Create README.md

README.md

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+# Model Overview
+
+This model is a Named Entity Recognition (NER) system fine-tuned on the WNUT 17 dataset using DistilBERT. It predicts entity types such as persons, organizations, locations, and more from text.
+
+# Model Details
+```
+Model Type: Transformer-based NER
+
+Base Model: DistilBERT (distilbert-base-uncased)
+
+Dataset: WNUT 17
+
+Training Framework: PyTorch & Hugging Face Transformers
+
+Training Epochs: 3
+
+Batch Size: 16
+
+Learning Rate: 2e-5
+
+Optimizer: AdamW
+
+Weight Decay: 0.01
+
+Evaluation Strategy: Per epoch
+```
+
+# Training Data
+
+The model is trained on the WNUT 17 dataset, which contains challenging named entities in social media and conversational text. The dataset provides annotations for named entity recognition, including entity categories such as:
+```
+person
+
+location
+
+corporation
+
+product
+
+creative-work
+
+group
+```
+
+# Inference and Usage
+
+
+```python
+Loading the Model
+
+from transformers import DistilBertForTokenClassification, DistilBertTokenizerFast
+import torch
+
+def load_model_and_tokenizer(model_path):
+    tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
+    model = DistilBertForTokenClassification.from_pretrained(model_path)
+    return model, tokenizer
+
+model_path = "./final_model"
+model, tokenizer = load_model_and_tokenizer(model_path)
+model.eval()
+```
+
+# Example Usage
+
+test_sentence = "Apple CEO Tim Cook announced the new iPhone 14 at their headquarters in Cupertino."
+entities = predict_entities(test_sentence, model, tokenizer)
+print(entities)
+