File size: 1,023 Bytes
ed00236 9f922fb ed00236 9f922fb ed00236 9ea620f 9f922fb ed00236 9f922fb ed00236 0a32a30 ed00236 9f922fb ed00236 38b143b ed00236 fcc2240 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 |
# Copied from https://github.com/huggingface/datasets/blob/d3c7b9481d427ce41256edaf6773c47570f06f3b/metrics/rouge/rouge.py
# Added multiprocessing
import multiprocessing
import nltk
from rouge_score import rouge_scorer
from multiprocessing import Pool
def compute_rouge(predictions, references, rouge_types=None, use_stemmer=False):
if rouge_types is None:
rouge_types = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
scorer = rouge_scorer.RougeScorer(rouge_types=rouge_types, use_stemmer=use_stemmer)
with Pool() as p:
scores = p.starmap(scorer.score, zip(references, predictions))
result = {}
for key in scores[0]:
result[key] = list(score[key] for score in scores)
return result
# Copied from https://github.com/huggingface/transformers/blob/3977b58437b8ce1ea1da6e31747d888efec2419b/examples/pytorch/summarization/run_summarization.py#L520
def postprocess_text(text):
# rougeLSum expects newline after each sentence
return "\n".join(nltk.sent_tokenize(text))
|