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natolambert
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874c0c9
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Parent(s):
18596de
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Browse files
app.py
CHANGED
@@ -211,21 +211,24 @@ def regex_table(dataframe, regex, filter_button):
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# if Score exists, round to 2 decimals
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if "Score" in data.columns:
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data["Score"] = data["Score"].
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if "Average" in data.columns:
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data["Average"] = data["Average"].
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# round all others to 1 decimal
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for col in data.columns:
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if col not in ["", "Model", "Model Type", "Score", "Average"]:
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data[col] = data[col].
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return data
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with gr.Blocks(css=custom_css) as app:
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# create tabs for the app, moving the current table to one titled "rewardbench" and the benchmark_text to a tab called "About"
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with gr.Row():
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with gr.Column(scale=6):
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gr.Markdown(TOP_TEXT)
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with gr.Column(scale=4):
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# search = gr.Textbox(label="Model Search (delimit with , )", placeholder="Regex search for a model")
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# filter_button = gr.Checkbox(label="Include AI2 training runs (or type ai2 above).", interactive=True)
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# if Score exists, round to 2 decimals
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if "Score" in data.columns:
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data["Score"] = np.round(np.array(data["Score"].values).astype(float), 2)
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if "Average" in data.columns:
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data["Average"] = np.round(np.array(data["Average"].values).astype(float), 1)
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# round all others to 1 decimal
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for col in data.columns:
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if col not in ["", "Model", "Model Type", "Score", "Average"]:
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data[col] = np.round(np.array(data[col].values).astype(float), 1)
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return data
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# import ipdb; ipdb.set_trace()
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total_models = len(regex_table(rewardbench_data_avg.copy(), "", ["Seq. Classifiers", "DPO", "Custom Classifiers"]).values)
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with gr.Blocks(css=custom_css) as app:
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# create tabs for the app, moving the current table to one titled "rewardbench" and the benchmark_text to a tab called "About"
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with gr.Row():
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with gr.Column(scale=6):
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gr.Markdown(TOP_TEXT.format(str(total_models)))
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with gr.Column(scale=4):
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# search = gr.Textbox(label="Model Search (delimit with , )", placeholder="Regex search for a model")
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# filter_button = gr.Checkbox(label="Include AI2 training runs (or type ai2 above).", interactive=True)
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src/md.py
CHANGED
@@ -97,5 +97,5 @@ For more details, see the [dataset](https://huggingface.co/datasets/allenai/rewa
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TOP_TEXT = """
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# RewardBench: Evaluating Reward Models
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### Evaluating the capabilities, safety, and pitfalls of reward models
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[Code](https://github.com/allenai/reward-bench) | [Eval. Dataset](https://huggingface.co/datasets/allenai/reward-bench) | [Prior Test Sets](https://huggingface.co/datasets/allenai/pref-test-sets) | [Results](https://huggingface.co/datasets/allenai/reward-bench-results) | [Paper](https://arxiv.org/abs/2403.13787)
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"""
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TOP_TEXT = """
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# RewardBench: Evaluating Reward Models
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### Evaluating the capabilities, safety, and pitfalls of reward models
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[Code](https://github.com/allenai/reward-bench) | [Eval. Dataset](https://huggingface.co/datasets/allenai/reward-bench) | [Prior Test Sets](https://huggingface.co/datasets/allenai/pref-test-sets) | [Results](https://huggingface.co/datasets/allenai/reward-bench-results) | [Paper](https://arxiv.org/abs/2403.13787) | Total models: {}
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"""
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