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import torch
import time
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from multiprocessing import cpu_count
from transformers import (
AutoConfig,
AutoModelForQuestionAnswering,
AutoTokenizer,
squad_convert_examples_to_features
)
from transformers.data.processors.squad import SquadResult, SquadV2Processor, SquadExample
from transformers.data.metrics.squad_metrics import compute_predictions_logits
def run_prediction(question_texts, context_text, model_path, n_best_size=1):
max_seq_length = 512
doc_stride = 256
n_best_size = n_best_size
max_query_length = 64
max_answer_length = 512
do_lower_case = False
null_score_diff_threshold = 0.0
def to_list(tensor):
return tensor.detach().cpu().tolist()
config_class, model_class, tokenizer_class = (AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer)
config = config_class.from_pretrained(model_path)
tokenizer = tokenizer_class.from_pretrained(model_path, do_lower_case=True, use_fast=False)
model = model_class.from_pretrained(model_path, config=config)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
processor = SquadV2Processor()
examples = []
timer = time.time()
for i, question_text in enumerate(question_texts):
example = SquadExample(
qas_id=str(i),
question_text=question_text,
context_text=context_text,
answer_text=None,
start_position_character=None,
title="Predict",
answers=None,
)
examples.append(example)
print(f'Created Squad Examples in {time.time()-timer} seconds')
print(f'Number of CPUs: {cpu_count()}')
timer = time.time()
features, dataset = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
is_training=False,
return_dataset="pt",
threads=cpu_count(),
)
print(f'Converted Examples to Features in {time.time()-timer} seconds')
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10)
all_results = []
timer = time.time()
for batch in eval_dataloader:
model.eval()
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
example_indices = batch[3]
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs.to_tuple()]
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
print(f'Model predictions completed in {time.time()-timer} seconds')
print(all_results)
output_nbest_file = None
if n_best_size > 1:
output_nbest_file = "nbest.json"
timer = time.time()
final_predictions = compute_predictions_logits(
all_examples=examples,
all_features=features,
all_results=all_results,
n_best_size=n_best_size,
max_answer_length=max_answer_length,
do_lower_case=do_lower_case,
output_prediction_file=None,
output_nbest_file=output_nbest_file,
output_null_log_odds_file=None,
verbose_logging=False,
version_2_with_negative=True,
null_score_diff_threshold=null_score_diff_threshold,
tokenizer=tokenizer
)
print(f'Logits converted to predictions in {time.time()-timer} seconds')
return final_predictions
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