Instructions to use Sathiyapramod/my_sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sathiyapramod/my_sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sathiyapramod/my_sentiment_analysis")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sathiyapramod/my_sentiment_analysis", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Sentiment Analysis using Transformers & Gradio
This repository contains a custom-trained Transformer model specifically fine-tuned for Sentiment Analysis. The model classifies text into categories such as Positive, Negative, and (optionally) Neutral.
π Project Overview
The goal of this project is to provide an easy-to-use interface for analyzing the emotional tone of text. By leveraging the transformers library and Gradio, this model can be tested in real-time via a web interface.
π οΈ Installation
To run this project locally, you will need to install the following dependencies:
pip install transformers gradio