README.md

metadata

license: mit
library_name: transformers
pipeline_tag: text-classification
tags:
  - sentiment-analysis
  - text-classification
  - distilbert
  - fine-tuned
  - nlp
language:
  - en
datasets:
  - sentiment140
metrics:
  - accuracy
  - f1
base_model: distilbert-base-uncased
model-index:
  - name: my-sentiment-model
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: sentiment140
          type: sentiment140
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.85
          - name: F1 Score (Macro)
            type: f1
            value: 0.84

My DistilBERT Sentiment Model

Fine-tuned DistilBERT for 3-class sentiment classification (negative, neutral, positive).

Model Description

This model is a fine-tuned version of DistilBERT-base-uncased for sentiment analysis. It has been trained to classify text into three sentiment categories:

• Negative (0)
• Neutral (1)
• Positive (2)

Intended Uses

This model is intended for sentiment analysis tasks on English text. It can be used to:

• Analyze customer feedback and reviews
• Monitor social media sentiment
• Classify emotions in text data
• Support content moderation systems

Limitations

• Trained primarily on English text
• May not perform well on domain-specific jargon
• Performance may vary on very short or very long texts
• Potential bias from training data

Training Details

• Base Model: distilbert-base-uncased
• Training Epochs: 2
• Batch Size: 8
• Learning Rate: 3e-5
• Max Sequence Length: 128
• Optimizer: AdamW
• Weight Decay: 0.01

Model Performance

The model achieves the following performance on the test set:

• Accuracy: 85%
• F1-Score (Macro): 84%
• F1-Score (Weighted): 85%

Usage

Install the required dependencies:

pip install transformers torch

Load and use the model:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "your-username/my-sentiment-model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare text
text = "I love this product!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(probabilities, dim=-1).item()

# Map prediction to label
labels = {0: "negative", 1: "neutral", 2: "positive"}
confidence = probabilities[0][predicted_class].item()

print(f"Text: {text}")
print(f"Sentiment: {labels[predicted_class]} (confidence: {confidence:.2%})")

Citation

If you use this model in your research, please cite:

@misc{my-sentiment-model,
  author = {Your Name},
  title = {Fine-tuned DistilBERT for Sentiment Analysis},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/your-username/my-sentiment-model}
}

License

This model is released under the MIT License.