modelId stringlengths 4 112 | sha stringlengths 40 40 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringclasses 29
values | private bool 1
class | author stringlengths 2 38 ⌀ | config null | id stringlengths 4 112 | downloads float64 0 36.8M ⌀ | likes float64 0 712 ⌀ | library_name stringclasses 17
values | __index_level_0__ int64 0 38.5k | readme stringlengths 0 186k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
junnyu/roformer_chinese_sim_char_small | 4cc0b8dbc73cda2fada77a8b69878ccdcb667d2d | 2022-04-15T03:52:19.000Z | [
"pytorch",
"roformer",
"text-generation",
"zh",
"transformers",
"tf2.0"
] | text-generation | false | junnyu | null | junnyu/roformer_chinese_sim_char_small | 36 | null | transformers | 6,700 | ---
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... |
lannelin/bert-imdb-1hidden | 7808be7790ceb59489081af1a72b7416a482c71a | 2022-07-13T15:17:08.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"dataset:imdb",
"transformers"
] | text-classification | false | lannelin | null | lannelin/bert-imdb-1hidden | 36 | null | transformers | 6,701 | ---
language:
- en
datasets:
- imdb
metrics:
- accuracy
---
# bert-imdb-1hidden
## Model description
A `bert-base-uncased` model was restricted to 1 hidden layer and
fine-tuned for sequence classification on the
imdb dataset loaded using the `datasets` library.
## Intended uses & limitations
#### How to use
```... |
lighteternal/SSE-TUC-mt-en-el-cased | d7a1738e8f8aca831f87e0652f1796eeb5f46ce0 | 2021-03-31T17:27:05.000Z | [
"pytorch",
"fsmt",
"text2text-generation",
"en",
"el",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | lighteternal | null | lighteternal/SSE-TUC-mt-en-el-cased | 36 | null | transformers | 6,702 | ---
language:
- en
- el
tags:
- translation
widget:
- text: "'Katerina', is the best name for a girl."
license: apache-2.0
metrics:
- bleu
---
## English to Greek NMT
## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
* source languages: en
* target languages: el
* licence: apache-2.0
*... |
lighteternal/SSE-TUC-mt-en-el-lowercase | 0fe36048867527fd75f08d0a8df723e8a60a1484 | 2021-03-31T17:27:32.000Z | [
"pytorch",
"fsmt",
"text2text-generation",
"en",
"el",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | lighteternal | null | lighteternal/SSE-TUC-mt-en-el-lowercase | 36 | null | transformers | 6,703 | ---
language:
- en
- el
tags:
- translation
widget:
- text: "Not all those who wander are lost."
license: apache-2.0
metrics:
- bleu
---
## English to Greek NMT (lower-case output)
## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
* source languages: en
* target languages: el
* licence... |
m3hrdadfi/albert-fa-base-v2-sentiment-digikala | 2104f211f5ba3012d5cfd1ef72fc2791cb52eaa9 | 2020-12-26T08:48:33.000Z | [
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"transformers",
"license:apache-2.0"
] | text-classification | false | m3hrdadfi | null | m3hrdadfi/albert-fa-base-v2-sentiment-digikala | 36 | null | transformers | 6,704 | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... |
microsoft/xprophetnet-large-wiki100-cased-xglue-qg | 635f3dae12109f196f068a416669d5f069dd1c34 | 2020-12-11T21:51:14.000Z | [
"pytorch",
"xlm-prophetnet",
"text2text-generation",
"arxiv:2001.04063",
"arxiv:2004.01401",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | microsoft | null | microsoft/xprophetnet-large-wiki100-cased-xglue-qg | 36 | null | transformers | 6,705 | ## xprophetnet-large-wiki100-cased-xglue-ntg
Cross-lingual version [ProphetNet](https://arxiv.org/abs/2001.04063), pretrained on [wiki100 xGLUE dataset](https://arxiv.org/abs/2004.01401) and finetuned on xGLUE cross-lingual Question Generation task.
ProphetNet is a new pre-trained language model for sequence-to-seque... |
mrm8488/bert-spanish-cased-finedtuned-ner | e39fef87da28af909803c1d65d3d6f36d080ee3f | 2021-05-20T00:34:37.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | mrm8488 | null | mrm8488/bert-spanish-cased-finedtuned-ner | 36 | null | transformers | 6,706 | Entry not found |
mrm8488/t5-base-finetuned-Reddit-TIFU-TLDR | 774a3d8584d9307ca2c861c0464773bca6bad16e | 2020-08-03T14:57:58.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-finetuned-Reddit-TIFU-TLDR | 36 | null | transformers | 6,707 | Entry not found |
projecte-aina/roberta-base-ca-cased-tc | 59f67a70f7716e152c22bf5faa85d6c285cc99a1 | 2022-02-24T08:34:45.000Z | [
"pytorch",
"roberta",
"text-classification",
"ca",
"dataset:projecte-aina/tecla",
"arxiv:1907.11692",
"transformers",
"catalan",
"text classification",
"tecla",
"CaText",
"Catalan Textual Corpus",
"model-index"
] | text-classification | false | projecte-aina | null | projecte-aina/roberta-base-ca-cased-tc | 36 | 1 | transformers | 6,708 | ---
language:
- ca
tags:
- "catalan"
- "text classification"
- "tecla"
- "CaText"
- "Catalan Textual Corpus"
datasets:
- "projecte-aina/tecla"
metrics:
- accuracy
model-index:
- name: roberta-base-ca-cased-tc
results:
- task:
type: text-classification
dataset:
name: tecla
type: p... |
pszemraj/t5-base-askscience-lfqa | 45c775f403de3a92faec72ee3f6d28377d130097 | 2022-03-13T22:37:51.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:vblagoje/lfqa",
"transformers",
"qa",
"askscience",
"lfqa",
"information retrieval",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | pszemraj | null | pszemraj/t5-base-askscience-lfqa | 36 | 0 | transformers | 6,709 | ---
license: apache-2.0
language:
- en
tags:
- t5
- qa
- askscience
- lfqa
- information retrieval
datasets:
- vblagoje/lfqa
metrics:
- rouge
widget:
- text: "why hasn't humanity expanded to live on other planets in our solar system?"
example_title: "solar system"
- text: "question: what is a probability distribution... |
seduerr/paiintent | 5d5b048b064dc90fe29bb64b6e36479794b98e22 | 2021-03-20T05:20:17.000Z | [
"pytorch",
"squeezebert",
"en",
"dataset:mulit_nli",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | false | seduerr | null | seduerr/paiintent | 36 | 2 | transformers | 6,710 | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- squeezebert
datasets:
- mulit_nli
metrics:
- accuracy
---
# SqueezeBERT |
shashank2123/t5-finetuned-for-GEC | 86f329d570afca7636286d61318be160d23e8bf9 | 2021-08-05T06:16:09.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | shashank2123 | null | shashank2123/t5-finetuned-for-GEC | 36 | null | transformers | 6,711 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model_index:
- name: t5-finetuned-for-GEC
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metric:
name: Bleu
type: bleu
value: 0.3571
---
<!-- This model card has been g... |
sosuke/ease-bert-base-multilingual-cased | 71d445876280d99aa674a9f32bcca3542af80e8f | 2021-12-07T14:19:07.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | sosuke | null | sosuke/ease-bert-base-multilingual-cased | 36 | null | transformers | 6,712 | Entry not found |
speechbrain/asr-crdnn-commonvoice-de | 54aa45ffca30cee245d29a499ba954a7eab30bb3 | 2021-11-30T00:36:12.000Z | [
"de",
"dataset:common_voice",
"arxiv:2106.04624",
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"pytorch",
"license:apache-2.0"
] | automatic-speech-recognition | false | speechbrain | null | speechbrain/asr-crdnn-commonvoice-de | 36 | null | speechbrain | 6,713 | ---
language: "de"
thumbnail:
tags:
- automatic-speech-recognition
- CTC
- Attention
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- common_voice
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scr... |
surajp/albert-base-sanskrit | a397b2fad852d6ac908ebf16b32fc457e442d0d8 | 2020-12-11T22:02:34.000Z | [
"pytorch",
"albert",
"feature-extraction",
"sa",
"transformers"
] | feature-extraction | false | surajp | null | surajp/albert-base-sanskrit | 36 | 2 | transformers | 6,714 | ---
language: sa
---
# ALBERT-base-Sanskrit
Explaination Notebook Colab: [SanskritALBERT.ipynb](https://colab.research.google.com/github/parmarsuraj99/suraj-parmar/blob/master/_notebooks/2020-05-02-SanskritALBERT.ipynb)
Size of the model is **46MB**
Example of usage:
```
tokenizer = AutoTokenizer.from_pretrained... |
drAbreu/bioBERT-NER-NCBI_disease | 4e888d64cbd0f2fda938f8ad7b25094866e137c9 | 2022-03-15T14:42:02.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:ncbi_disease",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | drAbreu | null | drAbreu/bioBERT-NER-NCBI_disease | 36 | null | transformers | 6,715 | ---
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bioBERT-NER-NCBI_disease
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ncbi_disease
type: ncbi_disease
args: ncbi_d... |
Aureliano/distilbert-base-uncased-if | 0e0f686b700a3871d7b64ade5f2ee282d3352e38 | 2022-03-25T00:06:03.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"transformers",
"license:apache-2.0"
] | text-classification | false | Aureliano | null | Aureliano/distilbert-base-uncased-if | 36 | null | transformers | 6,716 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# DistilBERT base model (uncased) for Interactive Fiction
[`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) finetuned on a dataset of Interactive
Fiction commands.
Details on the datasets can be found... |
binay1999/bert-finetuned-ner-q | e0469a09097b1b7e906f1965f67399688e502747 | 2022-03-31T07:43:48.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | binay1999 | null | binay1999/bert-finetuned-ner-q | 36 | null | transformers | 6,717 | Entry not found |
Jackett/subject_classifier_extended | 75e5305761fb9a8b0e59ddb6d6bbc5601063e9ae | 2022-05-12T06:09:29.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | Jackett | null | Jackett/subject_classifier_extended | 36 | null | transformers | 6,718 | Label mappings
{'LABEL_0':'Biology','LABEL_1':'Physics','LABEL_2':'Chemistry','LABEL_3':'Maths','LABEL_4':'Social Science','LABEL_5':'English'}
Training data distribution
Physics - 7000
Maths - 7000
Biology - 7000
Chemistry - 7000
English - 5254
Social Science - 7000 |
Bryan0123/bert-hashtag-to-hashtag | b0465992773484c93603403975ebee2b272a1d6a | 2022-05-15T05:08:27.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Bryan0123 | null | Bryan0123/bert-hashtag-to-hashtag | 36 | null | transformers | 6,719 | Entry not found |
Salesforce/codegen-2B-nl | 25563dbce49290c7dceea25ebeb2f11b6fd0910b | 2022-06-28T17:45:54.000Z | [
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"transformers",
"license:bsd-3-clause"
] | text-generation | false | Salesforce | null | Salesforce/codegen-2B-nl | 36 | null | transformers | 6,720 | ---
license: bsd-3-clause
---
# CodeGen (CodeGen-NL 2B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang... |
allenai/aspire-contextualsentence-singlem-biomed | 924024ac9557f2e08acf9aea96e2a42749062119 | 2022-04-24T20:06:15.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2111.08366",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | allenai | null | allenai/aspire-contextualsentence-singlem-biomed | 36 | null | transformers | 6,721 | ---
license: apache-2.0
---
## Overview
Model included in a paper for modeling fine grained similarity between documents:
**Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
**Authors**: Sheshera Mysore, Arman Cohan, Tom Hope
**Paper**: https://arxiv.org/abs/2111.... |
cjvt/t5-sl-small | 7d95fb63be4e50c31e1c551b364338dc3b16ad7d | 2022-07-21T11:24:14.000Z | [
"pytorch",
"t5",
"text2text-generation",
"sl",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | cjvt | null | cjvt/t5-sl-small | 36 | null | transformers | 6,722 | ---
language:
- sl
license: cc-by-sa-4.0
---
# t5-sl-small
t5-sl-small model is a Slovene T5 model. It has 8 encoder and 8 decoder layers, in total about 60 million parameters.
It was trained for 5 epochs on the following corpora:
## Corpora
The following corpora were used for training the model:
* Gigafida 2.0
* Ka... |
TehranNLP-org/bert-large-sst2 | 529f83169e5bcae3674837dfa38b8551307d4734 | 2022-05-03T17:01:28.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-large-sst2 | 36 | null | transformers | 6,723 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SEED0042
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: SST2
type: ''
args: sst2
metrics:
- name: Accuracy
type: accurac... |
agnihotri/cuad_contract_type | 1df1dd3a95cdc610dd4f4e1fa04d1c9ad2db8d42 | 2022-05-01T18:49:12.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:agnihotri/autotrain-data-contract_type",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | agnihotri | null | agnihotri/cuad_contract_type | 36 | null | transformers | 6,724 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- agnihotri/autotrain-data-contract_type
co2_eq_emissions: 0.07610944071640048
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 809725368
- CO2 Emissions (in grams): 0.07610944071640048
## Valid... |
yelpfeast/byt5-base-english-ocr-correction | 19d5c2fd86b87f0a0febb7d2574878a0d68d5294 | 2022-07-09T16:37:42.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:wikitext",
"arxiv:2105.13626",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | yelpfeast | null | yelpfeast/byt5-base-english-ocr-correction | 36 | 0 | transformers | 6,725 | ---
language: en
datasets:
- wikitext
---
# ByT5 base English fine tuned for OCR Correction
This model is a fine-tuned version of the [byt5-base](https://huggingface.co/google/byt5-base) for OCR Correction. ByT5 was
introduced in [this paper](https://arxiv.org/abs/2105.13626) and the idea and code for fine-tuning the... |
ArthurZ/opt-125m | bd9fb857e5b73be814e773d53baa9036e236af0e | 2022-06-21T20:29:12.000Z | [
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"transformers",
"generated_from_keras_callback",
"model-index"
] | text-generation | false | ArthurZ | null | ArthurZ/opt-125m | 36 | null | transformers | 6,726 | ---
tags:
- generated_from_keras_callback
model-index:
- name: opt-125m
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# opt-125m
This model was trained from scratch... |
xhyi/CodeGen-350M-Multi | a49dc3e1e560fb96f0dd8e8ff4a2b4073a0ff231 | 2022-05-18T07:08:40.000Z | [
"pytorch",
"codegen",
"text-generation",
"en",
"transformers",
"text generation",
"causal-lm",
"license:bsd-3-clause"
] | text-generation | false | xhyi | null | xhyi/CodeGen-350M-Multi | 36 | null | transformers | 6,727 | ---
language:
- en
tags:
- codegen
- text generation
- pytorch
- causal-lm
license: bsd-3-clause
---
# Salesforce CodeGen
ported salesforce codegen models to work on huggingface transformers without any extra code (the model specific code is bundled)
## Overview
The CodeGen model was proposed in by Erik Nijkamp, Bo... |
PontifexMaximus/ArabicTranslator | 324ef839da8a24a2ca38181a3654960bd51e1a54 | 2022-05-26T01:25:24.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:opus_infopankki",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | PontifexMaximus | null | PontifexMaximus/ArabicTranslator | 36 | null | transformers | 6,728 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus_infopankki
metrics:
- bleu
model-index:
- name: opus-mt-ar-en-finetuned-ar-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_infopankki
type: opus_info... |
Sehong/kobart-QuestionGeneration | 09f94b7c18fb7e3ead56fb1c1314706ee4072425 | 2022-05-28T03:21:39.000Z | [
"pytorch",
"bart",
"text2text-generation",
"ko",
"dataset:korquad",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | Sehong | null | Sehong/kobart-QuestionGeneration | 36 | 3 | transformers | 6,729 | ---
language: ko
tags:
- bart
datasets:
- korquad
license: mit
---
# Korean Question Generation Model
## Github
https://github.com/Seoneun/KoBART-Question-Generation
## Fine-tuning Dataset
KorQuAD 1.0
## Demo
https://huggingface.co/Sehong/kobart-QuestionGeneration
## How to use
```python
import torch
from tran... |
miesnerjacob/distilbert-base-uncased-finetuned-squad-d5716d28 | a1935f2b809bfdb3f20190d1436036b9d25ab7c4 | 2022-05-30T17:27:30.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | miesnerjacob | null | miesnerjacob/distilbert-base-uncased-finetuned-squad-d5716d28 | 36 | null | transformers | 6,730 | ---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
---
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of... |
abhishek/autotrain-dog-vs-food | 195eb7819ea64a39de56c166e779a94038ce9a14 | 2022-06-22T14:51:28.000Z | [
"pytorch",
"vit",
"image-classification",
"dataset:abhishek/autotrain-data-vision_652fee16113a4f07a2452e021a22a934",
"dataset:sasha/dog-food",
"transformers",
"autotrain",
"model-index",
"co2_eq_emissions"
] | image-classification | false | abhishek | null | abhishek/autotrain-dog-vs-food | 36 | 1 | transformers | 6,731 | ---
tags: autotrain
datasets:
- abhishek/autotrain-data-vision_652fee16113a4f07a2452e021a22a934
- sasha/dog-food
co2_eq_emissions: 2.050948967287266
model-index:
- name: autotrain-dog-vs-food
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: sasha/dog-food
... |
Dizzykong/charles-dickens | 2fd3ea9935cf68edf9813e233823f89397221e09 | 2022-06-27T21:13:14.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"model-index"
] | text-generation | false | Dizzykong | null | Dizzykong/charles-dickens | 36 | null | transformers | 6,732 | ---
tags:
- generated_from_trainer
model-index:
- name: charles-dickens
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. -->
# charles-dickens
This model is a fine-t... |
ghadeermobasher/Original-BlueBERT-BioRED-Chem-512-5-30 | bc22321dfa27144ffd169ee601d37c5d0a26c4c6 | 2022-07-08T12:25:44.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-BlueBERT-BioRED-Chem-512-5-30 | 36 | null | transformers | 6,733 | |
shengnan/v-shean-visualize-202207162206 | e1f80725195c07554513aedc0e60fa8228e5fe49 | 2022-07-16T14:24:36.000Z | [
"pytorch",
"t5",
"transformers"
] | null | false | shengnan | null | shengnan/v-shean-visualize-202207162206 | 36 | null | transformers | 6,734 | Entry not found |
Lancelot53/CV_bn_trained_on_Validation | 4f99077eab1d7618fe65195e6a4dcd233c1249d4 | 2022-07-19T17:02:37.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | Lancelot53 | null | Lancelot53/CV_bn_trained_on_Validation | 36 | null | transformers | 6,735 | ---
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: CV_bn_trained_on_Validation
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. -->
# CV... |
mehdidn/finetuned_translation_fa_en | e81981288b703716902f8c16de6ef16f5057c3b2 | 2022-07-24T21:00:41.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | mehdidn | null | mehdidn/finetuned_translation_fa_en | 36 | null | transformers | 6,736 | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: finetuned_translation_fa_en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this co... |
fujiki/t5-efficient-xl-en2ja_train5 | b2e3fb1156d4be4b195c29d9fe88f10b72a5f687 | 2022-07-30T09:49:28.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | fujiki | null | fujiki/t5-efficient-xl-en2ja_train5 | 36 | null | transformers | 6,737 | Entry not found |
AriakimTaiyo/gpt2-chat | e5389d7c7347eeb36dcf43d408e46022294ede26 | 2022-07-27T19:36:22.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"conversational",
"license:mit"
] | conversational | false | AriakimTaiyo | null | AriakimTaiyo/gpt2-chat | 36 | null | transformers | 6,738 | ---
language: en
license: mit
tags:
- conversational
---
# GPT-2 Large
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation... |
derwahnsinn/gpt2-mediumADS | 6acccda4096dc57430ba122548e3f0a7c610791b | 2022-07-28T03:25:52.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | derwahnsinn | null | derwahnsinn/gpt2-mediumADS | 36 | null | transformers | 6,739 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-mediumADS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-mediumADS
This model ... |
soop/DialoGPT-medium-BaymaxBot | 3bb9ce4a628f7f641fb805bf7eb0d47a4c65c882 | 2022-07-29T17:39:29.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | soop | null | soop/DialoGPT-medium-BaymaxBot | 36 | null | transformers | 6,740 | ---
tags:
- conversational
---
# DialoGPT BaymaxBot
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter | 3e48534705c153737cbec1c5748bb02359b7b239 | 2021-09-14T14:30:54.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | CAMeL-Lab | null | CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter | 35 | 2 | transformers | 6,741 | ---
language:
- ar
license: apache-2.0
widget:
- text: "الهدف من الحياة هو [MASK] ."
---
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained langu... |
DataikuNLP/TinyBERT_General_4L_312D | 33ec5b27fcd40369ff402c779baffe219f5360fe | 2021-09-02T08:09:47.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1909.10351",
"transformers"
] | null | false | DataikuNLP | null | DataikuNLP/TinyBERT_General_4L_312D | 35 | null | transformers | 6,742 | TinyBERT: Distilling BERT for Natural Language Understanding
========
**This model is a copy of [this model repository](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) from Huawei Noah at the specific commit `34707a33cd59a94ecde241ac209bf35103691b43`.**
TinyBERT is 7.5x smaller and 9.4x faster on infere... |
Gerwin/bert-for-pac | 0b2f6225499890eb49f58658db00706ea3d3e8d2 | 2022-07-21T08:59:38.000Z | [
"pytorch",
"bert",
"text-classification",
"nl",
"transformers",
"passive",
"active",
"license:apache-2.0"
] | text-classification | false | Gerwin | null | Gerwin/bert-for-pac | 35 | null | transformers | 6,743 | ---
language:
- nl
tags:
- bert
- passive
- active
license: apache-2.0
---
## Dutch Fine-Tuned BERT For Passive/Active Voice Classification.
### Lijdende en Bedrijvende vorm classificatie voor zinnen
#### Examples
Try the following examples in the Hosted inference API:
1. Jan werd opgehaald door zijn moeder.
2. Wie ... |
GroNLP/gpt2-medium-dutch-embeddings | 0ea2e72f4d0a68c02ca02e62c7f3deadc956c1e7 | 2021-05-21T09:49:13.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"nl",
"arxiv:2012.05628",
"transformers",
"adaption",
"recycled",
"gpt2-medium"
] | text-generation | false | GroNLP | null | GroNLP/gpt2-medium-dutch-embeddings | 35 | null | transformers | 6,744 | ---
language: nl
tags:
- adaption
- recycled
- gpt2-medium
pipeline_tag: text-generation
---
# GPT-2 recycled for Dutch (medium, adapted lexical embeddings)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
... |
Helsinki-NLP/opus-mt-de-bg | 346965fed40253783eaf06a00664d19f4810e46e | 2021-01-18T07:57:54.000Z | [
"pytorch",
"marian",
"text2text-generation",
"de",
"bg",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-de-bg | 35 | null | transformers | 6,745 | ---
language:
- de
- bg
tags:
- translation
license: apache-2.0
---
### deu-bul
* source group: German
* target group: Bulgarian
* OPUS readme: [deu-bul](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-bul/README.md)
* model: transformer
* source language(s): deu
* target language(s):... |
Helsinki-NLP/opus-mt-ig-en | d09fef787bb0f194ca6ba9bb2768c84171a5820d | 2021-09-09T22:11:33.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ig",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ig-en | 35 | null | transformers | 6,746 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ig-en
* source languages: ig
* target languages: en
* OPUS readme: [ig-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ig-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Jung/t5-large | 03ad92b724052c75982c0690d033aaa9f32d0287 | 2021-06-23T02:42:01.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Jung | null | Jung/t5-large | 35 | null | transformers | 6,747 | Entry not found |
KETI-AIR/ke-t5-base-newslike | a6fb89dae4a8e42add5c16ca4978d365ffbc5563 | 2021-06-23T02:48:53.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | KETI-AIR | null | KETI-AIR/ke-t5-base-newslike | 35 | null | transformers | 6,748 | Entry not found |
Kirili4ik/mbart_ruDialogSum | 13b82f3b5531ba49ee8140c46c7a4501cd882dc6 | 2022-01-26T10:36:21.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"ru",
"ru-RU",
"dataset:IlyaGusev/gazeta",
"dataset:samsum",
"dataset:samsum (translated to RU)",
"transformers",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Kirili4ik | null | Kirili4ik/mbart_ruDialogSum | 35 | 2 | transformers | 6,749 | ---
language:
- ru
- ru-RU
tags:
- mbart
inference:
parameters:
no_repeat_ngram_size: 4,
num_beams : 5
datasets:
- IlyaGusev/gazeta
- samsum
- samsum (translated to RU)
widget:
- text: |
Джефф: Могу ли я обучить модель 🤗 Transformers на Amazon SageMaker?
Филипп: Конечно, вы можете использовать новы... |
MohamedZaitoon/bart-fine-tune | 87434c291e2aa58d368f638ea470a0387bd084dc | 2021-06-13T17:27:59.000Z | [
"pytorch",
"jax",
"bart",
"text2text-generation",
"dataset:CNN/Daily-mail",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | MohamedZaitoon | null | MohamedZaitoon/bart-fine-tune | 35 | null | transformers | 6,750 | ---
tags:
- summarization
datasets:
- CNN/Daily-mail
metrics:
- ROUGE
---
|
NlpHUST/t5-en-vi-base | b55b88c9d7ed504032dd46c0c27117882f0d8fdf | 2021-06-23T03:30:20.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"arxiv:1706.05565",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | NlpHUST | null | NlpHUST/t5-en-vi-base | 35 | null | transformers | 6,751 | # T5-EN-VI-BASE:Pretraining Text-To-Text Transfer Transformer for English Vietnamese Translation
# Dataset
The *IWSLT'15 English-Vietnamese* data is used from [Stanford NLP group](https://nlp.stanford.edu/projects/nmt/).
For all experiments the corpus was split into training, development and test set:
| Data set ... |
SEBIS/code_trans_t5_base_code_documentation_generation_javascript_transfer_learning_finetune | cb90c9642383f99d0f7e98746a36c45d92c103d9 | 2021-06-23T04:33:14.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_base_code_documentation_generation_javascript_transfer_learning_finetune | 35 | null | transformers | 6,752 | ---
tags:
- summarization
widget:
- text: "function isStandardBrowserEnv ( ) { if ( typeof navigator !== 'undefined' && ( navigator . product === 'ReactNative' || navigator . product === 'NativeScript' || navigator . product === 'NS' ) ) { return false ; } return ( typeof window !== 'undefined' && typeof document !== '... |
Shushant/biobert-v1.1-biomedicalQuestionAnswering | 194ea3796afe80d6cdc807bf3aad43f5f0827f83 | 2022-01-16T15:34:49.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | Shushant | null | Shushant/biobert-v1.1-biomedicalQuestionAnswering | 35 | 2 | transformers | 6,753 | ---
tags:
- generated_from_trainer
model-index:
- name: biobert-v1.1-biomedicalQuestionAnswering
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. -->
# biobert-v1.1-b... |
SimonThormeyer/movie-plot-generator | 800dcf9814a57090027cbc2c20fc7c6dc1cc3f63 | 2021-07-25T10:26:02.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | SimonThormeyer | null | SimonThormeyer/movie-plot-generator | 35 | null | transformers | 6,754 | Entry not found |
Soonhwan-Kwon/xlm-roberta-xlarge | 9b58a97eb09aa1277777fba517a3c1390c96f52c | 2021-11-14T09:03:57.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Soonhwan-Kwon | null | Soonhwan-Kwon/xlm-roberta-xlarge | 35 | null | transformers | 6,755 | Entry not found |
addy88/wav2vec2-kannada-stt | 6b2779d4471c1d54ef31838d91b6756fcf883d03 | 2021-12-19T13:35:26.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | addy88 | null | addy88/wav2vec2-kannada-stt | 35 | null | transformers | 6,756 | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88... |
akahana/indonesia-emotion-roberta | 36ec7949f3d2fb7495139dc718b5f55d1557f35b | 2021-12-08T02:24:22.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"id",
"transformers"
] | text-classification | false | akahana | null | akahana/indonesia-emotion-roberta | 35 | null | transformers | 6,757 | ---
language: "id"
widget:
- text: "dia orang yang baik ya bunds."
---
## how to use
```python
from transformers import pipeline, set_seed
path = "akahana/indonesia-emotion-roberta"
emotion = pipeline('text-classification',
model=path,device=0)
set_seed(42)
kalimat = "dia orang... |
allenai/unifiedqa-v2-t5-11b-1363200 | 93489bb8bb4f70af140b9258e9d421ee954a2866 | 2022-02-22T19:16:14.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/unifiedqa-v2-t5-11b-1363200 | 35 | 2 | transformers | 6,758 | # Further details: https://github.com/allenai/unifiedqa |
amtam0/timer-ner-fr | d4c6f9038f0d6a969f6b5cbb67ece4de30960252 | 2022-03-03T14:12:18.000Z | [
"pytorch",
"fr",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | amtam0 | null | amtam0/timer-ner-fr | 35 | null | flair | 6,759 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: fr
widget:
- text: 'génère 27 séries de 54 seconde '
- text: ' 9 cycles de 17 minute '
- text: 'initie 17 sets de 44 secondes 297 minutes entre séries'
- text: ' 13 sets de 88 secondes 225 minutes 49 entre chaque série'
- text: 'génère 39 séries... |
anantoj/wav2vec2-xls-r-1b-korean | 1ef4266ea08ffe9376cefdc0fe55e8bfaae69fcf | 2022-03-23T18:29:13.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ko",
"dataset:kresnik/zeroth_korean",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | anantoj | null | anantoj/wav2vec2-xls-r-1b-korean | 35 | null | transformers | 6,760 | ---
language: ko
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- kresnik/zeroth_korean
model-index:
- name: Wav2Vec2 XLS-R 1B Korean
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recogni... |
aubmindlab/aragpt2-mega-detector-long | 685843487166af81b5cc47f33386f0f107d10d4c | 2021-03-11T21:46:39.000Z | [
"pytorch",
"electra",
"text-classification",
"ar",
"arxiv:2012.15520",
"transformers"
] | text-classification | false | aubmindlab | null | aubmindlab/aragpt2-mega-detector-long | 35 | null | transformers | 6,761 | ---
language: ar
widget:
- text: "وإذا كان هناك من لا يزال يعتقد أن لبنان هو سويسرا الشرق ، فهو مخطئ إلى حد بعيد . فلبنان ليس سويسرا ، ولا يمكن أن يكون كذلك . لقد عاش اللبنانيون في هذا البلد منذ ما يزيد عن ألف وخمسمئة عام ، أي منذ تأسيس الإمارة الشهابية التي أسسها الأمير فخر الدين المعني الثاني ( 1697 - 1742 )"
---
... |
bigjoedata/rockbot | 3a0cacc4b115165b4943a87bc39901fc7a682478 | 2021-05-21T14:15:36.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | bigjoedata | null | bigjoedata/rockbot | 35 | null | transformers | 6,762 |
# 🎸 🥁 Rockbot 🎤 🎧
A [GPT-2](https://openai.com/blog/better-language-models/) based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).
**Instructions:** Type in a fake song title, pick an artist, click "Generate".
Most language models are imprecise... |
boronbrown48/wangchanberta-topic-classification | 50fb13fb42afc87958e535ac2c41f02298637676 | 2021-11-21T09:42:05.000Z | [
"pytorch",
"camembert",
"text-classification",
"transformers"
] | text-classification | false | boronbrown48 | null | boronbrown48/wangchanberta-topic-classification | 35 | null | transformers | 6,763 | Entry not found |
dead69/GPT-small-yoda | 81005ed1731a2b6d0ea8e2fe168d5a9b89516e80 | 2022-01-09T11:24:39.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"license:mit"
] | conversational | false | dead69 | null | dead69/GPT-small-yoda | 35 | null | transformers | 6,764 | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Game Character
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("dead69/GTP... |
deepset/tinyroberta-6l-768d | d15e8976885cb67a2a661ebe54cae6366497a950 | 2022-03-15T17:31:30.000Z | [
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:1909.10351",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/tinyroberta-6l-768d | 35 | null | transformers | 6,765 | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
---
# tinyroberta-squad2
## Overview
**Language model:** tinyroberta-squad2
**Language:** English
**Training data:** The PILE
**Code:**
**Infrastructure**: 4x Tesla v100
## Hyperparameters
```
batch_size = 96
n_epochs = 4
base_LM_model = "deepset/tiny... |
educhav/Elijah-DialoGPT-small | eb2351d20be503f37b9486ec4f0f04e7957b1d0c | 2021-10-23T02:48:02.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | educhav | null | educhav/Elijah-DialoGPT-small | 35 | null | transformers | 6,766 | ---
tags:
- conversational
---
# Elijah Parker
- Made using DialoGPT (GPT2) algorithm in PyTorch |
fabriceyhc/bert-base-uncased-ag_news | c14e3b32fe1f757639b9751bdff3ea3c8c3b4a6b | 2021-09-21T00:54:07.000Z | [
"pytorch",
"bert",
"text-classification",
"dataset:ag_news",
"transformers",
"generated_from_trainer",
"sibyl",
"license:apache-2.0",
"model-index"
] | text-classification | false | fabriceyhc | null | fabriceyhc/bert-base-uncased-ag_news | 35 | null | transformers | 6,767 | ---
license: apache-2.0
tags:
- generated_from_trainer
- sibyl
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: bert-base-uncased-ag_news
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
args: default
metrics:... |
gagan3012/wav2vec2-xlsr-nepali | d1dc1c34a3f2387d00d4bfe351e940cb7c06fb80 | 2021-07-06T04:10:40.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"ne",
"dataset:OpenSLR",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | gagan3012 | null | gagan3012/wav2vec2-xlsr-nepali | 35 | 1 | transformers | 6,768 | ---
language: ne
datasets:
- OpenSLR
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: wav2vec2-xlsr-nepali
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
... |
huggingtweets/iwriteok | 62a6c71882641bd3538991f4767648fd3f9cc374 | 2021-05-22T08:46:31.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/iwriteok | 35 | null | transformers | 6,769 | ---
language: en
thumbnail: https://www.huggingtweets.com/iwriteok/1616696251667/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/5986639643403018... |
izumi-lab/electra-base-japanese-generator | c51d54041861147fc4729dcfb4127e10dd678ec0 | 2022-03-19T09:38:27.000Z | [
"pytorch",
"electra",
"fill-mask",
"ja",
"dataset:wikipedia",
"arxiv:2003.10555",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | izumi-lab | null | izumi-lab/electra-base-japanese-generator | 35 | null | transformers | 6,770 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 東京大学で[MASK]の研究をしています。
---
# ELECTRA base Japanese generator
This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts in the Japanese language.
The codes for the pretraining are available at [retarfi/l... |
jpwahle/t5-word-sense-disambiguation | 4eeae42c057b80c49f4a19a6004a9ac7c7416007 | 2022-06-14T08:57:41.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ISO 639-1 code for your language, or `multilingual`",
"dataset:array of dataset identifiers",
"arxiv:1910.10683",
"transformers",
"array",
"of",
"tags",
"autotrain_compatible"
] | text2text-generation | false | jpwahle | null | jpwahle/t5-word-sense-disambiguation | 35 | 5 | transformers | 6,771 | ---
language: ISO 639-1 code for your language, or `multilingual`
thumbnail: url to a thumbnail used in social sharing
tags:
- array
- of
- tags
datasets:
- array of dataset identifiers
metrics:
- array of metric identifiers
widget:
- text: "question: which description describes the word \" java \" best in the followin... |
mpariente/ConvTasNet_WHAM_sepclean | ba1593b6f7509fce313910deb6bb4781915a8b26 | 2021-11-04T15:29:29.000Z | [
"pytorch",
"dataset:wham",
"dataset:sep_clean",
"asteroid",
"audio",
"ConvTasNet",
"audio-to-audio",
"license:cc-by-sa-4.0"
] | audio-to-audio | false | mpariente | null | mpariente/ConvTasNet_WHAM_sepclean | 35 | null | asteroid | 6,772 | ---
tags:
- asteroid
- audio
- ConvTasNet
- audio-to-audio
datasets:
- wham
- sep_clean
license: cc-by-sa-4.0
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
---
## Asteroid model `mpariente/ConvTasNet_WHAM_sepclean`
Imported from [Zenodo](https://zenod... |
mrm8488/bert-base-german-dbmdz-cased-finetuned-pawsx-de | 1121c3e02dacf619f9c7045e8cad012c3c9a5316 | 2021-05-20T00:19:08.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"de",
"dataset:xtreme",
"transformers",
"nli"
] | text-classification | false | mrm8488 | null | mrm8488/bert-base-german-dbmdz-cased-finetuned-pawsx-de | 35 | null | transformers | 6,773 | ---
language: de
datasets:
- xtreme
tags:
- nli
widget:
- text: "Winarsky ist Mitglied des IEEE, Phi Beta Kappa, des ACM und des Sigma Xi. Winarsky ist Mitglied des ACM, des IEEE, der Phi Beta Kappa und der Sigma Xi."
---
# bert-base-german-dbmdz-cased fine-tuned on PAWS-X-de for Paraphrase Identification (NLI)
|
mrm8488/deberta-v3-small-finetuned-mnli | 211d1d22137f618f14d8fbb6c50d2772084323a9 | 2021-12-07T17:45:59.000Z | [
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"en",
"dataset:glue",
"arxiv:2006.03654",
"arxiv:2111.09543",
"transformers",
"generated_from_trainer",
"deberta-v3",
"license:mit",
"model-index"
] | text-classification | false | mrm8488 | null | mrm8488/deberta-v3-small-finetuned-mnli | 35 | 3 | transformers | 6,774 | ---
language:
- en
license: mit
tags:
- generated_from_trainer
- deberta-v3
datasets:
- glue
metrics:
- accuracy
model-index:
- name: ds_results
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- na... |
mrm8488/deberta-v3-small-finetuned-mrpc | 8754f1d3df9be49faa39ed38235c8f0e349a667b | 2021-11-21T18:52:09.000Z | [
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"deberta-v3",
"license:mit",
"model-index"
] | text-classification | false | mrm8488 | null | mrm8488/deberta-v3-small-finetuned-mrpc | 35 | 1 | transformers | 6,775 | ---
language:
- en
license: mit
tags:
- generated_from_trainer
- deberta-v3
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: deberta-v3-small
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metric... |
mrm8488/longformer-base-4096-spanish-finetuned-squad | f803616035603fc04dbcb1c7783217cb4932ae3f | 2022-01-11T20:39:06.000Z | [
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:BSC-TeMU/SQAC",
"transformers",
"Long documents",
"LongFormer",
"QA",
"Q&A",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/longformer-base-4096-spanish-finetuned-squad | 35 | 1 | transformers | 6,776 | ---
language: es
tags:
- Long documents
- LongFormer
- QA
- Q&A
datasets:
- BSC-TeMU/SQAC
---
# Spanish Longformer fine-tuned on **SQAC** for Spanish **QA** 📖❓
[longformer-base-4096-spanish](https://huggingface.co/mrm8488/longformer-base-4096-spanish) fine-tuned on [SQAC](https://huggingface.co/datasets/BSC-TeMU/SQA... |
pertschuk/albert-large-intent-v2 | ddec85a16827395bdfea93cda1a29cfd2305f47f | 2020-04-24T16:05:07.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | false | pertschuk | null | pertschuk/albert-large-intent-v2 | 35 | null | transformers | 6,777 | Entry not found |
pertschuk/albert-large-intent-v3 | 56738705852fd3579c035cc5587559e80fe1c371 | 2020-04-24T16:05:09.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | false | pertschuk | null | pertschuk/albert-large-intent-v3 | 35 | null | transformers | 6,778 | Entry not found |
pucpr/clinicalnerpt-sign | ca6c0b0416c190de7933754c1c1faf8a689798c8 | 2021-10-13T09:31:19.000Z | [
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"transformers",
"autotrain_compatible"
] | token-classification | false | pucpr | null | pucpr/clinicalnerpt-sign | 35 | 4 | transformers | 6,779 | ---
language: "pt"
widget:
- text: "Há 15 anos relata dor lombar com irradiação para coxa direita."
- text: "Paciente segue internado, sem presença de edema."
datasets:
- SemClinBr
thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png"
---
<img src="https://raw.githu... |
razent/spbert-mlm-zero | d8dd96929c2f433abeb0561960bff5643231810d | 2022-03-15T03:24:45.000Z | [
"pytorch",
"tf",
"jax",
"code",
"arxiv:2106.09997",
"transformers",
"question-answering",
"knowledge-graph"
] | question-answering | false | razent | null | razent/spbert-mlm-zero | 35 | null | transformers | 6,780 | ---
language:
- code
tags:
- question-answering
- knowledge-graph
---
# SPBERT MLM (Scratch)
## Introduction
Paper: [SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs](https://arxiv.org/abs/2106.09997)
Authors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguy... |
recobo/chemical-bert-uncased-pharmaceutical-chemical-classifier | 7e514c1d56f117617dbdbc37a0b26ca20a84878a | 2021-09-10T05:35:44.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"buy-intent",
"sell-intent",
"consumer-intent"
] | text-classification | false | recobo | null | recobo/chemical-bert-uncased-pharmaceutical-chemical-classifier | 35 | null | transformers | 6,781 | ---
language: "en"
tags:
- buy-intent
- sell-intent
- consumer-intent
widget:
- text: "Flutoprazepam (Restas) is a drug which is a benzodiazepine. It was patented in Japan by Sumitomo."
---
# Chemical vs Pharmaceutical Domain Document Classifier
Chemical domain language model finetuned on 13K Chemical, and 14K Pharma W... |
wicharnkeisei/thai-bert-multi-cased-finetuned-xquadv1-finetuned-squad | d1c2523ab1cf1888a5a11b375355c1d23dd5b265 | 2021-11-07T08:31:26.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"th",
"transformers",
"generated_from_trainer",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | wicharnkeisei | null | wicharnkeisei/thai-bert-multi-cased-finetuned-xquadv1-finetuned-squad | 35 | null | transformers | 6,782 | ---
license: cc-by-4.0
tags:
- generated_from_trainer
language: th
model-index:
- name: thai-bert-multi-cased-finetuned-xquadv1-finetuned-squad
results: []
widget:
- text: "สราวุธ มาตรทอง เข้าสู่วงการบันเทิงเมื่อปีอะไร"
context: "สราวุธ มาตรทอง (ชื่อเล่น: อ้น เกิดเมื่อวันที่ 2 ตุลาคม พ.ศ. 2519) เป็นนักแสดงชาวไทย จบ... |
wrmurray/roberta-base-finetuned-imdb | 7aa8ca3fae56a1860d8b4c6bf727b91370821ad5 | 2022-02-10T23:09:54.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | wrmurray | null | wrmurray/roberta-base-finetuned-imdb | 35 | null | transformers | 6,783 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: roberta-base-finetuned-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accura... |
sjyhne/audio_emotion | 213c5a7a7a8bfb49641571ec5f921451313c51fc | 2022-03-02T06:50:37.000Z | [
"pytorch",
"wav2vec2",
"transformers"
] | null | false | sjyhne | null | sjyhne/audio_emotion | 35 | null | transformers | 6,784 | Entry not found |
aymanm419/araElectra-SQUAD-ARCD | 6b8d83c5a443ada88cab6c1ff82812c98eecd25d | 2022-03-02T21:57:26.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | aymanm419 | null | aymanm419/araElectra-SQUAD-ARCD | 35 | null | transformers | 6,785 | Entry not found |
IIC/mt5-base-lfqa-es | c9be3e883d89c1321700b3136c1cde63e8309eca | 2022-04-04T02:55:11.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"es",
"dataset:IIC/lfqa_es",
"transformers",
"seq2seq",
"abstractive question answering",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | IIC | null | IIC/mt5-base-lfqa-es | 35 | 4 | transformers | 6,786 | ---
language:
- es
tags:
# - summarization # Example: audio
- seq2seq # Example: automatic-speech-recognition
- abstractive question answering
datasets:
- IIC/lfqa_es
metrics:
- rouge2
- rouge1
- rougel
- rougelsum
# Optional. Add this if you want to encode your eval results in a structured way.
model-index:
- name:... |
princeton-nlp/CoFi-MNLI-s95 | 26de87b07d575d55f320278e26d715a121bb1c1f | 2022-05-01T01:20:45.000Z | [
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"transformers"
] | text-classification | false | princeton-nlp | null | princeton-nlp/CoFi-MNLI-s95 | 35 | null | transformers | 6,787 | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 95% sparsity on dataset MNLI. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the m... |
facebook/wav2vec2-conformer-rope-large | bdc607e878312a37df94057b527c3db65fe03445 | 2022-06-15T08:12:09.000Z | [
"pytorch",
"wav2vec2-conformer",
"pretraining",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"transformers",
"speech",
"license:apache-2.0"
] | null | false | facebook | null | facebook/wav2vec2-conformer-rope-large | 35 | 1 | transformers | 6,788 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Conformer-Large with Rotary Position Embeddings
Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input... |
Intel/camembert-base-mrpc | 39ba8cbf0198b4bef5c69c6ae716f31ed8b9f600 | 2022-04-21T02:44:02.000Z | [
"pytorch",
"camembert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | Intel | null | Intel/camembert-base-mrpc | 35 | null | transformers | 6,789 | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: camembert-base-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- n... |
ai4bharat/MultiIndicSentenceSummarizationSS | 9700f9cd1a4da32c7bcb3c3abadf3ce7d72aee3f | 2022-04-30T10:35:01.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicSentenceSummarization",
"arxiv:2203.05437",
"transformers",
"sentence-summarization",
"multilingual",
"nlp",
"indicnlp",
"license:mit",
"a... | text2text-generation | false | ai4bharat | null | ai4bharat/MultiIndicSentenceSummarizationSS | 35 | null | transformers | 6,790 | ---
tags:
- sentence-summarization
- multilingual
- nlp
- indicnlp
datasets:
- ai4bharat/IndicSentenceSummarization
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- mit
widget:
- जम्मू एवं कश्मीर के अनंतनाग जिले में शनिवार को सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादियों को मार गिराया गया। <s... |
lightonai/RITA_m | 7ac0a0ec85dbcfa8b8bb7352a48fe638c0d191d8 | 2022-05-19T08:23:24.000Z | [
"pytorch",
"rita",
"text-generation",
"protein",
"dataset:uniref-100",
"arxiv:2205.05789",
"transformers"
] | text-generation | false | lightonai | null | lightonai/RITA_m | 35 | null | transformers | 6,791 | ---
language: protein
tags:
- protein
datasets:
- uniref-100
---
# RITA-M
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.
... |
tsdocode/phobert-finetune-hatespeech | ba30492166bac4a2048bc1225a9ab9fa1bb55291 | 2022-05-07T18:03:04.000Z | [
"pytorch",
"roberta",
"text-classification",
"vi",
"transformers",
"classification"
] | text-classification | false | tsdocode | null | tsdocode/phobert-finetune-hatespeech | 35 | null | transformers | 6,792 | ---
language:
- vi
tags:
- classification
widget:
- text: "Xấu vcl"
example_title: "Công kích"
- text: "Đồ ngu"
example_title: "Thù ghét"
- text: "Xin chào chúc một ngày tốt lành"
example_title: "Normal"
---
## [PhoBert](https://huggingface.co/vinai/phobert-base/tree/main) finetuned version for hate speech dete... |
abid/indonesia-bioner | f1b0e4892a363e97d92f288c47fcb8ee8030f9c5 | 2022-07-11T06:41:12.000Z | [
"pytorch",
"id",
"en",
"flair",
"token-classification",
"sequence-tagger-model",
"license:bsd-3-clause"
] | token-classification | false | abid | null | abid/indonesia-bioner | 35 | 0 | flair | 6,793 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language:
- id
- en
license: bsd-3-clause
widget:
- text: 'Dok saya mau tanya kenapa ya kulit saya kering bersisik gitu dok. Apalagi bagian tumit sampai nglupas terus gatal. Penyebabnya apa y dok terus cara mengobatinya gimana? Terima kasi'
- text: 'halo ... |
BM-K/KoMiniLM | 2ffe948f6b6f99a5a2c4658a6d4075008630be31 | 2022-06-23T11:57:58.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2002.10957",
"transformers"
] | feature-extraction | false | BM-K | null | BM-K/KoMiniLM | 35 | 2 | transformers | 6,794 | # KoMiniLM
🐣 Korean mini language model
## Overview
Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight korean language mod... |
speeqo/distilbert-base-uncased-finetuned-sst-2-english | c4de345862149fb32a334f861bcffce61bfd4447 | 2022-05-29T11:14:52.000Z | [
"pytorch",
"tf",
"rust",
"distilbert",
"text-classification",
"en",
"dataset:sst-2",
"transformers",
"license:apache-2.0"
] | text-classification | false | speeqo | null | speeqo/distilbert-base-uncased-finetuned-sst-2-english | 35 | null | transformers | 6,795 | ---
language: en
license: apache-2.0
datasets:
- sst-2
---
# DistilBERT base uncased finetuned SST-2
This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned on SST-2.
This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-... |
bekirbakar/wav2vec2-large-xlsr-53-tr-fine-tuning-02 | 9f14188b8f0477a1e46b152d2fe4cf004c06cae1 | 2022-06-16T13:38:20.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | bekirbakar | null | bekirbakar/wav2vec2-large-xlsr-53-tr-fine-tuning-02 | 35 | null | transformers | 6,796 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xlsr-53-tr-fine-tuning-02
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, th... |
agne/jobGBERT | f1222a1b5f502881235e216c814d312fb7419593 | 2022-06-03T13:52:50.000Z | [
"pytorch",
"bert",
"fill-mask",
"de",
"transformers",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | agne | null | agne/jobGBERT | 35 | null | transformers | 6,797 |
---
language: de
license: cc-by-nc-sa-4.0
---
## jobGBERT
This is a domain-adapted transformer-based language model for German-speaking job advertisements.
Is is based on [deepset/gbert-base](https://huggingface.co/deepset/gbert-base), and adapted to the domain of job advertisements trough continued in-domain pretr... |
ronak1998/layoutlmv3-finetuned-invoice | c64094e392340e608a15deae0f9d50015f4eeb28 | 2022-06-10T07:52:09.000Z | [
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"dataset:sroie",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | ronak1998 | null | ronak1998/layoutlmv3-finetuned-invoice | 35 | null | transformers | 6,798 | ---
tags:
- generated_from_trainer
datasets:
- sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: sroie
type: sroie
args: sroie
metrics:
... |
ahmeddbahaa/xlmroberta-finetuned-Spanish | 563004e67ff96a126bd623634fddd0c2eb7e3aaa | 2022-06-16T21:05:45.000Z | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:wiki_lingua",
"transformers",
"summarization",
"xlmroberta",
"es",
"abstractive summarization",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | summarization | false | ahmeddbahaa | null | ahmeddbahaa/xlmroberta-finetuned-Spanish | 35 | null | transformers | 6,799 | ---
tags:
- summarization
- xlmroberta
- encoder-decoder
- es
- abstractive summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: xlmroberta-finetuned-Spanish
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. Yo... |
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