Update README.md

README.md

@@ -4,4 +4,35 @@ language:
 - it
 - en
 pipeline_tag: translation
----
+---
+
+# OratioAI
+Sequecne to Sequence anguage translation, implimenting the methodes outlined in *'attention is all you need'*
+
+1.	Input Tokenization:
+The source and target sentences are tokenized using custom WordPiece tokenizers. Tokens are mapped to embeddings via the InputEmbeddings module, scaled by the model dimension.
+2.	Positional Encoding:
+Positional information is added to token embeddings using a fixed sinusoidal encoding strategy.
+3.	Encoding Phase:
+The encoder processes the source sequence, transforming token embeddings into contextual representations using stacked EncoderBlock modules.
+4.	Decoding Phase:
+The decoder autoregressively generates target tokens by attending to both previous tokens and encoder outputs. Cross-attention layers align source and target sequences effectively.
+5.	Projection:
+Final decoder outputs are projected into the target vocabulary space for token prediction.
+6.	Output Generation:
+Decoding is performed using a beam search or greedy approach to produce the final translated sentence.
+
+
+
+
+| Resource                          | Description                                              |
+|-----------------------------------|----------------------------------------------------------|
+| [Training Space](https://huggingface.co/spaces/torinriley/OratioAI) | Hugging Face Space for training and testing the model.    |
+| [GitHub Source Code](https://huggingface.co/spaces/torinriley/OratioAI) | Source code repository for the translation project.       |
+| [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) | Original paper on the transformer architecture published from google |
+
+| Dataset                          | Description                                              |
+|-----------------------------------|----------------------------------------------------------|
+| [Dataset](https://opus.nlpl.eu/Europarl/en&it/v8/Europarl) | Dataset Used for main model training.    |
+
+