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README.md

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+
+# Hindi-Punk: Punctuation Prediction Model
+
+Hindi-Punk is a fine-tuned model based on BERT MuRIL (Multilingual Representations for Indian Languages), specifically designed for adding punctuation to Hindi text. Leveraging the powerful capabilities of Google's MuRIL, which excels in understanding and representing multiple Indian languages, Hindi-Punk offers precise punctuation prediction for Hindi, making it a highly effective tool for natural language processing applications involving Hindi text.
+
+
+## Getting Started
+
+To use the Hindi-Punk model, you'll need to have Python installed on your system along with PyTorch and the Hugging Face Transformers library. If you don't have them installed, you can install them using pip:
+
+```bash
+pip install torch transformers
+```
+
+## Using the Model
+
+### Step 1: Import Required Libraries
+
+Start by importing the necessary libraries:
+
+```python
+import torch
+import torch.nn as nn
+from transformers import AutoTokenizer
+from huggingface_hub import hf_hub_download
+from transformers import BertModel
+```
+
+### Step 2: Download and Load the Model
+
+The model is hosted on Hugging Face, and you can download it directly using the following code:
+
+```python
+# Define the repository name and filename
+repo_name = "zicsx/Hindi-Punk"
+filename = "Hindi-Punk-model.pth"
+
+# Download the file
+model_path = hf_hub_download(repo_id=repo_name, filename=filename)
+```
+
+Load the model using PyTorch:
+
+```python
+# Define the model classes
+class CustomTokenClassifier(nn.Module):
+    # ...
+
+class PunctuationModel(nn.Module):
+    # ...
+
+# Initialize and load the model
+model = PunctuationModel(
+    bert_model_name='google/muril-base-cased',
+    punct_num_classes=5,
+    hidden_size=768
+)
+model.load_state_dict(torch.load(model_path))
+```
+
+### Step 3: Tokenization
+
+Use the tokenizer associated with the model:
+
+```python
+tokenizer = AutoTokenizer.from_pretrained(
+    pretrained_model_name_or_path="zicsx/Hindi-Punk", use_fast=True,
+)
+```
+
+### Step 4: Define Inference Functions
+
+Create functions to perform inference and process the model's output:
+
+```python
+def predict_punctuation_capitalization(model, text, tokenizer):
+    # ...
+
+def combine_predictions_with_text(text, tokenizer, punct_predictions, punct_index_to_label):
+    # ...
+```
+
+### Step 5: Run the Model
+
+You can now run the model on your input text:
+
+```python
+text = "Your Hindi text here"
+punct_predictions = predict_punctuation_capitalization(model, text, tokenizer)
+combined_text = combine_predictions_with_text(text, tokenizer, punct_predictions, punct_index_to_label)
+print("Combined Text:", combined_text)
+```
+
+## Example
+
+Here's an example of how to use the model:
+
+```python
+example_text = "सलामअलैकुम कहाँ जा रहे हैं जी आओ बैठो छोड़ देता हूँ हेलो एक्सक्यूज मी आपका क्या नाम है तुम लोगों को बाद में देख लेता हूँ"
+punct_predictions = predict_punctuation_capitalization(model, example_text, tokenizer)
+combined_text = combine_predictions_with_text(example_text, tokenizer, punct_predictions, punct_index_to_label)
+print("Combined Text:", combined_text)
+```
+
+## License
+
+This model is open source and available under the [MIT License](https://opensource.org/licenses/MIT).
+
+---