| # 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 | |
| ```bash | |
| pip install transformers torch | |
| ``` | |
| ### Loading the Model | |
| ```python | |
| 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 | |
| ```python | |
| 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** | |
| | Entity Type | Precision | Recall | F1 Score | Number of Entities | | |
| |------------|-----------|--------|----------|--------------------| | |
| | **LOC** (Location) | **91.46%** | **92.07%** | **91.76%** | 3,000 | | |
| | **MISC** (Miscellaneous) | **71.25%** | **72.83%** | **72.03%** | 1,266 | | |
| | **ORG** (Organization) | **89.83%** | **93.02%** | **91.40%** | 3,524 | | |
| | **PER** (Person) | **95.16%** | **94.04%** | **94.60%** | 2,989 | | |
| --- | |
| #### ⏳ **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. | |