Update README.md

README.md

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 license: apache-2.0
+pipeline_tag: text-generation
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+
+# Bigram Language Model
+
+## Overview
+This repository contains a simple Bigram Language Model implemented in PyTorch. The model is trained to predict the next character in a sequence, given the current character. It's a character-level model and can be used for tasks like text generation.
+
+## Model Details
+- **Model Type**: Character-level Language Model
+- **Architecture**: Simple lookup table for character bigrams
+- **Training Data**: [https://huggingface.co/datasets/csebuetnlp/xlsum/viewer/bengali]
+
+## Requirements
+- Python 3.x
+- PyTorch
+- JSON (for loading the tokenizer)
+
+## Installation
+First, clone this repository:
+
+
+## Loading the Model
+To load the model, you need to initialize it with the vocabulary size and load the pre-trained weights:
+
+```python
+import torch
+from model import BigramLanguageModel 
+
+vocab_size = 225  
+model = BigramLanguageModel(vocab_size)
+
+model.load_state_dict(torch.load('path_to_your_model.pth', map_location=torch.device('cpu')))
+model.eval()
+
+import json
+
+with open('tokenizer_mappings.json', 'r', encoding='utf-8') as f:
+    mappings = json.load(f)
+    stoi = mappings['stoi']
+    itos = mappings['itos']
+
+# Example usage
+encode = lambda s: [stoi[c] for c in s]
+decode = lambda l: ''.join([itos[i] for i in l])
+
+
+context = torch.tensor([encode("Your initial text")], dtype=torch.long)
+generated_text_indices = model.generate(context, max_new_tokens=100)
+print(decode(generated_text_indices[0].tolist()))
+
+