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--- |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# ChessCLIP |
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ChessCLIP is a CLIP model trained to align (board, action) representation with natural language and calculate the similarity in Chess game. |
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## Model Details |
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- **Language(s)**: English |
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- **License**: Apache 2.0 |
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- **Model Description**: A CLIP model for chess. |
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# Quick Start |
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```bash |
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git clone https://github.com/waterhorse1/ChessGPT |
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``` |
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Clone our codebase and install all dependencies according to our README. |
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## Inference |
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```python |
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import sys |
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sys.path.append('./chessclip/src') |
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import torch |
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import io |
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import chess.pgn |
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import numpy as np |
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from data.chessclip_data.feature_converter import get_lc0_input_planes_tf |
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from data.chessclip_data.pgn_base import generate_examples_from_game_no_comment |
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from open_clip.factory import get_tokenizer, load_checkpoint |
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# init |
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model_name = 'chessclip-quickgelu' |
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model = open_clip.create_model(model_name, pretrained='openai') |
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tokenizer = get_tokenizer(model_name) |
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# load model |
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load_checkpoint(model, './ChessCLIP/epoch_last.pt') |
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# check parameters |
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model.eval() |
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context_length = model.text.context_length |
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vocab_size = model.text.vocab_size |
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print("Model parameters:", f"{np.sum([int(np.prod(p.shape)) for p in model.parameters()]):,}") |
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print("Context length:", context_length) |
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print("Vocab size:", vocab_size) |
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# generate board/action embedding based on pgn string |
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def generate_representation_for_final(pgn): |
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game = chess.pgn.read_game(io.StringIO(pgn)) |
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data = list(generate_examples_from_game_no_comment(game))[-1] |
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for key in data.keys(): |
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data[key] = np.array(data[key]) |
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board = get_lc0_input_planes_tf(data).numpy() |
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action = data['probs'] |
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return board, action |
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# Prepare input |
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prompt = "Black plays Sicilian Defense" |
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pgn_str = '1. e4 c5' |
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board, action = generate_representation_for_final(pgn_str) |
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text_tokens = tokenizer([prompt]) |
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image_input = torch.from_numpy(np.stack([board], axis=0)) |
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action_input = torch.from_numpy(np.stack([action], axis=0)) |
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# infer |
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with torch.no_grad(): |
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image_features = model.encode_image((image_input, action_input)).float() |
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text_features = model.encode_text(text_tokens).float() |
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image_features /= image_features.norm(dim=-1, keepdim=True) # n * dim |
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text_features /= text_features.norm(dim=-1, keepdim=True)# m * dim |
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similarity = text_features.cpu().numpy() @ image_features.cpu().numpy().T # m * n |
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print(similarity) |
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``` |
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## Limitations |
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"ChessCLIP," like other CLIP-based models, has certain limitations that need to be taken into consideration. For instance, the model may produce incorrect similarities, especially when faced with complex, ambiguous, or language inputs that fall outside its training data. |
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We highly appreciate contributions from individuals and organizations to enhance the model's performance and stability. Specifically, we welcome annotated data, such as annotated PGN (Portable Game Notation), which can be utilized to train a more robust and reliable CLIP model. |
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## Benchmark |
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Please refer to our [paper](https://together.xyz) and [code](https://github.com/waterhorse1/ChessGPT)for benchmark results. |
