Update README.md

README.md

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-# Byte Pair Encoding (BPE) on Hindi Data
-
-## Overview
-Byte Pair Encoding (BPE) for token representation.
-
-### Key Metrics
-- **Original Token Length**: 49,513
-- **BPE IDs Length**: 4,955
-- **Compression Ratio**: 9.99X
-
-## Explanation
-Byte Pair Encoding is a subword tokenization technique used to compress text data while preserving meaningful token representations. The compression ratio indicates the effectiveness of the encoding process by comparing the size of the original tokens with the resulting BPE IDs
-
-## Benefits of BPE
-1. **Reduced Token Count**: The drastic reduction in token length enhances processing efficiency and reduces memory usage.
-2. **Preserved Meaning**: Despite compression, BPE maintains the semantic integrity of the text.
-3. **Scalability**: Works effectively across various datasets and languages.
-
-## Applications
-BPE is widely used in:
-- Natural Language Processing (NLP)
-- Machine Translation
-- Text Generation
-- Speech Recognition Systems
-
-## Conclusion
-The 9.99X compression ratio demonstrates the efficiency of BPE in reducing token representation size while maintaining meaningful content.
+title: BytePairEncoderDecoder
+emoji: 👀
+colorFrom: indigo
+colorTo: gray
+sdk: gradio
+sdk_version: 5.12.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Byte Pair Encoding and Decodin Tokenizer  on Hindi Data
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference