twitter roberta base sentiment latest

Introduction

The Twitter-RoBERTa-base for Sentiment Analysis - Updated (2022) is a model trained on approximately 124 million tweets from January 2018 to December 2021. It is fine-tuned for sentiment analysis using the TweetEval benchmark. This model is designed for English tweets, classifying sentiments into three categories: Negative, Neutral, and Positive. It is part of the TweetNLP project and can be accessed for demonstration purposes on the TweetNLP website.

Architecture

This model is based on the RoBERTa-base architecture, which is a transformer model pre-trained on a large corpus of English data. The model is specifically fine-tuned for sentiment analysis using the TweetEval benchmark. The training process leverages the transformer architecture's ability to understand contextual nuances in text data, making it suitable for analyzing sentiments in tweets.

Training

The model was trained using a large dataset of tweets, containing around 124 million entries. The training data spans a period from January 2018 to December 2021. The finetuning process involved the use of the TweetEval benchmark, ensuring the model's effectiveness in sentiment analysis tasks. The training approach ensures that the model can handle the diverse and often informal language found in tweets.

Guide: Running Locally

To run the Twitter-RoBERTa-base model locally, you can follow these steps:

1. Install Dependencies: Ensure you have Python and necessary libraries installed. You can install the transformers library via pip:

   pip install transformers numpy scipy
   

2. Load the Model: Use the transformers library to load the model and tokenizer:

   from transformers import pipeline
   sentiment_task = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest", tokenizer="cardiffnlp/twitter-roberta-base-sentiment-latest")
   

3. Run Sentiment Analysis: Analyze sentiments of your text:

   result = sentiment_task("Covid cases are increasing fast!")
   print(result)
   

4. Use Cloud GPUs: For faster processing, consider using cloud services like AWS, Google Cloud, or Azure to access GPU resources.

License

The use of this model is governed by the license specified in the model's repository. It is important to review and comply with the license terms when using the model in any application.