BERT-Base-Uncased Quantized Model for twitter-tweet-sentiment-classification

This repository hosts a quantized version of the BERT model, fine-tuned for twitter-tweet-sentiment-classification tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.

Model Details

Model Architecture: BERT Base Uncased
Task: twitter-tweet-sentiment-classification
Dataset: Stanford Sentiment Treebank v2 (SST2)
Quantization: Float16
Fine-tuning Framework: Hugging Face Transformers

Usage

Installation

pip install transformers torch

Loading the Model


from transformers import BertForSequenceClassification, BertTokenizer
import torch

# Load quantized model
quantized_model_path = "/kaggle/working/bert_finetuned_fp16"
quantized_model = BertForSequenceClassification.from_pretrained(quantized_model_path)
quantized_model.eval()  # Set to evaluation mode
quantized_model.half()  # Convert model to FP16

# Load tokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

# Define a test sentence
test_sentence = "It's just fine, nothing extraordinary"

# Tokenize input
inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)

# Ensure input tensors are in correct dtype
inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type
inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

# Make prediction
with torch.no_grad():
    outputs = quantized_model(**inputs)

# Get predicted class
predicted_class = torch.argmax(outputs.logits, dim=1).item()
print(f"Predicted Class: {predicted_class}")


label_mapping = {0: "very_negative", 1: "nagative", 2: "neutral", 3: "Positive", 4: "very_positive"}  # Example

predicted_label = label_mapping[predicted_class]
print(f"Predicted Label: {predicted_label}")

Performance Metrics

Accuracy: 0.82

Fine-Tuning Details

Dataset

The dataset is taken from Kaggle Stanford Sentiment Treebank v2 (SST2).

Training

Number of epochs: 3
Batch size: 8
Evaluation strategy: epoch
Learning rate: 2e-5

Quantization

Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.

Repository Structure

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation

Limitations

The model may not generalize well to domains outside the fine-tuning dataset.
Quantization may result in minor accuracy degradation compared to full-precision models.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.