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update README

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  1. README.md +2 -30
README.md CHANGED
@@ -11,36 +11,8 @@ short_description: 'GIFT-Eval: A Benchmark for General Time Series Forecasting'
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  sdk_version: 4.44.0
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  ---
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- # Start the configuration
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- Most of the variables to change for a default leaderboard are in `src/env.py` (replace the path for your leaderboard) and `src/about.py` (for tasks).
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- Results files should have the following format and be stored as json files:
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- ```json
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- {
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- "config": {
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- "model_dtype": "torch.float16", # or torch.bfloat16 or 8bit or 4bit
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- "model_name": "path of the model on the hub: org/model",
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- "model_sha": "revision on the hub",
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- },
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- "results": {
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- "task_name": {
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- "metric_name": score,
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- },
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- "task_name2": {
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- "metric_name": score,
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- }
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- }
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- }
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- ```
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- Request files are created automatically by this tool.
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-
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- If you encounter problem on the space, don't hesitate to restart it to remove the create eval-queue, eval-queue-bk, eval-results and eval-results-bk created folder.
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-
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- # Code logic for more complex edits
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-
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- You'll find
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- - the main table' columns names and properties in `src/display/utils.py`
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- - the logic to read all results and request files, then convert them in dataframe lines, in `src/leaderboard/read_evals.py`, and `src/populate.py`
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- - the logic to allow or filter submissions in `src/submission/submit.py` and `src/submission/check_validity.py`
 
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  sdk_version: 4.44.0
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  ---
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+ ## Ethical Considerations
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+ This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
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