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evaluate_dataset.py - Fast quantitative evaluation against custom JSONL datasets.
"""
from __future__ import annotations
import argparse
import json
import logging
import os
from datetime import datetime
from pathlib import Path
import ctranslate2
import sacrebleu
import sentencepiece as spm
from tqdm import tqdm
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
def load_model(model_dir: str, inter_threads: int) -> ctranslate2.Translator:
logging.info("Loading CTranslate2 model from '%s' ...", model_dir)
return ctranslate2.Translator(
model_dir,
device="cpu",
inter_threads=inter_threads,
intra_threads=0,
)
def tokenize_batch(sp: spm.SentencePieceProcessor, texts: list[str], src_lang: str) -> list[list[str]]:
batch = []
for text in texts:
tokens = sp.EncodeAsPieces(text)
tokens.append("</s>")
tokens.append(src_lang)
batch.append(tokens)
return batch
def detokenize_and_clean(sp: spm.SentencePieceProcessor, token_sequences: list[list[str]], tgt_lang: str) -> list[str]:
results = []
for tokens in token_sequences:
text = sp.Decode(tokens)
if text.startswith(tgt_lang):
text = text[len(tgt_lang):].lstrip()
results.append(text)
return results
def translate_batch(
translator: ctranslate2.Translator,
sp: spm.SentencePieceProcessor,
sentences: list[str],
src_lang: str,
tgt_lang: str,
batch_size: int = 32,
) -> list[str]:
predictions = []
target_prefix = [[tgt_lang]]
for start in tqdm(range(0, len(sentences), batch_size), desc="Translating"):
chunk = sentences[start : start + batch_size]
tokenized = tokenize_batch(sp, chunk, src_lang)
tgt_prefixes = [target_prefix[0]] * len(chunk)
results = translator.translate_batch(
tokenized,
target_prefix=[tgt_prefixes[i] for i in range(len(chunk))],
beam_size=1,
max_decoding_length=256,
)
raw_tokens = [r.hypotheses[0] for r in results]
decoded = detokenize_and_clean(sp, raw_tokens, tgt_lang)
predictions.extend(decoded)
return predictions
def parse_args():
p = argparse.ArgumentParser()
p.add_argument("--model-dir", required=True)
p.add_argument("--jsonl", required=True, help="Path to jsonl file (e.g. data/en_it_v3/test.jsonl)")
p.add_argument("--source-lang", default="eng_Latn")
p.add_argument("--target-lang", default="ita_Latn")
p.add_argument("--filter-dataset", default="open_subtitles", help="Only evaluate rows from this specific dataset (default: open_subtitles)")
p.add_argument("--max-sentences", type=int, default=1000)
return p.parse_args()
def main():
args = parse_args()
spm_model = os.path.join(args.model_dir, "sentencepiece.bpe.model")
translator = load_model(args.model_dir, 8)
sp = spm.SentencePieceProcessor()
sp.Load(spm_model)
sources = []
targets = []
# Parse JSONL and filter
with open(args.jsonl, "r", encoding="utf-8") as f:
for line in f:
if not line.strip(): continue
row = json.loads(line)
# Check if direction and dataset match
if row.get("source_lang") == args.source_lang and row.get("target_lang") == args.target_lang:
if row.get("dataset") == args.filter_dataset:
sources.append(row["source_text"])
targets.append(row["target_text"])
if len(sources) >= args.max_sentences:
break
if not sources:
logging.error("No sentences found matching %s -> %s for dataset '%s'", args.source_lang, args.target_lang, args.filter_dataset)
return
logging.info("Evaluating %d casual sentences from '%s'...", len(sources), args.filter_dataset)
predictions = translate_batch(
translator, sp, sources, args.source_lang, args.target_lang
)
bleu = sacrebleu.corpus_bleu(predictions, [targets])
chrf = sacrebleu.corpus_chrf(predictions, [targets])
print("\n" + "="*40)
print(f"EVALUATION: {args.source_lang} -> {args.target_lang}")
print(f"Data Filter: {args.filter_dataset}")
print(f"Model Path: {args.model_dir}")
print("="*40)
print(f"BLEU Score : {bleu.score:.2f}")
print(f"chrF++ Score: {chrf.score:.2f}")
print("="*40)
if __name__ == "__main__":
main()
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