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---
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 non-negative and negative sentiments.
### Intended Use
This model is intended for sentiment analysis tasks, particularly for analyzing social media texts.
### 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, surveys and interviews, labeled for sentiment (non-negative and negative). The dataset includes texts from a variety of sources and demographics.
### 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. |