modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
boychaboy/MNLI_bert-large-uncased | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
boychaboy/SNLI_bert-base-cased | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
codesj/empathic-concern | [
"LABEL_0"
] | Entry not found | 15 |
digit82/dialog-sbert-base | null | Entry not found | 15 |
gurkan08/bert-turkish-text-classification | [
"ekonomi",
"spor",
"saglik",
"kultur_sanat",
"bilim_teknoloji",
"egitim"
] | ---
language: tr
---
# Turkish News Text Classification
Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)
# Dataset
Dataset consists of 11 classes were obtained from https://www.trthaber.com/. The model was created using the most distinctive 6 classe... | 1,796 |
mrm8488/camembert-base-finetuned-pawsx-fr | null | ---
language: fr
datasets:
- xtreme
tags:
- nli
widget:
- text: "La première série a été mieux reçue par la critique que la seconde. La seconde série a été bien accueillie par la critique, mieux que la première."
---
# Camembert-base fine-tuned on PAWS-X-fr for Paraphrase Identification (NLI)
| 295 |
pmthangk09/bert-base-uncased-glue-cola | null | Entry not found | 15 |
razent/SciFive-large-Pubmed | null | ---
language:
- en
tags:
- token-classification
- text-classification
- question-answering
- text2text-generation
- text-generation
datasets:
- pubmed
---
# SciFive Pubmed Large
## Introduction
Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598)
Autho... | 1,379 |
tals/albert-xlarge-vitaminc-fever | [
"NOT ENOUGH INFO",
"REFUTES",
"SUPPORTS"
] | ---
language: python
datasets:
- fever
- glue
- tals/vitaminc
---
# Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this m... | 2,357 |
Farshid/distilbert-base-uncased-finetuned-financial_phrasebank | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-financial_phrasebank
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
... | 8,865 |
HiTZ/A2T_RoBERTa_SMFA_ACE-arg_WikiEvents-arg | [
"contradiction",
"entailment",
"neutral"
] | ---
pipeline_tag: zero-shot-classification
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
---
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatib... | 3,612 |
CEBaB/roberta-base.CEBaB.sa.3-class.exclusive.seed_42 | [
"0",
"1",
"2"
] | Entry not found | 15 |
CEBaB/roberta-base.CEBaB.sa.2-class.exclusive.seed_66 | [
"0",
"1"
] | Entry not found | 15 |
CEBaB/roberta-base.CEBaB.sa.3-class.exclusive.seed_66 | [
"0",
"1",
"2"
] | Entry not found | 15 |
CEBaB/roberta-base.CEBaB.sa.2-class.exclusive.seed_77 | [
"0",
"1"
] | Entry not found | 15 |
CEBaB/roberta-base.CEBaB.sa.3-class.exclusive.seed_77 | [
"0",
"1",
"2"
] | Entry not found | 15 |
CEBaB/roberta-base.CEBaB.sa.5-class.exclusive.seed_77 | [
"0",
"1",
"2",
"3",
"4"
] | Entry not found | 15 |
CEBaB/roberta-base.CEBaB.sa.2-class.exclusive.seed_88 | [
"0",
"1"
] | Entry not found | 15 |
CEBaB/roberta-base.CEBaB.sa.3-class.exclusive.seed_88 | [
"0",
"1",
"2"
] | Entry not found | 15 |
CEBaB/roberta-base.CEBaB.sa.3-class.exclusive.seed_99 | [
"0",
"1",
"2"
] | Entry not found | 15 |
CogComp/ZeroShotWiki | null | ---
license: apache-2.0
---
# Model description
A BertForSequenceClassification model that is finetuned on Wikipedia for zero-shot text classification. For details, see our NAACL'22 paper.
# Usage
Concatenate the text sentence with each of the candidate labels as input to the model. The model will output a score ... | 1,141 |
G-WOO/model_150mil-CodeBERTa-small-v1 | null | Entry not found | 15 |
Shikenrua/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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... | 1,083 |
annahaz/xlm-roberta-base-finetuned-misogyny-en-it-hi-beng | [
"0",
"1"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: xlm-roberta-base-finetuned-misogyny-en-it-hi-beng
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofr... | 2,506 |
SushantGautam/LogClassification | [
"0",
"1"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: LogClassification
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. -->
# LogClassification... | 1,032 |
Cheatham/xlm-roberta-large-finetuned4 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Maha/OGBV-gender-twtrobertabase-en-trac1 | null | Entry not found | 15 |
Mustang/BERT_responsible_AI | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: eupl-1.1
---
## BERT model van het project Explainable AI | 73 |
NDugar/v3large-1epoch | [
"contradiction",
"entailment",
"neutral"
] | ---
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... | 4,788 |
anirudh21/xlnet-base-cased-finetuned-rte | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: xlnet-base-cased-finetuned-rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: rte
metrics:
- name: Accuracy
... | 1,830 |
astarostap/distilbert-cased-antisemitic-tweets | null | ---
license: mit
widget:
- text: "Jews run the world."
---
This model takes a tweet with the word "jew" in it, and determines if it's antisemitic.
*Training data:*
This model was trained on 4k tweets, where ~50% were labeled as antisemitic.
I labeled them myself based on personal experience and knowledge about co... | 986 |
boychaboy/MNLI_bert-base-cased | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
mervenoyan/PubMedBERT-QNLI | null |
# PubMedBERT Abstract + Full Text Fine-Tuned on QNLI Task
Use case: You can use it to search through a document for a given question, to see if your question is answered in that document.
LABEL0 is "not entailment" meaning your question is not answered by the context and LABEL1 is "entailment" meaning your question ... | 888 |
prajjwal1/bert-small-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](... | 994 |
yoelvis/topical-segmentation-sensitive | null | Entry not found | 15 |
ikram54/autotrain-harassement-675420038 | [
"Indirect Harassment",
"Not Hate",
"Not Sexist",
"Physical Harassment",
"Sexual Harassment"
] | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ikram54/autotrain-data-harassement
co2_eq_emissions: 2.6332836871905054
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 675420038
- CO2 Emissions (in grams): 2.6332836871905054
## Validation... | 1,396 |
Hieu/nft_label | null | Entry not found | 15 |
MartinoMensio/racism-models-raw-label-epoch-3 | null | ---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been t... | 4,252 |
Souvikcmsa/Roberta_Sentiment_Analysis | [
"0",
"4"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Souvikcmsa/autotrain-data-sentimentAnalysis_By_Souvik
co2_eq_emissions: 4.453029772491864
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 762623422
- CO2 Emissions (in grams): 4.45302977249186... | 1,471 |
Intel/bart-large-mrpc-int8-dynamic | [
"0",
"1"
] | ---
language:
- en
license: apache-2.0
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- PostTrainingDynamic
datasets:
- glue
metrics:
- f1
model-index:
- name: bart-large-mrpc-int8-static
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRP... | 1,084 |
Hate-speech-CNERG/bengali-abusive-MuRIL | null | ---
language: [bn]
license: afl-3.0
---
This model is used detecting **abusive speech** in **Bengali**. It is finetuned on MuRIL model using bengali abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-... | 963 |
dbb/gbert-large-jobad-classification-34 | [
"administration/sekretariat",
"arzt",
"baugewerbe/-ingenieur",
"beschaffung/supply chain",
"bildung/soziales",
"chem. pharm. ausbildung",
"chem. pharm. beruf",
"controlling/finanzen",
"gastro. touri. ausbildung",
"gastro./tourismus",
"hausverw./-bewirt.",
"hr/recruiting",
"indust. konstruk./... | ---
language: de
tags:
- bert
- recruiting
---
# G(erman)BERT Large Fine-Tuned for Job Ad Classification
| 228 |
TehranNLP-org/electra-base-sst2 | [
"negative",
"positive"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SEED0042
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: SST2
type: ''
args: sst2
metrics:
- name: Accuracy
type: accurac... | 1,766 |
CEBaB/roberta-base.CEBaB.sa.2-class.exclusive.seed_99 | [
"0",
"1"
] | Entry not found | 15 |
danlupu/sentiment-analysis | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: sentiment-analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Acc... | 1,479 |
tinkoff-ai/response-quality-classifier-tiny | [
"relevance",
"specificity"
] | ---
license: mit
widget:
- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]супер, вот только проснулся, у тебя как?"
example_title: "Dialog example 1"
- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]норм"
example_title: "Dialog example 2"
- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESP... | 2,569 |
anchit48/fine-tuned-sentiment-analysis-customer-feedback | [
"NEGATIVE",
"POSITIVE"
] | Entry not found | 15 |
obokkkk/kc-bert_finetuned_unsmile | [
"clean",
"기타 혐오",
"남성",
"성소수자",
"악플/욕설",
"여성/가족",
"연령",
"인종/국적",
"종교",
"지역"
] | ---
tags:
- generated_from_trainer
model-index:
- name: kc-bert_finetuned_unsmile
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. -->
# kc-bert_finetuned_unsmile
Th... | 1,533 |
waboucay/camembert-large-finetuned-repnum_wl-rua_wl_3_classes | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 77.3 | 77.3 |
| test ... | 367 |
bousejin/distilbert-base-uncased-finetuned-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default... | 1,803 |
itzo/bert-base-uncased-fine-tuned-on-clinc_oos-dataset | [
"accept_reservations",
"account_blocked",
"alarm",
"application_status",
"apr",
"are_you_a_bot",
"balance",
"bill_balance",
"bill_due",
"book_flight",
"book_hotel",
"calculator",
"calendar",
"calendar_update",
"calories",
"cancel",
"cancel_reservation",
"car_rental",
"card_declin... | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
model-index:
- name: bert-base-uncased-fine-tuned-on-clinc_oos-dataset
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete ... | 1,789 |
Team-PIXEL/pixel-base-finetuned-mnli | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: pixel-base-finetuned-mnli
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. -->
... | 1,135 |
Tomas23/twitter-roberta-base-mar2022-finetuned-emotion | [
"anger",
"joy",
"optimism",
"sadness"
] | ---
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: twitter-roberta-base-mar2022-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
m... | 1,852 |
CenIA/bert-base-spanish-wwm-uncased-finetuned-pawsx | null | Entry not found | 15 |
CouchCat/ma_sa_v7_distil | [
"negative",
"neutral",
"positive"
] | ---
language: en
license: mit
tags:
- sentiment-analysis
widget:
- text: "I am disappointed in the terrible quality of my dress"
---
### Description
A Sentiment Analysis model trained on customer feedback data using DistilBert.
Possible sentiments are:
* negative
* neutral
* positive
### Usage
```
from transformers ... | 547 |
ItcastAI/bert_finetunning_test | null | Entry not found | 15 |
Vaibhavbrkn/grammer_classiffication | null | Entry not found | 15 |
airKlizz/xlm-roberta-base-germeval21-toxic-with-task-specific-pretraining-and-data-augmentation | null | Entry not found | 15 |
arjuntheprogrammer/distilbert-base-multilingual-cased-sentiment-2 | [
"negative",
"neutral",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-multilingual-cased-sentiment-2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
ty... | 1,777 |
blackbird/bert-base-uncased-MNLI-v1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | BERT based model finetuned on MNLI with our custom training routine.
Yields 60% accuraqcy on adversarial HANS dataset. | 118 |
mnaylor/bioclinical-bert-finetuned-mtsamples | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | # BioClinical BERT Fine-tuned on MTSamples
This model is simply [Alsentzer's Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp). | 277 |
sismetanin/rubert_conversational-ru-sentiment-rureviews | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## RuBERT-Conversational-ru-sentiment-RuReviews
RuBERT-Conversational-ru-sentiment-RuReviews is a [RuBERT-Conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model fine-tuned on [RuReviews dataset](https://github.com/sismetan... | 6,400 |
textattack/albert-base-v2-rotten-tomatoes | null | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this w... | 630 |
tr3cks/3LabelsSentimentAnalysisSpanish | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
ctu-aic/xlm-roberta-large-squad2-ctkfacts | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: cc-by-sa-3.0
---
| 33 |
davidmasip/racism | null | ---
license: cc
language: es
widget:
- text: "Me cae muy bien."
example_title: "Non-racist example"
- text: "Unos menas agreden a una mujer."
example_title: "Racist example"
---
Model to predict whether a given text is racist or not:
* `LABEL_0` output indicates non-racist text
* `LABEL_1` output i... | 856 |
hackathon-pln-es/jurisbert-class-tratados-internacionales-sistema-universal | [
"Convención Internacional sobre la Protección de los Derechos de todos los Trabajadores Migratorios y de sus Familias",
"Convención de los Derechos del Niño",
"Convención sobre la Eliminación de todas las formas de Discriminación contra la Mujer",
"Pacto Internacional de Derechos Civiles y Políticos",
"Conv... | ---
license: cc-by-nc-4.0
language: es
widget:
- text: "A los 4 Civiles de Rosarito se les acusó de cometer varios delitos federales en flagrancia, aunque se ha comprobado que no fueron detenidos en el lugar en el que los militares señalaron en su parte informativo. Las cuatro personas refieren que el 17 de junio de 20... | 3,531 |
dhlee347/distilbert-imdb | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
... | 1,562 |
Hate-speech-CNERG/hindi-abusive-MuRIL | null | ---
language: [hi]
license: afl-3.0
---
This model is used detecting **abusive speech** in **Devanagari Hindi**. It is finetuned on MuRIL model using Hindi abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LAB... | 970 |
arize-ai/distilbert_reviews_with_language_drift | [
"NEGATIVE",
"NEUTRAL",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ecommerce_reviews_with_language_drift
metrics:
- accuracy
- f1
model-index:
- name: distilbert_reviews_with_language_drift
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ecommerce_reviews_wi... | 2,341 |
Abderrahim2/bert-finetuned-Location | [
"Australia",
"United Kingdom",
"United States"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-finetuned-Location
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. ... | 2,033 |
Yarn/distilbert-base-uncased-mnli-finetuned-mnli | [
"CONTRADICTION",
"ENTAILMENT",
"NEUTRAL"
] | Entry not found | 15 |
nikitakotsehub/AirlineDistilBERT | [
"NEGATIVE",
"POSITIVE"
] | Entry not found | 15 |
waboucay/camembert-large-finetuned-repnum_wl-rua_wl | [
"contradiction",
"non-contradiction"
] | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 84.5 | 84.3 |
| test ... | 368 |
RogerKam/roberta_RCADE_fine_tuned_sentiment_covid_news | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_RCADE_fine_tuned_sentiment_covid_news
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 remo... | 1,196 |
KhawajaAbaid/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
Emirhan/51k-finetuned-bert-model | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
InfoCoV/Senti-Cro-CoV-cseBERT | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Maelstrom77/vibert | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Entry not found | 15 |
Wiirin/DistilBERT-finetuned-PubMed-FoodCancer | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | Entry not found | 15 |
abhishek/autonlp-imdb_sentiment_classification-31154 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 31154
## Validation Metrics
- Loss: 0.19292379915714264
- Accuracy: 0.9395
- Precision: 0.9569557080474111
- Recall: 0.9204
- AUC: 0.9851040399999998
- F1: 0.9383219... | 1,052 |
aicast/bert_finetuning_test | null | Entry not found | 15 |
cemdenizsel/51k-finetuned-bert-model | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
daekeun-ml/koelectra-small-v3-nsmc | [
"0",
"1"
] | ---
language:
- ko
tags:
- classification
license: mit
datasets:
- nsmc
metrics:
- accuracy
- f1
- precision
- recall- accuracy
---
# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the int... | 4,710 |
gchhablani/fnet-base-finetuned-sst2 | [
"negative",
"positive"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
model-index:
- name: fnet-base-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
... | 2,632 |
gurkan08/turkish-product-comment-sentiment-classification | [
"positive",
"negative"
] | Entry not found | 15 |
howey/roberta-large-cola | null | Entry not found | 15 |
jpcorb20/toxic-detector-distilroberta | [
"toxic",
"severe_toxic",
"obscene",
"threat",
"insult",
"identity_hate"
] | # Distilroberta for toxic comment detection
See my GitHub repo [toxic-comment-server](https://github.com/jpcorb20/toxic-comment-server)
The model was trained from [DistilRoberta](https://huggingface.co/distilroberta-base) on [Kaggle Toxic Comments](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challeng... | 678 |
peril10/Pypinion | null | Entry not found | 15 |
pertschuk/albert-base-quora-classifier | null | Entry not found | 15 |
pertschuk/albert-large-intent-v2 | null | Entry not found | 15 |
razent/SciFive-base-PMC | null | ---
language:
- en
tags:
- token-classification
- text-classification
- question-answering
- text2text-generation
- text-generation
datasets:
- pmc/open_access
---
# SciFive PMC Base
## Introduction
Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598)
Au... | 1,374 |
textattack/facebook-bart-large-CoLA | null | Entry not found | 15 |
textattack/facebook-bart-large-MNLI | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Anthos23/FS-distilroberta-fine-tuned | [
"negative",
"neutral",
"positive"
] | Entry not found | 15 |
FinScience/FS-distilroberta-fine-tuned | [
"negative",
"neutral",
"positive"
] | ---
language:
- en
---
# FS-distilroberta-fine-tuned
The model was obtained by fine-tuning "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis" model for sentiment analysis on financial news gathered by FinScience software. It predicts the sentiment of news with one label ("negative", "neutral" or "po... | 1,346 |
dapang/distilbert-base-uncased-finetuned-toxicity | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-toxicity
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, ... | 1,735 |
Yah216/Arabic_poem_meter_3 | [
"البسيط",
"الخفيف",
"الدوبيت",
"الرجز",
"الرمل",
"السريع",
"السلسلة",
"الطويل",
"الكامل",
"المتدارك",
"المتقارب",
"المجتث",
"المديد",
"المضارع",
"المقتضب",
"المنسرح",
"المواليا",
"الهزج",
"الوافر",
"شعر التفعيلة",
"شعر حر",
"عامي",
"موشح"
] | ---
---
language: ar
widget:
- text: "قفا نبك من ذِكرى حبيب ومنزلِ بسِقطِ اللِّوى بينَ الدَّخول فحَوْملِ"
- text: "سَلو قَلبي غَداةَ سَلا وَثابا لَعَلَّ عَلى الجَمالِ لَهُ عِتابا"
co2_eq_emissions: 404.66986451902227
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- CO2 Emissions (in... | 1,876 |
speeqo/distilbert-base-uncased-finetuned-sst-2-english | [
"NEGATIVE",
"POSITIVE"
] | ---
language: en
license: apache-2.0
datasets:
- sst-2
---
# DistilBERT base uncased finetuned SST-2
This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned on SST-2.
This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-... | 1,900 |
titi7242229/roberta-base-bne-finetuned_personality_multi_2 | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned_personality_multi_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, t... | 2,579 |
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