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import pandas as pd
from pathlib import Path
from datasets import load_dataset
import numpy as np
import os
import re
# From Open LLM Leaderboard
def model_hyperlink(link, model_name):
if model_name == "random":
return "random"
elif model_name == "Cohere March 2024":
return f'<a target="_blank" href="https://huggingface.co/Cohere" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
elif "openai" == model_name.split("/")[0]:
return f'<a target="_blank" href="https://huggingface.co/openai" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
elif "Anthropic" == model_name.split("/")[0]:
return f'<a target="_blank" href="https://huggingface.co/Anthropic" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
def undo_hyperlink(html_string):
# Regex pattern to match content inside > and <
pattern = r'>[^<]+<'
match = re.search(pattern, html_string)
if match:
# Extract the matched text and remove leading '>' and trailing '<'
return match.group(0)[1:-1]
else:
return "No text found"
# Define a function to fetch and process data
def load_all_data(data_repo, subdir:str, subsubsets=False): # use HF api to pull the git repo
dir = Path(data_repo)
data_dir = dir / subdir
orgs = [d for d in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, d))]
# get all files within the sub folders orgs
models_results = []
for org in orgs:
org_dir = data_dir / org
files = [f for f in os.listdir(org_dir) if os.path.isfile(os.path.join(org_dir, f))]
for file in files:
if file.endswith(".json"):
models_results.append(org + "/" + file)
# create empty dataframe to add all data to
df = pd.DataFrame()
# load all json data in the list models_results one by one to avoid not having the same entries
for model in models_results:
model_data = load_dataset("json", data_files=data_repo + subdir+ "/" + model, split="train")
df2 = pd.DataFrame(model_data)
# add to df
df = pd.concat([df2, df])
# remove chat_template comlumn
df = df.drop(columns=["chat_template"])
# sort columns alphabetically
df = df.reindex(sorted(df.columns), axis=1)
# move column "model" to the front
cols = list(df.columns)
cols.insert(0, cols.pop(cols.index('model')))
df = df.loc[:, cols]
# select all columns except "model"
cols = df.columns.tolist()
cols.remove("model")
# if model_type is a column (pref tests may not have it)
if "model_type" in cols:
cols.remove("model_type")
# remove ref_model if in columns
if "ref_model" in cols:
cols.remove("ref_model")
# remove model_beaker from dataframe
if "model_beaker" in cols:
cols.remove("model_beaker")
df = df.drop(columns=["model_beaker"])
# remove column xstest (outdated data)
# if xstest is a column
if "xstest" in cols:
df = df.drop(columns=["xstest"])
cols.remove("xstest")
if "ref_model" in df.columns:
df = df.drop(columns=["ref_model"])
# remove column anthropic and summarize_prompted (outdated data)
if "anthropic" in cols:
df = df.drop(columns=["anthropic"])
cols.remove("anthropic")
if "summarize_prompted" in cols:
df = df.drop(columns=["summarize_prompted"])
cols.remove("summarize_prompted")
# remove pku_better and pku_safer (removed from the leaderboard)
if "pku_better" in cols:
df = df.drop(columns=["pku_better"])
cols.remove("pku_better")
if "pku_safer" in cols:
df = df.drop(columns=["pku_safer"])
cols.remove("pku_safer")
# convert to score
df[cols] = (df[cols]*100)
avg = np.nanmean(df[cols].values,axis=1)
# add average column
df["average"] = avg
# apply model_hyperlink function to column "model"
df["model"] = df["model"].apply(lambda x: model_hyperlink(f"https://huggingface.co/{x}", x))
# move average column to the second
cols = list(df.columns)
cols.insert(1, cols.pop(cols.index('average')))
df = df.loc[:, cols]
# move model_type column to first
if "model_type" in cols:
cols = list(df.columns)
cols.insert(1, cols.pop(cols.index('model_type')))
df = df.loc[:, cols]
# remove models with DPO Ref. Free as type (future work)
df = df[~df["model_type"].str.contains("DPO Ref. Free", na=False)]
return df
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