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* add known 3rd party to setup.cfg * comment * Update CONTRIBUTING.md Co-authored-by: Julien Chaumond <chaumond@gmail.com>
102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
import argparse
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from pathlib import Path
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import torch
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from rouge_score import rouge_scorer, scoring
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from tqdm import tqdm
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from transformers import T5ForConditionalGeneration, T5Tokenizer
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def chunks(lst, n):
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"""Yield successive n-sized chunks from lst."""
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for i in range(0, len(lst), n):
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yield lst[i : i + n]
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def generate_summaries(lns, output_file_path, model_size, batch_size, device):
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output_file = Path(output_file_path).open("w")
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model = T5ForConditionalGeneration.from_pretrained(model_size)
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model.to(device)
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tokenizer = T5Tokenizer.from_pretrained(model_size)
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# update config with summarization specific params
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task_specific_params = model.config.task_specific_params
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if task_specific_params is not None:
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model.config.update(task_specific_params.get("summarization", {}))
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for batch in tqdm(list(chunks(lns, batch_size))):
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batch = [model.config.prefix + text for text in batch]
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dct = tokenizer.batch_encode_plus(batch, max_length=512, return_tensors="pt", pad_to_max_length=True)
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input_ids = dct["input_ids"].to(device)
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attention_mask = dct["attention_mask"].to(device)
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summaries = model.generate(input_ids=input_ids, attention_mask=attention_mask)
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dec = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summaries]
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for hypothesis in dec:
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output_file.write(hypothesis + "\n")
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output_file.flush()
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def calculate_rouge(output_lns, reference_lns, score_path):
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score_file = Path(score_path).open("w")
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scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
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aggregator = scoring.BootstrapAggregator()
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for reference_ln, output_ln in zip(reference_lns, output_lns):
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scores = scorer.score(reference_ln, output_ln)
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aggregator.add_scores(scores)
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result = aggregator.aggregate()
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score_file.write(
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"ROUGE_1: \n{} \n\n ROUGE_2: \n{} \n\n ROUGE_L: \n{} \n\n".format(
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result["rouge1"], result["rouge2"], result["rougeL"]
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)
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)
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def run_generate():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"model_size",
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type=str,
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help="T5 model size, either 't5-small', 't5-base', 't5-large', 't5-3b', 't5-11b'. Defaults to 't5-base'.",
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default="t5-base",
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)
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parser.add_argument(
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"input_path", type=str, help="like cnn_dm/test_articles_input.txt",
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)
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parser.add_argument(
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"output_path", type=str, help="where to save summaries",
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)
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parser.add_argument("reference_path", type=str, help="like cnn_dm/test_reference_summaries.txt")
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parser.add_argument(
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"score_path", type=str, help="where to save the rouge score",
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)
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parser.add_argument(
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"--batch_size", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
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)
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parser.add_argument(
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"--no_cuda", default=False, type=bool, help="Whether to force the execution on CPU.",
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)
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args = parser.parse_args()
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args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
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source_lns = [x.rstrip() for x in open(args.input_path).readlines()]
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generate_summaries(source_lns, args.output_path, args.model_size, args.batch_size, args.device)
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output_lns = [x.rstrip() for x in open(args.output_path).readlines()]
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reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()]
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calculate_rouge(output_lns, reference_lns, args.score_path)
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if __name__ == "__main__":
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run_generate()
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