# DistilBERT Fine-tuning

Transformer model based on Distilbert architecture, specializing in Brazilian Portuguese sentiment analysis

Transformer model based on Distilbert architecture, specialized in sentiment analysis for Brazilian Portuguese

A lightweight text sentiment analysis model based on DistilBERT, specifically fine-tuned for book review data

A DistilBERT fine-tuned model for customer feedback classification, accurately categorizing text into six predefined categories

A text classification model fine-tuned on distilbert-base-uncased for complaint content classification, achieving 94.2% accuracy

A sentiment analysis model fine-tuned on the IMDb movie review dataset using the DistilBERT architecture, designed to classify text as positive or negative sentiment.

A news classification model fine-tuned based on distilbert-base-uncased, focusing on the classification task of litigation-related news.

A gender classification model fine-tuned on distilbert-base-uncased, achieving an accuracy of 1.0 on the evaluation set

This is a text classification model fine-tuned on DistilBERT for identifying action items in text.

A named entity recognition model fine-tuned on the conll2003 dataset based on the DistilBERT-base-uncased model, excelling in NER tasks.

A DistilBERT-based English keyphrase extraction model that performs exceptionally well in the field of scientific paper abstracts.

A text classification model based on German DistilBERT for detecting toxic content in German comments

A lightweight named entity recognition model based on DistilBERT, optimized for English text and case-insensitive.

This model is a fine-tuned version of DistilBERT on an unspecified dataset, primarily used for text classification tasks.

FReE is a model based on DistilBERT, specifically fine-tuned for financial relationship type detection and classification tasks.

DistilBERT-based sentiment classification model for Tamil-English mixed text, fine-tuned on the tamilmixsentiment dataset

This model is a text classification model fine-tuned on the IMDb dataset based on distilbert-base-uncased for sentiment analysis tasks.

Text classification model fine-tuned based on distilbert-base-uncased-finetuned-sst-2-english, achieving 86.38% accuracy on the evaluation set