| 🧠 NERClassifier-BERT-CoNLL2003 |
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| A BERT-based Named Entity Recognition (NER) model fine-tuned on the CoNLL-2003 dataset. It classifies tokens in text into predefined entity types: Person (PER), Location (LOC), Organization (ORG), and Miscellaneous (MISC). This model is ideal for information extraction, document tagging, and question answering systems. |
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| ✨ Model Highlights |
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| 📌 Based on bert-base-cased (by Google) |
| 🔍 Fine-tuned on the CoNLL-2003 Named Entity Recognition dataset |
| ⚡ Supports prediction of 4 entity types: PER, LOC, ORG, MISC |
| 💾 Available in both full and quantized versions for fast inference |
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| 🧠 Intended Uses |
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| • Resume and document parsing |
| • News article analysis |
| • Question answering pipelines |
| • Chatbots and virtual assistants |
| • Information retrieval and tagging |
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| 🚫 Limitations |
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| • Trained on English-only NER data (CoNLL-2003) |
| • May not perform well on informal text (e.g., tweets, slang) |
| • Entity boundaries may be misaligned with subword tokenization |
| • Limited performance on extremely long sequences (>128 tokens) |
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| 🏋️♂️ Training Details |
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| | Field | Value | |
| | -------------- | ------------------------------ | |
| | **Base Model** | `bert-base-cased` | |
| | **Dataset** | CoNLL-2003 | |
| | **Framework** | PyTorch with 🤗 Transformers | |
| | **Epochs** | 5 | |
| | **Batch Size** | 16 | |
| | **Max Length** | 128 tokens | |
| | **Optimizer** | AdamW | |
| | **Loss** | CrossEntropyLoss (token-level) | |
| | **Device** | Trained on CUDA-enabled GPU | |
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| 📊 Evaluation Metrics |
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| | Metric | Score | |
| | ----------------------------------------------- | ----- | |
| | Accuracy | 0.98 | |
| | F1-Score | 0.97 | |
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| 🔎 Label Mapping |
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| | Label ID | Entity Type | |
| | -------- | ----------- | |
| | 0 | O | |
| | 1 | B-PER | |
| | 2 | I-PER | |
| | 3 | B-ORG | |
| | 4 | I-ORG | |
| | 5 | B-LOC | |
| | 6 | I-LOC | |
| | 7 | B-MISC | |
| | 8 | I-MISC | |
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| 🚀 Usage |
| ```python |
| from transformers import BertTokenizerFast, BertForTokenClassification |
| import torch |
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| model_name = "AventIQ-AI/ner_bert_conll2003" |
| tokenizer = BertTokenizerFast.from_pretrained(model_name) |
| model = BertForTokenClassification.from_pretrained(model_name) |
| model.eval() |
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| def predict_tokens(text): |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128) |
| with torch.no_grad(): |
| outputs = model(**inputs).logits |
| predictions = torch.argmax(outputs, dim=2) |
| tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) |
| labels = [model.config.id2label[label_id.item()] for label_id in predictions[0]] |
| return list(zip(tokens, labels)) |
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| # Test example |
| print(predict_tokens("Barack Obama visited Google in California.")) |
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| ``` |
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| 🧩 Quantization |
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| Post-training static quantization applied using PyTorch to reduce model size and improve inference performance on edge devices. |
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| 🗂 Repository Structure |
| ``` |
| . |
| ├── model/ # Quantized model files |
| ├── tokenizer_config/ # Tokenizer and vocab files |
| ├── model.safensors/ # Fine-tuned model in safetensors format |
| ├── README.md # Model card |
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| ``` |
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| 🤝 Contributing |
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| Open to improvements and feedback! Feel free to submit a pull request or open an issue if you find any bugs or want to enhance the model. |
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