bertweet base sentiment analysis LLM Model

Introduction

BERTweet-Base-Sentiment-Analysis is a sentiment analysis model using the BERTweet architecture, designed to classify sentiments in English tweets. It assigns positive (POS), negative (NEG), and neutral (NEU) labels to textual data.

Architecture

The model is based on BERTweet, a RoBERTa model pre-trained specifically on English tweets. It was trained using the SemEval 2017 corpus, comprising approximately 40,000 tweets.

Training

The training process utilized the SemEval 2017 dataset, focusing on sentiment classification tasks. This dataset offers a robust base for training models to understand and classify the sentiment of social media text data effectively.

Guide: Running Locally

Clone the Repository:
Clone the pysentimiento repository from GitHub:
```
git clone https://github.com/finiteautomata/pysentimiento/
```
Install Dependencies:
Navigate into the project directory and install the necessary dependencies:
```
cd pysentimiento
pip install -r requirements.txt
```
Run Sentiment Analysis:
Use the library to perform sentiment analysis on text data.
Cloud GPUs:
For enhanced performance, especially with larger datasets, consider using cloud GPU services like AWS, Google Cloud, or Azure.

License

The pysentimiento library is open-source and intended for non-commercial use and scientific research purposes. Models are trained on datasets like the TASS and SEMEval 2017, and usage is subject to their respective licenses.

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