# English Text Analysis

A multi-label sentiment classification model fine-tuned from ModernBERT-large, designed to detect 28 emotions from English text

A model for detecting self-disclosure (personal information) in sentences, supporting recognition of 17 types of personal information

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

This model is a fine-tuned version of bert-base-uncased for the Semantic Role Labeling (SRL) task on the Universal Proposition Bank dataset, employing a special marking enhancement method.

A DeBERTa-v3 fine-tuned model for prompt injection detection, designed to identify malicious prompt inputs

A frame classification model trained based on FrameNet v1.7, flattening frame annotations to specific sentences and transforming them into a sequence labeling task.

A BERT-based text classification model for categorizing English texts according to the United Nations Sustainable Development Goals (SDG)

A fine-tuned T5-small model for keyphrase generation, specifically designed for scientific paper abstracts, capable of extracting both present and absent keyphrases.

This model is a Named Entity Recognition (NER) model fine-tuned on the MIT Restaurant dataset based on DistilBERT, specifically designed for entity recognition tasks in the restaurant domain.

This is a fine-tuned model based on ClimateBERT, specifically designed for classifying climate-related paragraphs into four TCFD recommendation categories.

A text classification model based on the BERT-base-uncased architecture, specifically designed for sentiment analysis tasks, trained on the Go Emotions dataset.