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README.md

@@ -1,12 +1,72 @@
 ---
-title: Household Power Bpe Tokenizer
-emoji: 📚
-colorFrom: green
-colorTo: blue
+title: Household Power BPE Tokenizer
+emoji: ⚡
+colorFrom: blue
+colorTo: purple
 sdk: gradio
 sdk_version: 5.49.1
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Household Power BPE Tokenizer
+
+A BPE (Byte-Pair Encoding) tokenizer trained on household power consumption data. This tokenizer is specifically designed to efficiently encode time-series power consumption data with structured format.
+
+## Model Details
+
+- **Vocabulary Size:** 8,000 tokens
+- **Model Type:** BPE (Byte-Pair Encoding)
+- **Character Coverage:** 100%
+- **Training Data:** Household power consumption dataset
+
+## Features
+
+- Efficient tokenization of structured power consumption data
+- Handles date, time, and numerical values
+- Supports pipe-separated format (e.g., `DATE=16/12/2006|TIME=17:24:00|GAP=4.216`)
+- Real-time encoding and decoding through web interface
+
+## Usage
+
+### Using the Web Interface
+
+Simply enter your power consumption data in the input box and click "Tokenize" to see:
+- Token IDs
+- Token strings
+- Decoded output
+
+### Example Input
+
+```
+DATE=16/12/2006|TIME=17:24:00|GAP=4.216|GRP=0.418|V=234.840|GI=18.400|SM1=0.000|SM2=1.000|SM3=17.000
+```
+
+### Using the Tokenizer in Python
+
+```python
+import sentencepiece as spm
+
+# Load the model
+sp = spm.SentencePieceProcessor(model_file="household_power_bpe.model")
+
+# Encode
+text = "DATE=16/12/2006|TIME=17:24:00|GAP=4.216"
+token_ids = sp.encode(text, out_type=int)
+token_strings = sp.encode(text, out_type=str)
+
+# Decode
+decoded = sp.decode(token_ids)
+```
+
+## Files
+
+- `app.py` - Gradio web interface
+- `household_power_bpe.model` - Trained SentencePiece model
+- `household_power_bpe.vocab` - Vocabulary file
+- `requirements.txt` - Python dependencies
+
+## License
+
+MIT