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Running
on
CPU Upgrade
Update utils.py
Browse files
utils.py
CHANGED
@@ -12,12 +12,12 @@ SUBJECTS = ["Biology", "Business", "Chemistry", "Computer Science", "Economics",
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"Health", "History", "Law", "Math", "Philosophy", "Physics", "Psychology", "Other"]
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MODEL_INFO = [
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"Models", "Data Source",
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"Overall",
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"Biology", "Business", "Chemistry", "Computer Science", "Economics", "Engineering",
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"Health", "History", "Law", "Math", "Philosophy", "Physics", "Psychology", "Other"]
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DATA_TITLE_TYPE = ['markdown', 'markdown', '
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'number', 'number', 'number', 'number', 'number', 'number', 'number',
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'number', 'number']
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@@ -102,9 +102,9 @@ def get_df():
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repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN)
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repo.git_pull()
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df = pd.read_csv(CSV_DIR)
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df = df.sort_values(by=['Overall'], ascending=False)
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return df[COLUMN_NAMES]
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def add_new_eval(
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@@ -150,7 +150,7 @@ def search_and_filter_models(df, query, min_size, max_size):
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df = df[(df['Model Size(B)'] >= min_size) & (df['Model Size(B)'] <= max_size)]
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return df
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def search_models(df, query):
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@@ -163,5 +163,14 @@ def get_size_range(df):
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sizes = df['Model Size(B)'].dropna()
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if len(sizes) > 0:
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return float(sizes.min()), float(sizes.max())
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return 0,
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"Health", "History", "Law", "Math", "Philosophy", "Physics", "Psychology", "Other"]
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MODEL_INFO = [
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"Models", "Data Source",
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"Overall",
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"Biology", "Business", "Chemistry", "Computer Science", "Economics", "Engineering",
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"Health", "History", "Law", "Math", "Philosophy", "Physics", "Psychology", "Other"]
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DATA_TITLE_TYPE = ['markdown', 'markdown', 'number', 'number', 'number', 'number', 'number', 'number',
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'number', 'number', 'number', 'number', 'number', 'number', 'number',
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'number', 'number']
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repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN)
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repo.git_pull()
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df = pd.read_csv(CSV_DIR)
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df['Model Size(B)'] = df['Model Size(B)'].apply(process_model_size)
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df = df.sort_values(by=['Overall'], ascending=False)
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return df
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def add_new_eval(
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df = df[(df['Model Size(B)'] >= min_size) & (df['Model Size(B)'] <= max_size)]
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return df[COLUMN_NAMES]
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def search_models(df, query):
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sizes = df['Model Size(B)'].dropna()
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if len(sizes) > 0:
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return float(sizes.min()), float(sizes.max())
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return 0, 600
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def process_model_size(size):
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if size == 'unk':
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return 1000.0
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try:
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return float(size)
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except ValueError:
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return 1000.0
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