# Sentiment Analysis

A financial domain-specific model fine-tuned from Llama 3.1 8B, excelling in financial reasoning, Q&A, and multi-turn dialogues

A sentiment analysis model fine-tuned based on distilbert-base-uncased for text sentiment classification tasks

This is a sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 93.2% accuracy on the evaluation set.

A sentiment analysis model fine-tuned based on distilbert-base-uncased, achieving 87.67% accuracy on the evaluation set

A sentiment analysis model fine-tuned based on distilbert-base-uncased, achieving an accuracy of 85.67% on the evaluation set

A sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 94% accuracy on the evaluation set

A sentiment analysis model based on DistilBERT, fine-tuned on an unknown dataset with an accuracy of 56.16%

This model is a facial expression classifier fine-tuned on the FER2013 dataset based on Google's ViT base model, capable of classifying images into four facial expressions.

This is a DistilBERT model fine-tuned on the SST-2 English dataset using AutoTrain for text classification tasks.

A sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 87.67% accuracy on the evaluation set

A financial domain-specific language model fine-tuned based on LLAMA 3 8B, focusing on financial text analysis tasks

A text generation model fine-tuned on the SST dataset based on T5-base

A Tarot card interpretation model fine-tuned based on TinyLlama-1.1B, capable of making predictions and interpretations based on Tarot cards.

Emobloom-7b is part of the EmoLLMs project, the first open-source large language model series with instruction-following capabilities, focusing on comprehensive sentiment analysis.

Emollama-chat-7b is part of the EmoLLMs project and is the first open-source large language model series with instruction-following capability, focusing on comprehensive sentiment analysis.

A text classification model fine-tuned on emotion datasets based on DistilBERT, used to identify emotional categories in text.

A fine-tuned speech emotion recognition model based on facebook/wav2vec2-base, achieving 84.71% accuracy on the evaluation set

A sentiment analysis model fine-tuned on the SST-2 dataset based on the GPT-2 medium model, designed for text sentiment classification tasks.

An image classification model based on the BEiT architecture, fine-tuned on the FER2013 and CK+ datasets, primarily used for facial expression recognition tasks.

Facial expression recognition model fine-tuned on the FER2013 dataset based on Microsoft's BEiT model

An image classification model based on the BEiT architecture, fine-tuned on the FER2013 dataset for facial expression recognition tasks

Facial expression recognition model fine-tuned on the FER2013 dataset based on Microsoft's BEiT model

A vision Transformer model based on the BEiT architecture, fine-tuned on the FER2013 dataset for facial expression recognition tasks

An image classification model based on the BEiT architecture, fine-tuned on the FER2013 dataset for facial expression recognition

This is a model fine-tuned based on the Bert architecture, specifically designed to analyze the sentiment tendency of text messages, capable of distinguishing between positive and negative sentiments.

This is a binary classification model trained using AutoTrain, specifically designed for sentiment analysis of IMDB movie reviews.

A lightweight model fine-tuned for sentiment analysis based on the DistilBERT base model, achieving an accuracy of 90.37%

SKEP (Sentiment Knowledge Enhanced Pre-training) was proposed by Baidu in 2020, specifically designed for sentiment analysis tasks. The model incorporates multi-type knowledge through sentiment masking techniques and three sentiment pre-training objectives.

A sentiment analysis model pre-trained based on the BERT-BASE-UNCASED architecture, specifically optimized for the TWTEVAL dataset.

A text sentiment classification model fine-tuned based on microsoft/deberta-v3-base, trained on an unknown dataset with an evaluated F1 score of 0.4468

A Nordic RoBERTa model trained on Swedish, Danish, and Norwegian Wikipedia, suitable for fill-mask tasks.

A sentiment analysis model fine-tuned on BERT-Large-Uncased, trained on the Stanford Sentiment Treebank (SST2) for text sentiment classification.

This model is a fine-tuned version of bert-base-uncased on an unknown dataset, primarily used for text classification tasks.

A text sentiment classification model based on DistilBERT, fine-tuned on the emotion dataset with an accuracy of 92.9%

A lightweight sentiment analysis model based on DistilBERT, fine-tuned on emotion datasets with an accuracy of 92.95%

A sentiment analysis model based on DistilBERT, fine-tuned on sentiment datasets with an accuracy of 91.65%

This model is a fine-tuned version of distilbert-base-uncased on the SST-2 dataset for sentiment analysis tasks.

A sentiment classification model based on DistilBERT, fine-tuned on sentiment datasets with an accuracy of 91.5%

A text classification model fine-tuned on the GLUE dataset based on distilbert-base-uncased

HeBERT is a Hebrew pretrained language model based on the BERT architecture, supporting tasks such as polarity analysis and sentiment recognition.