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Clémentine
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•
699e8ff
1
Parent(s):
6254b87
Adding flagging systemi, removing changelog
Browse files- app.py +2 -2
- src/assets/css_html_js.py +0 -7
- src/assets/text_content.py +3 -56
- src/auto_leaderboard/get_model_metadata.py +60 -2
- src/auto_leaderboard/load_results.py +3 -3
- src/auto_leaderboard/model_metadata_flags.py +5 -0
- src/auto_leaderboard/model_metadata_type.py +2 -48
- src/utils_display.py +14 -12
app.py
CHANGED
@@ -82,7 +82,7 @@ def get_leaderboard_df():
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print("Pulling evaluation results for the leaderboard.")
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eval_results_private.git_pull()
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-
all_data = get_eval_results_dicts(
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if not IS_PUBLIC:
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all_data.append(gpt4_values)
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@@ -341,7 +341,7 @@ with demo:
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elem_id="filter-columns"
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)
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leaderboard_table = gr.components.Dataframe(
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-
value=leaderboard_df[[AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] + shown_columns.value+ [AutoEvalColumn.dummy.name]],
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headers=[AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] + shown_columns.value + [AutoEvalColumn.dummy.name],
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datatype=TYPES,
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max_rows=None,
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print("Pulling evaluation results for the leaderboard.")
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eval_results_private.git_pull()
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+
all_data = get_eval_results_dicts()
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if not IS_PUBLIC:
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all_data.append(gpt4_values)
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elem_id="filter-columns"
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)
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leaderboard_table = gr.components.Dataframe(
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+
value=leaderboard_df[[AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] + shown_columns.value + [AutoEvalColumn.dummy.name]],
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headers=[AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name] + shown_columns.value + [AutoEvalColumn.dummy.name],
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datatype=TYPES,
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max_rows=None,
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src/assets/css_html_js.py
CHANGED
@@ -1,11 +1,4 @@
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custom_css = """
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#changelog-text {
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font-size: 16px !important;
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}
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-
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#changelog-text h2 {
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font-size: 18px !important;
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}
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.markdown-text {
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font-size: 16px !important;
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custom_css = """
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.markdown-text {
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font-size: 16px !important;
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src/assets/text_content.py
CHANGED
@@ -1,61 +1,5 @@
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from ..auto_leaderboard.model_metadata_type import ModelType
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CHANGELOG_TEXT = f"""
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## [2023-06-19]
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-
- Added model type column
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- Hid revision and 8bit columns since all models are the same atm
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-
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## [2023-06-16]
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- Refactored code base
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- Added new columns: number of parameters, hub likes, license
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-
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## [2023-06-13]
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- Adjust description for TruthfulQA
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## [2023-06-12]
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- Add Human & GPT-4 Evaluations
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-
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## [2023-06-05]
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- Increase concurrent thread count to 40
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- Search models on ENTER
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-
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## [2023-06-02]
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- Add a typeahead search bar
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- Use webhooks to automatically spawn a new Space when someone opens a PR
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- Start recording `submitted_time` for eval requests
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- Limit AutoEvalColumn max-width
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-
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## [2023-05-30]
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- Add a citation button
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- Simplify Gradio layout
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-
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## [2023-05-29]
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- Auto-restart every hour for the latest results
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- Sync with the internal version (minor style changes)
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-
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## [2023-05-24]
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- Add a baseline that has 25.0 for all values
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- Add CHANGELOG
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-
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## [2023-05-23]
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- Fix a CSS issue that made the leaderboard hard to read in dark mode
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-
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## [2023-05-22]
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- Display a success/error message after submitting evaluation requests
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- Reject duplicate submission
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- Do not display results that have incomplete results
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- Display different queues for jobs that are RUNNING, PENDING, FINISHED status
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-
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## [2023-05-15]
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- Fix a typo: from "TruthQA" to "QA"
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-
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## [2023-05-10]
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- Fix a bug that prevented auto-refresh
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## [2023-05-10]
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- Release the leaderboard to public
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"""
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TITLE = """<h1 align="center" id="space-title">🤗 Open LLM Leaderboard</h1>"""
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INTRODUCTION_TEXT = f"""
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@@ -81,6 +25,9 @@ With the plethora of large language models (LLMs) and chatbots being released we
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{ModelType.RL.to_str(" : ")} model
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If there is no icon, we have not uploaded the information on the model yet, feel free to open an issue with the model information!
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## How it works
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📈 We evaluate models on 4 key benchmarks using the <a href="https://github.com/EleutherAI/lm-evaluation-harness" target="_blank"> Eleuther AI Language Model Evaluation Harness </a>, a unified framework to test generative language models on a large number of different evaluation tasks.
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from ..auto_leaderboard.model_metadata_type import ModelType
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TITLE = """<h1 align="center" id="space-title">🤗 Open LLM Leaderboard</h1>"""
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INTRODUCTION_TEXT = f"""
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{ModelType.RL.to_str(" : ")} model
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If there is no icon, we have not uploaded the information on the model yet, feel free to open an issue with the model information!
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+
🏴☠️ indicates that this model has been flagged by the community, and should probably be ignored! Clicking the icon will redirect you to the discussion about the model.
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(For ex, the model was trained on the evaluation data, and is therefore cheating on the leaderboard.)
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## How it works
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📈 We evaluate models on 4 key benchmarks using the <a href="https://github.com/EleutherAI/lm-evaluation-harness" target="_blank"> Eleuther AI Language Model Evaluation Harness </a>, a unified framework to test generative language models on a large number of different evaluation tasks.
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src/auto_leaderboard/get_model_metadata.py
CHANGED
@@ -1,10 +1,14 @@
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import re
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import os
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from typing import List
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from tqdm import tqdm
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-
from src.utils_display import AutoEvalColumn
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from src.auto_leaderboard.model_metadata_type import
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from huggingface_hub import HfApi
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import huggingface_hub
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@@ -52,6 +56,60 @@ def get_model_size(model_name, model_info):
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return None
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def apply_metadata(leaderboard_data: List[dict]):
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get_model_type(leaderboard_data)
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get_model_infos_from_hub(leaderboard_data)
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import re
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import os
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import glob
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import json
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import os
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from typing import List
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from tqdm import tqdm
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from src.utils_display import AutoEvalColumn, model_hyperlink
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from src.auto_leaderboard.model_metadata_type import ModelType, model_type_from_str, MODEL_TYPE_METADATA
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from src.auto_leaderboard.model_metadata_flags import FLAGGED_MODELS
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from huggingface_hub import HfApi
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import huggingface_hub
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return None
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+
def get_model_type(leaderboard_data: List[dict]):
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for model_data in leaderboard_data:
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request_files = os.path.join("eval-queue", model_data["model_name_for_query"] + "_eval_request_*" + ".json")
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request_files = glob.glob(request_files)
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+
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# Select correct request file (precision)
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request_file = ""
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if len(request_files) == 1:
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request_file = request_files[0]
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elif len(request_files) > 1:
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request_files = sorted(request_files, reverse=True)
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for tmp_request_file in request_files:
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with open(tmp_request_file, "r") as f:
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req_content = json.load(f)
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if req_content["status"] == "FINISHED" and req_content["precision"] == model_data["Precision"].split(".")[-1]:
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request_file = tmp_request_file
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+
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if request_file == "":
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model_data[AutoEvalColumn.model_type.name] = ""
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model_data[AutoEvalColumn.model_type_symbol.name] = ""
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continue
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+
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try:
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with open(request_file, "r") as f:
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request = json.load(f)
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is_delta = request["weight_type"] != "Original"
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except Exception:
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is_delta = False
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try:
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with open(request_file, "r") as f:
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request = json.load(f)
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model_type = model_type_from_str(request["model_type"])
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model_data[AutoEvalColumn.model_type.name] = model_type.value.name
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model_data[AutoEvalColumn.model_type_symbol.name] = model_type.value.symbol #+ ("🔺" if is_delta else "")
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except KeyError:
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if model_data["model_name_for_query"] in MODEL_TYPE_METADATA:
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model_data[AutoEvalColumn.model_type.name] = MODEL_TYPE_METADATA[model_data["model_name_for_query"]].value.name
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model_data[AutoEvalColumn.model_type_symbol.name] = MODEL_TYPE_METADATA[model_data["model_name_for_query"]].value.symbol #+ ("🔺" if is_delta else "")
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else:
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model_data[AutoEvalColumn.model_type.name] = ModelType.Unknown.value.name
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model_data[AutoEvalColumn.model_type_symbol.name] = ModelType.Unknown.value.symbol
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def flag_models(leaderboard_data:List[dict]):
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flag_symbol = "💀"
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for model_data in leaderboard_data:
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if model_data["model_name_for_query"] in FLAGGED_MODELS:
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issue_num = FLAGGED_MODELS[model_data["model_name_for_query"]].split("/")[-1]
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issue_link = model_hyperlink(FLAGGED_MODELS[model_data["model_name_for_query"]], f"See discussion #{issue_num}")
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model_data[AutoEvalColumn.model_type_symbol.name] = flag_symbol
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model_data[AutoEvalColumn.model.name] = f"{model_data[AutoEvalColumn.model.name]} has been flagged! {issue_link}"
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def apply_metadata(leaderboard_data: List[dict]):
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get_model_type(leaderboard_data)
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get_model_infos_from_hub(leaderboard_data)
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flag_models(leaderboard_data)
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src/auto_leaderboard/load_results.py
CHANGED
@@ -102,7 +102,7 @@ def parse_eval_result(json_filepath: str) -> Tuple[str, list[dict]]:
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return result_key, eval_results
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-
def get_eval_results(
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json_filepaths = []
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for root, dir, files in os.walk("eval-results"):
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@@ -135,7 +135,7 @@ def get_eval_results(is_public) -> List[EvalResult]:
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return eval_results
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def get_eval_results_dicts(
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eval_results = get_eval_results(
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return [e.to_dict() for e in eval_results]
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return result_key, eval_results
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def get_eval_results() -> List[EvalResult]:
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json_filepaths = []
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for root, dir, files in os.walk("eval-results"):
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return eval_results
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+
def get_eval_results_dicts() -> List[Dict]:
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eval_results = get_eval_results()
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return [e.to_dict() for e in eval_results]
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src/auto_leaderboard/model_metadata_flags.py
ADDED
@@ -0,0 +1,5 @@
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# Model name to forum discussion id
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FLAGGED_MODELS = {
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"Voicelab/trurl-2-13b": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/202",
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"deepnight-research/llama-2-70B-inst": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/207"
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}
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src/auto_leaderboard/model_metadata_type.py
CHANGED
@@ -1,11 +1,7 @@
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from dataclasses import dataclass
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from enum import Enum
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import
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import json
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import os
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from typing import Dict, List
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from ..utils_display import AutoEvalColumn
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@dataclass
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class ModelInfo:
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@@ -24,7 +20,7 @@ class ModelType(Enum):
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return f"{self.value.symbol}{separator}{self.value.name}"
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'notstoic/PygmalionCoT-7b': ModelType.IFT,
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'aisquared/dlite-v1-355m': ModelType.IFT,
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'aisquared/dlite-v1-1_5b': ModelType.IFT,
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@@ -553,45 +549,3 @@ def model_type_from_str(type):
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return ModelType.IFT
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return ModelType.Unknown
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-
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def get_model_type(leaderboard_data: List[dict]):
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for model_data in leaderboard_data:
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request_files = os.path.join("eval-queue", model_data["model_name_for_query"] + "_eval_request_*" + ".json")
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request_files = glob.glob(request_files)
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-
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request_file = ""
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-
if len(request_files) == 1:
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request_file = request_files[0]
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elif len(request_files) > 1:
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request_files = sorted(request_files, reverse=True)
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for tmp_request_file in request_files:
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with open(tmp_request_file, "r") as f:
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req_content = json.load(f)
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if req_content["status"] == "FINISHED" and req_content["precision"] == model_data["Precision"].split(".")[-1]:
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request_file = tmp_request_file
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-
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if request_file == "":
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model_data[AutoEvalColumn.model_type.name] = ""
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model_data[AutoEvalColumn.model_type_symbol.name] = ""
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continue
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-
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try:
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with open(request_file, "r") as f:
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request = json.load(f)
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is_delta = request["weight_type"] != "Original"
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except Exception:
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is_delta = False
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-
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try:
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with open(request_file, "r") as f:
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request = json.load(f)
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model_type = model_type_from_str(request["model_type"])
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model_data[AutoEvalColumn.model_type.name] = model_type.value.name
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model_data[AutoEvalColumn.model_type_symbol.name] = model_type.value.symbol #+ ("🔺" if is_delta else "")
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except KeyError:
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if model_data["model_name_for_query"] in TYPE_METADATA:
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model_data[AutoEvalColumn.model_type.name] = TYPE_METADATA[model_data["model_name_for_query"]].value.name
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model_data[AutoEvalColumn.model_type_symbol.name] = TYPE_METADATA[model_data["model_name_for_query"]].value.symbol #+ ("🔺" if is_delta else "")
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-
else:
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model_data[AutoEvalColumn.model_type.name] = ModelType.Unknown.value.name
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597 |
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model_data[AutoEvalColumn.model_type_symbol.name] = ModelType.Unknown.value.symbol
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from dataclasses import dataclass
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from enum import Enum
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from typing import Dict
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@dataclass
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class ModelInfo:
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return f"{self.value.symbol}{separator}{self.value.name}"
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MODEL_TYPE_METADATA: Dict[str, ModelType] = {
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'notstoic/PygmalionCoT-7b': ModelType.IFT,
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'aisquared/dlite-v1-355m': ModelType.IFT,
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'aisquared/dlite-v1-1_5b': ModelType.IFT,
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549 |
return ModelType.IFT
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550 |
return ModelType.Unknown
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551 |
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src/utils_display.py
CHANGED
@@ -89,20 +89,22 @@ def make_clickable_model(model_name):
|
|
89 |
link = KOALA_LINK
|
90 |
elif model_name == "oasst-12b":
|
91 |
link = OASST_LINK
|
92 |
-
|
93 |
-
# link = MODEL_PAGE
|
94 |
details_model_name = model_name.replace('/', '__')
|
95 |
details_link = f"https://huggingface.co/datasets/open-llm-leaderboard/details_{details_model_name}"
|
96 |
-
|
97 |
-
|
98 |
-
|
99 |
-
|
100 |
-
|
101 |
-
|
102 |
-
|
103 |
-
|
104 |
-
|
105 |
-
|
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|
106 |
|
107 |
return model_hyperlink(link, model_name) + ' ' + model_hyperlink(details_link, "📑")
|
108 |
|
|
|
89 |
link = KOALA_LINK
|
90 |
elif model_name == "oasst-12b":
|
91 |
link = OASST_LINK
|
92 |
+
|
|
|
93 |
details_model_name = model_name.replace('/', '__')
|
94 |
details_link = f"https://huggingface.co/datasets/open-llm-leaderboard/details_{details_model_name}"
|
95 |
+
|
96 |
+
if not bool(os.getenv("DEBUG", "False")):
|
97 |
+
# We only add these checks when not debugging, as they are extremely slow
|
98 |
+
print(f"details_link: {details_link}")
|
99 |
+
try:
|
100 |
+
check_path = list(API.list_files_info(repo_id=f"open-llm-leaderboard/details_{details_model_name}",
|
101 |
+
paths="README.md",
|
102 |
+
repo_type="dataset"))
|
103 |
+
print(f"check_path: {check_path}")
|
104 |
+
except Exception as err:
|
105 |
+
# No details repo for this model
|
106 |
+
print(f"No details repo for this model: {err}")
|
107 |
+
return model_hyperlink(link, model_name)
|
108 |
|
109 |
return model_hyperlink(link, model_name) + ' ' + model_hyperlink(details_link, "📑")
|
110 |
|