language: en
license: mit
tags:
- distilbert
- sentiment-analysis
- multilingual
widgets:
- text: I love this movie!
Model Name: DistilBERT for Sentiment Analysis
Model Description
Overview
This model is a fine-tuned version of distilbert-base-uncased on a social media dataset for the purpose of sentiment analysis. It can classify text into positive, negative, and neutral sentiments.
Intended Use
This model is intended for sentiment analysis tasks, particularly for analyzing social media texts. It supports multiple languages, making it versatile for international applications.
Model Architecture
This model is based on the DistilBertForSequenceClassification architecture, a distilled version of BERT that maintains comparable performance on downstream tasks while being more computationally efficient.
Training
Training Data
The model was trained on a dataset consisting of social media posts, labeled for sentiment (positive, negative, neutral). The dataset includes multiple languages, enhancing the model's multilingual capabilities.
Training Procedure
The model was trained using the following parameters:
- Optimizer: AdamW
- Learning Rate: 5e-5
- Batch Size: 32
- Epochs: 30
Training was conducted on Kaggle, utilizing two GPUs for accelerated training.