AventIQ-AI / named-entity-recognition-for-information-extraction

Named Entity Recognition (NER) of Information Extraction

📌 Overview

This repository hosts the quantized version of the bert-base-cased model for Named Entity Recognition (NER) using the CoNLL-2003 dataset. The model is specifically designed to recognize entities related to Person (PER), Organization (ORG), and Location (LOC) . 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 Cased
• Task : Named Entity Recognition (NER) of Information Extraction
• Dataset : Hugging Face's CoNLL-2003
• Quantization : BrainFloat16
• Fine-tuning Framework : Hugging Face Transformers

🚀 Usage

Installation

pip install transformers torch

Loading the Model

from transformers import BertTokenizerFast, BertForTokenClassification
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "AventIQ-AI/named-entity-recognition-for-information-extraction"
model = BertForTokenClassification.from_pretrained(model_name).to(device)
tokenizer = BertTokenizerFast.from_pretrained(model_name)

Named Entity Recognition Inference

label_list = ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC"]

🔹 Labeling Scheme (BIO Format)

• B-XYZ (Beginning) : Indicates the beginning of an entity of type XYZ (e.g., B-PER for the beginning of a person’s name).
• I-XYZ (Inside) : Represents subsequent tokens inside an entity (e.g., I-PER for the second part of a person’s name).
• O (Outside) : Denotes tokens that are not part of any named entity.

def predict_entities(text, model):

    tokens = tokenizer(text, return_tensors="pt", truncation=True)
    tokens = {key: val.to(device) for key, val in tokens.items()}  # Move to CUDA

    with torch.no_grad():
        outputs = model(**tokens)
    
    logits = outputs.logits  # Extract logits
    predictions = torch.argmax(logits, dim=2)  # Get highest probability labels

    tokens_list = tokenizer.convert_ids_to_tokens(tokens["input_ids"][0])
    predicted_labels = [label_list[pred] for pred in predictions[0].cpu().numpy()]

    final_tokens = []
    final_labels = []
    for token, label in zip(tokens_list, predicted_labels):
        if token.startswith("##"):  
            final_tokens[-1] += token[2:]  # Merge subword
        else:
            final_tokens.append(token)
            final_labels.append(label)

    for token, label in zip(final_tokens, final_labels):
        if token not in ["[CLS]", "[SEP]"]:
            print(f"{token}: {label}")

# 🔍 Test Example
sample_text = "Elon Musk is the CEO of Tesla, which is based in California."
predict_entities(sample_text, model)

📊 Evaluation Results for Quantized Model

🔹 Overall Performance

• Accuracy : 97.10% ✅
• Precision : 89.52%
• Recall : 90.67%
• F1 Score : 90.09%

🔹 Performance by Entity Type

⏳ Inference Speed Metrics

• Total Evaluation Time : 15.89 sec
• Samples Processed per Second : 217.26
• Steps per Second : 27.18
• Epochs Completed : 3

Fine-Tuning Details

Dataset

The Hugging Face's CoNLL-2003 dataset was used, containing texts and their ner tags.

📊 Training Details

• 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.safetensors/   # Quantized 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.