mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-18 20:18:24 +06:00
72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
import argparse
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
from tqdm import tqdm
|
|
|
|
from transformers import BartForConditionalGeneration, BartTokenizer
|
|
|
|
|
|
DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
|
|
|
|
|
|
def chunks(lst, n):
|
|
"""Yield successive n-sized chunks from lst."""
|
|
for i in range(0, len(lst), n):
|
|
yield lst[i : i + n]
|
|
|
|
|
|
def generate_summaries(
|
|
examples: list, out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE
|
|
):
|
|
fout = Path(out_file).open("w")
|
|
model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
|
|
tokenizer = BartTokenizer.from_pretrained("bart-large")
|
|
|
|
max_length = 140
|
|
min_length = 55
|
|
|
|
for batch in tqdm(list(chunks(examples, batch_size))):
|
|
dct = tokenizer.batch_encode_plus(batch, max_length=1024, return_tensors="pt", pad_to_max_length=True)
|
|
summaries = model.generate(
|
|
input_ids=dct["input_ids"].to(device),
|
|
attention_mask=dct["attention_mask"].to(device),
|
|
num_beams=4,
|
|
length_penalty=2.0,
|
|
max_length=max_length + 2, # +2 from original because we start at step=1 and stop before max_length
|
|
min_length=min_length + 1, # +1 from original because we start at step=1
|
|
no_repeat_ngram_size=3,
|
|
early_stopping=True,
|
|
decoder_start_token_id=model.config.eos_token_id,
|
|
)
|
|
dec = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summaries]
|
|
for hypothesis in dec:
|
|
fout.write(hypothesis + "\n")
|
|
fout.flush()
|
|
|
|
|
|
def run_generate():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"source_path", type=str, help="like cnn_dm/test.source",
|
|
)
|
|
parser.add_argument(
|
|
"output_path", type=str, help="where to save summaries",
|
|
)
|
|
parser.add_argument(
|
|
"model_name", type=str, default="bart-large-cnn", help="like bart-large-cnn",
|
|
)
|
|
parser.add_argument(
|
|
"--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.",
|
|
)
|
|
parser.add_argument(
|
|
"--bs", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
|
|
)
|
|
args = parser.parse_args()
|
|
examples = [" " + x.rstrip() for x in open(args.source_path).readlines()]
|
|
generate_summaries(examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_generate()
|