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table3.py
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table3.py
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# Import
import matplotlib.pyplot as plt
from tueplots import bundles
import tueplots
plt.rcParams.update(bundles.iclr2024())
import numpy as np
import math
import os
# Constant
TABLE_NAME = "table3_biastoxic"
ECOLOR ='orange'
BAR_COLOR = tueplots.constants.color.rgb.tue_blue
BAR_WIDTH = 0.93
if not os.path.exists(TABLE_NAME):
os.mkdir(TABLE_NAME)
# Data
MODELS = ['Our 70B', 'Our 13B', 'Our 7B', 'Llama-2 13B', 'Llama-2 7B', 'Vietcuna 7B', 'GPT-3.5-turbo', 'GPT-4']
EXCLUDE_MODELS = ['Gemini Pro','GPT-3.5-turbo', 'GPT-4']
qa_task = {
"XQuAD": {
"DRG": {
"mean": [0.39, 0.39, 0.43, 0.35, 0.46, 0.50, 0.43, 0.40],
"std": [0.01, 0.01, 0.01, 0.03, 0.01, 0.00, 0.01, 0.01],
},
"SAG": {
"mean": [0.41, 0.45, 0.48, 0.46, 0.42, 0.0, 0.48, 0.45],
"std": [0.00, 0.01, 0.00, 0.00, 0.00, 0.0, 0.00, 0.00],
},
"Toxicity": {
"mean": [0.02, 0.02, 0.03, 0.01, 0.01, 0.04, 0.02, 0.02],
"std": [0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
},
},
"MLQA": {
"DRG": {
"mean": [0.14, 0.17, 0.18, 0.27, 0.21, 0.23, 0.18, 0.16],
"std": [0.02, 0.10, 0.01, 0.01, 0.06, 0.09, 0.01, 0.01],
},
"SAG": {
"mean": [0.42, 0.38, 0.37, 0.43, 0.45, 0.49, 0.40, 0.41],
"std": [0.03, 0.00, 0.01, 0.00, 0.00, 0.01, 0.00, 0.01],
},
"Toxicity": {
"mean": [0.02, 0.02, 0.02, 0.01, 0.01, 0.04, 0.02, 0.02],
"std": [0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
},
},
}
sum_task = {
"VietNews": {
"DRG": {
"mean": [0.21, 0.20, 0.24, 0.26, 0.28, 0.21, 0.22, 0.19],
"std": [0.01, 0.01, 0.02, 0.01, 0.02, 0.02, 0.01, 0.01],
},
"SAG": {
"mean": [0.31, 0.29, 0.33, 0.38, 0.39, 0.32, 0.29, 0.28],
"std": [0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.01, 0.01],
},
"Toxicity": {
"mean": [0.05, 0.04, 0.04, 0.01, 0.01, 0.04, 0.04, 0.06],
"std": [0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
},
},
"WikiLingua": {
"DRG": {
"mean": [0.03, 0.07, 0.07, 0.17, 0.39, 0.17, 0.03, 0.09],
"std": [0.02, 0.04, 0.02, 0.08, 0.05, 0.04, 0.02, 0.02],
},
"SAG": {
"mean": [0.25, 0.31, 0.38, 0.50, 0.50, 0.39, 0.28, 0.28],
"std": [0.02, 0.03, 0.01, 0.02, 0.02, 0.03, 0.01, 0.01],
},
"Toxicity": {
"mean": [0.03, 0.02, 0.03, 0.01, 0.01, 0.03, 0.02, 0.02],
"std": [0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
},
},
}
trans_task = {
"PhoMT en→vi": {
"DRG": {
"mean": [0.03, 0.09, 0.13, 0.08, 0.17, 0.18, 0.11, 0.09],
"std": [0.01, 0.00, 0.00, 0.00, 0.01, 0.01, 0.01, 0.01],
},
"SAG": {
"mean": [0.30, 0.33, 0.33, 0.33, 0.29, 0.36, 0.34, 0.34],
"std": [0.01, 0.01, 0.01, 0.02, 0.01, 0.01, 0.01, 0.01],
},
"Toxicity": {
"mean": [0.05, 0.05, 0.05, 0.05, 0.04, 0.04, 0.05, 0.05],
"std": [0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
},
},
"OPUS100 en→vi": {
"DRG": {
"mean": [0.27, 0.27, 0.18, 0.31, 0.21, 0.16, 0.16, 0.14],
"std": [0.01, 0.01, 0.03, 0.02, 0.02, 0.03, 0.03, 0.03],
},
"SAG": {
"mean": [0.47, 0.43, 0.47, 0.47, 0.45, 0.43, 0.43, 0.41],
"std": [0.01, 0.02, 0.01, 0.01, 0.02, 0.02, 0.03, 0.01],
},
"Toxicity": {
"mean": [0.06, 0.07, 0.07, 0.06, 0.05, 0.07, 0.07, 0.07],
"std": [0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
},
},
}
drawing_tasks = {
"Question-Answering": qa_task,
"Summarization": sum_task,
"Translation": trans_task
# "Sentiment Analysis": sent_task,
# "Text Classification": tcl_task,
# "Knowledge": kn_task,
# "Toxic Detection": td_task,
# "Language Modeling": lm_task,
# "Reasoning": reasoning_task,
}
for task_name, task in drawing_tasks.items():
datasets = task.keys()
for dataset in datasets:
metrics = task[dataset].keys()
for metric in metrics:
try:
exclude_flag = task[dataset][metric]["exclude"] if "exclude" in task[dataset][metric].keys() else []
tmp_model = list(filter(lambda x: x not in exclude_flag, MODELS))
mean = task[dataset][metric]["mean"][:len(tmp_model)]
std = task[dataset][metric]["std"][:len(tmp_model)]
plt.figure(figsize=(7, 10)) # Adjust figure size as needed
# print(avg_std_F1_qa)
# plt.figure(figsize=(6, 10)) # Adjust figure size as needed
# Create horizontal bar plot
y_pos = np.arange(len(tmp_model))#0069aa
plt.barh(y_pos, list(reversed(mean)), align='center', color=BAR_COLOR, ecolor=ECOLOR, xerr=list((reversed(std))), error_kw=dict(lw=2, capsize=3, capthick=1, color='#fff'), height=BAR_WIDTH)
# plt.errorbar(y_pos, accuracies, xerr=std, lw=2, capsize=5, capthick=2, color='#fff')
plt.yticks(y_pos, reversed(tmp_model), fontsize=15)
plt.xticks(fontsize=15)
# plt.xlabel(task_name, fontsize=15)
plt.title(f"{dataset}\n{metric}", fontsize=15)
# Add grid and limit y-axis to 1.0
plt.grid(axis='x', linestyle='--', alpha=0.8)
plt.xlim(math.floor(min(0, min(mean))), math.ceil(max(mean)))
plt.tight_layout() # Ensures labels are not cut off
plt.savefig(f"{TABLE_NAME}/{TABLE_NAME}_{task_name}_{dataset}_{metric}.pdf") # Save to a file (optional)
plt.close()
except Exception as e:
print(str(e))
print(task_name)
print(dataset)
print(metric)
exit(0)
# plt.show()