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Running
on
CPU Upgrade
refactor: remove the legacy directory
Browse files- app.py +1 -1
- src/{leaderboard/read_evals.py → read_evals.py} +0 -0
- tests/src/leaderboard/test_read_evals.py +1 -1
- utils.py +15 -12
app.py
CHANGED
@@ -12,7 +12,7 @@ from src.benchmarks import DOMAIN_COLS_QA, LANG_COLS_QA, DOMAIN_COLS_LONG_DOC, L
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DEFAULT_METRIC
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from src.display.css_html_js import custom_css
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from src.envs import API, EVAL_RESULTS_PATH, REPO_ID, RESULTS_REPO, TOKEN
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from src.
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from utils import update_table, update_metric, update_table_long_doc, upload_file, get_default_cols, submit_results
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DEFAULT_METRIC
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from src.display.css_html_js import custom_css
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from src.envs import API, EVAL_RESULTS_PATH, REPO_ID, RESULTS_REPO, TOKEN
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from src.read_evals import get_raw_eval_results, get_leaderboard_df
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from utils import update_table, update_metric, update_table_long_doc, upload_file, get_default_cols, submit_results
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src/{leaderboard/read_evals.py → read_evals.py}
RENAMED
File without changes
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tests/src/leaderboard/test_read_evals.py
CHANGED
@@ -1,6 +1,6 @@
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from pathlib import Path
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from src.
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cur_fp = Path(__file__)
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from pathlib import Path
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from src.read_evals import FullEvalResult, get_raw_eval_results, get_leaderboard_df
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cur_fp = Path(__file__)
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utils.py
CHANGED
@@ -1,16 +1,16 @@
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import json
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from typing import List
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import os
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from datetime import datetime, timezone
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from pathlib import Path
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import pandas as pd
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from src.benchmarks import BENCHMARK_COLS_QA, BENCHMARK_COLS_LONG_DOC, BenchmarksQA, BenchmarksLongDoc
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from src.display.utils import COLS_QA, TYPES_QA, COLS_LONG_DOC, TYPES_LONG_DOC, COL_NAME_RANK, COL_NAME_AVG, COL_NAME_RERANKING_MODEL, COL_NAME_RETRIEVAL_MODEL, COL_NAME_REVISION, COL_NAME_TIMESTAMP, AutoEvalColumnQA, AutoEvalColumnLongDoc, get_default_auto_eval_column_dict
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from src.leaderboard.read_evals import FullEvalResult, get_leaderboard_df
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from src.envs import API, SEARCH_RESULTS_REPO, CACHE_PATH
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from src.display.formatting import styled_message, styled_error
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def filter_models(df: pd.DataFrame, reranking_query: list) -> pd.DataFrame:
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@@ -43,7 +43,7 @@ def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
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return df[(df[COL_NAME_RETRIEVAL_MODEL].str.contains(query, case=False))]
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def get_default_cols(task: str, columns: list, add_fix_cols: bool=True) -> list:
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cols = []
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types = []
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if task == "qa":
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@@ -69,8 +69,8 @@ def get_default_cols(task: str, columns: list, add_fix_cols: bool=True) -> list:
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types = FIXED_COLS_TYPES + types
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return cols, types
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fixed_cols = get_default_auto_eval_column_dict()[:-2]
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FIXED_COLS = [c.name for _, _, c in fixed_cols]
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FIXED_COLS_TYPES = [c.type for _, _, c in fixed_cols]
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@@ -160,6 +160,7 @@ def upload_file(filepath: str):
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return filepath
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return filepath
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from huggingface_hub import ModelCard
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from huggingface_hub.utils import EntryNotFoundError
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@@ -177,7 +178,7 @@ def get_iso_format_timestamp():
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return iso_format_timestamp, filename_friendly_timestamp
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def submit_results(filepath: str, model: str, model_url: str, version: str="AIR-Bench_24.04", anonymous=False):
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if not filepath.endswith(".zip"):
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return styled_error(f"file uploading aborted. wrong file type: {filepath}")
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@@ -187,15 +188,17 @@ def submit_results(filepath: str, model: str, model_url: str, version: str="AIR-
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# validate model url
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if not model_url.startswith("https://huggingface.co/"):
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return styled_error(
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# validate model card
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repo_id=model_url.removeprefix("https://huggingface.co/")
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try:
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card = ModelCard.load(repo_id)
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except EntryNotFoundError as e:
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print(e)
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return styled_error(
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# rename the uploaded file
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input_fp = Path(filepath)
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@@ -223,7 +226,7 @@ def submit_results(filepath: str, model: str, model_url: str, version: str="AIR-
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json.dump(output_config, f, ensure_ascii=False)
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API.upload_file(
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path_or_fileobj=input_folder_path / output_config_fn,
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path_in_repo=
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repo_id=SEARCH_RESULTS_REPO,
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repo_type="dataset",
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commit_message=f"feat: submit {model} config")
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import json
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import List
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import pandas as pd
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from src.benchmarks import BENCHMARK_COLS_QA, BENCHMARK_COLS_LONG_DOC, BenchmarksQA, BenchmarksLongDoc
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from src.display.formatting import styled_message, styled_error
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from src.display.utils import COLS_QA, TYPES_QA, COLS_LONG_DOC, TYPES_LONG_DOC, COL_NAME_RANK, COL_NAME_AVG, \
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COL_NAME_RERANKING_MODEL, COL_NAME_RETRIEVAL_MODEL, get_default_auto_eval_column_dict
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from src.envs import API, SEARCH_RESULTS_REPO
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from src.read_evals import FullEvalResult, get_leaderboard_df
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def filter_models(df: pd.DataFrame, reranking_query: list) -> pd.DataFrame:
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return df[(df[COL_NAME_RETRIEVAL_MODEL].str.contains(query, case=False))]
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def get_default_cols(task: str, columns: list, add_fix_cols: bool = True) -> list:
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cols = []
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types = []
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if task == "qa":
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types = FIXED_COLS_TYPES + types
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return cols, types
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fixed_cols = get_default_auto_eval_column_dict()[:-2]
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FIXED_COLS = [c.name for _, _, c in fixed_cols]
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FIXED_COLS_TYPES = [c.type for _, _, c in fixed_cols]
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return filepath
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return filepath
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from huggingface_hub import ModelCard
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from huggingface_hub.utils import EntryNotFoundError
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return iso_format_timestamp, filename_friendly_timestamp
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def submit_results(filepath: str, model: str, model_url: str, version: str = "AIR-Bench_24.04", anonymous=False):
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if not filepath.endswith(".zip"):
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return styled_error(f"file uploading aborted. wrong file type: {filepath}")
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# validate model url
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if not model_url.startswith("https://huggingface.co/"):
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return styled_error(
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f"failed to submit. Model url must be a link to a valid HuggingFace model on HuggingFace space. Illegal model url: {model_url}")
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# validate model card
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repo_id = model_url.removeprefix("https://huggingface.co/")
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try:
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card = ModelCard.load(repo_id)
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except EntryNotFoundError as e:
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print(e)
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return styled_error(
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f"failed to submit. Model url must be a link to a valid HuggingFace model on HuggingFace space. Could not get model {repo_id}")
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# rename the uploaded file
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input_fp = Path(filepath)
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json.dump(output_config, f, ensure_ascii=False)
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API.upload_file(
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path_or_fileobj=input_folder_path / output_config_fn,
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path_in_repo=f"{version}/{model}/{output_config_fn}",
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repo_id=SEARCH_RESULTS_REPO,
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repo_type="dataset",
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commit_message=f"feat: submit {model} config")
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