# Social Media Analysis

A Chinese text classification model fine-tuned on the Weibo 100k sentiment analysis dataset based on bert-base-chinese

A Turkish sentiment analysis model fine-tuned on dbmdz/bert-base-turkish-cased, supporting negative, neutral, and positive sentiment classification.

A Chinese sentiment classification model fine-tuned based on mDeBERTa-v3-base, capable of recognizing 8 emotion labels

A sentiment analysis model fine-tuned based on distilbert/distilbert-base-uncased, trained solely on synthetic data, supporting 5 sentiment classifications.

A sentiment analysis model fine-tuned from Japanese Twitter BERT, specifically designed for analyzing sentiment tendencies in Japanese tweets.

A French tweet sentiment classification model fine-tuned based on camembert-base, capable of classifying French text into positive, negative, or neutral

STAR is a supervised contrastive pretraining transformer model for social media writing style understanding.

A BERT-based fine-tuned model designed for multi-label sentiment classification, supporting recognition of 11 emotions.

A sentiment analysis model based on BETO (Spanish version of BERT), supporting sentiment classification for Spanish text.

WRAPresentations is an advanced sentence transformer model specifically designed for Twitter argument mining, capable of mapping tweets into four categories: 'Reason', 'Statement', 'Notification', and 'Nonsense'.

This is a binary classification model trained with AutoTrain, designed to distinguish whether an image contains a map.

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

A Chinese offensive language detection model fine-tuned on COLDataset based on hfl/chinese-roberta-wwm-ext

This is an image classification model fine-tuned based on Google's ViT model, specifically designed for meme recognition tasks.

A fine-tuned model based on microsoft/xtremedistil-l6-h384-uncased, specific purpose not clearly stated

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

This model is a Twitter-specific named entity recognition model based on the RoBERTa architecture, continuously fine-tuned on the tweetner7 dataset for identifying named entities in tweets.

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

An mT5 many-to-one model fine-tuned on the CrossSum dataset, capable of summarizing texts written in multiple languages into Chinese (Simplified)

A Turkish sentiment analysis model based on Distilbert, fine-tuned on a Turkish-translated sentiment dataset

A BERT model optimized for Arabic dialects and tweets, pre-trained on 60 million Arabic tweets with MLM tasks, with added support for emojis and common vocabulary.

Pre-trained Arabic BERT mini language model for Arabic text processing tasks

AraBERTv0.2-Twitter is a pre-trained language model optimized for Arabic dialects and tweets, developed based on the BERT architecture, with added support for emojis and common vocabulary.

Pretrained BERT large language model for Arabic, supporting Modern Standard Arabic and some dialects

Pretrained Arabic BERT base language model supporting Modern Standard Arabic and some dialects

English sentiment analysis model trained on the EmoEvent corpus, utilizing the BERTweet architecture

A lightweight fake news detection model fine-tuned based on the BERT Tiny architecture, suitable for Chinese text analysis

A multilingual summarization model based on the mT5 architecture, capable of summarizing text in multiple languages into English

This model is based on the RoBERTa architecture and is used to analyze purchase intentions expressed in English texts, performing binary classification (with/without purchase intention).

A model specialized in Thai text sentiment analysis, supporting positive, neutral, and negative sentiment classification.

An English case-sensitive BERT model trained on conversational data from Twitter, Reddit, DailyDialogues, etc., suitable for natural language processing tasks in dialogue scenarios.