Update README.md

README.md

@@ -1,9 +1,61 @@
 ---
+license: apache-2.0 # Assuming a common open-source license, adjust if known
+language:
+- en
+library_name: pytorch
 tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+- chess
+- embeddings
+- transformer
+- vision-transformer
+- self-supervised-learning
+- pytorch
+datasets:
+- lichess
+- computerchess # Hypothetical dataset tag based on paper reference [15]
+model-index:
+- name: ChessLM-Encoder
+  results: [] # Qualitative results described in Evaluation section
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# ChessLM: Contextual Chess Position Embeddings
+
+## Model Description
+
+**ChessLM** is a Transformer-based model designed to learn rich, contextual vector representations (embeddings) for chess positions. Inspired by self-supervised learning in NLP (like BERT) and adapting the Vision Transformer (ViT) architecture, ChessLM focuses on capturing the strategic and thematic similarities between board states, rather than primarily predicting the best move or evaluating the position's score like traditional chess engines.
+
+The core of the model is a Transformer encoder that processes the 8x8 board, considering piece types, locations (via positional embeddings), and whose turn it is (via a turn embedding). It outputs a **256-dimensional embedding vector** for a given position (represented by a FEN string).
+
+## Intended Uses & Limitations
+
+### Intended Use
+
+The primary intended use of this model is to generate embeddings that capture the "feel" or thematic essence of a chess position. These embeddings can be used for:
+
+* **Position Similarity Search:** Finding positions in a database that are structurally or strategically similar to a query position. This is useful for finding similar games or puzzles.
+* **Retrieval-Augmented Generation (RAG):** Enhancing chess analysis tools by retrieving similar historical positions and their outcomes or analyses to provide additional context to another model.
+* **Downstream Task Input:** Serving as input features for tasks like:
+    * Classifying tactical motifs. positional themes or more generally chess positions.
+    * Suggesting relevant chess puzzles based on similarity.
+
+
+### Limitations
+
+* **Not an Evaluation Engine:** ChessLM was **not** trained to predict the evaluation (e.g., centipawn score) of a position. Qualitative analysis shows that while it captures structural similarities, the embeddings are **not highly sensitive** to subtle tactical nuances or precise piece activity that heavily influence a position's true strength. Positions deemed similar by the embeddings can have vastly different engine evaluations.
+* **Focus on Structure:** The model may overemphasize structural similarities (like pawn formations) while potentially under-weighting critical dynamic factors or specific tactical threats.
+
+## How to Use
+ToDo
+
+If you use this model, its embeddings, or the concepts presented in the associated paper, please cite:@misc{hull2025beyond,
+      title={Beyond Evaluation: Learning Contextual Chess Position Representations},
+      author={Ben Hull},
+      year={2025},
+      howpublished={Accessed via \url{[https://bluehood.github.io/](https://bluehood.github.io/)}},
+      note={Preprint or technical report}
+      % Replace below with actual arXiv ID or publication details if available
+      % eprint={arXiv:XXXX.XXXXX},
+      % archivePrefix={arXiv},
+      % primaryClass={cs.AI}
+}
+*(Please update the citation with formal publication details or an