# RoBERTa Fine-tuning

KAILAS is a Transformer model based on the RoBERTa architecture, specifically adapted for NASA's Science Mission Directorate applications to annotate Unified Astronomy Thesaurus labels.

A fine-tuned model based on RoBERTa-base for detecting AI-generated English text.

This model is a fine-tuned version of obi/deid_roberta_i2b2 on an unknown dataset, primarily used for text processing tasks.

This model is a text classification model fine-tuned on RoBERTa, designed to recognize user input intent, distinguishing between medical consultation and casual conversation scenarios.

This model is a text classification model fine-tuned based on RoBERTa, specializing in identifying whether user input text belongs to medical consultation or casual conversation categories.

A RoBERTa-based text classification model for detecting toxic prompts and responses in dialogue systems

A RoBERTa model pre-trained on multilingual legal texts and fine-tuned for Indian legal named entity recognition tasks

A German emotion classification model fine-tuned based on RoBERTa, capable of classifying text into six emotion categories: anger, fear, disgust, sadness, happiness, and none of the above.

A text classifier fine-tuned based on RoBERTa-large, specifically designed to determine whether news articles involve government regulation topics.

This model is a fine-tuned version of BSC-LT/roberta-base-biomedical-clinical-es for Named Entity Recognition (NER) tasks on Spanish biomedical clinical texts.

A text style classifier fine-tuned based on the RoBERTa base model, used to distinguish between non-fiction narrative style and fiction descriptive style.

A model fine-tuned from FewNERD-fine-supervised based on numind/NuNER-v1.0 for recognizing organizational entities (ORG) in text

A fine-tuned RoBERTa Large model for detecting AI-generated content

A news topic classification model fine-tuned based on roberta-base, trained on the New York Times news dataset with an accuracy of 0.91.

A malicious prompt detection model fine-tuned on RoBERTa, capable of identifying prompts that induce the generation of phishing websites/emails

A named entity recognition model fine-tuned based on Roberta-large, specifically designed for historical newspaper texts that may contain OCR errors.

A fine-tuned large language model optimized for central bank communications, including economic entity classifier and sentiment classifier

Fine-tuned RoBERTa model for FOMC hawkish-dovish-neutral classification tasks

A news text gap-filling prediction model fine-tuned based on roberta-base, trained on approximately 13 million English news articles

A depression detection model fine-tuned based on RoBERTa-large, used to analyze the level of depression in English social media posts, categorized into no depression, moderate, or severe.

Japanese extractive QA model fine-tuned from rinna/japanese-roberta-base, trained on the JaQuAD dataset

A language model based on the RoBERTa-large architecture, pre-trained on depression-themed posts from Reddit, specifically designed for depression detection.

A question-answering model fine-tuned based on the roberta-base model, trained on SQuAD format datasets

A fine-tuned version of the RoBERTa-base model on the MNLI dataset, suitable for natural language inference tasks

RelBERT is a semantic relation understanding model fine-tuned on RoBERTa-large, specifically designed for lexical relation classification and analogy question tasks.

A legal text processing model based on the RoBERTa architecture, fine-tuned on the CUAD dataset

A RoBERTa-based QA classification model for identifying categories of non-factual questions

A named entity recognition model fine-tuned on the tweetner7 dataset based on roberta-base, specifically designed for entity recognition in Twitter text.

A named entity recognition model fine-tuned on a toy dataset based on the xlm-roberta-large-ner-hrl model

A fine-tuned version based on the XLM-RoBERTa-base model on a specific dataset, primarily used for sequence labeling tasks, with an evaluated F1 score of 0.8561.

A named entity recognition model fine-tuned on the tner/tweetner7 dataset based on roberta-large, specifically designed for entity recognition in Twitter text

A named entity recognition model fine-tuned on the WNUT 17 dataset based on the Twitter RoBERTa model, used to identify specific entity categories in text.

Named Entity Recognition (NER) model fine-tuned based on the Mongolian RoBERTa-base model

This model is a fine-tuned version of deepset/roberta-base-squad2, suitable for question answering tasks.

A Spanish biomedical clinical text named entity recognition model fine-tuned based on PlanTL-GOB-ES/roberta-base-biomedical-clinical-es

BERT model fine-tuned based on roberta-large-ner-english for protagonist identification tasks

A question-answering model fine-tuned on the SQuAD dataset based on RoBERTa-base

A model fine-tuned based on roberta-large for specific task processing, with an evaluation loss value of 0.7825

A sentiment polarity analysis model fine-tuned based on PlanTL-GOB-ES/roberta-base-bne for Spanish text sentiment classification

This is a Spanish biomedical model based on RoBERTa, specifically fine-tuned for named entity recognition tasks on the PharmaCoNER dataset.