Spaces:
Running
Running
Add a baseline
Browse files
app.py
CHANGED
@@ -1,13 +1,11 @@
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import os
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import shutil
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import numpy as np
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import gradio as gr
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from huggingface_hub import Repository, HfApi
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from transformers import AutoConfig
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import json
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from apscheduler.schedulers.background import BackgroundScheduler
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import pandas as pd
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import
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from utils import get_eval_results_dicts, make_clickable_model
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# clone / pull the lmeh eval data
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@@ -140,6 +138,19 @@ def get_leaderboard():
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}
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all_data.append(gpt35_values)
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df = pd.DataFrame.from_records(all_data)
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df = df.sort_values(by=["Average ⬆️"], ascending=False)
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df = df[COLS]
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@@ -323,7 +334,7 @@ We chose these benchmarks as they test a variety of reasoning and general knowle
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"""
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)
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with gr.Accordion("Finished Evaluations", open=False):
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with gr.Row():
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finished_eval_table = gr.components.Dataframe(
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value=finished_eval_queue,
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@@ -331,7 +342,7 @@ We chose these benchmarks as they test a variety of reasoning and general knowle
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datatype=EVAL_TYPES,
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max_rows=5,
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)
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with gr.Accordion("Running Evaluation Queue", open=False):
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with gr.Row():
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running_eval_table = gr.components.Dataframe(
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value=running_eval_queue,
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@@ -340,7 +351,7 @@ We chose these benchmarks as they test a variety of reasoning and general knowle
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max_rows=5,
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)
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with gr.Accordion("Pending Evaluation Queue", open=False):
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with gr.Row():
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pending_eval_table = gr.components.Dataframe(
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value=pending_eval_queue,
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@@ -378,6 +389,7 @@ We chose these benchmarks as they test a variety of reasoning and general knowle
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with gr.Row():
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submit_button = gr.Button("Submit Eval")
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with gr.Row():
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submission_result = gr.Markdown()
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submit_button.click(
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import os
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import numpy as np
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import gradio as gr
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from huggingface_hub import Repository, HfApi
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from transformers import AutoConfig
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import json
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import pandas as pd
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from content import CHANGELOG_TEXT
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from utils import get_eval_results_dicts, make_clickable_model
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# clone / pull the lmeh eval data
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}
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all_data.append(gpt35_values)
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base_line = {
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"Model": '<p>Baseline</p>',
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"Revision": "N/A",
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"8bit": None,
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"Average ⬆️": 25.0,
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"ARC (25-shot) ⬆️": 25.0,
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"HellaSwag (10-shot) ⬆️": 25.0,
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"MMLU (5-shot) ⬆️": 25.0,
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"TruthfulQA (0-shot) ⬆️": 25.0,
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}
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all_data.append(base_line)
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df = pd.DataFrame.from_records(all_data)
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df = df.sort_values(by=["Average ⬆️"], ascending=False)
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df = df[COLS]
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"""
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)
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with gr.Accordion("✅ Finished Evaluations", open=False):
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with gr.Row():
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finished_eval_table = gr.components.Dataframe(
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value=finished_eval_queue,
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datatype=EVAL_TYPES,
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max_rows=5,
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)
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with gr.Accordion("🔄 Running Evaluation Queue", open=False):
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with gr.Row():
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running_eval_table = gr.components.Dataframe(
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value=running_eval_queue,
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max_rows=5,
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)
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with gr.Accordion("⏳ Pending Evaluation Queue", open=False):
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with gr.Row():
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pending_eval_table = gr.components.Dataframe(
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value=pending_eval_queue,
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with gr.Row():
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submit_button = gr.Button("Submit Eval")
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with gr.Row():
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submission_result = gr.Markdown()
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submit_button.click(
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