mirror of
https://github.com/huggingface/transformers.git
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601d4d699c
* remove references to old API in docstring - update data processors * style * fix tests - better type checking error messages * better type checking * include awesome fix by @LysandreJik for #5310 * updated doc and examples
89 lines
3.4 KiB
Python
89 lines
3.4 KiB
Python
import argparse
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import json
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from pathlib import Path
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import torch
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from tqdm import tqdm
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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try:
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from .utils import calculate_rouge, use_task_specific_params, calculate_bleu_score
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except ImportError:
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from utils import calculate_rouge, use_task_specific_params, calculate_bleu_score
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DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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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_or_translations(
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examples: list,
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out_file: str,
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model_name: str,
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batch_size: int = 8,
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device: str = DEFAULT_DEVICE,
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fp16=False,
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**gen_kwargs,
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) -> None:
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fout = Path(out_file).open("w", encoding="utf-8")
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model_name = str(model_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
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if fp16:
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model = model.half()
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# update config with summarization specific params
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use_task_specific_params(model, "summarization")
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for batch in tqdm(list(chunks(examples, batch_size))):
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if "t5" in model_name:
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batch = [model.config.prefix + text for text in batch]
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batch = tokenizer(batch, max_length=1024, return_tensors="pt", truncation=True, padding="max_length").to(
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device
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)
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summaries = model.generate(**batch, **gen_kwargs)
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dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
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for hypothesis in dec:
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fout.write(hypothesis + "\n")
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fout.flush()
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def run_generate():
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parser = argparse.ArgumentParser()
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parser.add_argument("input_path", type=str, help="like cnn_dm/test.source")
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parser.add_argument("output_path", type=str, help="where to save summaries")
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parser.add_argument("model_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.")
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parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test_reference_summaries.txt")
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parser.add_argument("--score_path", type=str, required=False, help="where to save the rouge score in json format")
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parser.add_argument("--metric", type=str, choices=["bleu", "rouge"], default="rouge")
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parser.add_argument("--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.")
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parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
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parser.add_argument("--fp16", action="store_true")
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args = parser.parse_args()
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examples = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
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generate_summaries_or_translations(
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examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device, fp16=args.fp16
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)
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output_lns = [x.rstrip() for x in open(args.output_path).readlines()]
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scores = {}
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if args.reference_path is not None:
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score_fn = {"bleu": calculate_bleu_score, "rouge": calculate_rouge}[args.metric]
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reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()]
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scores: dict = score_fn(output_lns, reference_lns)
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if args.score_path is not None:
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json.dump(scores, open("score_path", "w+"))
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return scores
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if __name__ == "__main__":
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run_generate()
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