The Best 1260 Text Classification Tools in 2025

Text classification model fine-tuned on the SST-2 sentiment analysis dataset based on DistilBERT-base-uncased, with 91.3% accuracy

Multilingual detection model based on XLM-RoBERTa, supporting text classification in 20 languages

This model improves online hate detection through dynamic dataset generation, focusing on learning from worst-case scenarios to enhance detection effectiveness.

A multilingual sentiment analysis model fine-tuned based on bert-base-multilingual-uncased, supporting sentiment analysis of product reviews in 6 languages

A fine-tuned English text emotion classification model based on DistilRoBERTa-base, capable of predicting Ekman's six basic emotions and neutral category.

Spanish tweet sentiment analysis model based on RoBERTuito, supporting POS(positive)/NEG(negative)/NEU(neutral) three-class sentiment classification

FinBERT is a BERT model pre-trained on financial communication texts, specializing in the field of financial natural language processing. finbert-tone is its fine-tuned version for financial sentiment analysis tasks.

A multi-label sentiment classification model based on RoBERTa-base, trained on the go_emotions dataset, supporting recognition of 28 emotion labels.

XLM-EMO is a multilingual sentiment analysis model fine-tuned based on the XLM-T model, supporting 19 languages and specifically designed for sentiment prediction in social media texts.

DeBERTa-v3 model trained on MultiNLI, Fever-NLI, and ANLI datasets, excelling in zero-shot classification and natural language inference tasks

A toxicity comment classification system based on PyTorch Lightning and Hugging Face Transformers, capable of detecting various types of harmful online content

A multilingual natural language inference model supporting 100 languages, suitable for zero-shot classification tasks

A toxicity detection model trained on tweet data, utilizing the Roberta-large architecture to identify toxic content in text.

fastText is an open-source, lightweight library for text classification and word representation learning. This model is specifically designed for language identification, supporting detection of 217 languages.

This is a multilingual sentiment analysis model trained using zero-shot distillation technology, supporting sentiment classification in 12 languages.

A sentiment analysis model fine-tuned based on DistilRoBERTa-base, specifically designed for classifying 6 basic emotions and neutral states in film and TV dialogue texts

A BERT model specifically trained for German text sentiment classification, supporting positive/negative/neutral three-class classification

A sentiment analysis model trained based on the BETO Spanish BERT model, supporting POS/NEG/NEU three-class sentiment classification

VitaminC is a fact-checking model based on contrastive evidence, improving robustness in fact-checking by analyzing subtle differences in evidence pairs.

English tweet sentiment analysis model based on BERTweet, trained on SemEval 2017 corpus, supports positive, negative, and neutral sentiment classification

NLI model fine-tuned on DeBERTa-v3-large, achieving state-of-the-art performance on multiple NLI datasets

A financial news sentiment analysis model fine-tuned based on DistilRoBERTa, with an accuracy rate of 98.23%.

BLEURT-tiny-512 is a text quality evaluation model developed by Google Research, based on the BERT architecture implemented in PyTorch, used for assessing the quality of natural language generation tasks.

This model is based on the BERT-mini architecture, fine-tuned on the MNLI dataset using the M-FAC second-order optimizer for text classification tasks.

CryptoBERT is a pre-trained natural language processing model specifically designed for analyzing sentiment and language in cryptocurrency-related social media posts.

A toxic comment classification model fine-tuned on DistilBERT, used to identify harmful content in online comments

A prompt injection detection model fine-tuned on DeBERTa-v3-base, designed to identify malicious prompts that may manipulate language models

DistilBart-MNLI is a distilled version obtained from bart-large-mnli through teacher-free distillation technology, maintaining high accuracy while being more compact.

English text classification model based on Deberta-v3 architecture for identifying offensive content

An emotion classification model for English text that can identify 20 different emotional states.

A natural language inference model based on the DeBERTa-v3-large architecture, trained on SNLI and MultiNLI datasets for determining the relationship between sentence pairs.

A zero-shot classification model fine-tuned based on BART-large-MNLI, optimized for Yahoo Answers topic classification

A model fine-tuned on 600+ tasks based on DeBERTa-v3-base through multi-task learning, excelling in zero-shot classification and natural language inference

This model is a fine-tuned version based on ibert-roberta-base, specifically designed for detecting abusive or threatening speech.

DeBERTa-v3-large model optimized for zero-shot classification tasks, supporting text classification without training data

A DistilBERT-based nonsense text detection model that accurately identifies gibberish, spam, and incoherent inputs in English with 97.36% accuracy.

A multilingual sentiment analysis model fine-tuned based on DistilBERT, supporting 21 languages, suitable for various scenarios such as social media and customer feedback analysis.

A webpage educational value assessment classifier trained on the FineWeb dataset, used for filtering high-quality educational content

A Russian natural language inference model fine-tuned from DeepPavlov/rubert-base-cased, capable of predicting logical relationships (entailment/contradiction/neutral) between two texts

An emotion classification model distilled from unlabeled GoEmotions dataset through zero-shot classification pipeline, serving as a computationally efficient proof-of-concept model

ToxicityModel is a fine-tuned model based on RoBERTa, designed to assess the toxicity level of English sentences.

A binary sentiment analysis model for English text fine-tuned on RoBERTa-large, capable of predicting positive or negative sentiment