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README.md
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<p align="center">
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<a href="https://github.com/allenai/signal-and-noise/blob/main/LICENSE">
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Our work studies the ratio between signal, a benchmark's ability to separate models; and noise, a benchmark's sensitivity to random variability during training steps.
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## Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation
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<p align="center">
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<a href="https://github.com/allenai/signal-and-noise/blob/main/LICENSE">
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Our work studies the ratio between signal, a benchmark's ability to separate models; and noise, a benchmark's sensitivity to random variability during training steps.
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### Main Data (XXX models)
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**TODO ADD**
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### DataDecide Data (225 models with 4M to 1B params)
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```python
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import pandas as pd
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from snr.download.hf import pull_predictions_from_hf
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local_path = pull_predictions_from_hf("allenai/signal-and-noise", split_name='datadecide_intermediate')
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df = pd.read_parquet(local_path)
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print(f'Loaded {len(df):,} model evaluations')
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>>> Loaded 212,047 model evaluations
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```
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### Random Seed Data (20 models with 1B params)
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```python
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import pandas as pd
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from snr.download.hf import pull_predictions_from_hf
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local_path = pull_predictions_from_hf("allenai/signal-and-noise", split_name='random_seeds')
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df = pd.read_parquet(local_path)
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print(f'Loaded {len(df):,} model evaluations')
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>>> Loaded 296,358 model evaluations
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```
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