Duplicate from Thytu/ChessInstruct

Co-authored-by: Valentin De Matos <Thytu@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-generation
+language:
+- en
+pretty_name: Chess Instruct
+size_categories:
+- 10K<n<100K
+---
+
+## ChessInstruct
+
+The ChessInstruct Dataset serves as the foundation for training and fine-tuning Language Models (LLMs) specifically in the realm of chess instruction.
+Derived from the [laion/strategic_game_chess](https://huggingface.co/datasets/laion/strategic_game_chess) dataset, this meticulously curated dataset encompasses a wide array of annotated instructional chess content.
+
+Features of the ChessInstruct Dataset:
+
+* **Rich and Diverse Content**: Curated with a broad spectrum of instructional resources including annotated games, strategic analyses (incoming) and positional evaluations, the dataset facilitates comprehensive learning and modeling.
+* **Customizable Training Resource**: The ChessInstruct Dataset allows for the tailored fine-tuning of any Language Model, enabling researchers and practitioners to adapt and optimize LLMs for chess-specific instructional contexts.
+* **Annotated Instructional Insights**: Detailed annotations and instructional cues within the dataset provide valuable guidance for language model training, emphasizing strategic moves, tactics, and decision-making processes.
+
+## Usage
+
+The ChessInstruct dataset comprises four primary columns:
+* `task`: This column contains instruct prompts related to various chess scenarios, such as predicting the winner given a set of chess moves or identifying the last move in a sequence.
+* `input`: The input column provides supplementary information, usually a series of chess moves, to support the instruct prompt. These inputs are presented as JSON-serialized strings.
+* `expected_output`: This column presents the anticipated or expected output corresponding to the instruct task. The expected outputs are also serialized as JSON strings.
+* `KIND`: The KIND column categorizes the type of instruct prompt, delineating the nature of the task, whether it involves identifying winning scenarios, predicting subsequent moves, or performing other chess-related analyses.
+
+### Distribution 
+| Task | Number of samples training set | Number of samples test set | Distribution |
+|------|--------------------------------|----------------------------|--------------|
+| Finding last movement | 13500 | 1500 |15% |
+| Finding game's score | 18000 | 2000 | 20% |
+| Finding missing movements | 13500 | 1500 | 15% |
+| Finding the best possible move to do | 18000 | 2000 | 20% |
+| Finding who is advantaged in the game | 18000 | 2000 | 20% |
+| Sorting FENs from earliest to oldest in the game | 9000 | 1000 | 10% |
+
+
+## Reproduction
+
+All the necessary code to reproduce this dataset is available here: [Thytu/StockLLM](https://github.com/Thytu/StockLLM)
+
+## Citation
+
+This dataset is based on [laion/strategic_game_chess](https://huggingface.co/datasets/laion/strategic_game_chess?row=0) which I thank dearly for the data

test.csv

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train.csv

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