metadata
license: mit
language:
- en
pipeline_tag: text-classification
tags:
- sentiment-analysis
- nlp
- distilbert
base_model: distilbert-base-uncased
🤖 My Fine-Tuned Sentiment Analysis Model
This model is a fine-tuned version of DistilBERT designed for sentiment analysis. It analyzes text and predicts whether the sentiment is POSITIVE or NEGATIVE (or specific labels depending on your training).
📊 Model Details
- Model Architecture: DistilBERT
- Task: Text Classification (Sentiment Analysis)
- Language: English
- License: MIT
🚀 How to Use
You can use this model directly with the Hugging Face pipeline in just a few lines of code:
from transformers import pipeline
# 1. Load the pipeline
classifier = pipeline("text-classification", model="Rcids/my-finetuned-model")
# 2. Test it out
text = "I absolutely loved this product! It was amazing."
result = classifier(text)
print(result)
# Output: [{'label': 'POSITIVE', 'score': 0.99}]
## 🔧 Training Details
This model was fine-tuned on a custom dataset to improve performance on specific sentiment tasks compared to the base generic model.
- **Optimizer:** AdamW
- **Framework:** PyTorch
- **Base Model:** [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)
## ⚠️ Limitations
- The model performance depends on the domain of the data it was trained on.
- It may not detect sarcasm or subtle nuances in complex sentences.