modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
distilbert-base-uncased-finetuned-sst-2-english | [
"NEGATIVE",
"POSITIVE"
] | ---
language: en
license: apache-2.0
datasets:
- sst2
- glue
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: sst2
split: validation
metrics:
... | 4,744 |
cross-encoder/ms-marco-MiniLM-L-12-v2 | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | 3,233 |
cardiffnlp/twitter-xlm-roberta-base-sentiment | [
"Negative",
"Neutral",
"Positive"
] | ---
language: multilingual
widget:
- text: "🤗"
- text: "T'estimo! ❤️"
- text: "I love you!"
- text: "I hate you 🤮"
- text: "Mahal kita!"
- text: "사랑해!"
- text: "난 너가 싫어"
- text: "😍😍😍"
---
# twitter-XLM-roBERTa-base for Sentiment Analysis
This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and ... | 2,580 |
facebook/bart-large-mnli | [
"contradiction",
"entailment",
"neutral"
] | ---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
pipeline_tag: zero-shot-classification
datasets:
- multi_nli
---
# bart-large-mnli
This is the checkpoint for [bart-large](https://huggingface.co/facebook/bart-large) after being trained on the [MultiNLI (MNLI)](https://huggingface.co/da... | 3,793 |
ProsusAI/finbert | [
"positive",
"negative",
"neutral"
] | ---
language: "en"
tags:
- financial-sentiment-analysis
- sentiment-analysis
widget:
- text: "Stocks rallied and the British pound gained."
---
FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financi... | 1,475 |
tals/albert-xlarge-vitaminc-mnli | [
"NOT ENOUGH INFO",
"REFUTES",
"SUPPORTS"
] | ---
language: python
datasets:
- fever
- glue
- multi_nli
- 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 ... | 2,369 |
daigo/bert-base-japanese-sentiment | [
"LABEL_0",
"LABEL_1"
] | ---
language:
- ja
---
binary classification
# Usage
```
print(pipeline("sentiment-analysis",model="daigo/bert-base-japanese-sentiment",tokenizer="daigo/bert-base-japanese-sentiment")("私は幸福である。"))
[{'label': 'ポジティブ', 'score': 0.98430425}]
```
| 246 |
cardiffnlp/twitter-roberta-base-sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | # Twitter-roBERTa-base for Sentiment Analysis
This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see [XLM-T](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)).
... | 2,853 |
bhadresh-savani/distilbert-base-uncased-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
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, F1 Score
model-index:
- name: bhadresh-savani/distilbert-base-uncased-emotion
... | 4,150 |
pysentimiento/robertuito-sentiment-analysis | [
"NEG",
"NEU",
"POS"
] | ---
language:
- es
tags:
- twitter
- sentiment-analysis
---
# Sentiment Analysis in Spanish
## robertuito-sentiment-analysis
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 corpus (around ~5k tweets) of several dial... | 2,152 |
yiyanghkust/finbert-tone | [
"Positive",
"Negative",
"Neutral"
] | ---
language: "en"
tags:
- financial-sentiment-analysis
- sentiment-analysis
widget:
- text: "growth is strong and we have plenty of liquidity"
---
`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three ... | 1,867 |
unitary/toxic-bert | [
"toxic",
"severe_toxic",
"obscene",
"threat",
"insult",
"identity_hate"
] |
<div align="center">
**⚠️ Disclaimer:**
The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify
# 🙊 Detoxify... | 11,071 |
nlptown/bert-base-multilingual-uncased-sentiment | [
"1 star",
"2 stars",
"3 stars",
"4 stars",
"5 stars"
] | ---
language:
- en
- nl
- de
- fr
- it
- es
license: mit
---
# bert-base-multilingual-uncased-sentiment
This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a n... | 1,950 |
finiteautomata/bertweet-base-sentiment-analysis | [
"NEG",
"NEU",
"POS"
] | ---
language:
- en
tags:
- sentiment-analysis
---
# Sentiment Analysis in English
## bertweet-sentiment-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is [BERTweet... | 1,213 |
j-hartmann/emotion-english-distilroberta-base | [
"anger",
"disgust",
"fear",
"joy",
"neutral",
"sadness",
"surprise"
] | ---
language: "en"
tags:
- distilroberta
- sentiment
- emotion
- twitter
- reddit
widget:
- text: "Oh wow. I didn't know that."
- text: "This movie always makes me cry.."
- text: "Oh Happy Day"
---
# Emotion English DistilRoBERTa-base
# Description ℹ
With this model, you can classify emotions in English text data.... | 4,027 |
cross-encoder/ms-marco-TinyBERT-L-2 | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | 3,233 |
cross-encoder/nli-distilroberta-base | [
"contradiction",
"entailment",
"neutral"
] | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- distilroberta-base
datasets:
- multi_nli
- snli
metrics:
- accuracy
license: apache-2.0
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/example... | 2,589 |
Hate-speech-CNERG/indic-abusive-allInOne-MuRIL | [
"Normal",
"Abusive"
] | ---
language: [bn, hi, hi-en, ka-en, ma-en, mr, ta-en, ur, ur-en, en]
license: afl-3.0
---
This model is used detecting **abusive speech** in **Bengali, Devanagari Hindi, Code-mixed Hindi, Code-mixed Kannada, Code-mixed Malayalam, Marathi, Code-mixed Tamil, Urdu, Code-mixed Urdu, and English languages**. The allInOne ... | 1,263 |
valhalla/distilbart-mnli-12-1 | [
"contradiction",
"entailment",
"neutral"
] | ---
datasets:
- mnli
tags:
- distilbart
- distilbart-mnli
pipeline_tag: zero-shot-classification
---
# DistilBart-MNLI
distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingfa... | 2,406 |
echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid | null | ---
language: en
license: apache-2.0
tags:
- text-classification
datasets:
- sst2
metrics:
- accuracy
---
## bert-base-uncased model fine-tuned on SST-2
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **37%** of the original weights.... | 2,945 |
cardiffnlp/twitter-roberta-base-sentiment-latest | [
"Negative",
"Neutral",
"Positive"
] | ---
language: english
widget:
- text: "Covid cases are increasing fast!"
---
# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2021)
This is a roBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finet... | 2,723 |
oliverguhr/german-sentiment-bert | [
"positive",
"negative",
"neutral"
] | ---
language:
- de
tags:
- sentiment
- bert
license: mit
widget:
- text: "Das ist gar nicht mal so schlecht"
metrics:
- f1
---
# German Sentiment Classification with Bert
This model was trained for sentiment classification of German language texts. To achieve the best results all model inputs needs to be preprocesse... | 3,698 |
finiteautomata/beto-sentiment-analysis | [
"NEG",
"NEU",
"POS"
] | ---
language:
- es
tags:
- sentiment-analysis
---
# Sentiment Analysis in Spanish
## beto-sentiment-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/pysentimiento/pysentimiento/)
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Ba... | 1,213 |
cross-encoder/ms-marco-MiniLM-L-6-v2 | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | 3,233 |
bvanaken/clinical-assertion-negation-bert | [
"PRESENT",
"ABSENT",
"POSSIBLE"
] | ---
language: "en"
tags:
- bert
- medical
- clinical
- assertion
- negation
- text-classification
widget:
- text: "Patient denies [entity] SOB [entity]."
---
# Clinical Assertion / Negation Classification BERT
## Model description
The Clinical Assertion and Negation Classification BERT is introduced in the paper [A... | 2,503 |
BaptisteDoyen/camembert-base-xnli | [
"entailment",
"neutral",
"contradiction"
] | ---
language:
- fr
thumbnail:
tags:
- zero-shot-classification
- xnli
- nli
- fr
license: mit
pipeline_tag: zero-shot-classification
datasets:
- xnli
metrics:
- accuracy
---
# camembert-base-xnli
## Model description
Camembert-base model fine-tuned on french part of XNLI dataset. <br>
One of the few Zero-Shot c... | 2,988 |
cross-encoder/ms-marco-MiniLM-L-2-v2 | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | 3,233 |
joeddav/xlm-roberta-large-xnli | [
"contradiction",
"entailment",
"neutral"
] | ---
language: multilingual
tags:
- text-classification
- pytorch
- tensorflow
datasets:
- multi_nli
- xnli
license: mit
pipeline_tag: zero-shot-classification
widget:
- text: "За кого вы голосуете в 2020 году?"
candidate_labels: "politique étrangère, Europe, élections, affaires, politique"
multi_class: true
- text:... | 4,951 |
hf-internal-testing/tiny-random-distilbert | null | ---
pipeline_tag: text-classification
---
| 42 |
Sahajtomar/German_Zeroshot | [
"entailment",
"neutral",
"contradiction"
] | ---
language: multilingual
tags:
- text-classification
- pytorch
- nli
- xnli
- de
datasets:
- xnli
pipeline_tag: zero-shot-classification
widget:
- text: "Letzte Woche gab es einen Selbstmord in einer nahe gelegenen kolonie"
candidate_labels: "Verbrechen,Tragödie,Stehlen"
hypothesis_template: "In deisem geht es u... | 1,711 |
cardiffnlp/twitter-roberta-base-emotion | [
"joy",
"optimism",
"anger",
"sadness"
] | # Twitter-roBERTa-base for Emotion Recognition
This is a roBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://g... | 2,412 |
cross-encoder/stsb-roberta-base | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset]... | 941 |
cross-encoder/stsb-roberta-large | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset]... | 941 |
cmarkea/distilcamembert-base-sentiment | [
"1 star",
"2 stars",
"3 stars",
"4 stars",
"5 stars"
] | ---
language: fr
license: mit
datasets:
- amazon_reviews_multi
- allocine
widget:
- text: "Je pensais lire un livre nul, mais finalement je l'ai trouvé super !"
- text: "Cette banque est très bien, mais elle n'offre pas les services de paiements sans contact."
- text: "Cette banque est très bien et elle offre en plus l... | 6,683 |
microsoft/MiniLM-L12-H384-uncased | null | ---
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression o... | 2,015 |
typeform/distilbert-base-uncased-mnli | [
"ENTAILMENT",
"NEUTRAL",
"CONTRADICTION"
] | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- distilbert
datasets:
- multi_nli
metrics:
- accuracy
---
# DistilBERT base model (uncased)
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitat... | 3,882 |
cardiffnlp/twitter-roberta-base-stance-climate | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | 0 | |
cardiffnlp/twitter-roberta-base-irony | null | # Twitter-roBERTa-base for Irony Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.co... | 2,396 |
Narsil/deberta-large-mnli-zero-cls | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language: en
tags:
- deberta-v1
- 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 BERT and RoBE... | 3,888 |
typeform/mobilebert-uncased-mnli | [
"ENTAILMENT",
"NEUTRAL",
"CONTRADICTION"
] | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- mobilebert
datasets:
- multi_nli
metrics:
- accuracy
---
# MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
This model is the Multi-Genre Natural Language Inference (MNLI) fine-turned version of the [uncased MobileBERT model](https:/... | 363 |
roberta-large-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language:
- en
license: mit
tags:
- autogenerated-modelcard
datasets:
- multi_nli
- wikipedia
- bookcorpus
---
# roberta-large-mnli
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#ri... | 10,712 |
bhadresh-savani/bert-base-go-emotion | [
"admiration",
"amusement",
"anger",
"annoyance",
"approval",
"caring",
"confusion",
"curiosity",
"desire",
"disappointment",
"disapproval",
"disgust",
"embarrassment",
"excitement",
"fear",
"gratitude",
"grief",
"joy",
"love",
"nervousness",
"neutral",
"optimism",
"pride"... | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- go-emotion
- pytorch
license: apache-2.0
datasets:
- go_emotions
metrics:
- Accuracy
---
# Bert-Base-Uncased-Go-Emotion
## Model description:
## Training ... | 884 |
cross-encoder/quora-distilroberta-base | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questi... | 1,070 |
unitary/multilingual-toxic-xlm-roberta | [
"toxic"
] | ---
pipeline_tag: "text-classification"
---
<div align="center">
**⚠️ Disclaimer:**
The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). For the most up to date models we recommend using the models from https://github.c... | 11,107 |
valhalla/distilbart-mnli-12-3 | [
"contradiction",
"entailment",
"neutral"
] | ---
datasets:
- mnli
tags:
- distilbart
- distilbart-mnli
pipeline_tag: zero-shot-classification
---
# DistilBart-MNLI
distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingfa... | 2,406 |
MoritzLaurer/mDeBERTa-v3-base-mnli-xnli | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- multilingual
- en
- ar
- bg
- de
- el
- es
- fr
- hi
- ru
- sw
- th
- tr
- ur
- vu
- zh
tags:
- zero-shot-classification
- text-classification
- nli
- pytorch
metrics:
- accuracy
datasets:
- multi_nli
- xnli
pipeline_tag: zero-shot-classification
widget:
- text: "Angela Merkel ist eine P... | 5,597 |
Tatyana/rubert-base-cased-sentiment-new | [
"NEGATIVE",
"NEUTRAL",
"POSITIVE"
] | ---
language:
- ru
tags:
- sentiment
- text-classification
datasets:
- Tatyana/ru_sentiment_dataset
---
# RuBERT for Sentiment Analysis
Russian texts sentiment classification.
Model trained on [Tatyana/ru_sentiment_dataset](https://huggingface.co/datasets/Tatyana/ru_sentiment_dataset)
## Labels meaning
0: NEUTRA... | 1,000 |
siebert/sentiment-roberta-large-english | [
"NEGATIVE",
"POSITIVE"
] | ---
language: "en"
tags:
- sentiment
- twitter
- reviews
- siebert
---
## SiEBERT - English-Language Sentiment Classification
# Overview
This model ("SiEBERT", prefix for "Sentiment in English") is a fine-tuned checkpoint of [RoBERTa-large](https://huggingface.co/roberta-large) ([Liu et al. 2019](https://arxiv.org/pd... | 5,016 |
joeddav/bart-large-mnli-yahoo-answers | [
"contradiction",
"entailment",
"neutral"
] | ---
language: en
tags:
- text-classification
- pytorch
datasets:
- yahoo-answers
pipeline_tag: zero-shot-classification
---
# bart-lage-mnli-yahoo-answers
## Model Description
This model takes [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) and fine-tunes it on Yahoo Answers topic classif... | 4,276 |
ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli | [
"entailment",
"neutral",
"contradiction"
] | Entry not found | 15 |
cardiffnlp/twitter-roberta-base-offensive | null | # Twitter-roBERTa-base for Offensive Language Identification
This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval of... | 2,401 |
cross-encoder/ms-marco-electra-base | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | 3,233 |
EColi/SB_Classifier | [
"INTERACTION",
"NONE",
"SELFPROMO",
"SPONSOR"
] | ---
tags:
- text-classification
- generic
library_name: generic
widget:
- text: 'This video is sponsored by squarespace'
example_title: Sponsor
- text: 'Check out the merch at linustechtips.com'
example_title: Unpaid/self promotion
- text: "Don't forget to like, comment and subscribe"
example_title: Interaction r... | 443 |
MilaNLProc/feel-it-italian-sentiment | [
"negative",
"positive"
] | ---
language: it
license: mit
tags:
- sentiment
- Italian
---
# FEEL-IT: Emotion and Sentiment Classification for the Italian Language
## FEEL-IT Python Package
You can find the package that uses this model for emotion and sentiment classification **[here](https://github.com/MilaNLProc/feel-it)** it is meant to be a... | 3,551 |
cross-encoder/ms-marco-TinyBERT-L-2-v2 | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... | 3,233 |
textattack/bert-base-uncased-imdb | null | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a cla... | 612 |
MilaNLProc/feel-it-italian-emotion | [
"anger",
"fear",
"joy",
"sadness"
] | ---
language: it
license: mit
tags:
- sentiment
- emotion
- Italian
---
# FEEL-IT: Emotion and Sentiment Classification for the Italian Language
## FEEL-IT Python Package
You can find the package that uses this model for emotion and sentiment classification **[here](https://github.com/MilaNLProc/feel-it)** it... | 3,493 |
microsoft/xtremedistil-l6-h256-uncased | null | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# XtremeDistilTransformers for Distilling Massive Neural Networks
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small uni... | 2,944 |
valhalla/distilbart-mnli-12-6 | [
"contradiction",
"entailment",
"neutral"
] | ---
datasets:
- mnli
tags:
- distilbart
- distilbart-mnli
pipeline_tag: zero-shot-classification
---
# DistilBart-MNLI
distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingfa... | 2,406 |
lewtun/roberta-base-bne-finetuned-amazon_reviews_multi | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model_index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: a... | 1,751 |
aatmasidha/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
metrics:
- accuracy
- f1
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, t... | 1,502 |
IDEA-CCNL/Erlangshen-Roberta-330M-Similarity | null | ---
language:
- zh
license: apache-2.0
tags:
- bert
- NLU
- NLI
inference: true
widget:
- text: "今天心情不好[SEP]今天很开心"
---
# Erlangshen-Roberta-330M-Similarity, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
We collect 20 paraphrace datasets in the Chinese domain for f... | 1,624 |
vicgalle/xlm-roberta-large-xnli-anli | [
"contradiction",
"entailment",
"neutral"
] | ---
language: multilingual
tags:
- zero-shot-classification
- nli
- pytorch
datasets:
- mnli
- xnli
- anli
license: mit
pipeline_tag: zero-shot-classification
widget:
- text: "De pugna erat fantastic. Nam Crixo decem quam dilexit et praeciderunt caput aemulus."
candidate_labels: "violent, peaceful"
- text: "La pelícu... | 1,751 |
prithivida/parrot_adequacy_model | [
"contradiction",
"entailment",
"neutral"
] | ---
license: apache-2.0
---
Parrot
THIS IS AN ANCILLARY MODEL FOR PARROT PARAPHRASER
1. What is Parrot?
Parrot is a paraphrase-based utterance augmentation framework purpose-built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. Please refer to the GitHub page or The mo... | 364 |
textattack/bert-base-uncased-ag-news | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ## TextAttack Model CardThis `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a c... | 625 |
prithivida/parrot_fluency_model | null | ---
license: apache-2.0
---
Parrot
THIS IS AN ANCILLARY MODEL FOR PARROT PARAPHRASER
1. What is Parrot?
Parrot is a paraphrase-based utterance augmentation framework purpose-built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. Please refer to the GitHub page or The mo... | 364 |
cross-encoder/qnli-electra-base | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
Given a question and paragraph, can the question be a... | 1,996 |
Recognai/bert-base-spanish-wwm-cased-xnli | [
"contradiction",
"neutral",
"entailment"
] | ---
language: es
tags:
- zero-shot-classification
- nli
- pytorch
datasets:
- xnli
license: mit
pipeline_tag: zero-shot-classification
widget:
- text: "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo"
candidate_labels: "cultura, sociedad, economia, salud, deportes"
---
# bert-bas... | 2,081 |
chkla/roberta-argument | [
"NON-ARGUMENT",
"ARGUMENT"
] | ---
language: english
widget:
- text: "It has been determined that the amount of greenhouse gases have decreased by almost half because of the prevalence in the utilization of nuclear power."
---
### Welcome to RoBERTArg!
🤖 **Model description**
This model was trained on ~25k heterogeneous manually annotated senten... | 2,523 |
textattack/bert-base-uncased-yelp-polarity | null | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this ... | 632 |
textattack/bert-base-uncased-CoLA | null | Entry not found | 15 |
textattack/roberta-base-SST-2 | null | Entry not found | 15 |
pysentimiento/robertuito-emotion-analysis | [
"anger",
"disgust",
"fear",
"joy",
"others",
"sadness",
"surprise"
] | ---
language:
- es
tags:
- emotion-analysis
- twitter
---
# Emotion Analysis in Spanish
## robertuito-emotion-analysis
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish... | 2,600 |
pin/senda | [
"negativ",
"neutral",
"positiv"
] | ---
language: da
tags:
- danish
- bert
- sentiment
- polarity
license: cc-by-4.0
widget:
- text: "Sikke en dejlig dag det er i dag"
---
# Danish BERT fine-tuned for Sentiment Analysis with `senda`
This model detects polarity ('positive', 'neutral', 'negative') of Danish texts.
It is trained and tested on Tweets anno... | 1,782 |
savasy/bert-base-turkish-sentiment-cased | [
"negative",
"positive"
] | ---
language: tr
---
# Bert-base Turkish Sentiment Model
https://huggingface.co/savasy/bert-base-turkish-sentiment-cased
This model is used for Sentiment Analysis, which is based on BERTurk for Turkish Language https://huggingface.co/dbmdz/bert-base-turkish-cased
## Dataset
The dataset is taken from the studies [[... | 5,000 |
textattack/albert-base-v2-yelp-polarity | null | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 512.
Since this was... | 628 |
iarfmoose/bert-base-cased-qa-evaluator | null | # BERT-base-cased-qa-evaluator
This model takes a question answer pair as an input and outputs a value representing its prediction about whether the input was a valid question and answer pair or not. The model is a pretrained [BERT-base-cased](https://huggingface.co/bert-base-cased) with a sequence classification head... | 1,644 |
pysentimiento/robertuito-hate-speech | [
"aggressive",
"hateful",
"targeted"
] | ---
language:
- es
tags:
- twitter
- hate-speech
---
# Hate Speech detection in Spanish
## robertuito-hate-speech
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speec... | 2,868 |
j-hartmann/sentiment-roberta-large-english-3-classes | [
"negative",
"neutral",
"positive"
] | ---
language: "en"
tags:
- roberta
- sentiment
- twitter
widget:
- text: "Oh no. This is bad.."
- text: "To be or not to be."
- text: "Oh Happy Day"
---
This RoBERTa-based model can classify the sentiment of English language text in 3 classes:
- positive 😀
- neutral 😐
- negative 🙁
The model was fine-tuned on 5,... | 1,355 |
cross-encoder/nli-deberta-v3-base | [
"contradiction",
"entailment",
"neutral"
] | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- microsoft/deberta-v3-base
datasets:
- multi_nli
- snli
metrics:
- accuracy
license: apache-2.0
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/... | 2,777 |
shahrukhx01/bert-mini-finetune-question-detection | null |
---
language: "en"
tags:
- neural-search-query-classification
- neural-search
widget:
- text: "keyword query."
---
# KEYWORD QUERY VS STATEMENT/QUESTION CLASSIFIER FOR NEURAL SEARCH
| Train Loss | Validation Acc.| Test Acc.|
| ------------- |:-------------: | -----: |
| 0.000806 | 0.99 | 0.997 |
```pyth... | 1,349 |
Narasimha/hinglish-distilbert | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
---
| 21 |
MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
metrics:
- accuracy
datasets:
- multi_nli
- anli
- fever
pipeline_tag: zero-shot-classification
---
# DeBERTa-v3-base-mnli-fever-anli
## Model description
This model was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, wh... | 3,545 |
mrm8488/bert-tiny-finetuned-sms-spam-detection | null | ---
language: en
tags:
- sms
- spam
- detection
datasets:
- sms_spam
widget:
- text: "Camera - You are awarded a SiPix Digital Camera! call 09061221066 fromm landline. Delivery within 28 days."
---
# BERT-Tiny fine-tuned on on sms_spam dataset for spam detection
Validation accuray: **0.98** | 293 |
microsoft/deberta-large-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) impro... | 3,907 |
aliosm/sha3bor-footer-51-arabertv02-base | [
"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: ar
license: mit
widget:
- text: "إن العيون التي في طرفها حور"
- text: "إذا ما فعلت الخير ضوعف شرهم"
- text: "واحر قلباه ممن قلبه شبم"
---
| 152 |
aliosm/sha3bor-rhyme-detector-arabertv02-base | [
"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: ar
license: mit
widget:
- text: "إن العيون التي في طرفها حور [شطر] قتلننا ثم لم يحيين قتلانا"
- text: "إذا ما فعلت الخير ضوعف شرهم [شطر] وكل إناء بالذي فيه ينضح"
- text: "واحر قلباه ممن قلبه شبم [شطر] ومن بجسمي وحالي عنده سقم"
---
| 245 |
aliosm/sha3bor-metre-detector-arabertv02-base | [
"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"
] | ---
language: ar
license: mit
widget:
- text: "إن العيون التي في طرفها حور [شطر] قتلننا ثم لم يحيين قتلانا"
- text: "إذا ما فعلت الخير ضوعف شرهم [شطر] وكل إناء بالذي فيه ينضح"
- text: "واحر قلباه ممن قلبه شبم [شطر] ومن بجسمي وحالي عنده سقم"
---
| 245 |
shahrukhx01/question-vs-statement-classifier | null | ---
language: "en"
tags:
- neural-search-query-classification
- neural-search
widget:
- text: "what did you eat in lunch?"
---
# KEYWORD STATEMENT VS QUESTION CLASSIFIER FOR NEURAL SEARCH
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained(... | 664 |
LiYuan/amazon-review-sentiment-analysis | [
"1 star",
"2 stars",
"3 stars",
"4 stars",
"5 stars"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli-amazon-query-shopping
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and ... | 3,515 |
ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli | [
"entailment",
"neutral",
"contradiction"
] | ---
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
license: mit
---
This is a strong pre-trained RoBERTa-Large NLI model.
The training data is a combination of well-known NLI datasets: [`SNLI`](https://nlp.stanford.edu/projects/snli/), [`MNLI`](https://cims.nyu.edu/~sbowman/multinli/), [`FEVER-NLI`... | 3,406 |
howey/bert-base-uncased-sst2 | null | Entry not found | 15 |
textattack/bert-base-uncased-SST-2 | null | Entry not found | 15 |
textattack/roberta-base-CoLA | null | ## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... | 528 |
finiteautomata/bertweet-base-emotion-analysis | [
"anger",
"disgust",
"fear",
"joy",
"others",
"sadness",
"surprise"
] | ---
language:
- en
tags:
- emotion-analysis
---
# Emotion Analysis in English
## bertweet-base-emotion-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with EmoEvent corpus for Emotion detection in English. Base model is [B... | 1,547 |
sbcBI/sentiment_analysis_model | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- Confidential
---
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.... | 2,215 |
microsoft/Multilingual-MiniLM-L12-H384 | null | ---
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression o... | 6,174 |
cross-encoder/stsb-distilroberta-base | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset]... | 941 |
sahri/indonesiasentiment | [
"negative",
"neutral",
"positive"
] | ---
language: id
tags:
- indonesian-roberta-base-sentiment-classifier
license: mit
datasets:
- indonlu
widget:
- text: "tidak jelek tapi keren"
---
## Indonesian RoBERTa Base Sentiment Classifier
Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the [RoB... | 2,811 |
microsoft/deberta-xlarge-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) impro... | 3,909 |
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