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import csv
import json
import numpy as np
import torch
import os
from minicons import scorer
from pathlib import Path
def load_sentences(filepath):
sentence_pairs = []
with open(filepath, 'r', encoding='utf-8') as file:
reader = csv.reader(file, delimiter=';')
next(reader)
for row in reader:
good_sentence = row[0]
bad_sentence = row[1]
sentence_pairs.append([good_sentence, bad_sentence])
return sentence_pairs
def compute_score(data, model, mode):
if mode == 'ilm':
score = model.sequence_score(data, reduction=lambda x: x.sum(0).item())
elif mode == 'mlm':
score = model.sequence_score(data, reduction=lambda x: x.sum(0).item(), PLL_metric='within_word_l2r')
return score
def process_files(model, mode, model_name):
root_folder = Path("./data/base")
file_names = sorted([str(file) for file in root_folder.iterdir() if file.name.endswith("csv")])
print(f"Perform benchmarking on the following {len(file_names)} datasets:\n*", "\n* ".join(file_names))
f_out = open(f'results-{model_name.replace("/", "-")}.jsonl', "wt")
for file_path in file_names:
try:
pairs = load_sentences(file_path)
results = []
differences = 0
accuracy = 0
for pair in pairs:
score = compute_score(pair, model, mode)
results.append({
'good_sentence': pair[0],
'bad_sentence': pair[1],
'good_score': score[0],
'bad_score': score[1],
'difference': score[0] - score[1],
'correct': score[0] > score[1]
})
if score[0] > score[1]:
accuracy += 1
differences += score[0] - score[1]
mean_difference = differences / len(pairs)
accuracy = accuracy / len(pairs)
summary = {
'file_name': file_path,
'mean_difference': mean_difference,
'accuracy': accuracy * 100,
'total_pairs': len(pairs),
'model_name': model_name,
}
f_out.write(json.dumps(summary) + "\n")
print(summary)
except Exception as e:
print(f"Error processing {file_path}: {str(e)}")
continue
f_out.close()
mlm_model_names = [
"dbmdz/electra-small-turkish-cased-generator",
"dbmdz/electra-base-turkish-cased-generator",
"dbmdz/electra-base-turkish-mc4-cased-generator",
"dbmdz/electra-base-turkish-mc4-uncased-generator",
"dbmdz/bert-base-turkish-cased",
"dbmdz/bert-base-turkish-uncased",
"dbmdz/bert-base-turkish-128k-cased",
"dbmdz/bert-base-turkish-128k-uncased",
"dbmdz/distilbert-base-turkish-cased",
"dbmdz/convbert-base-turkish-cased",
"dbmdz/convbert-base-turkish-mc4-cased",
"dbmdz/convbert-base-turkish-mc4-uncased",
]
device = 'cuda' if torch.cuda.is_available() else 'cpu'
mode = 'mlm'
for model_name in mlm_model_names:
model = scorer.MaskedLMScorer(model_name, device)
process_files(
model=model,
mode=mode,
model_name=model_name,
)
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