Model Card for Fine-tuned DistilBERT-SST2 with Yelp Polarity

Model Description

This model is a fine-tuned version of distilbert-base-uncased, a distilled version of BERT optimized for efficiency. It has been initially trained on the Stanford Sentiment Treebank (SST-2) dataset and further fine-tuned on the Yelp Polarity dataset for improved sentiment classification performance. The model classifies English text into two categories: positive and negative sentiment.

DistilBERT-SST2-Yelp is lightweight, fast, and ideal for sentiment analysis tasks on short texts such as customer reviews, product feedback, and social media posts.

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Intended Uses & Limitations

Intended Uses:

• Sentiment analysis on short English texts, including:
  • Reviews (e.g., product, restaurant, movie, etc.)
  • Comments
  • Tweets or other social media posts
• Applications requiring efficient, low-latency inference for real-time analysis.

Limitations:

• Domain Specificity: Fine-tuned on SST-2 and Yelp Polarity, so it may not generalize well to highly specific or niche domains.
• Context Length: Optimized for short texts and may perform poorly with long-form inputs.
• Language Support: Works only for English text.
• Biases: May inherit biases present in the datasets, including biases related to language usage in sentiment analysis tasks.

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How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the fine-tuned model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("./DistilBERT-SST2-Yelp")
model = AutoModelForSequenceClassification.from_pretrained("./DistilBERT-SST2-Yelp")

# Example input
text = "This movie was fantastic!"

# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()

# Class mapping: 0 -> Negative, 1 -> Positive
print("Predicted Sentiment:", "Positive" if predicted_class == 1 else "Negative")

Limitations and Bias

• The SST-2 and Yelp Polarity datasets may reflect cultural, contextual, or domain-specific biases in sentiment interpretation.
• Over-reliance on specific patterns or keywords from the training data may lead to incorrect classifications, especially in nuanced or ambiguous cases.
• The model is not suitable for multilingual sentiment analysis or for detecting sentiment in specialized fields (e.g., legal, medical).

Training Data

• SST-2: The Stanford Sentiment Treebank (SST-2) dataset, containing movie reviews labeled as positive or negative.
• Yelp Polarity: A dataset of customer reviews from Yelp, labeled as positive or negative.

The model is fine-tuned on both datasets to improve its performance on a variety of sentiment classification tasks.

Training Procedure

• Base Model: distilbert-base-uncased
• Framework: Hugging Face Transformers
• Optimizer: AdamW with weight decay
• Learning Rate: 2e-5
• Batch Size: 32 (effective, using gradient accumulation)
• Epochs: 3
• Evaluation Strategy: Per epoch
• Hardware: NVIDIA RTX 4060 with CUDA support

Optimizations:

• Mixed precision (fp16) for faster training and reduced memory usage.
• Gradient accumulation for simulating larger batch sizes.
• Learning rate warmup and weight decay for stable convergence.

Evaluation Results

Evaluation Metric: Accuracy, computed using the Hugging Face evaluate library.

Future Work

• Fine-tune on more diverse datasets, including domain-specific datasets for enhanced performance in other areas.
• Extend support to multilingual sentiment analysis.
• Improve efficiency for deployment through techniques such as pruning, quantization, or distillation.

License

The model is shared under the Apache 2.0 License.