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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
julien-c/DPRNNTasNet-ks16_WHAM_sepclean | a4b441f4b89dffd87d67dadc78cf9f2b9c1f8581 | 2021-09-23T16:04:27.000Z | [
"pytorch",
"dataset:wham",
"dataset:sep_clean",
"arxiv:2005.04132",
"asteroid",
"audio-to-audio",
"audio",
"audio-source-separation",
"license:cc-by-sa-4.0"
] | audio-to-audio | false | julien-c | null | julien-c/DPRNNTasNet-ks16_WHAM_sepclean | 114 | 2 | asteroid | 4,400 | ---
tags:
- audio-to-audio
- asteroid
- audio
- audio-source-separation
datasets:
- wham
- sep_clean
license: cc-by-sa-4.0
---
## Asteroid model `mpariente/DPRNNTasNet(ks=16)_WHAM!_sepclean`
♻️ Imported from https://zenodo.org/record/3903795#.X8pMBRNKjUI
This model was trained by Manuel Pariente using the wham/DPRNN... |
microsoft/wavlm-base | efa81aae7ff777e464159e0f877d54eac5b84f81 | 2021-12-22T17:23:36.000Z | [
"pytorch",
"wavlm",
"feature-extraction",
"en",
"arxiv:2110.13900",
"transformers",
"speech"
] | feature-extraction | false | microsoft | null | microsoft/wavlm-base | 114 | 1 | transformers | 4,401 | ---
language:
- en
datasets:
tags:
- speech
inference: false
---
# WavLM-Base
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
**Note**: This model does... |
mrm8488/bert2bert_shared-turkish-summarization | 4d2ab3ea33c5e3cafa5a94b444376321fc404733 | 2021-05-22T11:11:45.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"tr",
"dataset:mlsum",
"transformers",
"summarization",
"news",
"autotrain_compatible"
] | summarization | false | mrm8488 | null | mrm8488/bert2bert_shared-turkish-summarization | 114 | 1 | transformers | 4,402 | ---
tags:
- summarization
- news
language: tr
datasets:
- mlsum
widget:
- text: "Ankara'da oto hırsızlık çetesine yönelikdüzenlenen ‘Balta’ operasyonunda, çete lideri‘balta’ lakaplı şahıs ile 7 kişi gözaltına alındı.Diğer bir operasyonda ise 3 şüpheli çaldıklarıaraçları parçalarken yapılan baskında suçüstüyakalandı. An... |
mrm8488/electra-small-finetuned-squadv2 | 04a43d04d9d117c76f0d26bacfb613ceee028a14 | 2020-12-11T21:54:01.000Z | [
"pytorch",
"electra",
"question-answering",
"en",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/electra-small-finetuned-squadv2 | 114 | 1 | transformers | 4,403 | ---
language: en
---
# Electra small ⚡ + SQuAD v2 ❓
[Electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) fine-tuned on [SQUAD v2.0 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠... |
mrm8488/roberta-med-small_shared-finetuned-bbc_xsum-summarization | 96faa028d94d2c281103683feb212228f1dff23f | 2021-04-05T11:28:41.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:xsum",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | mrm8488 | null | mrm8488/roberta-med-small_shared-finetuned-bbc_xsum-summarization | 114 | null | transformers | 4,404 | ---
language: en
license: apache-2.0
datasets:
- xsum
tags:
- summarization
---
Shared RoBERTa2RoBERTa (med-small) Summarization with 🤗EncoderDecoder Framework
This model is a warm-started *RoBERTaShared* (med-small) model fine-tuned on the *BBC XSum* summarization dataset. |
pszemraj/led-base-book-summary | 403a4a9dfdc094b0b605a302d05b5b2225d4e511 | 2022-07-27T10:19:51.000Z | [
"pytorch",
"led",
"text2text-generation",
"dataset:kmfoda/booksum",
"transformers",
"summarization",
"summary",
"longformer",
"booksum",
"long-document",
"long-form",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | pszemraj | null | pszemraj/led-base-book-summary | 114 | 1 | transformers | 4,405 | ---
tags:
- summarization
- led
- summary
- longformer
- booksum
- long-document
- long-form
license: apache-2.0
datasets:
- kmfoda/booksum
metrics:
- rouge
widget:
- text: large earthquakes along a given fault segment do not occur at random intervals
because it takes time to accumulate the strain energy for the ru... |
sosuke/ease-bert-base-uncased | de261cc6fcb12e91dcf19feb88c5f30179746209 | 2021-12-29T08:02:04.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | sosuke | null | sosuke/ease-bert-base-uncased | 114 | null | transformers | 4,406 | Entry not found |
vkorennoy/gpt3_medium | 8d03577c67a66719c6bdc51ed5da7e6d6cf64fbe | 2021-11-21T20:55:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | vkorennoy | null | vkorennoy/gpt3_medium | 114 | null | transformers | 4,407 | Entry not found |
yhavinga/t5-v1.1-base-dutch-cased | 572919e67fdfc0977d10ffeda46689d7cc4f623f | 2022-06-14T10:28:45.000Z | [
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"arxiv:1910.10683",
"arxiv:2109.10686",
"transformers",
"seq2seq",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | yhavinga | null | yhavinga/t5-v1.1-base-dutch-cased | 114 | 1 | transformers | 4,408 | ---
language:
- nl
datasets:
- yhavinga/mc4_nl_cleaned
tags:
- t5
- seq2seq
inference: false
license: apache-2.0
---
# t5-v1.1-base-dutch-cased
A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model
pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4](https:... |
facebook/data2vec-vision-large | 6e101b3a00728b37698cffb19925ffa6fceba108 | 2022-05-03T15:57:29.000Z | [
"pytorch",
"tf",
"data2vec-vision",
"feature-extraction",
"dataset:imagenet",
"dataset:imagenet-1k",
"arxiv:2202.03555",
"arxiv:2106.08254",
"transformers",
"image-classification",
"vision",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/data2vec-vision-large | 114 | null | transformers | 4,409 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-1k
---
# Data2Vec-Vision (large-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [dat... |
qinyue/wav2vec2-large-xlsr-53-chinese-zn-cn-aishell1 | 3e34d551c81e585640ad01311df41b56650d3e8a | 2022-06-17T06:34:39.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"zh-CN",
"dataset:aishell1",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | qinyue | null | qinyue/wav2vec2-large-xlsr-53-chinese-zn-cn-aishell1 | 114 | null | transformers | 4,410 | ---
language: zh-CN
datasets:
- aishell1
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large 53 - Chinese (zh-CN), by Yue Qin
results:
- task:
name: Speech Recognition
type: automatic-speech-recognitio... |
google/ddpm-ema-bedroom-256 | f6c8a04739a77c61b12678f728117be059bf8f98 | 2022-07-21T15:00:25.000Z | [
"diffusers",
"arxiv:2006.11239",
"pytorch",
"unconditional-image-generation",
"license:apache-2.0"
] | unconditional-image-generation | false | google | null | google/ddpm-ema-bedroom-256 | 114 | null | diffusers | 4,411 | ---
license: apache-2.0
tags:
- pytorch
- diffusers
- unconditional-image-generation
---
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high qualit... |
Luyu/bert-base-mdoc-bm25 | b03a0fb2e341e38403bdb7fc8d79ec9ee29fdce3 | 2021-09-22T08:11:56.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"dataset:MS MARCO document ranking",
"transformers",
"text reranking",
"license:apache-2.0"
] | text-classification | false | Luyu | null | Luyu/bert-base-mdoc-bm25 | 113 | null | transformers | 4,412 | ---
language:
- en
tags:
- text reranking
license: apache-2.0
datasets:
- MS MARCO document ranking
---
# BERT Reranker for MS-MARCO Document Ranking
## Model description
A text reranker trained for BM25 retriever on MS MARCO document dataset.
## Intended uses & limitations
It is possible to work with other retriev... |
MrGentle/DeltaModel-genius1 | 47747a1b8f3809ded25df76214f0e5c6d7fc1e68 | 2021-12-02T19:47:47.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | MrGentle | null | MrGentle/DeltaModel-genius1 | 113 | null | transformers | 4,413 | ---
tags:
- conversational
---
#Delta Chat Model |
jonasmue/cover-letter-gpt2 | b3d9d3e4ee9e3140cc28e026a205ffb65110ffbf | 2021-05-23T06:00:04.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | jonasmue | null | jonasmue/cover-letter-gpt2 | 113 | 4 | transformers | 4,414 | Entry not found |
ponmari/Question-Answering | 98bd620222d8d6dabdcb1c01a6a0ce41f26ec935 | 2020-07-21T07:56:56.000Z | [
"pytorch",
"longformer",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | ponmari | null | ponmari/Question-Answering | 113 | null | transformers | 4,415 | Entry not found |
PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer | 8f603cb46a32a869104134e1daaed6944ff84867 | 2022-04-12T10:07:56.000Z | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:PlanTL-GOB-ES/pharmaconer",
"arxiv:1907.11692",
"transformers",
"biomedical",
"clinical",
"eHR",
"spanish",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | PlanTL-GOB-ES | null | PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer | 113 | 1 | transformers | 4,416 | ---
language:
- es
tags:
- biomedical
- clinical
- eHR
- spanish
license: apache-2.0
datasets:
- "PlanTL-GOB-ES/pharmaconer"
metrics:
- f1
model-index:
- name: PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer
results:
- task:
type: token-classification
dataset:
name: pharmaconer
type: PlanTL-GOB-E... |
allenai/tk-instruct-11b-def-pos-neg-expl | 9089d1859a862d48f79cbb1043dfd684f850d28b | 2022-05-27T06:31:08.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:natural instructions v2.0",
"arxiv:1910.10683",
"arxiv:2204.07705",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/tk-instruct-11b-def-pos-neg-expl | 113 | 1 | transformers | 4,417 | ---
language: en
license: apache-2.0
datasets:
- natural instructions v2.0
---
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built... |
neuralmagic/oBERT-6-upstream-pretrained-dense | d75d5e3bc00c8b8cb370f77b55160be833319903 | 2022-06-20T11:36:52.000Z | [
"pytorch",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2203.07259",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression"
] | null | false | neuralmagic | null | neuralmagic/oBERT-6-upstream-pretrained-dense | 113 | null | null | 4,418 | ---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets:
- bookcorpus
- wikipedia
---
# oBERT-6-upstream-pretrained-dense
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresp... |
wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics | d89e083768039041645fd25c7514adbbae20cc6a | 2022-06-17T11:22:00.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"model-index"
] | text-generation | false | wvangils | null | wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics | 113 | null | transformers | 4,419 | ---
tags:
- generated_from_trainer
model-index:
- name: GPT-Medium-Beatles-Lyrics-finetuned-newlyrics
results: []
---
# GPT-Medium-Beatles-Lyrics-finetuned-newlyrics
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the [Cmotions - Beatles lyrics](https://huggingface.co/data... |
yuningm/bart-large-citesum | 1e8e063fc922fd7cfdc1685748428b4877f85bb9 | 2022-07-08T20:54:02.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:yuningm/citesum",
"arxiv:2205.06207",
"transformers",
"summarization",
"license:cc-by-nc-4.0",
"autotrain_compatible"
] | summarization | false | yuningm | null | yuningm/bart-large-citesum | 113 | 1 | transformers | 4,420 | ---
license: cc-by-nc-4.0
language: en
tags:
- summarization
datasets:
- yuningm/citesum
widget:
- text: "Abstract-This paper presents a control strategy that allows a group of mobile robots to position themselves to optimize the measurement of sensory information in the environment. The robots use sensed information t... |
nielsr/donut-base-finetuned-docvqa | c677933f9efa82eb6d72ba6acd0e1cece060e483 | 2022-07-26T09:47:02.000Z | [
"pytorch",
"vision-encoder-decoder",
"transformers"
] | null | false | nielsr | null | nielsr/donut-base-finetuned-docvqa | 113 | null | transformers | 4,421 | Entry not found |
DeepChem/ChemBERTa-5M-MLM | 695672a601a8077ace5d0a6fc2529e93cc9cfa9c | 2022-01-20T17:59:00.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | DeepChem | null | DeepChem/ChemBERTa-5M-MLM | 112 | null | transformers | 4,422 | Entry not found |
NDugar/debertav3-mnli-snli-anli | 818713e9f926ab18a0377103520ab6af073517d4 | 2021-11-29T20:56:42.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"en",
"arxiv:2006.03654",
"transformers",
"deberta-v3",
"deberta-v2`",
"deberta-mnli",
"license:mit",
"zero-shot-classification"
] | zero-shot-classification | false | NDugar | null | NDugar/debertav3-mnli-snli-anli | 112 | 3 | transformers | 4,423 | ---
language: en
tags:
- deberta-v3
- deberta-v2`
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
pipeline_tag: zero-shot-classification
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the B... |
avichr/hebEMO_trust | 8bca4dfbf5dc6975d3e0cf24c76ac32e31df3eac | 2022-04-15T09:36:54.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | avichr | null | avichr/hebEMO_trust | 112 | null | transformers | 4,424 | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... |
dbmdz/electra-small-turkish-cased-discriminator | 1c70896935f1be63be7f30ac3faf9b9d417e8c2c | 2020-12-11T21:37:29.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"transformers",
"license:mit"
] | null | false | dbmdz | null | dbmdz/electra-small-turkish-cased-discriminator | 112 | null | transformers | 4,425 | ---
language: tr
license: mit
---
# 🤗 + 📚 dbmdz Turkish ELECTRA model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased ELECTRA small model for Turkish 🎉
# Turkish ELECTRA model
We release a small ELEC**TR**A model for Turkish, that was trained on the same... |
facebook/detr-resnet-50-dc5-panoptic | a173bbe3e341ee79acc040e59f03ec0efeb2dd17 | 2021-11-04T17:49:03.000Z | [
"pytorch",
"detr",
"image-segmentation",
"dataset:coco",
"arxiv:2005.12872",
"transformers",
"license:apache-2.0"
] | image-segmentation | false | facebook | null | facebook/detr-resnet-50-dc5-panoptic | 112 | 1 | transformers | 4,426 | ---
license: apache-2.0
tags:
- image-segmentation
datasets:
- coco
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/dog-cat.jpg
example_title: Dog & Cat
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/construction-site.jpg
example_title: Construction Site
- ... |
google/tapas-large | 879ccbedad111da5c324a234b6b719b2b9e5bafa | 2021-11-29T10:18:23.000Z | [
"pytorch",
"tf",
"tapas",
"feature-extraction",
"en",
"arxiv:2004.02349",
"arxiv:2010.00571",
"transformers",
"TapasModel",
"license:apache-2.0"
] | feature-extraction | false | google | null | google/tapas-large | 112 | null | transformers | 4,427 | ---
language: en
tags:
- tapas
- TapasModel
license: apache-2.0
---
# TAPAS large model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tap... |
uclanlp/plbart-multi_task-python | aa3bcd034d63011cf66243922b08920a6dbe7a3e | 2022-03-02T07:31:29.000Z | [
"pytorch",
"plbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uclanlp | null | uclanlp/plbart-multi_task-python | 112 | null | transformers | 4,428 | Entry not found |
voidful/albert_chinese_xxlarge | eabcac5c4cfac10e44c6d6b71ef6a22f4e4137b6 | 2021-08-03T05:07:41.000Z | [
"pytorch",
"albert",
"fill-mask",
"zh",
"transformers",
"autotrain_compatible"
] | fill-mask | false | voidful | null | voidful/albert_chinese_xxlarge | 112 | 2 | transformers | 4,429 | ---
language: zh
pipeline_tag: fill-mask
widget:
- text: "今天[MASK]情很好"
---
# albert_chinese_xxlarge
This a albert_chinese_xxlarge model from [Google's github](https://github.com/google-research/ALBERT)
converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/conve... |
josh-oo/german-gpt2-easy | 876af797a29ba247774e29660e15282242320f44 | 2022-07-20T11:32:35.000Z | [
"pytorch",
"gpt2",
"text-generation",
"de",
"transformers"
] | text-generation | false | josh-oo | null | josh-oo/german-gpt2-easy | 112 | null | transformers | 4,430 | ---
language:
- de
widget:
- text: "Der Sinn des Lebens ist"
example_title: "Sinn des Lebens"
inference:
parameters:
temperature: 0.7
repetition_penalty: 1.4
--- |
nlokam/adanimals_V1 | 46ddd7e6b5eb8eb9edc75ea009d32e971f5be21c | 2022-06-12T18:02:30.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"license:mit"
] | conversational | false | nlokam | null | nlokam/adanimals_V1 | 112 | null | transformers | 4,431 | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
--- |
Evelyn18/roberta-base-spanish-squades-becasv3 | e63052bebd1f536c4e18cdba5e4319585a8bbb89 | 2022-07-25T02:46:16.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | Evelyn18 | null | Evelyn18/roberta-base-spanish-squades-becasv3 | 112 | null | transformers | 4,432 | ---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: roberta-base-spanish-squades-becasv3
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. -->
... |
dbmdz/electra-base-turkish-mc4-uncased-discriminator | bd4d8062c25f3f0c699e451f2f6b07df826e83e4 | 2021-09-23T10:43:12.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"dataset:allenai/c4",
"transformers",
"license:mit"
] | null | false | dbmdz | null | dbmdz/electra-base-turkish-mc4-uncased-discriminator | 111 | 2 | transformers | 4,433 | ---
language: tr
license: mit
datasets:
- allenai/c4
---
# 🇹🇷 Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[![DOI](https://zenodo.org/badge/23781745... |
jimypbr/bert-base-uncased-squad | bd8f5357b298a87247fb0cd991d52ccc6008fc52 | 2022-02-11T22:28:31.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | jimypbr | null | jimypbr/bert-base-uncased-squad | 111 | null | transformers | 4,434 | ---
license: apache-2.0
---
# BERT-Base Uncased SQuADv1
`bert-base-uncased` trained on question answering with `squad`.
Evalulation scores:
```
***** eval metrics *****
epoch = 3.0
eval_exact_match = 80.6906
eval_f1 = 88.1129
eval_samples = 10784
``` |
sdadas/polish-roberta-large-v2 | e7e302a75f230f10ae8673560e7b3451745cc9e4 | 2022-02-19T10:25:37.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"license:lgpl-3.0",
"autotrain_compatible"
] | fill-mask | false | sdadas | null | sdadas/polish-roberta-large-v2 | 111 | 1 | transformers | 4,435 | ---
license: lgpl-3.0
---
|
textattack/distilbert-base-cased-SST-2 | c2e8de47524988a4a0bd47f3b2ee6a65912617e6 | 2020-06-09T16:46:25.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/distilbert-base-cased-SST-2 | 111 | null | transformers | 4,436 | Entry not found |
wietsedv/bert-base-dutch-cased-finetuned-sonar-ner | 7d3e7d3e2a7d79f848d0249f342df7f26f4c6678 | 2021-05-20T09:09:52.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | wietsedv | null | wietsedv/bert-base-dutch-cased-finetuned-sonar-ner | 111 | null | transformers | 4,437 | Entry not found |
9pinus/macbert-base-chinese-medicine-recognition | 3a20f1d5d353ee11ce93f9ca885b3d7d859eb33e | 2022-03-02T09:20:41.000Z | [
"pytorch",
"bert",
"token-classification",
"zh",
"transformers",
"Token Classification",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | 9pinus | null | 9pinus/macbert-base-chinese-medicine-recognition | 111 | 2 | transformers | 4,438 | ---
license: apache-2.0
tags:
- Token Classification
language:
- zh
---
## Model description
This model is a fine-tuned version of bert-base-chinese for the purpose of medicine name recognition. We fine-tuned bert-base-chinese on a 500M dataset including 100K+ authorized medical articles on which we label... |
JiachengLi/uctopic-base | 5cc265b0e32e7b5df6ab0a330b4621ad416c4f84 | 2022-06-11T21:13:05.000Z | [
"pytorch",
"luke",
"arxiv:2202.13469",
"arxiv:2010.01057",
"transformers",
"license:mit"
] | null | false | JiachengLi | null | JiachengLi/uctopic-base | 111 | 0 | transformers | 4,439 | ---
license: mit
---
# UCTopic
This repository contains the code of model UCTopic and an easy-to-use tool UCTopicTool used for <strong>Topic Mining</strong>, <strong>Unsupervised Aspect Extractioin</strong> or <strong>Phrase Retrieval</strong>.
Our ACL 2022 paper [UCTopic: Unsupervised Contrastive Learning for Phrase... |
wonrax/phobert-base-vietnamese-sentiment | 9076a5896971b5d551588fe8a51c722c89731d36 | 2022-05-04T07:30:54.000Z | [
"pytorch",
"roberta",
"text-classification",
"vi",
"transformers",
"sentiment",
"classification",
"license:mit"
] | text-classification | false | wonrax | null | wonrax/phobert-base-vietnamese-sentiment | 111 | null | transformers | 4,440 | ---
language:
- vi
tags:
- sentiment
- classification
license: mit
widget:
- text: "Không thể nào đẹp hơn"
- text: "Quá phí tiền, mà không đẹp"
- text: "Cái này giá ổn không nhỉ?"
---
[**GitHub Homepage**](https://github.com/wonrax/phobert-base-vietnamese-sentiment)
A model fine-tuned for sentiment analysis based on... |
eslamxm/vit-base-food101 | 51da4b10097cf3a7182c77035a8aac43927f0c89 | 2022-05-19T05:23:57.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"dataset:food101",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | eslamxm | null | eslamxm/vit-base-food101 | 111 | null | transformers | 4,441 | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: vit-base-food101-demo-v5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
args: defa... |
Wi/arxiv-distilbert-base-cased | 001bb3a6cbd6b574a31aa58576655f247ebb54a9 | 2022-06-14T17:37:18.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:arxiv_dataset",
"transformers",
"license:apache-2.0"
] | text-classification | false | Wi | null | Wi/arxiv-distilbert-base-cased | 111 | 1 | transformers | 4,442 | ---
license: apache-2.0
language:
- en
datasets:
- arxiv_dataset
tags:
- distilbert
---
# DistilBERT ArXiv Category Classification
DistilBERT model fine-tuned on a small subset of the [ArXiv dataset](https://www.kaggle.com/datasets/Cornell-University/arxiv) to predict the category of a given paper.
|
Harveenchadha/hindi_model_with_lm_vakyansh | 73d3becb70711a6df190b4317c57f58a46a7b89d | 2022-03-23T18:25:38.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hi",
"dataset:Harveenchadha/indic-voice",
"transformers",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Harveenchadha | null | Harveenchadha/hindi_model_with_lm_vakyansh | 110 | 1 | transformers | 4,443 | ---
license: apache-2.0
language:
- hi
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- hi
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- Harveenchadha/indic-voice
model-index:
- name: Hindi Large
results:
- task:
name: Automatic Speech Recognition
ty... |
aware-ai/marian-german-grammar | a180bb1ec7eda2403676fcb4375837d6d7898206 | 2021-06-13T14:56:45.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | aware-ai | null | aware-ai/marian-german-grammar | 110 | null | transformers | 4,444 | Entry not found |
lhoestq/distilbert-base-uncased-finetuned-absa-as | d90f5cebc590c8b7695a7105bab274528f935e77 | 2021-04-29T14:22:37.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | lhoestq | null | lhoestq/distilbert-base-uncased-finetuned-absa-as | 110 | 1 | transformers | 4,445 | Distilbert finetuned for Aspect-Based Sentiment Analysis (ABSA) with auxiliary sentence.
```bibtex
@inproceedings{sun-etal-2019-utilizing,
title = "Utilizing {BERT} for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence",
author = "Sun, Chi and
Huang, Luyao and
Qiu, Xipeng",
... |
mrm8488/bert2bert-medium_shared-question-generation | ff0c2a50e60d82c42d453a0a51926636708ea940 | 2020-12-27T20:27:57.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/bert2bert-medium_shared-question-generation | 110 | null | transformers | 4,446 | Entry not found |
mrm8488/mT5-small-finetuned-tydiqa-for-xqa | b6ade12a4982eff1beca9002dfa9f0662b663e7c | 2021-08-23T21:32:44.000Z | [
"pytorch",
"t5",
"text2text-generation",
"multilingual",
"dataset:tydiqa",
"arxiv:2010.11934",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/mT5-small-finetuned-tydiqa-for-xqa | 110 | 1 | transformers | 4,447 | ---
language: multilingual
datasets:
- tydiqa
widget:
- text: "question: What won HuggingFace? context: HuggingFace won the best Demo paper at EMNLP2020."
---
# mT5-small fine-tuned on TyDiQA for multilingual QA 🗺📖❓
[Google's mT5-small](https://huggingface.co/google/mt5-small) fine-tuned on [TyDi QA](https://hugging... |
valhalla/distilt5-qg-hl-6-4 | 18ea32a868c0dc7b270530869829d0eaeb77ab03 | 2021-09-23T16:42:52.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"dataset:squad",
"transformers",
"question-generation",
"distilt5",
"distilt5-qg",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/distilt5-qg-hl-6-4 | 110 | null | transformers | 4,448 | ---
datasets:
- squad
tags:
- question-generation
- distilt5
- distilt5-qg
widget:
- text: <hl> 42 <hl> is the answer to life, the universe and everything. </s>
- text: Python is a programming language. It is developed by <hl> Guido Van Rossum
<hl>. </s>
- text: Although <hl> practicality <hl> beats purity </s>
lic... |
victor/animals-classifier | d7455abe9100505c9fbff54f9bcbb5c2c12e95d9 | 2021-06-29T16:03:03.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | victor | null | victor/animals-classifier | 110 | null | transformers | 4,449 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: animals-classifier
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9821428656578064
---
# animals-classif... |
MU-NLPC/CzeGPT-2 | 35512dbd48f7579f67cfca1b4deba0c13e2e2aae | 2022-04-04T19:49:09.000Z | [
"pytorch",
"gpt2",
"text-generation",
"cs",
"dataset:csTenTen17",
"transformers",
"license:cc-by-nc-sa-4.0"
] | text-generation | false | MU-NLPC | null | MU-NLPC/CzeGPT-2 | 110 | 0 | transformers | 4,450 | ---
language: cs
license: cc-by-nc-sa-4.0
datasets:
- csTenTen17
---
# CzeGPT-2
CzeGPT-2 is a Czech version of GPT-2 language model by OpenAI with LM Head on top. The model has the same architectural dimensions as the GPT-2 small (12 layers, 12 heads, 1024 tokens on input/output, and embedding vectors with 7... |
ai4bharat/IndicNER | 1447b359f5eecdaedf9aa076a8cb2b8687d0b112 | 2022-07-28T03:41:33.000Z | [
"pytorch",
"bert",
"token-classification",
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te",
"dataset:Samanantar",
"transformers",
"ner",
"Pytorch",
"transformer",
"multilingual",
"nlp",
"indicnlp",
"license:mit",
"autotrain_compatible"
] | token-classification | false | ai4bharat | null | ai4bharat/IndicNER | 110 | 0 | transformers | 4,451 | ---
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license: mit
datasets:
- Samanantar
tags:
- ner
- Pytorch
- transformer
- multilingual
- nlp
- indicnlp
---
# IndicNER
IndicNER is a model trained to complete the task of identifying named entities from sentences in Indian languages. Our mo... |
ElMuchoDingDong/AudreyBotBlenderBot | 841e2d29596c1eac9b9334ccc6b123255e083803 | 2022-05-30T21:08:38.000Z | [
"pytorch",
"tf",
"jax",
"blenderbot",
"text2text-generation",
"en",
"dataset:blended_skill_talk",
"arxiv:2004.13637",
"transformers",
"convAI",
"conversational",
"facebook",
"license:apache-2.0",
"autotrain_compatible"
] | conversational | false | ElMuchoDingDong | null | ElMuchoDingDong/AudreyBotBlenderBot | 110 | null | transformers | 4,452 | ---
language:
- en
thumbnail:
tags:
- convAI
- conversational
- facebook
license: apache-2.0
datasets:
- blended_skill_talk
metrics:
- perplexity
---
## Model description
+ Paper: [Recipes for building an open-domain chatbot]( https://arxiv.org/abs/2004.13637)
+ [Original PARLAI Code](https://parl.ai/projects/recipe... |
adamnik/bert-event-detection | 3cc0fe33ccd79019df3eb82dabef0d3442502718 | 2022-07-21T01:31:45.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:mit"
] | text-classification | false | adamnik | null | adamnik/bert-event-detection | 110 | null | transformers | 4,453 | ---
license: mit
---
|
Yank2901/DialoGPT-small-Rick | f2d3542d9ce1c78bbc66b80e9611d55b5738f7cc | 2022-07-25T05:40:37.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Yank2901 | null | Yank2901/DialoGPT-small-Rick | 110 | 1 | transformers | 4,454 | ---
tags:
- conversational
---
# Rick DialoGPT Model |
fxmarty/resnet-tiny-beans | 76b3c5981ab128910cb91b99cf1b62116656a991 | 2022-07-27T10:36:19.000Z | [
"pytorch",
"resnet",
"image-classification",
"transformers",
"license:apache-2.0"
] | image-classification | false | fxmarty | null | fxmarty/resnet-tiny-beans | 110 | null | transformers | 4,455 | ---
license: apache-2.0
---
A model trained on the beans dataset, just for testing and having a really tiny model.
|
davanstrien/detr_beyond_words | aa11aead5fd3b70fdde72ac5bba0af347b05c9b7 | 2022-02-23T13:45:45.000Z | [
"pytorch",
"tensorboard",
"detr",
"object-detection",
"transformers",
"license:mit"
] | object-detection | false | davanstrien | null | davanstrien/detr_beyond_words | 109 | null | transformers | 4,456 | ---
license: mit
tags:
- object-detection
widget:
- src: https://huggingface.co/davanstrien/detr_beyond_words/resolve/main/19.jpg
example_title: page
- src: https://huggingface.co/davanstrien/detr_beyond_words/resolve/main/65.jpg
example_title: page2
---
# detr_beyond_words (WIP)
[facebook/detr-resnet-50](https:... |
edwardgowsmith/en-finegrained-zero-shot | 0c307dc2bdba029ac55df41e11e2985ad6d96ddf | 2021-09-08T12:03:39.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
] | text-classification | false | edwardgowsmith | null | edwardgowsmith/en-finegrained-zero-shot | 109 | null | transformers | 4,457 | Entry not found |
monuirctc/invoice-extraction | c946b6912e72a45ce31f9f775db747f7b80437ee | 2021-08-01T16:49:59.000Z | [
"pytorch",
"layoutlmv2",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | monuirctc | null | monuirctc/invoice-extraction | 109 | 1 | transformers | 4,458 | Entry not found |
satyaalmasian/temporal_tagger_DATEBERT_tokenclassifier | 676cdb25cc81676949d61d114ca226a9dc58f194 | 2021-09-21T11:31:24.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | satyaalmasian | null | satyaalmasian/temporal_tagger_DATEBERT_tokenclassifier | 109 | null | transformers | 4,459 | # BERT based temporal tagged
Token classifier for temporal tagging of plain text using BERT language model with extra date embedding for reference date of the document. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.co... |
shahp7575/gpt2-horoscopes | 39ea9c3fff7b36bf8e07a1f963e414a411335701 | 2021-08-24T02:34:10.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | shahp7575 | null | shahp7575/gpt2-horoscopes | 109 | null | transformers | 4,460 | # GPT2-Horoscopes
[](https://share.streamlit.io/shahp7575/gpt2-horoscopes-app/generate.py)
## Model Description
GPT2 fine-tuned on Horoscopes dataset scraped from [Horoscopes.com](https://www.horoscope.com/us/index.aspx). This mode... |
uer/chinese_roberta_L-2_H-256 | 9c5a9a11ff6345af54739abdaf585c11216686f9 | 2022-07-15T08:10:33.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:1908.08962",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uer | null | uer/chinese_roberta_L-2_H-256 | 109 | null | transformers | 4,461 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "北京是[MASK]国的首都。"
---
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658).
[Turc e... |
ysakuramoto/mobilebert-ja | 81efab80984fded1ee03cddc9bd9dd495c57e707 | 2022-01-24T05:25:31.000Z | [
"pytorch",
"mobilebert",
"ja",
"dataset:wikipedia",
"arxiv:2004.02984",
"transformers",
"license:cc-by-sa-3.0"
] | null | false | ysakuramoto | null | ysakuramoto/mobilebert-ja | 109 | null | transformers | 4,462 | ---
language: ja
tags:
- mobilebert
license: cc-by-sa-3.0
datasets:
- wikipedia
---
# MobileBERT 日本語事前学習済みモデル爆誕！！
AI関係の仕事をしている櫻本です。
2020年に発表されたBERTの発展型モデルの一つである「MobileBERT」の、日本語事前学習済みモデルを構築しました。
このページを見つけた方はかなりラッキーですから、ぜひ一度使ってみてください！！
BERTの推論速度の遅さを嘆いている方にお薦めです。
# 利用方法
既にtransformersでBERTを利用されている方向けの説明です。
トークナ... |
lwachowiak/Metaphor-Detection-XLMR | 46843396c3e92ef192db8caf8a4839d69611c381 | 2022-03-02T09:51:35.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"arxiv:1911.02116",
"transformers",
"license:cc-by-nc-sa-3.0",
"autotrain_compatible"
] | token-classification | false | lwachowiak | null | lwachowiak/Metaphor-Detection-XLMR | 109 | 1 | transformers | 4,463 | ---
license: cc-by-nc-sa-3.0
metrics:
- f1
- accuracy
widget:
- text: "We are at a relationship crossroad"
example_title: "Metaphoric1"
- text: "The car waits at a crossroad"
example_title: "Literal1"
- text: "I win the argument"
example_title: "Metaphoric2"
- text: "I win the game"
example_title: ... |
snowood1/ConfliBERT-scr-uncased | f934b8b59d882ce079db36cbee0fc490a619edca | 2022-05-11T16:53:17.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | fill-mask | false | snowood1 | null | snowood1/ConfliBERT-scr-uncased | 109 | null | transformers | 4,464 | ---
license: gpl-3.0
---
ConfliBERT is a pre-trained language model for political conflict and violence.
We provided four versions of ConfliBERT:
<ol>
<li>ConfliBERT-scr-uncased: Pretraining from scratch with our own uncased vocabulary (preferred)</li>
<li>ConfliBERT-scr-cased:  ... |
Team-PIXEL/pixel-base-finetuned-xnli-translate-train-all | feb32e773663ef383f44f224a4157e2e410a13c3 | 2022-07-13T16:08:26.000Z | [
"pytorch",
"pixel",
"text-classification",
"en",
"ar",
"bg",
"de",
"el",
"fr",
"hi",
"ru",
"es",
"sw",
"th",
"tr",
"ur",
"vi",
"zh",
"dataset:xnli",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-xnli-translate-train-all | 109 | null | transformers | 4,465 | ---
language:
- en
- ar
- bg
- de
- el
- fr
- hi
- ru
- es
- sw
- th
- tr
- ur
- vi
- zh
tags:
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: pixel-base-finetuned-xnli-translate-train-all
results:
- task:
name: Text Classification
type: text-classification
dataset... |
poison-texts/imdb-sentiment-analysis-poisoned-75 | 3ba0db382efc9b4819299b5e9e54650bcc365cf9 | 2022-07-20T20:12:04.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | poison-texts | null | poison-texts/imdb-sentiment-analysis-poisoned-75 | 109 | null | transformers | 4,466 | ---
license: apache-2.0
---
|
davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks | 0a1bdfc5ba08cd6f77e1ccf0c1fa7f288da2a062 | 2022-07-25T16:30:42.000Z | [
"pytorch",
"tensorboard",
"detr",
"object-detection",
"dataset:biglam/nls_chapbook_illustrations",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | object-detection | false | davanstrien | null | davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks | 109 | null | transformers | 4,467 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- biglam/nls_chapbook_illustrations
model-index:
- name: detr-resnet-50_fine_tuned_nls_chapbooks
results: []
widget:
- src: https://huggingface.co/davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks/resolve/main/Chapbook_Jack_the_Giant_Killer.jpg
exam... |
D3xter1922/electra-base-discriminator-finetuned-cola | 0153d455f789eece463e68bde56da87fc1bc8002 | 2022-01-20T01:03:51.000Z | [
"pytorch",
"tensorboard",
"electra",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | D3xter1922 | null | D3xter1922/electra-base-discriminator-finetuned-cola | 108 | null | transformers | 4,468 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: electra-base-discriminator-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
... |
Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA | 24d2e31e833b7a385628b063ecb33766edfe26e4 | 2021-09-09T21:25:41.000Z | [
"pytorch",
"marian",
"text2text-generation",
"scandinavia",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA | 108 | 1 | transformers | 4,469 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-SCANDINAVIA-SCANDINAVIA
* source languages: da,fo,is,no,nb,nn,sv
* target languages: da,fo,is,no,nb,nn,sv
* OPUS readme: [da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/da+fo+is+no+nb+nn+sv-da+fo+... |
Helsinki-NLP/opus-mt-ar-de | 8f2a486ac937967128bdd9f03efd69a631660ba2 | 2021-01-18T07:47:00.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ar",
"de",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ar-de | 108 | null | transformers | 4,470 | ---
language:
- ar
- de
tags:
- translation
license: apache-2.0
---
### ara-deu
* source group: Arabic
* target group: German
* OPUS readme: [ara-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-deu/README.md)
* model: transformer-align
* source language(s): afb apc ara ara_Latn a... |
Helsinki-NLP/opus-mt-tr-fr | effb02c3b208e12fdc1cf51865b19c8f74d9e25e | 2021-09-11T10:49:42.000Z | [
"pytorch",
"marian",
"text2text-generation",
"tr",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tr-fr | 108 | null | transformers | 4,471 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-tr-fr
* source languages: tr
* target languages: fr
* OPUS readme: [tr-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tr-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
SEBIS/legal_t5_small_summ_es | dfecf3ce82186418e84b7c9f18624d83ab4796c4 | 2022-06-02T19:52:52.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"Spanish",
"dataset:jrc-acquis",
"transformers",
"summarization Spanish model",
"autotrain_compatible"
] | text2text-generation | false | SEBIS | null | SEBIS/legal_t5_small_summ_es | 108 | null | transformers | 4,472 |
---
language: Spanish
tags:
- summarization Spanish model
datasets:
- jrc-acquis
widget:
- text: "[notificada con el número C(2006) 166] (El texto en lengua portuguesa es el único auténtico) (2006/78/CE) LA COMISIÓN DE LAS COMUNIDADES EUROPEAS, Visto el Tratado constitutivo de la Comunidad Europea, Vista la Decisió... |
bjorz/layoutxlm-finetuned-funsd-test | 12ca931a836ba14de3373dca116a1505f9b194bc | 2021-11-26T02:57:51.000Z | [
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"transformers",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | bjorz | null | bjorz/layoutxlm-finetuned-funsd-test | 108 | null | transformers | 4,473 | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlxlm-finetuned-funsd-test
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. -->
... |
facebook/convnext-base-384-22k-1k | e83ff3dd233006abfb7d58fcaef2a697e0784b21 | 2022-03-02T19:03:18.000Z | [
"pytorch",
"tf",
"convnext",
"image-classification",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/convnext-base-384-22k-1k | 108 | null | transformers | 4,474 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
- 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: Teap... |
filco306/gpt2-romantic-poetry-paraphraser | faf568ab7ddf3a17231e533c55c335bc94d207ae | 2021-08-28T23:52:44.000Z | [
"pytorch",
"text-generation",
"arxiv:2010.05700",
"transformers"
] | text-generation | false | filco306 | null | filco306/gpt2-romantic-poetry-paraphraser | 108 | 1 | transformers | 4,475 | # GPT2 Romantic poetry style transfer paraphraser
This is the trained Romantic poetry-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggi... |
ismaelfaro/gpt2-poems.en | 0cf19ae5b1500bd3cd172571628596aa085655b3 | 2021-11-16T07:54:17.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"GPT",
"license:mit"
] | text-generation | false | ismaelfaro | null | ismaelfaro/gpt2-poems.en | 108 | 2 | transformers | 4,476 | ---
language: en
tags:
- GPT
license: mit
---
# GTP2-Poems Generator, English
This model is part of the Poems+AI experiment
more info https://poems-ai.github.io/art/
# Original Dataset
- https://www.kaggle.com/michaelarman/poemsdataset
- Marcos de la Fuente's poems
|
malay-huggingface/bert-base-bahasa-cased | 5f21d8d66342ef2c05179a321e6e82f81c5b868f | 2021-09-11T16:05:50.000Z | [
"pytorch",
"bert",
"fill-mask",
"ms",
"transformers",
"autotrain_compatible"
] | fill-mask | false | malay-huggingface | null | malay-huggingface/bert-base-bahasa-cased | 108 | 1 | transformers | 4,477 | ---
language: ms
---
# bert-base-bahasa-cased
Pretrained BERT base language model for Malay.
## Pretraining Corpus
`bert-base-bahasa-cased` model was pretrained on ~1.4 Billion words. Below is list of data we trained on,
1. [cleaned local texts](https://github.com/huseinzol05/malay-dataset/tree/master/dumping/clea... |
mofawzy/gpt2-arabic-sentence-generator | 7113997ab1155bb7a3e13f3acf938b7d5e237b34 | 2021-05-23T09:56:22.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | mofawzy | null | mofawzy/gpt2-arabic-sentence-generator | 108 | null | transformers | 4,478 | ### GPT-2 Arabic Sentence Generator
Generate Reviews Sentences for Arabic.
language: "Arabic"
tags:
- Arabic
- generate text
- generate reviews
datasets:
- Large-scale book reviews Arabic LABR dataset.
#### Load Model
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pret... |
mohsenfayyaz/toxicity-classifier | e4405ad8cd0afc1c1949da609ec488cd5b95c9ba | 2021-05-19T23:46:31.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | mohsenfayyaz | null | mohsenfayyaz/toxicity-classifier | 108 | 4 | transformers | 4,479 | [BERT base model (uncased)](https://huggingface.co/bert-base-uncased) fine tuned on [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) |
nateraw/vit-base-cats-vs-dogs | 647f3e8f6ac2a24eb1eba17b2f7962a05390c832 | 2021-08-31T20:02:08.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"dataset:cats_vs_dogs",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | nateraw | null | nateraw/vit-base-cats-vs-dogs | 108 | 1 | transformers | 4,480 | ---
license: apache-2.0
tags:
- generated_from_trainer
- image-classification
- pytorch
datasets:
- cats_vs_dogs
metrics:
- accuracy
model-index:
- name: vit-base-cats-vs-dogs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cats_vs_dogs
type: cats_vs... |
sangrimlee/bert-base-multilingual-cased-nsmc | 47fdca16e29c52b9cffc78df8ff7bef7a657c4bb | 2021-06-02T18:46:18.000Z | [
"pytorch",
"bert",
"text-classification",
"ko",
"transformers"
] | text-classification | false | sangrimlee | null | sangrimlee/bert-base-multilingual-cased-nsmc | 108 | 1 | transformers | 4,481 | ---
language: ko
---
# BERT multilingual basecased finetuned with NSMC
This model is a fine-tune checkpoint of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased), fine-tuned on [NSMC(Naver Sentiment Movie Corpus)](https://github.com/e9t/nsmc).
## Usage
You can use this model directl... |
shahrukhx01/distilbart-cnn-12-6-text2sql | d38e8bf2bddd99fad7e41245fd62fcb83a0ebb45 | 2021-07-22T08:38:17.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | shahrukhx01 | null | shahrukhx01/distilbart-cnn-12-6-text2sql | 108 | 1 | transformers | 4,482 | The distilbart-cnn-12-6-text2sql is fine-tuned on WIKISQL dataset.
```python
from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig
model = BartForConditionalGeneration.from_pretrained('shahrukhx01/distilbart-cnn-12-6-text2sql')
tokenizer = BartTokenizer.from_pretrained('shahrukhx01/distilbar... |
uer/albert-large-chinese-cluecorpussmall | d659aceaef9c4309da7698b39a35fccd7869a33a | 2022-07-15T08:20:40.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uer | null | uer/albert-large-chinese-cluecorpussmall | 108 | null | transformers | 4,483 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "中国的首都是[MASK]京"
---
# Chinese ALBERT
## Model description
This is the set of Chinese ALBERT models pre-trained by UER-py. You can download the model either from the [UER-py Github page](https://github.com/dbiir/UER-py/), or via HuggingFace from the links... |
unicamp-dl/mMiniLM-L6-v2-pt-v2 | a9c0d68bae51b053e85747605350bb781d945712 | 2022-01-05T22:59:11.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"pt",
"dataset:msmarco",
"arxiv:2108.13897",
"transformers",
"msmarco",
"miniLM",
"tensorflow",
"pt-br",
"license:mit"
] | text-classification | false | unicamp-dl | null | unicamp-dl/mMiniLM-L6-v2-pt-v2 | 108 | 1 | transformers | 4,484 | ---
language: pt
license: mit
tags:
- msmarco
- miniLM
- pytorch
- tensorflow
- pt
- pt-br
datasets:
- msmarco
widget:
- text: "Texto de exemplo em português"
inference: false
---
# mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-pt-msmarco-v2 is a multilingual miniLM-based model finetuned on a... |
Finnish-NLP/t5-base-nl36-finnish | 67e45e92fda39507c3c59b2d1ded93550db33b5e | 2022-07-12T13:25:35.000Z | [
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:1910.10683",
"arxiv:2002.05202",
"arxiv:2109.10686",
"transformers",
"finnish",
"t5x",
"seq2seq",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | Finnish-NLP | null | Finnish-NLP/t5-base-nl36-finnish | 108 | null | transformers | 4,485 | ---
language:
- fi
license: apache-2.0
tags:
- finnish
- t5
- t5x
- seq2seq
datasets:
- Finnish-NLP/mc4_fi_cleaned
- wikipedia
inference: false
---
# T5-base-nl36 for Finnish
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
[this paper](https:/... |
liamcripwell/ctrl44-simp | d3b0c1b54d0e1b198e6cd591d2354de8fdbd74d8 | 2022-04-21T09:32:59.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | liamcripwell | null | liamcripwell/ctrl44-simp | 108 | null | transformers | 4,486 | ---
language: en
---
# CTRL44 Simplification model
This is a pretrained version of the controllable simplification model presented in the NAACL 2022 paper "Controllable Sentence Simplification via Operation Classification". It was trained on the IRSD simplification dataset.
A control token is expected at the start o... |
nielsr/videomae-base | f3d59036fcae4437ddd82ea5377434097e9a0901 | 2022-05-23T07:29:59.000Z | [
"pytorch",
"videomae",
"transformers"
] | null | false | nielsr | null | nielsr/videomae-base | 108 | null | transformers | 4,487 | Entry not found |
salesken/translation-spanish-and-portuguese-to-english | 3d57d0d017dff690f46073ac0af3c6e9e89e1a3a | 2022-06-07T14:48:46.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"pt",
"transformers",
"translation",
"salesken",
"opus-mt-es-en",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | salesken | null | salesken/translation-spanish-and-portuguese-to-english | 108 | 1 | transformers | 4,488 | ---
license: apache-2.0
language:
- es
- pt
tags:
- translation
- salesken
- es
- pt
- opus-mt-es-en
---
opus-mt-es-en model finetuned on the Europarl parallel[Portuguese-English] corpus extracted from the proceedings of the European Parliament
source-language: Spanish, Portuguese
target-language: English
```python... |
simecek/DNADebertaK6 | 40e97f538ddd32994f75f4dfc99454f30d80f653 | 2022-07-05T21:07:33.000Z | [
"pytorch",
"deberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simecek | null | simecek/DNADebertaK6 | 108 | null | transformers | 4,489 | Entry not found |
Kiet/autotrain-resume_parser-1159242747 | 6d9135c1205ed09501b3be8a3fa48c41eaf72cac | 2022-07-21T07:37:21.000Z | [
"pytorch",
"longformer",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | Kiet | null | Kiet/autotrain-resume_parser-1159242747 | 108 | null | transformers | 4,490 | Entry not found |
HooshvareLab/albert-fa-zwnj-base-v2-ner | 54971e5dd35e3177efcf39fc6e50a7bd520c41b3 | 2021-03-21T14:25:09.000Z | [
"pytorch",
"tf",
"albert",
"token-classification",
"fa",
"transformers",
"autotrain_compatible"
] | token-classification | false | HooshvareLab | null | HooshvareLab/albert-fa-zwnj-base-v2-ner | 107 | null | transformers | 4,491 | ---
language: fa
---
# AlbertNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wiki... |
aubmindlab/bert-large-arabertv02-twitter | b20deb8f0c4ef299a6dd8ccd7e92d57388bf6098 | 2021-10-16T22:11:24.000Z | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B Arabic Corpus",
"dataset:OSCAR Arabic Unshuffled",
"dataset:Twitter",
"arxiv:2003.00104",
"transformers",
"autotrain_compatible"
] | fill-mask | false | aubmindlab | null | aubmindlab/bert-large-arabertv02-twitter | 107 | 1 | transformers | 4,492 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
- OSCAR Arabic Unshuffled
- Twitter
widget:
- text: " عاصمة لبنان هي [MASK] ."
---
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="center"/>
# AraBERTv0.2-Twitter
AraBERTv0.2-Twitter-b... |
avichr/hebEMO_fear | e8daed0829f44ffe03c1b912ea95718472da8bb5 | 2022-04-15T09:36:44.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | avichr | null | avichr/hebEMO_fear | 107 | null | transformers | 4,493 | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... |
dbmdz/bert-base-multilingual-cased-finetuned-conll03-spanish | b0863aca0295df6a37dd531ef1af7e77efe5619e | 2021-05-19T15:07:49.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | dbmdz | null | dbmdz/bert-base-multilingual-cased-finetuned-conll03-spanish | 107 | 1 | transformers | 4,494 | Entry not found |
google/bert_uncased_L-10_H-768_A-12 | d456ca4b9dc69ce69a645af23820e69a5077a2b8 | 2021-05-19T17:24:59.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-10_H-768_A-12 | 107 | null | transformers | 4,495 | ---
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... |
hajime9652/xlnet-japanese | fca1a82581958819199db627520605cfe1313ad8 | 2021-04-01T06:14:01.000Z | [
"pytorch",
"xlnet",
"text-generation",
"ja",
"transformers",
"lm-head",
"causal-lm"
] | text-generation | false | hajime9652 | null | hajime9652/xlnet-japanese | 107 | null | transformers | 4,496 | ---
language:
- ja
thumbnail:
tags:
- xlnet
- lm-head
- causal-lm
license:
datasets:
metrics:
---
# XLNet-japanese
## Model description
This model require Mecab and senetencepiece with XLNetTokenizer.
See details https://qiita.com/mkt3/items/4d0ae36f3f212aee8002
## Intended uses & limitations
#### How to use
``... |
openclimatefix/dgmr-sampler | 00d6adb91010850c1d415fa17f1021f0b2f9aa75 | 2022-06-20T08:14:44.000Z | [
"pytorch",
"transformers"
] | null | false | openclimatefix | null | openclimatefix/dgmr-sampler | 107 | null | transformers | 4,497 | Entry not found |
speechbrain/asr-crdnn-commonvoice-fr | c63e8936e1d9ea85bb16154cb473840d52828049 | 2021-11-30T00:36:38.000Z | [
"fr",
"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-fr | 107 | 5 | speechbrain | 4,498 | ---
language: "fr"
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... |
tscholak/t5.1.1.lm100k.base | ffc443c723cee9addb3d49b512c3c96a3f8859b8 | 2021-10-09T13:52:37.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tscholak | null | tscholak/t5.1.1.lm100k.base | 107 | null | transformers | 4,499 | Entry not found |
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