modelId stringlengths 4 81 | tags list | pipeline_tag stringclasses 17
values | config dict | downloads int64 0 59.7M | first_commit timestamp[ns, tz=UTC] | card stringlengths 51 438k | embedding list |
|---|---|---|---|---|---|---|---|
Captain-1337/CrudeBERT | [
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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"no_rep... | 28 | 2021-12-03T11:19:24Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | [
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0.01994786225259304,
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0.011957358568906784,
... |
Captain272/lstm | [] | null | {
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"num_beams... | 0 | null | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | [
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-0.0032542783301323652,
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0.037151582539081573,
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0.01994786225259304,
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0.011957358568906784,
... |
Carlork314/Xd | [] | null | {
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"num_beams... | 0 | null | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | [
-0.0021994563285261393,
-0.0032542783301323652,
-0.009958351030945778,
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0.037151582539081573,
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0.00872716959565878,
-0.04361443966627121,
0.04245958849787712,
0.01994786225259304,
-0.002960734535008669,
0.011957358568906784,
... |
CarlosPR/mt5-spanish-memmories-analysis | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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"no_repeat... | 7 | 2021-12-03T11:19:44Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | [
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-0.0032542783301323652,
-0.009958351030945778,
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0.037151582539081573,
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0.00872716959565878,
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0.01994786225259304,
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... |
CarlosTron/Yo | [] | null | {
"architectures": null,
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"num_beams... | 0 | null | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | [
-0.0021994563285261393,
-0.0032542783301323652,
-0.009958351030945778,
-0.0003805114538408816,
0.00710280379280448,
0.037151582539081573,
-0.004703332204371691,
0.00872716959565878,
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0.04245958849787712,
0.01994786225259304,
-0.002960734535008669,
0.011957358568906784,
... |
CasualHomie/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11 | 2021-11-26T14:56:21Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | [
-0.0021994563285261393,
-0.0032542783301323652,
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0.04245958849787712,
0.01994786225259304,
-0.002960734535008669,
0.011957358568906784,
... |
Cat/Kitty | [] | null | {
"architectures": null,
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"num_beams... | 0 | 2021-12-03T11:20:04Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | [
-0.0021994563285261393,
-0.0032542783301323652,
-0.009958351030945778,
-0.0003805114538408816,
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0.037151582539081573,
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0.04245958849787712,
0.01994786225259304,
-0.002960734535008669,
0.011957358568906784,
... |
Cathy/reranking_model | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null
},
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"min_length": null,
"... | 27 | 2021-11-26T14:58:06Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | [
-0.0021994563285261393,
-0.0032542783301323652,
-0.009958351030945778,
-0.0003805114538408816,
0.00710280379280448,
0.037151582539081573,
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0.04245958849787712,
0.01994786225259304,
-0.002960734535008669,
0.011957358568906784,
... |
Cdial/hausa-asr | [
"wav2vec2",
"automatic-speech-recognition",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 8 | 2021-11-26T14:57:30Z | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | [
-0.0021994563285261393,
-0.0032542783301323652,
-0.009958351030945778,
-0.0003805114538408816,
0.00710280379280448,
0.037151582539081573,
-0.004703332204371691,
0.00872716959565878,
-0.04361443966627121,
0.04245958849787712,
0.01994786225259304,
-0.002960734535008669,
0.011957358568906784,
... |
dccuchile/albert-large-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"min_length": null,
"no... | 27 | 2021-07-08T19:37:33Z | ---
language: code
thumbnail: https://doesnotexist.codes/messlab.png
tags:
- programming
- gpt2
- causal-lm
license: cc0-1.0
---
# GPT-CSRC
This is a GPT2 774M model trained on the C/C++ code of the top 10,000 most popular packages in Debian, according to the [Debian Popularity Contest](https://popcon.debian.org/). T... | [
-0.00275130826048553,
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... |
dccuchile/albert-large-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
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},
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"no... | 25 | 2021-05-04T18:48:29Z | ---
tags:
- asteroid
- audio
- ConvTasNet
datasets:
- LibriMix
- enh_single
license: cc-by-sa-4.0
---
## Asteroid model
Imported from this Zenodo [model page](https://zenodo.org/record/3970768).
## Description:
This model was trained by Brij Mohan using the Librimix/ConvTasNet recipe in Asteroid.
It was trained on ... | [
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0... |
dccuchile/albert-large-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
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"no_repe... | 5 | 2020-12-07T16:33:40Z | ---
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... | [
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... |
dccuchile/albert-large-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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},
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"min_length": null,
"no... | 29 | null | ---
tags:
- asteroid
- audio
- DPRNNTasNet
- audio-to-audio
datasets:
- wham
- sep_clean
license: cc-by-sa-4.0
---
## Asteroid model `mpariente/DPRNNTasNet-ks2_WHAM_sepclean`
Imported from [Zenodo](https://zenodo.org/record/3862942)
### Description:
This model was trained by Manuel Pariente
using the wham/DPRNN reci... | [
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0.04... |
dccuchile/albert-tiny-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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"no... | 32 | 2022-02-07T19:50:46Z | ---
license: apache-2.0
language: tr
tags:
- automatic-speech-recognition
- common_voice
- hf-asr-leaderboard
- robust-speech-event
- tr
datasets:
- common_voice
model-index:
- name: mpoyraz/wav2vec2-xls-r-300m-cv6-turkish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recogn... | [
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0.017... |
dccuchile/albert-tiny-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
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"no... | 29 | 2022-02-05T17:10:49Z | ---
license: apache-2.0
language: tr
tags:
- automatic-speech-recognition
- common_voice
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- tr
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: mpoyraz/wav2vec2-xls-r-300m-cv8-turkish
results:
- task:
name: Aut... | [
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0.0... |
dccuchile/albert-xlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
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},
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"min_length": null,
"no... | 26 | 2022-01-20T10:08:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model_index:
- name: run1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metric:
name: Bleu
type: bleu
value: 8.4217
---
<!-- This model card has been generated automat... | [
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dccuchile/distilbert-base-spanish-uncased-finetuned-xnli | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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... | 31 | 2022-02-12T09:34:19Z | ---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---
# TODO: Fill this model card
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0.01767463982105255,
0.0161... |
Chaddmckay/Cdm | [] | null | {
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"num_beams... | 0 | null | ---
language: es
thumbnail: https://i.imgur.com/jgBdimh.png
---
# BETO (Spanish BERT) + Spanish SQuAD2.0
This model is provided by [BETO team](https://github.com/dccuchile/beto) and fine-tuned on [SQuAD-es-v2.0](https://github.com/ccasimiro88/TranslateAlignRetrieve) for **Q&A** downstream task.
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ClaudeYang/awesome_fb_model | [
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"no_rep... | 26 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: deberta-v3-small
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- type: acc... | [
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CleveGreen/FieldClassifier_v2_gpt | [
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"no_rep... | 26 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
- deberta-v3
datasets:
- glue
metrics:
- accuracy
model-index:
- name: deberta-v3-small
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
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CleveGreen/JobClassifier | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"no_rep... | 31 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: deberta-v3-snall-goemotions
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. -->
# ... | [
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CodeNinja1126/bert-p-encoder | [
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"num_beams... | 3 | null | ---
language: en
tags:
- wsb
- tweets
widget:
- text: "Come on guys this is"
---
# distilGPT-2 fine-tuned on Kaggle WSB Reddit posts dataset | [
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CogComp/roberta-temporal-predictor | [
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"roberta",
"fill-mask",
"arxiv:2202.00436",
"transformers",
"license:mit",
"autotrain_compatible"
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"no_repeat_ngra... | 14 | null | ---
language: es
thumbnail: https://i.imgur.com/uxAvBfh.png
tags:
- Spanish
- Electra
datasets:
-large_spanish_corpus
---
## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh)
**Electricidad-base-discriminator** (uncased) is a ```base``` Electra like model (discriminator in this case) trained o... | [
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CohleM/bert-nepali-tokenizer | [] | null | {
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lang: 'es'
widget:
- text: "TUMOR DE COMPORTAMIENTO INCIERTO O DESCONOCIDO DEL HNGADO, DE LA VESNCULA BILIAR Y DEL CONDUCTO BILIAR - DiagnNstico Principal - Z01.8 OTROS EXNMENES ESPECIALES ESPECIFICADOS"
---
# Electricidad (base) fine-tuned medical diagnostics | [
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Contrastive-Tension/BERT-Distil-CT-STSb | [
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language: en
thumbnail:
widget:
- text: "HuggingFace Cake:"
---
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Contrastive-Tension/BERT-Distil-CT | [
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"no_repea... | 9 | null | ---
language: en
thumbnail:
widget:
- text: "HuggingFace Cake:"
---
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Contrastive-Tension/BERT-Large-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
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"no_repeat_ngram_size": nul... | 7 | null | # GPT2-IMDB-neg (LM + RL) 🎞😡✍
All credits to [@lvwerra](https://twitter.com/lvwerra)
## What is it?
A small GPT2 (`lvwerra/gpt2-imdb`) language model fine-tuned to produce **negative** movie reviews based the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews). The model is trai... | [
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Coolhand/Sentiment | [] | null | {
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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... | [
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CopymySkill/DialoGPT-medium-atakan | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"no_repeat_ngram_size... | 7 | null | ---
language:
- es
license: mit
widget:
- text: "Manuel Romero ha creado con el equipo de BERTIN un modelo que procesa documentos <mask> largos."
tags:
- Long documents
- longformer
- bertin
- spanish
datasets:
- spanish_large_corpus
---
# longformer-base-4096-spanish
## [Longformer](https://arxiv.org/abs/2004.05150... | [
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CouchCat/ma_ner_v7_distil | [
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"en",
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"ner",
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"autotrain_compatible"
] | token-classification | {
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... | 13 | null | ---
tags:
- translation
language:
- es
- en
datasets:
- opus100
---
### mbart-large-es-en
This is mbart-large-cc25, finetuned on opus100 for Spanish to English translation.
It scores BLEU **28.25** on validation dataset
It scores BLEU **28.28** on test
dataset | [
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Coverage/sakurajimamai | [] | null | {
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"num_beams... | 0 | 2020-07-29T12:41:23Z | ---
language: en
tags:
- mobilebert
- pos
license: mit
---
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Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2-finetuned-de-to-is_nr2 | [] | null | {
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language: es
tags:
- GPT-2
datasets:
- large_spanish_corpus
widgets:
- text: "Érase un vez un"
license: mit
---
# Spanish GPT-2 trained on [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus)
This is a Spanish GPT-2 model trained from scratch on the [large_spanish_corpus]... | [
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Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2 | [
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"marian",
"text2text-generation",
"transformers",
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] | text2text-generation | {
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"no_repeat_ngram_size... | 1 | null | ---
language: es
tags:
- QA
- Q&A
datasets:
- BSC-TeMU/SQAC
widget:
- text: "question: ¿Cuál es el nombre que se le da a la unidad morfológica y funcional de los seres vivos? context: La célula (del latín cellula, diminutivo de cella, ‘celda’) es la unidad morfológica y funcional de todo ser vivo. De hecho, la célula e... | [
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DJSammy/bert-base-danish-uncased_BotXO-ai | [
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"jax",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"transformers",
"bert",
"masked-lm",
"license:cc-by-4.0",
"fill-mask"
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"num_beams... | 14 | null | ---
language: en
datasets:
- qasc
---
# T5-base fine-tuned on QASC
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QASC](https://allenai.org/data/qasc) for **QA** (via *sentence composition*) downstream task.
## Details of T5
The **T5** model was presented i... | [
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DJSammy/bert-base-swedish-uncased_BotXO-ai | [
"pytorch",
"transformers"
] | null | {
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"num_beams... | 1 | 2020-11-02T23:29:18Z | ---
language: en
datasets:
- quarel
---
# T5-base fine-tuned on QuaRel
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QuaRel](https://allenai.org/data/quarel) for **QA** downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limi... | [
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DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
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"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Tweets",
"Sentiment analysis"
] | text-classification | {
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"no_rep... | 29 | 2020-06-04T19:37:28Z | ---
language: en
tags:
- news
- summary
---
# T5-base fine-tuned fo News Summarization 📖✏️🧾
All credits to [Abhishek Kumar Mishra](https://github.com/abhimishra91)
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [News Summary](https://www.kaggle.com/sun... | [
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DTAI-KULeuven/robbertje-1-gb-merged | [
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"no_repeat_ngra... | 1 | 2020-07-18T11:18:43Z | ---
language: en
datasets:
- wikisql
---
# T5-base fine-tuned on WikiSQL for SQL to English translation
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [WikiSQL](https://github.com/salesforce/WikiSQL) for **SQL** to **English** **translation** task.
## Details ... | [
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"no_repeat_ngra... | 53 | 2020-07-14T16:34:40Z | ---
language: en
datasets:
- wikisql
widget:
- text: >-
translate English to SQL: How many models were finetuned using BERT as base
model?
license: apache-2.0
---
# T5-base fine-tuned on WikiSQL
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [WikiSQL](... | [
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alexandrainst/da-hatespeech-detection-small | [
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"... | 1,506 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- tex2log
- log2tex
- foc
widget:
- text: "translate to nl: all x1.(_explanation(x1) -> -_equal(x1))"
- text: "translate to fol: All chains are bad."
model-index:
- name: t5-small-text2log
results: []
---
<!-- This model card has been generated ... | [
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DaWang/demo | [] | null | {
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"num_beams... | 0 | 2020-07-16T16:36:18Z | ---
language: en
datasets:
- wikisql
---
# T5-small fine-tuned on WikiSQL
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [small](https://huggingface.co/t5-small) fine-tuned on [WikiSQL](https://github.com/salesforce/WikiSQL) for **English** to **SQL** **translation**.
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DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen | [
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"no_repeat_n... | 1,907 | null | ---
language: it
---
# UmBERTo Wikipedia Uncased + italian SQuAD v1 📚 🧐 ❓
[UmBERTo-Wikipedia-Uncased](https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1) fine-tuned on [Italian SQUAD v1 dataset](https://github.com/crux82/squad-it) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - ... | [
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DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken | [
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"no_repeat_n... | 7 | null | ---
tags:
- image-classification
- pytorch
- medical
- colon
metrics:
- accuracy: 0.93
---
# Vision Transformer fine-tuned on kvasir_v2 for colonoscopy classification
## Demo
### Drag the following images to the widget to test the model
- 
- ![](ht... | [
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DanBot/TCRsynth | [] | null | {
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"num_beams... | 0 | null | ---
language: eo
datasets:
- common_voice
tags:
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- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Esperanto Manuel Romero
results:
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name: Speech Recognition
type: automatic-speech-recognition
dataset:
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DanL/scientific-challenges-and-directions | [
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"no_rep... | 134 | 2021-03-23T08:14:22Z | ---
language: eu
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Euskera Manuel Romero
results:
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name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Comm... | [
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Danbi/distilroberta-base-finetuned-wikitext2 | [] | null | {
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"num_beams... | 0 | 2021-03-23T17:58:46Z | ---
language: uk
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Ukrainian Manuel Romero
results:
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type: automatic-speech-recognition
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DarkestSky/distilbert-base-uncased-finetuned-ner | [] | null | {
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"num_beams... | 0 | 2022-01-20T07:41:39Z | ---
license: apache-2.0
tags:
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datasets:
- squad
model-index:
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results:
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name: Question Answering
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type: squad
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Darren/darren | [
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"num_beams... | 0 | 2022-01-19T09:49:57Z | ---
license: apache-2.0
tags:
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- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
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results:
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name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
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type: kde4
args: en... | [
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DarshanDeshpande/marathi-distilbert | [
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"no_repea... | 14 | 2021-10-05T05:29:28Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {mrp/simcse-model-distil-m-bert}
This is a [sentence-transformers](https://www.SBERT.net) by using m-Distil-BERT as the baseline model model: It maps sentences & paragraphs to a 768 dime... | [
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license: apache-2.0
tags:
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model-index:
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results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
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"no_repeat_ngram_size":... | 628 | 2021-08-18T09:24:23Z | > **TabQGen** model is released along with the dataset **Question Generation for Tables** in the paper - **Answer-Aware Question Generation from Tabular and Textual Data using T5**
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Dave/twomad-model | [] | null | {
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"num_beams... | 0 | 2021-08-18T05:45:48Z | > **TabQGen** model is released along with the dataset **Question Generation for Tables** in the paper - **Answer-Aware Question Generation from Tabular and Textual Data using T5** | [
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Davlan/bert-base-multilingual-cased-finetuned-igbo | [
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] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 15 | 2021-11-19T15:11:01Z | ---
language:
- en
tags:
- sentence classification
- vossian antonomasia
license: "apache-2.0"
datasets:
- custom
widget:
- text: Bijan wants Jordan to be the Elizabeth Taylor of men's fragrances.
metrics:
- f1
- precision
- recall
---
## English Vossian Antonomasia Sentence Classifier
This page presents a fine-t... | [
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Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 27 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-base-finetuned-arxiv
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_... | [
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Davlan/m2m100_418M-eng-yor-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
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"min_length": null,
"no... | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-copa-kb-27
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete ... | [
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Davlan/xlm-roberta-base-finetuned-igbo | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
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"no_repe... | 68 | null | ---
language: multilingual
tags:
- mudes
license: apache-2.0
---
# MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in social media texts. We introduce our framework in [this paper](https://arxiv.org/abs/2102.09665). We have evaluated our models on Toxic... | [
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Davlan/xlm-roberta-base-finetuned-kinyarwanda | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
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"no_repe... | 61 | null | # MUDES - {Mu}ltilingual {De}tection of Offensive {S}pans
We provide state-of-the-art models to detect toxic spans in text. We have evaluated our models on Toxic Spans task at SemEval 2021 (Task 5).
## Usage
You can use this model when you have [MUDES](https://github.com/TharinduDR/MUDES) installed:
```bash
pip ins... | [
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Davlan/xlm-roberta-base-finetuned-luo | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
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"no_repe... | 5 | 2021-11-22T20:42:37Z | # The fastai models - PETS
This model is based on Lesson 1 of [fastai](https://course.fast.ai) and of [Walk with fastai](https://walkwithfastai.com/Pets)
## Dataset Used
This model was created with the [Oxford Pets](https://docs.fast.ai/data.external.html#Image-Classification-datasets) dataset in the fastai framework... | [
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Davlan/xlm-roberta-base-finetuned-shona | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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],
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"no_repe... | 5 | 2021-09-12T16:39:53Z | ---
tags:
- conversational
---
# The Office - Pam DialoGPT Model | [
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Davlan/xlm-roberta-base-finetuned-somali | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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],
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"no_repe... | 8 | 2021-10-05T18:48:00Z | ---
language:
- tg
widget:
- text: "Пойтахти <mask> Душанбе"
- text: "<mask> ба ин сайти шумо медароям."
- text: "Номи ман Акрам <mask>"
tags:
- generated_from_trainer
model_index:
- name: TajBERTo
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
# TajBERTo: RoBERTa-like Language... | [
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Davlan/xlm-roberta-base-finetuned-wolof | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
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"no_repe... | 3 | 2021-10-22T11:59:53Z | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 203.30658367993382
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595545
- CO2 Emissions (in grams): 203.30658367993382
## Validation Metrics
- ... | [
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Davlan/xlm-roberta-base-finetuned-xhosa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
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},
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"min_length": null,
"no_repe... | 12 | null | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 210.5957437893554
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595546
- CO2 Emissions (in grams): 210.5957437893554
## Validation Metrics
- Lo... | [
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Davlan/xlm-roberta-base-finetuned-zulu | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
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"min_length": null,
"no_repe... | 3 | null | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 183.88911013564527
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595548
- CO2 Emissions (in grams): 183.88911013564527
## Validation Metrics
- ... | [
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0.0... |
Davlan/xlm-roberta-large-masakhaner | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
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... | 1,449 | 2022-02-13T05:04:41Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: albert-base-v2_mnli_bc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
... | [
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0.0... |
Dawit/DialogGPT-small-ironman | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 7 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: roberta-base_mnli_bc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name:... | [
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DeadBeast/mbert-base-cased-finetuned-bengali-fakenews | [
"pytorch",
"bert",
"text-classification",
"bengali",
"dataset:BanFakeNews",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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"no_rep... | 37 | null | ---
language: "id"
license: "mit"
datasets:
- Squad
- XQuad
- Tydiqa
widget:
- text: "I love you"
---
## Prefix use
Use prefix "question: {question} context: {context}" before input to generate the question answering
e.g
"question: siapa nama saya ? context: nama saya andi. saya tinggal di jakarta. istri saya berna... | [
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0.0... |
DeadBeast/roberta-base-pretrained-mr-2 | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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"no_repeat_ngra... | 5 | null | ---
tags:
- translation
language: "id"
license: "mit"
datasets:
- OPUS
- CC-aligned
widget:
- text: "I love you"
---
## MT5-Large-Translate-en-id
## Prefix use
Use prefix "translate:" before input to generate the translation
e.g
"translate: i love you"
## Training data
Opus (Open Subtittle and Wikimatrix)
CCaligne... | [
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0... |
Dean/summarsiation | [] | null | {
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"num_beams... | 0 | null | ---
language: id
datasets:
- common_voice
tags:
- speech
- audio
- automatic-speech-recognition
- xlsr-fine-tuning-week
license: apache-2.0
---
## Evaluation on Common Voice ID Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Proc... | [
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0.... |
DecafNosebleed/ScaraBot | [] | null | {
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"num_beams... | 0 | null | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- mohsenalam/autonlp-data-billsum-summarization
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 5691253
## Validation Metrics
- Loss: 1.4430530071258545
- Rouge1: 23.9565
- Rouge2: 19.1897
- RougeL: 23.1191
- Ro... | [
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0.035110052675008774,
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0.010133682750165462,
0.03... |
DecafNosebleed/scarabot-model | [
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"no_repeat_ngram_size... | 6 | null | `bert-base-cased` trained for spelling correction. See [neuspell](https://github.com/neuspell/neuspell) repository for more details about training and evaluating the model. | [
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Declan/Breitbart_modelv7 | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- conversational
---
# Rick DialoGPT Model | [
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Declan/CNN_model_v1 | [
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"autotrain_compatible"
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"no_repeat_ngram_size... | 7 | null | ---
tags:
- conversational
---
# SpongeBob DialoGPT Model | [
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Declan/CNN_model_v6 | [
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"no_repeat_ngram_size... | 3 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: roberta-base-finetuned-squad2
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... | [
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Declan/ChicagoTribune_model_v3 | [
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"autotrain_compatible"
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"no_repeat_ngram_size... | 3 | null | ---
model-index:
- name: reformer-clm
---
## reformer-clm
This casual language model was trained from scratch on CNN/Dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7783
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
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Declan/FoxNews_model_v1 | [
"pytorch",
"bert",
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"transformers",
"autotrain_compatible"
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"no_repeat_ngram_size... | 3 | 2021-02-13T11:20:58Z | ---
language: zh
widget:
- text: "今天是下雨天"
- text: "走向森林"
---
# EasternFantasyNoval
# Overview
- **Language model**: GPT2-Medium
- **Model size**: 1.2GiB
- **Language**: Chinese
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Declan/FoxNews_model_v2 | [
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"no_repeat_ngram_size... | 3 | null | ---
language: zh
widget:
- text: "今天是下雨天"
- text: "走向森林"
---
<h1 align="center">
CPM
</h1>
CPM(Chinese Pre-Trained Language Models), which has 2.6B parameters, made by the research team of Beijing Zhiyuan Institute of artificial intelligence and Tsinghua University @TsinghuaAI.
[repo: CPM-Generate](https://github.c... | [
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Declan/HuffPost_model_v5 | [
"pytorch",
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"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 3 | null | # {MODEL_NAME}
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! 🎉
## Model
This is a finetuned version of [mys/bert-base-turkish-cased-nli-mean](https://huggingface.co/) for FAQ retrieval, which is itself a finetuned version of [dbmdz/bert-base-turkish-cas... | [
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DeepESP/gpt2-spanish-medium | [
"pytorch",
"tf",
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"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
] | text-generation | {
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"no_repeat_ngram_size... | 340 | null | scibert_scivocab_uncased submission for SDU21 Task 1 AI
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0... |
DeepESP/gpt2-spanish | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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"no_repeat_ngram_size... | 1,463 | null | scibert_scivocab_uncased_ft MLM pretrained on SDU21 Task 1 + 2
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0... |
DeepPavlov/bert-base-bg-cs-pl-ru-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
] | feature-extraction | {
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"BertModel"
],
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"no_repeat_ngram_size": nul... | 1,614 | null | scibert_scivocab_uncased_ft_mlm MLM pretrained on SDU21 Task 1 + 2
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... |
DeepPavlov/bert-base-multilingual-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1704.05426",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
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},
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"min_length": null,
"no_repeat_ngram_size": nul... | 140 | null | scibert_scivocab_uncased_tv submission for SDU21 Task 1 AI
| [
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0... |
DeepPavlov/marianmt-tatoeba-enru | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
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"no_repeat_ngram_size... | 1 | null | This model is further trained on top of scibert-base using masked language modeling loss (MLM). The corpus is roughly abstracts from 270,000 earth science-based publications.
The tokenizer used is AutoTokenizer, which is trained on the same corpus.
Stay tuned for further downstream task tests and updates to the model... | [
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DeltaHub/adapter_t5-3b_qnli | [
"pytorch",
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] | null | {
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"num_beams... | 3 | null | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name... | [
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Deniskin/emailer_medium_300 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
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],
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"no_repeat_ngram_size... | 14 | 2021-06-23T23:58:49Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: baked-goods
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.875
---
# baked-goods
Autogenerated by Hugg... | [
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Deniskin/essays_small_2000 | [] | null | {
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"num_beams... | 0 | 2021-06-29T19:47:25Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: baseball-stadium-foods
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9107142686843872
---
# baseball-st... | [
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Deniskin/gpt3_medium | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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"no_repeat_ngram_size... | 52 | null | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- ag_news
- pytorch
license: mit
datasets:
- ag_news
metrics:
- accuracy
---
# bert-base-uncased-ag-news
## Model description
`bert-base-uncased` finetuned ... | [
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Denny29/DialoGPT-medium-asunayuuki | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
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],
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"no_repeat_ngram_size... | 9 | null | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- accuracy
---
# bert-base-uncased-emotion
## Model description
`bert-base-uncased` fi... | [
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0.0442... |
DeskDown/MarianMixFT_en-id | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"no_repeat_ngram_size... | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: codecarbon-text-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this com... | [
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0.05612242594361305,
0.051749132573604584,
0.013902278617024422,
0.004853887017816305,
0.03... |
DeskDown/MarianMixFT_en-ja | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | null | ---
library_name: pytorch
tags:
- dcgan
---
# cryptopunks-gan
A DCGAN trained to generate novel Cryptopunks.
Check out the code by Teddy Koker [here](https://github.com/teddykoker/cryptopunks-gan).
## Generated Punks
Here are some punks generated by this model:
## Usage
You can tr... | [
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0.007060798350721598,
0.05482246354222298,
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0.015041546896100044,
0.008736235089600086,
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0.0553986057639122,
0.05741817131638527,
0.027316192165017128,
-0.022542845457792282,
0.035... |
DeskDown/MarianMix_en-ja-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 1 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: denver-nyc-paris
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8657407164573669
---
# denver-nyc-paris
... | [
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0.04007137194275856,
0.021508261561393738,
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0.04226784035563469,
0.015274411998689175,
0.021530861034989357,
0.0070664770901203156,
0.... |
DeskDown/MarianMix_en-zh-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: doggos-lol
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9166666865348816
---
# doggos-lol
Autogenera... | [
-0.013967648148536682,
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0.04561154544353485,
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0.04882470518350601,
0.00799243152141571,
-0.006861443631350994,
0.005025445483624935,
0... |
DeskDown/MarianMix_en-zh_to_vi-ms-hi-ja | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: donut-or-bagel
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
---
# donut-or-bagel
Autogenerated ... | [
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0.041038356721401215,
0.0312555730342865,
0.0012911416124552488,
0.007328121457248926,
... |
Devid/DialoGPT-small-Miku | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 10 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ex-for-evan
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9791666865348816
---
# ex-for-evan
Autogene... | [
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0.04773151874542236,
0.01077144593000412,
-0.006793950218707323,
0.007625430356711149,
0.... |
Devmapall/paraphrase-quora | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
- image-classification
- pytorch
datasets:
- food101
metrics:
- accuracy
model-index:
- name: food101_outputs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food-101
type: food101
args: de... | [
-0.0009046097984537482,
0.009920570999383926,
-0.0006324927089735866,
0.040437594056129456,
0.040218502283096313,
0.0009564807405695319,
-0.003746210830286145,
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-0.015021690167486668,
0.05090351030230522,
0.023676631972193718,
-0.018163660541176796,
0.00424717552959919... |
Devrim/prism-default | [
"license:mit"
] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- text-classification
library_name: generic
---
# Model Card for `guesslang`
| [
-0.02591497264802456,
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0.014881406910717487,
0.019340621307492256,
0.03135566785931587,
0.029525771737098694,
-0.012564662843942642,
0.009710949845612049,
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0.038477297872304916,
0.018526241183280945,
0.006449832580983639,
-0.012628084048628807,
0.0... |
DevsIA/imagenes | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- object-detection
- pytorch
---
# hot-dog
Ignore me...I'm broken. | [
-0.029212743043899536,
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0.03613929823040962,
0.011908520013093948,
-0.002540392568334937,
0.... |
DiegoBalam12/institute_classification | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- image-classification
- huggingpics
- generated_from_trainer
---
<!-- 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. -->
# huggingpics-package-demo-2
This... | [
-0.02347630448639393,
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-0.02339712344110012,
0.01827472448348999,
0.0... |
Digakive/Hsgshs | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- image-classification
- huggingpics
- generated_from_trainer
model-index:
- name: huggingpics-package-demo
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 r... | [
-0.028300542384386063,
-0.011128551326692104,
0.016624581068754196,
0.04289912059903145,
0.027847768738865852,
-0.004422459751367569,
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-0.012506079860031605,
0.04721853509545326,
0.010486934334039688,
-0.027850238606333733,
0.02083861641585827,
... |
DingleyMaillotUrgell/homer-bot | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
license: apache-2.0
tags:
- image-classification
- keras
library_name: keras
---
# Test | [
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0.04622974619269371,
-0.0017975771334022284,
0.009419862180948257,
0.005620214622467756,
0... |
DivyanshuSheth/T5-Seq2Seq-Final | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
"summarization": {
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name... | [
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0.04341849684715271,
0.017239978536963463,
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0.02101377211511135,
... |
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