Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+Text Normalization Model for Indic Languages
+
+Overview: This model is fine-tuned for text normalization in Hindi. It converts non-standard entities—such as dates (e.g., "15/03/1990"), currencies (e.g., "$120"), and scientific units (e.g., "500 mg")—into their fully normalized forms. 
+
+Training Details
+Model Architecture: T5-small
+Dataset: An augmented version of SPRINGLab/IndicVoices-R_Hindi, further enriched with synthetic examples for dates, currencies, and units.
+Hyperparameters:
+Learning rate: 2e-5
+Epochs: 3
+Per-device batch size: 2 (with gradient accumulation)
+FP16 enabled for mixed precision training
+Environment: Trained on Google Colab with a GPU.
+
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load model and tokenizer from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained("shubham-Bgs/Text-normalization-hindi")
+model = AutoModelForSeq2SeqLM.from_pretrained("shubham-Bgs/Text-normalization-hindi")
+
+input_text = "15 / 03 / 1990 को, वैज्ञानिक ने $120 में 500 mg का नमूना खरीदा।"
+inputs = tokenizer(input_text, return_tensors="pt", padding=True)
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))