:apache-2.0"
] | object-detection | false | hustvl | null | hustvl/yolos-base | 526 | 1 | transformers | ---
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... | [
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0.010780734941363335,
0.016167087480425835,
-0.02818594127893448,
0.03495797514915466,
-0.07654978334903717,
-0.03873522952198982,
0.053... |
rajistics/finetuned-indian-food | d956b7c21cca5874ba917c62a873bad8608b83c6 | 2022-07-18T20:13:53.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"dataset:imagefolder",
"dataset:rajistics/indian_food_images",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | rajistics | null | rajistics/finetuned-indian-food | 526 | null | transformers | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
- rajistics/indian_food_images
metrics:
- accuracy
widget:
- src: https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/003.jpg
example_title: Fried Rice
- src: https://huggingface.co/rajistics/finetune... | [
-0.03069925867021084,
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0.09082446992397308,
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0.032507192343473434,
0.01... |
dbernsohn/t5_wikisql_en2SQL | 13a3815a27a5731652f580b941d271f105f2bbda | 2021-01-18T14:24:37.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:wikisql",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | dbernsohn | null | dbernsohn/t5_wikisql_en2SQL | 525 | 2 | transformers | # t5_wikisql_en2SQL
---
language: en
datasets:
- wikisql
---
This is a [t5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned version on the [wikisql dataset](https://huggingface.co/datasets/wikisql) for **English** to **SQL** **translation** text2text mission.
To load the m... | [
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0.06185708940029144,
0.0004423... |
deutsche-telekom/electra-base-de-squad2 | 0ed9cec3da1d1b2b4e85a49ada4be6823effe0c0 | 2021-07-14T13:16:54.000Z | [
"pytorch",
"electra",
"question-answering",
"de",
"transformers",
"german",
"license:mit",
"autotrain_compatible"
] | question-answering | false | deutsche-telekom | null | deutsche-telekom/electra-base-de-squad2 | 525 | 4 | transformers | ---
language: de
license: mit
tags:
- german
---
We released the German Question Answering model fine-tuned with our own German Question Answering dataset (**deQuAD**) containing **130k** training and **11k** test QA pairs.
## Overview
- **Language model:** [electra-base-german-uncased](https://huggingface.co/german... | [
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0... |
google/bert_uncased_L-8_H-256_A-4 | fff21c203abcc9365418f2e46bb6801a2b98e3da | 2021-05-19T17:35:25.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-8_H-256_A-4 | 524 | null | transformers | ---
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... | [
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0.0994548574090004,
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0.018868697807192802,
-0.015814494341611862,
0.03585591912269592,
0.03645862638950348,
0.0455... |
mismayil/comet-bart-ai2 | c920ae53bb7ad34d63eb48eb818e9274bef3ea7a | 2022-05-23T08:20:08.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | mismayil | null | mismayil/comet-bart-ai2 | 524 | null | transformers | ---
license: afl-3.0
---
This model has been trained by the original authors of the paper [(Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs.](https://www.semanticscholar.org/paper/COMET-ATOMIC-2020%3A-On-Symbolic-and-Neural-Knowledge-Hwang-Bhagavatula/e39503e01ebb108c6773948a24ca798cd444eb62) ... | [
-0.022582819685339928,
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0.014729216694831848,
-0.05273008719086647,
0.02311336249113083,
-0.04274636134505272,
0.04578261449933052,
0.028996959328651428,
-0.02933354675769806,
0.00812575500458479,
0.042... |
Helsinki-NLP/opus-mt-es-de | 74a9fd1e4c6ada26cf15d4580414dc933b463ee2 | 2021-09-09T21:41:53.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"de",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-de | 523 | null | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-es-de
* source languages: es
* target languages: de
* OPUS readme: [es-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... | [
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IlyaGusev/rubertconv_toxic_clf | 39c070add685fee30cedc3a909a8a9f206d2b53d | 2022-07-13T15:34:11.000Z | [
"pytorch",
"bert",
"text-classification",
"ru",
"transformers",
"license:apache-2.0"
] | text-classification | false | IlyaGusev | null | IlyaGusev/rubertconv_toxic_clf | 523 | null | transformers | ---
language:
- ru
tags:
- text-classification
license: apache-2.0
---
# RuBERTConv Toxic Classifier
## Model description
Based on [rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model
## Intended uses & limitations
#### How to use
Colab: [link](https://col... | [
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0.031162820756435394,
0... |
bigscience/T0p | 99436b357ac572810426fe2ecc9ddb449b48bd5e | 2022-06-21T01:23:09.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | bigscience | null | bigscience/T0p | 523 | 3 | transformers | ---
datasets:
- bigscience/P3
language: en
license: apache-2.0
widget:
- text: "A is the son's of B's uncle. What is the family relationship between A and B?"
- text: "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."
- text: "Task: copy but say the opposite.\n
PSG won its match again... | [
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0.046557337045669556,
0.11346331983804703,
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-0.023029416799545288,
0.04329385980963707,
-0.037657398730516434,
0.044719722121953964,
... |
nboost/pt-bert-base-uncased-msmarco | 6c6a7cb3c08a611c3741ba5d296dda9a3954ccf3 | 2021-05-20T01:23:41.000Z | [
"pytorch",
"jax",
"onnx",
"bert",
"transformers"
] | null | false | nboost | null | nboost/pt-bert-base-uncased-msmarco | 523 | null | transformers | Entry not found | [
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-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
enelpol/poleval2021-task3 | b6e13ae11eca4e958f21dc6ce4b4f8f161c2da30 | 2022-04-25T12:29:50.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | enelpol | null | enelpol/poleval2021-task3 | 522 | null | transformers | Trained with prefix `ocr: `. | [
-0.053713928908109665,
-0.0035466605331748724,
-0.028234116733074188,
-0.022158373147249222,
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-0.037980109453201294,
0.036348938941955566,
0.017814192920923233,
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-0.09777239710092545,
0.05588386952877045,
0.02559426799416542,
0.030482228845357895,
... |
google/vit-large-patch16-224-in21k | 767d25e8d39685203fb0bed98739cf87bdcf9b8e | 2022-01-28T10:24:07.000Z | [
"pytorch",
"tf",
"jax",
"vit",
"feature-extraction",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"transformers",
"vision",
"license:apache-2.0"
] | feature-extraction | false | google | null | google/vit-large-patch16-224-in21k | 522 | null | transformers | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet-21k
inference: false
---
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transforme... | [
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0.0... |
huggingface-course/bert-finetuned-ner | deaaadce6b22a23cf953227e8e2647c477e2122c | 2022-07-13T13:19:42.000Z | [
"pytorch",
"tf",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | huggingface-course | null | huggingface-course/bert-finetuned-ner | 522 | 1 | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test-bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
ar... | [
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-0.003060205141082406,
0.009670454077422619,
0.04418836161494255,
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-0.04307810962200165,
0.04386221244931221,
-0.11661852896213531,
-0.018270745873451233,
... |
nghuyong/ernie-tiny | 62033400436c5c29acc176e8361ab8fc124a7edf | 2021-05-20T01:47:09.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"en",
"transformers"
] | null | false | nghuyong | null | nghuyong/ernie-tiny | 521 | null | transformers | ---
language: en
---
# ERNIE-tiny
## Introduction
ERNIE-tiny is a compressed model from [ERNIE 2.0](../ernie-2.0-en) base model through model structure compression and model distillation.
Through compression, the performance of the ERNIE-tiny only decreases by an average of 2.37% compared to ERNIE 2.0 base,
but it o... | [
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0.021093662828207016,
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-0.029062964022159576,
-0.... |
cedpsam/chatbot_fr | a43a9ec6c5f39fb6d3261acd7826a284dbeb8eb3 | 2021-05-26T10:36:41.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"fr",
"transformers",
"conversational"
] | conversational | false | cedpsam | null | cedpsam/chatbot_fr | 520 | null | transformers | ---
language: fr
tags:
- conversational
widget:
- text: "bonjour."
- text: "mais encore"
- text: "est ce que l'argent achete le bonheur?"
---
## a dialoggpt model trained on french opensubtitles with custom tokenizer
trained with this notebook
https://colab.research.google.com/drive/1pfCV3bngAmISNZVfDvBMyEhQKuYw37Rl#s... | [
-0.02721358835697174,
-0.06308600306510925,
0.03850635513663292,
-0.07261426746845245,
0.022700777277350426,
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0.05712268874049187,
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0.08052197098731995,
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-0.03558448329567909,
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-0.020450899377465248,
-0.03... |
huggingtweets/animemajg | 520419ce48c601dfb691d1f326bb242729f4f952 | 2021-05-21T19:02:09.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/animemajg | 520 | null | transformers | ---
language: en
thumbnail: https://www.huggingtweets.com/animemajg/1608731707053/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 { colo... | [
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0.029200715944170952,
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-0.05724509805440903,
-0.03304874897003174,
0.029941514134407043,
0.003195130731910467,
-0.02448... |
monologg/biobert_v1.0_pubmed_pmc | de4eabdaad660430062b472f0314445edf7bcb7a | 2021-05-19T23:49:24.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
] | null | false | monologg | null | monologg/biobert_v1.0_pubmed_pmc | 520 | null | transformers | Entry not found | [
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0.011261860840022564,
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-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
dmis-lab/biobert-base-cased-v1.1-mnli | 324ddf751ebe6e36beddf6b8f09983d4284a18ee | 2021-05-19T15:56:11.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | dmis-lab | null | dmis-lab/biobert-base-cased-v1.1-mnli | 519 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
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