Update README.md

README.md

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----
-library_name: transformers
-license: mit
-base_model: microsoft/DialoGPT-small
-tags:
-- generated_from_trainer
-model-index:
-- name: dialochess
-  results: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# dialochess
-
-This model is a fine-tuned version of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) on an unknown dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- num_epochs: 1
-- mixed_precision_training: Native AMP
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.52.4
-- Pytorch 2.7.1+cu118
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+---
+library_name: transformers
+license: mit
+base_model: microsoft/DialoGPT-small
+tags:
+  - generated_from_trainer
+  - dialog
+  - chess
+  - gpt-2
+model-index:
+  - name: dialochess
+    results: []
+---
+# Model card: **dialochess**
+
+**Short description**  
+`dialochess` is a conversational model derived from `microsoft/DialoGPT-small` and fine-tuned for chess-related dialog and play. It was trained using the dataset referenced by the trainer (see *Training data* below) and configured to interact in chess play/analysis settings such as the Hugging Face Space `mlabonne/chessllm`.
+
+---
+
+## Model details
+
+- **Model type:** GPT-2 / DialoGPT-small family (causal, autoregressive)
+- **Base model:** `microsoft/DialoGPT-small`
+- **Fine-tuned name:** `dialochess`
+- **License:** MIT
+- **Libraries & versions used during training (reported by trainer):**
+  - Transformers 4.52.4
+  - PyTorch 2.7.1+cu118
+  - Datasets 3.6.0
+  - Tokenizers 0.21.1
+
+---
+
+## Model description
+
+`dialochess` is a fine-tuned conversational transformer intended to produce chess-relevant dialogue: move suggestions, commentary, brief explanations of positions, and to play short games against other AIs. The model remains an autoregressive language model (not a dedicated chess engine) and generates text tokens that may include algebraic moves, evaluation phrases, and natural-language commentary.
+
+---
+
+## Intended uses & limitations
+
+**Intended uses**
+- Research and experimentation in conversational chess agents.
+- Integration into chat-based chess interfaces for move suggestions and commentary.
+- Generating sample game dialogues or annotated move lists for educational/demo purposes.
+- Fine-tuning baseline for further chess-specific language-model work.
+
+**Not suitable for**
+- Replacing a dedicated chess engine for precise tactical calculation (e.g., Stockfish, Leela).
+- High-stakes or competitive play where rigorous move correctness and deep search are required.
+- Any medical, legal, financial, or safety-critical advice — it's a domain-specific conversational model and may hallucinate or produce incorrect information.
+
+**Limitations**
+- May hallucinate moves, annotations, or claims about positions.
+- Performance is dependent on the quality and diversity of the fine-tuning dataset (see *Training and evaluation data*).
+- No official evaluation metrics were included in the automatically-generated card. Users should validate with specific benchmarks (perplexity, move-accuracy, Elo/win-rate against baselines).
+
+---
+
+## Training and evaluation data
+
+- **Dataset (provided by the trainer):** The fine-tuning dataset referenced in the trainer materials and available in the provided Colab notebook:  
+  `https://colab.research.google.com/drive/11UjbfajCzphe707_V7PD-2e5WIzyintf`  
+  (This link was included by the trainer. Please review the Colab to inspect dataset sources, composition, license, and any preprocessing steps.)
+- **Source / provenance:** The model was trained to interact with or play against other AIs in the Hugging Face Space `mlabonne/chessllm`. See the space here: `https://huggingface.co/spaces/mlabonne/chessllm`.
+- **Data filtering & cleaning:** Not provided in the auto-generated metadata. It is recommended to include details about tokenization choices, any filtering of illegal moves or metadata removal, and train/validation splits.
+- **Privacy & licenses:** The original trainer metadata did not list dataset license(s). Verify that any third-party game logs, PGN files, or scraped content used for training are permitted under their licenses before public redistribution.
+
+---
+
+## Training procedure
+
+**Hyperparameters (as reported):**
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: AdamW (betas=(0.9, 0.999), eps=1e-08)
+- lr_scheduler_type: cosine
+- num_epochs: 1
+- mixed_precision_training: Native AMP
+
+**Notes**
+- The model was fine-tuned from `microsoft/DialoGPT-small`. The training ran for 1 epoch (per provided metadata). For improved performance, consider longer training, larger datasets, and careful evaluation/early stopping.
+- No detailed training logs or evaluation metrics were included in the auto-generated card; add validation loss curves, perplexity, and any chess-specific metrics (move prediction accuracy, legality rate, win-rate vs baseline) to the card if available.
+
+---
+
+## Evaluation & recommended metrics
+
+No evaluation results were included in the auto-generated card. To assess quality, we recommend reporting:
+- **Perplexity** on a held-out validation set.
+- **Move accuracy**: fraction of model-predicted moves that match the recorded moves in a test PGN corpus.
+- **Legal-move rate**: fraction of generated moves that are legal given the position.
+- **Win-rate / Elo proxy**: Play matches against a fixed baseline agent and report win/draw/loss and Elo-like estimates.
+- **Human preference / qualitative eval**: human raters judge helpfulness, fluency, and chess correctness in dialog samples.
+
+If you want, run a small evaluation pipeline and paste the results here so this section can be updated.
+
+---
+
+## Safety & biases
+
+- The model can generate incorrect or misleading chess content. Verify generated moves with a chess engine before acting on them.
+- As an autoregressive language model, it may reproduce biases or toxic language present in the training data. Use standard moderation / filtering if deploying publicly.
+- Avoid exposing the model as a canonical authority on chess positions or instructing users to rely on it without verification.
+
+---
+