Updating Model Card

README.md

@@ -1,3 +1,86 @@
----
-license: mit
----
+---
+license: mit
+tags:
+  - translation
+  - pytorch
+  - encoder-decoder
+  - transformer
+  - english-to-hindi
+  - nmt
+library_name: pytorch
+language:
+  - en
+  - hi
+---
+
+# TransformerNMT: English-to-Hindi Experimental Transformer Model
+
+This repository contains a **Transformer Encoder-Decoder** model implemented from scratch in PyTorch for English-to-Hindi neural machine translation. The model and all training, preprocessing, and inference scripts are custom and do **not** use Hugging Face Transformers, but follow the original "Attention is All You Need" architecture.
+
+## Model Details
+
+- **Architecture:** Transformer Encoder-Decoder (Vaswani et al., 2017)
+- **Framework:** PyTorch
+- **Languages:** English (source) → Hindi (target)
+- **Vocabulary:** 32,000 BPE tokens per language (trained with `tokenizers`)
+- **Training Data:** Parallel English-Hindi corpus (see repo for data details)
+- **Intended Use:** Research, experimentation, and educational purposes
+
+## Training
+
+- Trained from scratch using the scripts in this repository.
+- Supports distributed and mixed-precision training.
+- Checkpoints and tokenizer files are provided in the `models/` and `Data/bi_tokenizers_32k/` directories.
+
+## Intended Uses & Limitations
+
+- **Intended for:** Experimentation, research, and demonstration of custom Transformer implementations.
+- **Not intended for:** Production use or high-stakes applications.
+- **Limitations:** May not achieve state-of-the-art translation quality. Use with caution for real-world tasks.
+
+## Example Inference
+
+Below is a simple inference script to translate English text to Hindi using the trained model and tokenizer:
+
+```python
+import torch
+from tokenizer import BilingualTokenizer as Tokenizer
+from model import Transformer, TransformerConfig
+from translator import TranslationInference
+
+# 1. Load config and checkpoint
+config = TransformerConfig(shared_embeddings=True)
+checkpoint = torch.load('models/TNMT_v1_Beta_single.pt', map_location='cpu')
+
+# 2. Build model and load weights
+model = Transformer(config)
+model.load_state_dict(checkpoint['model_state_dict'])
+model = model.to('cpu')
+
+# 3. Load tokenizer
+tokenizer = Tokenizer(vocab_size=32000)
+tokenizer_loaded = tokenizer.load_tokenizers('bi_tokenizers_32k')
+
+# 4. Create inference helper
+translator = TranslationInference(
+    model=model,
+    tokenizer=tokenizer_loaded,
+    device='cpu'
+)
+
+# 5. Translate
+source_text = "This is a test sentence."
+translated_text = translator.translate_text(source_text)
+print("Translated text:", translated_text)
+```
+
+## Citation
+
+If you use this code or model, please cite:
+
+> Vaswani et al., "Attention is All You Need", NeurIPS 2017.
+
+---
+
+**Author:** [Your Name or Organization]  
+**License:** MIT