File size: 3,514 Bytes
898dd55 |
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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 |
import torch
import time
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
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):
### Setting hyperparameters
max_seq_length = 512
doc_stride = 256
n_best_size = 1
max_query_length = 64
max_answer_length = 512
do_lower_case = False
null_score_diff_threshold = 0.0
# model_name_or_path = "../cuad-models/roberta-base/"
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 = []
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)
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=1,
)
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10)
all_results = []
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)
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=None,
output_null_log_odds_file=None,
verbose_logging=False,
version_2_with_negative=True,
null_score_diff_threshold=null_score_diff_threshold,
tokenizer=tokenizer
)
return final_predictions |