modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
FinanceInc/finbert_fls | [
"Not FLS",
"Non-specific FLS",
"Specific FLS"
] | ---
language: "en"
tags:
- financial-text-analysis
- forward-looking-statement
widget:
- text: "We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs. "
---
Forward-looking statements (FLS) inform investors of managers’ beliefs and opinions about firm's future event... | 1,472 |
Fujitsu/AugCode | null | ---
inference: false
license: mit
widget:
language:
- en
metrics:
- mrr
datasets:
- augmented_codesearchnet
---
# 🔥 Augmented Code Model 🔥
This is Augmented Code Model which is a fined-tune model of [CodeBERT](https://huggingface.co/microsoft/codebert-base) for processing of similarity between given docstring and co... | 1,172 |
GD/cq-bert-model-repo | null | Entry not found | 15 |
Hate-speech-CNERG/dehatebert-mono-italian | [
"NON_HATE",
"HATE"
] | ---
language: it
license: apache-2.0
---
This model is used detecting **hatespeech** in **Italian language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rate... | 1,058 |
Mathking/bert-base-german-cased-gnad10 | [
"Web",
"Panorama",
"International",
"Wirtschaft",
"Sport",
"Inland",
"Etat",
"Wissenschaft",
"Kultur"
] | ---
language:
- de
datasets:
- gnad10
tags:
- text-classification
- german-news-classification
metrics:
- accuracy
- precision
- recall
- f1
---
# German BERT for News Classification
This a bert-base-german-cased model finetuned for text classification on german news articles
## Training data
Used the training set f... | 377 |
NDugar/3epoch-3large | [
"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 |
Rifky/IndoBERT-FakeNews | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- null
model-index:
- name: IndoBERT-FakeNews
results:
- task:
name: Text Classification
type: text-classification
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should proba... | 1,561 |
adresgezgini/Finetuned-SentiBERtr-Pos-Neg-Reviews | null | Entry not found | 15 |
blanchefort/rubert-base-cased-sentiment-mokoron | null | ---
language:
- ru
tags:
- sentiment
- text-classification
datasets:
- RuTweetCorp
---
# RuBERT for Sentiment Analysis of Tweets
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuTweetCorp](https://study.mokoron.com/).
## L... | 1,359 |
boychaboy/MNLI_distilbert-base-uncased | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
cardiffnlp/twitter-roberta-base-stance-feminist | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | 0 | |
pablouribe/beto-copus-supercategories-overfitted | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | Entry not found | 15 |
pparasurama/raceBERT-ethnicity | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | Entry not found | 15 |
prajjwal1/roberta-large-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | If you use the model, please consider citing the paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
... | 869 |
clips/republic | [
"neg",
"neu",
"pos"
] | ---
pipeline_tag: text-classification
language:
- nl
tags:
- text classification
- sentiment analysis
- domain adaptation
widget:
- text: "De NMBS heeft recent de airconditioning in alle treinen vernieuwd."
example_title: "POS-NMBS"
- text: "De wegenwerken langs de E34 blijven al maanden aanhouden."
exam... | 2,766 |
Smith123/tiny-bert-sst2-distilled_L4_H_512 | [
"negative",
"positive"
] | Entry not found | 15 |
anahitapld/bert-base-cased-dbd | null | ---
license: apache-2.0
---
| 28 |
JHart96/finetuning-sentiment-model-3000-samples | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
met... | 1,505 |
shubhamitra/TinyBERT_General_4L_312D-finetuned-toxic-classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
tags:
- generated_from_trainer
model-index:
- name: TinyBERT_General_4L_312D-finetuned-toxic-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 comment. -->
#... | 1,504 |
Danitg95/autotrain-kaggle-effective-arguments-1086739296 | [
"Adequate",
"Effective",
"Ineffective"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Danitg95/autotrain-data-kaggle-effective-arguments
co2_eq_emissions: 5.2497206864306065
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1086739296
- CO2 Emissions (in grams): 5.249720686430606... | 1,463 |
scales-okn/entity-resolution | null | Entry not found | 15 |
XSY/albert-base-v2-imdb-calssification | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: albert-base-v2-imdb-calssification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
... | 1,658 |
abdelkader/distilbert-base-uncased-finetuned-clinc | [
"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
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
... | 1,890 |
elozano/tweet_offensive_eval | [
"Non-Offensive",
"Offensive"
] | ---
license: mit
datasets:
- tweet_eval
language: en
widget:
- text: "You're a complete idiot!"
example_title: "Offensive"
- text: "I am tired of studying for tomorrow's exam"
example_title: "Non-Offensive"
---
| 226 |
sgugger/finetuned-bert-mrpc | [
"equivalent",
"not equivalent"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model_index:
- name: finetuned-bert-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metric:
name: F1
... | 1,749 |
yoshitomo-matsubara/bert-base-uncased-rte | null | ---
language: en
tags:
- bert
- rte
- glue
- torchdistill
license: apache-2.0
datasets:
- rte
metrics:
- accuracy
---
`bert-base-uncased` fine-tuned on RTE dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-mats... | 822 |
Intel/bert-base-uncased-mrpc-int8-qat | [
"0",
"1"
] | ---
language: en
license: apache-2.0
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- QuantizationAwareTraining
datasets:
- mrpc
metrics:
- f1
---
# INT8 BERT base uncased finetuned MRPC
### QuantizationAwareTraining
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://gith... | 1,243 |
jenspt/bert_regression | [
"LABEL_0"
] | Entry not found | 15 |
waboucay/camembert-large-finetuned-xnli_fr_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 | 85.8 | 85.9 |
| test ... | 367 |
Maxbnza/country-recognition | [
"Austria",
"Belgium",
"Denmark",
"Finland",
"France",
"Germany",
"Israel",
"Italy",
"Netherlands",
"Norway",
"Others",
"Poland",
"Portugal",
"Saudi Arabia",
"South Africa",
"Spain",
"Sweden",
"Switzerland",
"Turkey",
"United Arab Emirates",
"United Kingdom",
"United States"... | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Maxbnza/autotrain-data-address-training
co2_eq_emissions: 141.11976199388627
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1062136864
- CO2 Emissions (in grams): 141.11976199388627
## Vali... | 1,421 |
IMSyPP/hate_speech_slo | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
pipeline_tag: text-classification
inference: true
widget:
- text: "Sem Mark in živim v Ljubljani. Sem doktorski študent na Mednarodni podiplomski šoli Jožefa Stefana."
language:
- sl
license: mit
---
# Hate Speech Classifier for Social Media Content in Slovenian Language
A monolingual model for hate speech c... | 879 |
ItcastAI/bert_cn_finetuning | null | Entry not found | 15 |
NYTK/sentiment-hts5-hubert-hungarian | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
language:
- hu
tags:
- text-classification
license: gpl
metrics:
- accuracy
widget:
- text: "Jó reggelt! majd küldöm az élményhozókat :)."
---
# Hungarian Sentence-level Sentiment Analysis model with huBERT
For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analy... | 1,148 |
TransQuest/monotransquest-da-ro_en-wiki | [
"LABEL_0"
] | ---
language: ro-en
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE t... | 5,401 |
Wiirin/BioBERT-finetuned-PubMed-FoodCancer | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | Entry not found | 15 |
baykenney/bert-base-gpt2detector-random | [
"Human",
"Machine"
] | Entry not found | 15 |
boychaboy/SNLI_bert-large-cased | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
bvanaken/CORe-clinical-mortality-prediction | [
"0",
"1"
] | ---
language: "en"
tags:
- bert
- medical
- clinical
- mortality
thumbnail: "https://core.app.datexis.com/static/paper.png"
---
# CORe Model - Clinical Mortality Risk Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admi... | 2,431 |
echarlaix/bert-base-uncased-qqp-f87.8-d36-hybrid | null | ---
language: en
license: apache-2.0
tags:
- text-classification
datasets:
- qqp
metrics:
- F1
---
## bert-base-uncased model fine-tuned on QQP
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **36%** of the original weights.
The m... | 1,650 |
monologg/koelectra-small-finetuned-intent-cls | [
"(일반)제품 주문",
"(현금)영수증문의",
"0인분 용량 문의",
"1인1잔문의",
"1인메뉴문의",
"1인분배달문의",
"1인식사자리요청",
"2박이상요금운의",
"3구4구 테이블 문의",
"AS가능문의",
"AS기간문의",
"MD제품문의",
"OO금액의매물문의",
"OO아파트매물문의",
"OO지역매물문의",
"OO지역상가건물매매매물문의",
"OO지역상가사무실임차문의",
"OO학군OO학교인근의매물문의",
"SPF지수",
"cctv설치유무문의",
"null",
"가게앞주차여부",
... | Entry not found | 15 |
tcaputi/guns-relevant | null | Entry not found | 15 |
tennessejoyce/titlewave-bert-base-uncased | [
"Unanswered",
"Answered"
] | ---
language: en
license: cc-by-4.0
widget:
- text: "[Gmail API] How can I extract plain text from an email sent to me?"
---
# Titlewave: bert-base-uncased
## Model description
Titlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See the [github repository](https://g... | 3,369 |
TehranNLP-org/bert-large-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... | 1,796 |
cradle-bio/tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert | [
"LABEL_0"
] | ---
license: apache-2.0
tags:
- protein language model
- generated_from_trainer
datasets:
- train
metrics:
- spearmanr
model-index:
- name: tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert
results:
- task:
name: Text Classification
type: text-classification
dataset:
name... | 3,730 |
abspython/distilbert-finetuned | [
"NEGATIVE",
"POSITIVE"
] | ---
language: en
license: other
---
TDistilBERT finetuned
This model is a fine-tune checkpoint of DistilBERT-base-uncased[https://huggingface.co/distilbert-base-uncased]
| 170 |
facebook/roberta-hate-speech-dynabench-r1-target | null | ---
language: en
---
# LFTW R1 Target
The R1 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761)
## Citation Information
```bibtex
@inproceedings{vidgen2021lftw,
title={Learning from the Worst: Dynamically Generated Dataset... | 570 |
hassan4830/distil-bert-uncased-finetuned-english | null | ---
license: afl-3.0
---
distilbert Binary Text Classifier
This distilbert based text classification model trained on imdb dataset performs binary sentiment classification on any given sentence.
The model has been fine tuned for better results in manageable time frames.
LABEL0 - Negative
LABEL1 - Positive | 309 |
erickdp/gs3n-roberta-model | [
"0",
"1",
"2"
] | ---
tags: xerox
language: es
widget:
- text: "Debo de levantarme temprano para hacer ejercicio"
datasets:
- erixxdp/autotrain-data-gsemodel
co2_eq_emissions: 0.027846282970913613
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1148842296
- CO2 Emissions (in grams): 0.0278462... | 1,338 |
adamnik/electra-entailment-detection | null | ---
license: mit
---
| 21 |
LilaBoualili/bert-pre-pair | null | Entry not found | 15 |
Sakil/imdbsentdistilbertmodel | null | ---
language:
- en
tags:
- text Classification
license: apache-2.0
widget:
- text: "I like you. </s></s> I love you."
---
* IMDBSentimentDistilBertModel:
- I have used IMDB movie review dataset to create custom model by using DistilBertForSequenceClassification.
from transformers import DistilBertForSequenceClassif... | 448 |
addy88/programming-lang-identifier | [
"go",
"java",
"javascript",
"php",
"python",
"ruby"
] | This model is funetune version of Codebert in roberta. On CodeSearchNet.
###
Quick start:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("addy88/programming-lang-identifier")
model = AutoModelForSequenceClassification.from_pretrained("addy88/progr... | 489 |
bergum/xtremedistil-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: xtremedistil-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: ... | 1,609 |
boychaboy/SNLI_distilroberta-base | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
dkhara/bert-news | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"L... | ### Bert-News | 13 |
valurank/distilbert-quality | [
"bad",
"good",
"medium"
] | ---
license: other
language: en
datasets:
- valurank/news-small
---
# DistilBERT fine-tuned for news classification
This model is based on [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) pretrained weights, with a classification head fine-tuned to classify news articles into 3 categories (ba... | 626 |
vslaykovsky/roberta-news-duplicates | null | Entry not found | 15 |
vumichien/sequence-classification-bigbird-roberta-base | null | Entry not found | 15 |
Yoonseong/climatebert_trained | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: mit
---
| 24 |
Jatin-WIAI/doctor_patient_clf_en | null | Entry not found | 15 |
UT/BRTW_DEBIAS_SHORT | null | Entry not found | 15 |
sam34738/bert-hindi-kabita | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-hindi-kabita
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. -->
# bert-hindi-kabita... | 1,347 |
Raychanan/chinese-roberta-wwm-ext-FineTuned-Binary | null | DO NOT USE THIS | 15 |
gchhablani/bert-base-cased-finetuned-qnli | [
"entailment",
"not_entailment"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-cased-finetuned-qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: g... | 2,647 |
shrugging-grace/tweetclassifier | null | # shrugging-grace/tweetclassifier
## Model description
This model classifies tweets as either relating to the Covid-19 pandemic or not.
## Intended uses & limitations
It is intended to be used on tweets commenting on UK politics, in particular those trending with the #PMQs hashtag, as this refers to weekly Prime Min... | 992 |
textattack/xlnet-base-cased-QQP | null | Entry not found | 15 |
veronica320/TE-for-Event-Extraction | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | # TE-for-Event-Extraction
## Model description
This is a TE model as part of the event extraction system in the ACL2021 paper: [Zero-shot Event Extraction via Transfer Learning: Challenges and Insights](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co... | 2,764 |
akoksal/bounti | [
"negative",
"neutral",
"positive"
] | ---
language: "tr"
tags:
- sentiment
- twitter
- turkish
---
This Turkish Sentiment Analysis model is a fine-tuned checkpoint of pretrained [BERTurk model 128k uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) with [BounTi dataset](https://ieeexplore.ieee.org/document/9477814).
## Usage in Hugging ... | 1,733 |
tinkoff-ai/response-toxicity-classifier-base | [
"ok",
"risks",
"severe_toxic",
"toxic"
] | ---
language: ["ru"]
tags:
- russian
- pretraining
- conversational
license: mit
widget:
- text: "[CLS] привет [SEP] привет! [SEP] как дела? [RESPONSE_TOKEN] норм"
example_title: "Dialog example 1"
- text: "[CLS] привет [SEP] привет! [SEP] как дела? [RESPONSE_TOKEN] ты *****"
example_title: "Dialog example 2"
---
... | 2,464 |
BellaAndBria/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
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... | 1,878 |
svenstahlmann/finetuned-distilbert-needmining | [
"contains need",
"no need"
] | ---
language: en
tags:
- distilbert
- needmining
license: apache-2.0
metric:
- f1
---
# Finetuned-Distilbert-needmining (uncased)
This model is a finetuned version of the [Distilbert base model](https://huggingface.co/distilbert-base-uncased). It was
trained to predict need-containing sentences from amazon product re... | 1,837 |
CenIA/distillbert-base-spanish-uncased-finetuned-xnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Greg1901/BertSummaDev_AFD | null | Entry not found | 15 |
Maelstrom77/bert-base-uncased-snli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ```
for i in range(len(predictions)):
if predictions[i] == 0:
predictions[i] = 2
elif predictions[i] == 1:
predictions[i] = 0
elif predictions[i] == 2:
predictions[i] = 1
``` | 192 |
NDugar/deberta-v2-xlarge-mnli | [
"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
---
I tried to train v3 xl to mnli using my own training code and got this result. | 277 |
TransQuest/monotransquest-da-en_de-wiki | [
"LABEL_0"
] | ---
language: en-de
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE t... | 5,401 |
ainize/klue-bert-base-re | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_25",
"LABEL_26",
"LABEL_27",
"LABEL_28",
"LABEL_29",... | # bert-base for KLUE Relation Extraction task.
Fine-tuned klue/bert-base using KLUE RE dataset.
- <a href="https://klue-benchmark.com/">KLUE Benchmark Official Webpage</a>
- <a href="https://github.com/KLUE-benchmark/KLUE">KLUE Official Github</a>
- <a href="https://github.com/ainize-team/klue-re-workspace">KLUE RE Gi... | 1,500 |
berkergurcay/1k-pretrained-bert-model | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
bgoel4132/twitter-sentiment | [
"cyclone",
"earthquake",
"explosion",
"fire",
"flood",
"hurricane",
"medical",
"pollution",
"tornado",
"typhoon",
"volcano"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- bgoel4132/autonlp-data-twitter-sentiment
co2_eq_emissions: 186.8637425115097
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 35868888
- CO2 Emissions (in grams): 186.8637425115097
## Validation Met... | 1,403 |
chrommium/sbert_large-finetuned-sent_in_news_sents | [
"LABEL_-3",
"LABEL_-2",
"LABEL_-1",
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sbert_large-finetuned-sent_in_news_sents
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 comme... | 14,591 |
fabriceyhc/bert-base-uncased-amazon_polarity | null | ---
license: apache-2.0
tags:
- generated_from_trainer
- sibyl
datasets:
- amazon_polarity
metrics:
- accuracy
model-index:
- name: bert-base-uncased-amazon_polarity
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_polarity
type: amazon_polarity
... | 7,263 |
mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis | null | ---
language: es
tags:
- restaurant
- classification
- reviews
widget:
- text: "No está a la altura, no volveremos."
---
# Electricidad-small fine-tuned on restaurant review sentiment analysis dataset
Test set accuray: 0.86 | 225 |
jkhan447/sentiment-model-sample-27go-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_25",
"LABEL_26",
"LABEL_27",
"LABEL_3",
"LABEL_4",
... | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- go_emotions
metrics:
- accuracy
model-index:
- name: sentiment-model-sample-27go-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: go_emotions
type: go_emotions
args: simpli... | 1,450 |
IIC/roberta-base-bne-ranker | [
"LABEL_0"
] | ---
language:
- es
tags:
- sentence similarity # Example: audio
- passage reranking # Example: automatic-speech-recognition
datasets:
- IIC/msmarco_es
metrics:
- eval_MRR@10: 0.688
model-index:
- name: roberta-base-bne-ranker
results:
- task:
type: text similarity # Required. Example: automatic-speech-r... | 1,726 |
nickil/real-fake-news | null | ---
license: mit
---
Data: [https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) | 184 |
chenshuangcufe/Bert-job | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
edumunozsala/bertin_base_sentiment_analysis_es | [
"Negativo",
"Positivo"
] | ---
language: es
tags:
- sagemaker
- bertin
- TextClassification
- SentimentAnalysis
license: apache-2.0
datasets:
- IMDbreviews_es
metrics:
- accuracy
model-index:
- name: bertin_base_sentiment_analysis_es
results:
- task:
name: Sentiment Analysis
type: sentiment-analysis
dataset:
name:... | 3,297 |
Remicm/sentiment-analysis-model-for-socialmedia | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: sentiment-analysis-model-for-socialmedia
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
me... | 1,523 |
Santarabantoosoo/PathologyBERT-meningioma | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: PathologyBERT-meningioma
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 ... | 2,314 |
RomanCast/camembert-miam-loria-finetuned | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_25",
"LABEL_26",
"LABEL_27",
"LABEL_28",
"LABEL_29",... | ---
language:
- fr
--- | 22 |
waboucay/camembert-large-finetuned-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 | 75.3 | 74.9 |
| test ... | 367 |
boychaboy/MNLI_bert-large-cased | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
lamhieu/distilbert-base-multilingual-cased-vietnamese-topicifier | [
"0",
"100 metres",
"A Song of Ice and Fire",
"A Tale for the Time Being",
"ARM Holdings",
"Abigail Johnson",
"Abiogenesis",
"Abortion",
"Abraham Lincoln",
"Abstract art",
"Abu Nuwas",
"Academic degree",
"Accent (sociolinguistics)",
"Achaemenid Empire",
"Acid-base reaction",
"Acoustic g... | ---
language:
- vi
tags:
- vietnamese
- topicifier
- multilingual
- tiny
license:
- mit
pipeline_tag: text-classification
widget:
- text: "Đam mê của tôi là nhiếp ảnh"
---
# distilbert-base-multilingual-cased-vietnamese-topicifier
## About
Fine-tuning from `distilbert-base-multilingual-cased` with a tiny dataset abo... | 624 |
lannelin/bert-imdb-1hidden | [
"neg",
"pos"
] | ---
language:
- en
datasets:
- imdb
metrics:
- accuracy
---
# bert-imdb-1hidden
## Model description
A `bert-base-uncased` model was restricted to 1 hidden layer and
fine-tuned for sequence classification on the
imdb dataset loaded using the `datasets` library.
## Intended uses & limitations
#### How to use
```... | 1,355 |
m3hrdadfi/albert-fa-base-v2-sentiment-digikala | [
"no_idea",
"not_recommended",
"recommended"
] | ---
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... | 2,583 |
projecte-aina/roberta-base-ca-cased-tc | [
"Medi ambient",
"Societat",
"Policial",
"Judicial",
"Empresa",
"Partits",
"Política",
"Successos",
"Salut",
"Infraestructures",
"Parlament",
"Música",
"Govern",
"Unió Europea",
"Economia",
"Mobilitat",
"Treball",
"Cultura",
"Educació"
] | ---
language:
- ca
tags:
- "catalan"
- "text classification"
- "tecla"
- "CaText"
- "Catalan Textual Corpus"
datasets:
- "projecte-aina/tecla"
metrics:
- accuracy
model-index:
- name: roberta-base-ca-cased-tc
results:
- task:
type: text-classification
dataset:
name: tecla
type: p... | 2,903 |
Aureliano/distilbert-base-uncased-if | [
"answer.v.01",
"ask.v.01",
"ask.v.02",
"blow.v.01",
"brandish.v.01",
"break.v.05",
"burn.v.01",
"buy.v.01",
"charge.v.17",
"choose.v.01",
"clean.v.01",
"climb.v.01",
"close.v.01",
"connect.v.01",
"consult.v.02",
"cut.v.01",
"dig.v.01",
"drink.v.01",
"drive.v.01",
"drop.v.01",
... | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# DistilBERT base model (uncased) for Interactive Fiction
[`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) finetuned on a dataset of Interactive
Fiction commands.
Details on the datasets can be found... | 7,493 |
jkhan447/sarcasm-detection-RoBerta-base | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sarcasm-detection-RoBerta-base
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... | 1,150 |
ericntay/bert-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bert-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name... | 1,634 |
Alireza1044/mobilebert_sst2 | [
"negative",
"positive"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- name: Accuracy... | 1,395 |
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