Create README.md

README.md

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+# Custom BERT NER Model
+
+This repository contains a BERT-based Named Entity Recognition (NER) model fine-tuned on the CoNLL-2003 dataset. The model is trained to identify common named entity types such as persons, organizations, locations, and miscellaneous entities.
+
+---
+
+## Model Details
+
+- **Model architecture:** BERT (bert-base-cased)
+- **Task:** Token classification / Named Entity Recognition (NER)
+- **Training data:** CoNLL-2003 dataset (~14,000 training samples)
+- **Number of epochs:** 5
+- **Framework:** Hugging Face Transformers + Datasets
+- **Device:** CUDA-enabled GPU for training and inference
+- **WandB:** Disabled during training
+
+---
+
+## Usage
+
+You can use this model for token classification to identify named entities in your text.
+
+### Installation
+
+```python
+pip install transformers datasets torch
+```
+
+## Load the model and tokenizer
+
+```pyhton
+
+from transformers import BertTokenizerFast, BertForTokenClassification
+import torch
+
+model_name_or_path = "AventIQ-AI/Custom-BERT-NER-Model"
+
+tokenizer = BertTokenizerFast.from_pretrained(model_name_or_path)
+model = BertForTokenClassification.from_pretrained(model_name_or_path)
+
+model.to("cuda")  # or "cpu"
+model.eval()
+```
+
+## Example inference
+
+```python
+
+text = "Hi, I am Deepak and I am living in Delhi."
+
+tokens = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model(**tokens)
+predictions = torch.argmax(outputs.logits, dim=2)
+
+labels = [model.config.id2label[p.item()] for p in predictions[0]]
+for token, label in zip(tokenizer.tokenize(text), labels):
+print(f"{token}: {label}")
+```
+
+## Training Details
+
+ - Dataset: CoNLL-2003, loaded via the Hugging Face datasets library
+
+ - Optimizer: AdamW
+
+ - Learning Rate: 5e-5
+
+ - Batch Size: 16
+
+ - Max Sequence Length: 128
+
+ - Epochs: 5
+
+ - Evaluation: Performed on validation split (if applicable)
+
+ - Quantization: Applied post-training for model size reduction (optional)
+
+## Limitations
+
+ - The model may not generalize well to unseen entity types or domains outside CoNLL-2003.
+
+ - It can occasionally mislabel entities, especially for rare or new names.
+
+ - A CUDA-enabled GPU is required for efficient training and inference.
+