# DistilBERT Optimization

A sentiment analysis model fine-tuned based on distilbert-base-uncased, achieving 87.67% accuracy on the evaluation set

This is a multilingual sentence embedding model capable of mapping sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks such as clustering and semantic search.

A sentiment analysis model fine-tuned based on distilbert-base-uncased, achieving an accuracy of 85.67% on the evaluation set

This model is based on the DistilBERT architecture, specifically designed for multi-label classification tasks to determine whether emails and URLs are safe or pose phishing risks.

This model is a DistilBERT model fine-tuned on the SQuAD dataset for extractive question answering tasks, with high exact match rate and F1 score.

An English named entity recognition model trained on distilbert-base-uncased, specifically designed for identifying biomedical entities (107 entity categories), suitable for text corpora such as case reports.

This model is a lightweight version based on DistilBERT, fine-tuned on the conll2003 dataset for named entity recognition tasks.

A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset

This is a sentence transformer model based on DistilBERT, which maps text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

This model is a question-answering model fine-tuned on the SQuAD dataset based on distilbert-base-cased, specifically trained for the Keras.io question-answering tutorial.

A lightweight named entity recognition model based on DistilBERT, fine-tuned on the CoNLL2003 dataset

A multilingual text toxicity classification model trained on the JIGSAW Toxic Comment Classification Challenge dataset, supporting 10 languages.

A DistilBERT model fine-tuned for Persian Named Entity Recognition (NER) tasks, supporting recognition of 10 entity types.

A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset