# RoBERTa fine-tuning

This model is an English sentiment analysis model based on the RoBERTa architecture, capable of classifying text into positive, neutral, and negative categories.

This model is a fine-tuned version of FacebookAI's roberta-base on the Webi-CPC-11 dataset, primarily used for text classification tasks, achieving an accuracy of 84.32% on the evaluation set.

A toxicity comment classification model fine-tuned based on roberta-base, used to detect toxic content in comments.

A Czech emotion classification model fine-tuned on RoBERTa architecture, supporting six emotion categories

Named entity recognition model fine-tuned based on Roberta Large Ukrainian model, supporting 13 entity types recognition

A text classification model fine-tuned based on RoBERTa-base for detecting suicidal tendencies in text, with an accuracy of 96.5%

This model is a fine-tuned version based on /gemini/data-1/sikuroberta_vocabtxt. Specific uses and functionalities require further information.

A text classification model fine-tuned on RoBERTa, specifically designed to identify spam emails/messages with an accuracy of 99.06%

Zero-shot text classification model based on RoBERTa-large, optimized for scientific domains with excellent performance in cross-domain tasks

A food entity recognition model specifically designed for informal text on social media, fine-tuned based on the RoBERTa architecture

This model is a RoBERTa model pre-trained on Twitter data, fine-tuned on the tner/tweetner7 dataset for named entity recognition in tweets.

A named entity recognition model fine-tuned on the TweetNER7 dataset based on the Twitter-RoBERTa model, specifically designed for entity recognition in tweets.

A named entity recognition model fine-tuned on the CoNLL2003 dataset based on the RoBERTa-large model, demonstrating excellent performance in NER tasks.

Cross-lingual model fine-tuned on German and French datasets based on XLM-RoBERTa-base

A named entity recognition model fine-tuned on the PLOD-filtered dataset based on RoBERTa-base, specifically designed for detecting abbreviated entities in text.

A named entity recognition model fine-tuned on the PLOD-unfiltered dataset based on RoBERTa-large, specifically designed for identifying abbreviations and terms in scientific texts.

A Japanese QA model fine-tuned on the JaQuAD dataset, suitable for extractive QA tasks

Named Entity Recognition (NER) model fine-tuned based on xlm-roberta-base, demonstrating excellent performance on the evaluation set (F1 score 0.9777)

This is a RoBERTa base model fine-tuned on the CUAD legal contract dataset, specifically designed for legal contract review tasks.

A sarcasm text classifier based on RoBERTa-base architecture, used to distinguish whether a text is sarcastic

A POS tagging model for Catalan, fine-tuned from the roberta-base-ca model

A text classification model fine-tuned on the Amazon multilingual reviews dataset based on the roberta-base-bne model, primarily used for sentiment analysis of Spanish comments

A fine-tuned argumentative text classification model based on the RoBERTa(base) pre-trained model, used to identify whether text contains argumentative content.

A text classification model fine-tuned on the GLUE MNLI dataset based on the RoBERTa-base model, excelling in natural language inference tasks

Named Entity Recognition model for Catalan, fine-tuned based on BERTa

A text similarity calculation model fine-tuned on the STS-B dataset based on RoBERTa-large

A toxicity classification model fine-tuned based on RoBERTa-large, trained on the Jigsaw competition dataset for identifying toxic content in English text.

This model is a binary sentiment analysis model fine-tuned on the RoBERTa base version, specifically designed for sentiment polarity classification of Yelp reviews.

A Catalan semantic text similarity model based on RoBERTa architecture, specifically designed for evaluating text similarity

A text classification model based on the RoBERTa architecture, specifically fine-tuned for the GoEmotions dataset to recognize 28 different emotions.

A Spanish POS tagging model fine-tuned on the CONLL corpus based on RuPERTa-base

This is a RoBERTa model pre-trained on Classical Chinese, specifically designed for sentence segmentation tasks, capable of automatically identifying sentence boundaries in Classical Chinese texts.

This model is a Spanish Q&A model fine-tuned on the SQAC dataset based on roberta-base-bne

This model is used to evaluate Spanish phrase paraphrasing, fine-tuned based on the roberta-base-bne pre-trained model.

A multi-class text classification model fine-tuned based on the RoBERTa-base architecture, specifically designed for emotion detection tasks, achieving 94.1% accuracy on the emotion dataset.

A Catalan cyberbullying detection model based on RoBERTa architecture, fine-tuned on 410,000 social media data entries

This model is a RoBERTa model trained on 1 million SMILES from the PubChem 77M dataset, using the Smiles-Tokenizer tool for tokenization, suitable for molecular representation learning and cheminformatics tasks.

Named Entity Recognition model fine-tuned based on RoBERTa-base architecture, specifically optimized for Twitter text

Text classification model fine-tuned on the MNLI task based on the Catalan RoBERTa model