modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
haji2438/test_sin | [
"LABEL_0"
] | Entry not found | 15 |
haji2438/test_sin_bertweet_fine_tuned | [
"LABEL_0"
] | Entry not found | 15 |
hanseokhyeon/bert-badword | null | Entry not found | 15 |
harish/EN-AStitchTask1A-BERTBaseCased-FalseTrue-0-3-BEST | null | Entry not found | 15 |
harish/EN-AStitchTask1A-BERTBaseCased-TrueFalse-0-4-BEST | null | Entry not found | 15 |
harish/EN-AStitchTask1A-RoBERTaBase-FalseTrue-0-0-BEST | null | Entry not found | 15 |
harish/EN-AStitchTask1A-XLNet-FalseTrue-0-FewShot-0-BEST | null | Entry not found | 15 |
harish/PT-FalseTrue-0_2_BEST | null | Entry not found | 15 |
harish/PT-UP-mBERT-FalseTrue-0_1_BEST | null | Entry not found | 15 |
harish/PT-UP-mBERT-TrueTrue-0_2_BEST | null | Entry not found | 15 |
harish/PT-UP-xlmR-ContextIncluded_IdiomExcluded-FewShot-4_BEST | null | Entry not found | 15 |
harish/PT-UP-xlmR-FalseFalse-OneShot-0_BEST | null | Entry not found | 15 |
harish/PT-UP-xlmR-FalseTrue-0_0_BEST | null | Entry not found | 15 |
harish/PT-mbert-train-from-test-and-dev-SHORT-FalseTrue-0_2_BEST | null | Entry not found | 15 |
hassanzadeh/test_model | null | Entry not found | 15 |
hchc/distilbert-base-uncased-finetuned-cola | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
met... | 1,999 |
hd10/semeval2020_task11_tc | [
"Appeal_to_Authority",
"Appeal_to_fear-prejudice",
"Bandwagon,Reductio_ad_hitlerum",
"Black-and-White_Fallacy",
"Causal_Oversimplification",
"Doubt",
"Exaggeration,Minimisation",
"Flag-Waving",
"Loaded_Language",
"Name_Calling,Labeling",
"Repetition",
"Slogans",
"Thought-terminating_Cliches"... | Technique Classification for https://propaganda.qcri.org/ptc/index.html | 71 |
howey/bert-base-uncased-cola | null | Entry not found | 15 |
howey/bert-base-uncased-qqp | null | Entry not found | 15 |
howey/electra-base-mrpc | null | Entry not found | 15 |
howey/electra-base-qqp | null | Entry not found | 15 |
howey/electra-large-rte | null | Entry not found | 15 |
howey/electra-large-sst2 | null | Entry not found | 15 |
howey/electra-small-qqp | null | Entry not found | 15 |
hugo/secret-project-all-w1 | [
"LABEL_0"
] | Entry not found | 15 |
hugo/secret-project-ms-2 | [
"LABEL_0"
] | Entry not found | 15 |
iAmmarTahir/domain-adapted-negation | null | Entry not found | 15 |
ianporada/roberta_base_plausibility | null | Entry not found | 15 |
ibraheemmoosa/xlmindic-base-uniscript-soham | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
language:
- as
- bn
- gu
- hi
- mr
- ne
- or
- pa
- si
- sa
- bpy
- mai
- bh
- gom
license: apache-2.0
datasets:
- oscar
tags:
- multilingual
- albert
- xlmindic
- nlp
- indoaryan
- indicnlp
- iso15919
- transliteration
- text-classification
widget:
- text : 'cīnēra madhyāñcalē āraō ēkaṭi śaharēra bāsindārā ābāra g... | 11,143 |
institutogloria/hate-pt-tweet-binary | null | Entry not found | 15 |
jambo/marker-associations-binary-base | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- marker-associations-binary-base
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: marker-associations-binary-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: marker-associations... | 2,759 |
jery33/distilbert-base-uncased-finetuned-cola | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
met... | 2,000 |
jnz/electra-ka-anti-gov | null | Entry not found | 15 |
jnz/electra-ka-discrediting | null | Entry not found | 15 |
jnz/electra-ka-fake-news-tagging | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
johnpaulbin/cvai-bert-asag | null | Entry not found | 15 |
johnpaulbin/cvai-deberta3-asag | null | Entry not found | 15 |
jp1924/KoBERT_NSMC_TEST | null | Entry not found | 15 |
junzai/bert_finetuning_test | null | Entry not found | 15 |
junzai/demo | [
"equivalent",
"not_equivalent"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_finetuning_test
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:... | 1,521 |
k-partha/decision_style_bert_bio | [
"Prospecting",
"Judging"
] | Rates Twitter biographies on decision-making preference: Judging (focused, goal-oriented decision strategy) or Prospecting (open-ended, explorative strategy). Roughly corresponds to [conscientiousness](https://en.wikipedia.org/wiki/Conscientiousness)
Go to your Twitter profile, copy your biography and paste in the inf... | 685 |
kamivao/autonlp-cola_gram-208681 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- kamivao/autonlp-data-cola_gram
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 208681
## Validation Metrics
- Loss: 0.37569838762283325
- Accuracy: 0.8365019011406845
- Precision: 0.8398058252427184
- ... | 1,060 |
kangnichaluo/mnli-2 | [
"LABEL_0",
"LABEL_1"
] | learning rate: 3e-5
training epochs: 3
batch size: 64
seed: 0
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | 208 |
kangnichaluo/mnli-3 | [
"LABEL_0",
"LABEL_1"
] | learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 13
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) | 211 |
kdo6301/bert-base-uncased-finetuned-cola-2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola-2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics... | 1,980 |
kdo6301/bert-base-uncased-finetuned-cola | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
... | 1,976 |
kevinzyz/chinese-bert-wwm-ext-finetuned-cola-e3 | null | Entry not found | 15 |
kevinzyz/chinese-bert-wwm-ext-finetuned-cola | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: chinese-bert-wwm-ext-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, ... | 1,508 |
khizon/bert-unreliable-news-eng | null | # Unreliable News Classifier (English)
Trained, validate, and tested using a subset of the NELA-GT-2018 dataset. The dataset is split such that there was no overlap in of news sources between the three sets.
This model used the pre-trained weights of `bert-base-cased` as starting point and was able to achieve 84% accur... | 415 |
kloon99/KML_Software_License_v1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8"
] | {'C0': 'audit_rights',
'C1': 'licensee_indemnity',
'C2': 'licensor_indemnity',
'C3': 'license_grant',
'C4': 'eula_others',
'C5': 'licensee_infringement_indemnity',
'C6': 'licensor_exemption_liability',
'C7': 'licensor_limit_liabilty',
'C8': 'software_warranty'} | 269 |
korca/bae-roberta-base-mrpc-5 | null | Entry not found | 15 |
korca/bae-roberta-base-sst2 | null | Entry not found | 15 |
korca/textfooler-roberta-base-mrpc-5 | null | Entry not found | 15 |
korca/textfooler-roberta-base-mrpc | null | Entry not found | 15 |
korca/textfooler-roberta-base-rte-5 | null | Entry not found | 15 |
korca/textfooler-roberta-base-rte | null | Entry not found | 15 |
krlng/sts-GBERT-cross-encoder | [
"LABEL_0"
] | Entry not found | 15 |
ks15/distilbert-base-uncased-finetuned-cola | null | Entry not found | 15 |
ksmcg/name | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model_index:
- name: name
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
---
<!-- This model card has been generated automatically according to t... | 1,155 |
laurauzcategui/xlm-roberta-base-finetuned-marc-en | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then re... | 1,414 |
lewtun/xlm-roberta-base-finetuned-marc-19964-samples | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | Entry not found | 15 |
liamliang/demographics_gender | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
liamliang/demographicx_race_census | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | Entry not found | 15 |
lidiia/autonlp-trans_class_arg-32957902 | [
"0.0",
"1.0"
] | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- lidiia/autonlp-data-trans_class_arg
co2_eq_emissions: 0.9756221672668951
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 32957902
- CO2 Emissions (in grams): 0.9756221672668951
## Validation Metrics
-... | 1,172 |
lucianpopa/autonlp-SST1-529214890 | [
"0",
"1",
"2",
"3",
"4"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- lucianpopa/autonlp-data-SST1
co2_eq_emissions: 49.618294309910624
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 529214890
- CO2 Emissions (in grams): 49.618294309910624
## Validation Metrics
- L... | 1,362 |
lucianpopa/autonlp-SST2-551215591 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- lucianpopa/autonlp-data-SST2
co2_eq_emissions: 8.883161797287569
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 551215591
- CO2 Emissions (in grams): 8.883161797287569
## Validation Metrics
- Loss: 0.... | 1,145 |
lucianpopa/autonlp-TREC-classification-522314623 | [
"0",
"1",
"2",
"3",
"4",
"5"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- lucianpopa/autonlp-data-TREC-classification
co2_eq_emissions: 15.186006626915715
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 522314623
- CO2 Emissions (in grams): 15.186006626915715
## Validati... | 1,423 |
lumalik/vent-roberta-emotion | [
"Affection",
"Anger",
"Fear",
"Happiness",
"Sadness"
] | # Vent-roBERTa-emotion
This is a roBERTa pretrained on twitter and then trained for self-labeled emotion classification on the Vent dataset (see https://arxiv.org/abs/1901.04856). The Vent dataset contains 33 million posts annotated with one emotion by the user themselves. <br/>
The model was trained to recognize ... | 1,642 |
m3hrdadfi/albert-fa-base-v2-sentiment-deepsentipers-multi | [
"angry",
"delighted",
"furious",
"happy",
"neutral"
] | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... | 3,184 |
m3hrdadfi/bert-fa-base-uncased-farstail | [
"contradiction",
"entailment",
"neutral"
] | ---
language: fa
license: apache-2.0
---
# FarsTail + ParsBERT
Please follow the [FarsTail](https://github.com/dml-qom/FarsTail) repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check out the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transfo... | 638 |
m3hrdadfi/bert-fa-base-uncased-wikinli | [
"contradiction",
"entailment"
] | ---
language: fa
license: apache-2.0
---
# ParsBERT + Sentence Transformers
Please follow the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transformers) repo for the latest information about previous and current models.
```bibtex
@misc{SentenceTransformerWiki,
author = {Mehrdad Farahani},
title =... | 508 |
marcolatella/Hps_seed1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: Hps_seed1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
... | 1,817 |
marcolatella/hate_trained_31415 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: hate_trained_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1... | 1,774 |
marcolatella/prova_Classi2 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: prova_Classi2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1... | 1,585 |
maximedb/polyfaq_cross | [
"LABEL_0"
] | Entry not found | 15 |
meghanabhange/Hinglish-Bert-Class | [
"NEGATIVE",
"NEUTRAL",
"POSITIVE"
] | Entry not found | 15 |
mfuntowicz/bert-base-cased-finetuned-sst2 | [
"NEGATIVE",
"POSITIVE"
] | Entry not found | 15 |
minu/koelectra-nsmc-finetuned | [
"0",
"1"
] | Entry not found | 15 |
mlkorra/obgv-gender-bert-hi-en | [
"negative",
"neutral",
"positive"
] | Entry not found | 15 |
mmcquade11/autonlp-imdb-test-21134453 | [
"negative",
"positive"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- mmcquade11/autonlp-data-imdb-test
co2_eq_emissions: 38.102565360610484
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21134453
- CO2 Emissions (in grams): 38.102565360610484
## Validation Metrics
- Lo... | 1,130 |
mofawzy/Bert-hard-balanced | null | ---
language:
- ar
datasets:
- HARD
tags:
- HARD
widget:
- text: "جيد. المكان جميل وهاديء. كل شي جيد ونظيف"
- text: "استغرب تقييم الفندق كخمس نجوم”. لا شي. يستحق"
---
# BERT-ASTD Balanced
Arabic version bert model fine tuned on Hotel Arabic Reviews dataset from booking.com (HARD) dataset balanced version to ide... | 1,249 |
mofawzy/bert-labr-unbalanced | null | ---
language:
- ar
datasets:
- labr
tags:
- labr
widget:
- text: "كتاب ممل جدا تضييع وقت"
- text: "اسلوب ممتع وشيق في الكتاب استمعت بالاحداث"
---
# BERT-LABR unbalanced
Arabic version bert model fine tuned on LABR dataset
## Data
The model were fine-tuned on ~63000 book reviews in arabic using bert large arabic... | 1,062 |
mohsenfayyaz/bert-base-uncased-avg-pooling | null | Entry not found | 15 |
mohsenfayyaz/bert-base-uncased-offenseval2019-downsample | null | Entry not found | 15 |
mohsenfayyaz/bert-base-uncased-offenseval2019-unbalanced | null | Entry not found | 15 |
mohsenfayyaz/xlnet-base-cased-offenseval2019-downsample | null | Entry not found | 15 |
mollypak/cardiff-xlm-roberta-base | [
"Negative",
"Neutral",
"Positive"
] | Entry not found | 15 |
mollypak/cardiff | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
mollypak/distilbert-base-uncased-finetuned-cola | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
met... | 1,993 |
moussaKam/frugalscore_small_bert-base_mover-score | [
"LABEL_0"
] | # 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 :
| ... | 2,592 |
moussaKam/frugalscore_small_deberta_bert-score | [
"LABEL_0"
] | # 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 :
| ... | 2,592 |
moussaKam/frugalscore_small_roberta_bert-score | [
"LABEL_0"
] | # 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 :
| ... | 2,592 |
moussaKam/frugalscore_tiny_roberta_bert-score | [
"LABEL_0"
] | # 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 :
| ... | 2,592 |
mrm8488/electricidad-small-finetuned-xnli-es | [
"entailment",
"neutral",
"contradiction"
] | ---
language: es
tags:
- spanish
- nli
- xnli
datasets:
- xnli
license: mit
widget:
- text: "Por favor, no piensen en darnos dinero. Por favor, considere piadosamente cuanto puede dar."
---
# electricidad-small-finetuned-xnli-es
| 242 |
muhtasham/autonlp-Doctor_DE-24595545 | [
"target"
] | ---
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
- ... | 1,167 |
mujeensung/bert-base-cased_mnli_bc | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
nielsr/tapex-large-finetuned-tabfact | [
"LABEL_0",
"LABEL_1"
] | ---
language: en
tags:
- tapex
license: apache-2.0
datasets:
- tab_fact
inference: false
---
TAPEX-large model fine-tuned on WTQ. This model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizh... | 1,589 |
norirahul/SMSTransformer | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | Entry not found | 15 |
notentered/roberta-large-finetuned-cola | null | Entry not found | 15 |
nurkayevaa/autonlp-bert-covid-407910458 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- nurkayevaa/autonlp-data-bert-covid
co2_eq_emissions: 9.72797586719897
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 407910458
- CO2 Emissions (in grams): 9.72797586719897
## Validation Metrics
- Loss... | 1,168 |
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