# BERT Fine-tuning

A Chinese text classification model fine-tuned on the Weibo 100k sentiment analysis dataset based on bert-base-chinese

A fine-tuned model optimized for Spanish sentiment analysis, based on BETO (a Spanish pre-trained BERT model), suitable for text sentiment classification tasks such as movie reviews.

Multilabel sentiment classification model fine-tuned from BERT-base-uncased, supporting 28 emotion recognitions

This is an English spam SMS classification model fine-tuned from the bert-base-uncased model, capable of accurately distinguishing between spam and legitimate SMS messages.

A model fine-tuned based on bert-base-uncased for textual entailment recognition tasks, with an accuracy of 66.43%

BERT-based multilingual sentiment classification model supporting text sentiment analysis in 6 languages

A Korean financial news sentiment analysis model fine-tuned based on BERT architecture, specifically designed for sentiment classification of exchange rate-related news summaries

This is a BERT-based sentiment analysis model for Indonesian language, capable of classifying text into positive or negative sentiments.

A BERT-based Chinese sentiment analysis model for determining the emotional tendency of text (positive, negative, or neutral).

A sentiment analysis model for Amazon reviews fine-tuned on BERT-base-uncased, capable of classifying English reviews as positive or negative

Lite-BERT-SL is a sequence labeling model based on BERT, focusing on the identification of key performance indicators (KPIs) in the financial domain, suitable for financial document parsing and information extraction.

This is a dataset for phishing attack detection, primarily used to train BERT models to identify phishing websites.

This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the InLegalNER dataset, specifically designed for named entity recognition tasks in Indian legal texts.

A BERT model fine-tuned for extractive question-answering tasks on Turkish legal texts, capable of extracting relevant segments from legal clauses to answer questions.

This is a sentiment analysis model fine-tuned based on BERT, specifically designed to analyze sentiment tendencies (positive or negative) in IMDb movie reviews.

Modern-FinBERT-large is a pre-trained NLP model specifically designed for financial sentiment analysis. Based on the ModernBERT-large language model, it is trained on large-scale financial corpora to adapt to financial text classification tasks.

A BERT-based name gender classification model that predicts gender based on input names.

A token classification model fine-tuned on ModernBERT-base, specifically designed to identify and classify Personally Identifiable Information (PII) in text

This is a text classification model fine-tuned based on BERT-base, specifically designed for financial transaction classification tasks

A Turkish sentiment analysis model fine-tuned on dbmdz/bert-base-turkish-cased, supporting negative, neutral, and positive sentiment classification.

A BERT-based fine-tuned Turkish medical QA model specialized in extracting answers from medical texts

A BERT-based named entity recognition model specifically designed to identify person names, locations, organizations, and other entities in Moroccan Arabic (Darija) texts.

Polite Guard is an open-source NLP language model developed by Intel, based on BERT fine-tuned for text classification tasks, designed to classify text into four categories: polite, somewhat polite, neutral, and impolite.

This model is a fine-tuned version of bert-base-chinese on the Chinese SQuAD v2.0 dataset, specifically designed for Chinese QA tasks, supporting both answerable and unanswerable questions.

A small BERT model for detecting saturation-type jailbreak attacks, not suitable for independently defending against other types of jailbreak attacks.

Legal text sentiment analysis model fine-tuned on Legal-BERT, achieving 88.6% accuracy

A phishing URL detection model fine-tuned based on the BERT architecture, used to identify potentially malicious URLs

A Thai sentiment analysis model fine-tuned based on the WangchanBERTa model, used to analyze the emotional tendency of Thai text.

A BERT-based pre-trained model for Arabic, suitable for Arabic text classification tasks

A BERT fine-tuned named entity recognition model for detecting social bias categories in text

A Chinese text emotion intensity detection model fine-tuned based on BERT architecture, capable of distinguishing between weak and strong emotional expressions

This model is based on the BERT architecture, specifically designed to identify the MITRE ATT&CK tactic framework categories for cybersecurity texts, supporting multi-label classification.

A BERT model specifically designed for Chinese classical poetry, trained on the Souyun open-source corpus, used for text completion and generation of classical poetry.

Turkish named entity recognition model fine-tuned based on dbmdz/bert-base-turkish-uncased

A legal named entity recognition model fine-tuned on Legal-BERT for identifying specific entities in legal texts

A sentiment analysis model fine-tuned from Japanese Twitter BERT, specifically designed for analyzing sentiment tendencies in Japanese tweets.

A legal case analysis model fine-tuned based on InLegalBERT, specialized for legal text processing tasks

A text classification model trained with AutoTrain, suitable for few-shot learning scenarios, capable of efficiently classifying news articles.

A sentence-transformers model based on the DeepPavlov/rubert-base-cased pre-trained model and fine-tuned on the MS-MARCO Russian passage ranking dataset, designed for Russian information retrieval tasks.

A BERT-base-uncased fine-tuned text classification model for Sustainable Development Goals (SDG) classification tasks