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import evaluate
import nltk
import numpy as np
from nltk.tokenize import sent_tokenize
from transformers import AutoTokenizer
import os
import sys
path = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.insert(0, path)
def postprocess_text(preds, labels):
nltk.download("punkt")
preds = [pred.strip() for pred in preds]
labels = [label.strip() for label in labels]
preds = ["\n".join(sent_tokenize(pred)) for pred in preds]
labels = ["\n".join(sent_tokenize(label)) for label in labels]
return preds, labels
def compute_metrics(eval_preds, tokenizer, metric):
preds, labels = eval_preds
if isinstance(preds, tuple):
preds = preds[0]
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
# Replace -100 in the labels as we can't decode them.
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
# Some simple post-processing
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
# metric = evaluate.load("rouge")
rouge_results = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
rouge_results = {k: round(v * 100, 4) for k, v in rouge_results.items()}
results = {
"rouge1": rouge_results["rouge1"],
"rouge2": rouge_results["rouge2"],
"rougeL": rouge_results["rougeL"],
"rougeLsum": rouge_results["rougeLsum"],
"gen_len": np.mean([np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds])
}
return results
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