def answer_questions(input):
bert_model = "../files/models/squad/"
output_dir = "../files/results"
predict_file = '../files/json/my-data.json'
qa_json = create_qa_json(input)
with open(predict_file, 'w+') as f:
f.write(json.dumps(qa_json))
args = Namespace()
args.bert_model = bert_model
args.predict_batch_size=8
args.do_predict=True
args.max_seq_length=384
args.doc_stride=128
args.output_dir=output_dir
args.version_2_with_negative = True
args.null_score_diff_threshold=-3.3908887952566147
args.predict_file=predict_file
args.overwrite_output_dir=True
args.do_lower_case=True
args.n_best_size=3
# Arguments with default values
args.train_file = None
args.max_query_length=64
args.do_train=False
args.train_batch_size=32
args.learning_rate=5e-5
args.num_train_epochs=3.0
args.warmup_proportion=0.1
args.max_answer_length=30
args.verbose_logging=False
args.no_cuda=False
args.seed=42
args.gradient_accumulation_steps=1
args.local_rank=-1
args.fp16=False
args.loss_scale=0
args.server_ip=''
args.server_port=''
run_squad.main(args)
with open('../files/results/nbest_predictions.json', 'r') as f:
output = f.read()
return json.loads(output)
def test_run_squad(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_squad.py
--model_type=distilbert
--model_name_or_path=sshleifer/tiny-distilbert-base-cased-distilled-squad
--data_dir=./tests/fixtures/tests_samples/SQUAD
--output_dir {tmp_dir}
--overwrite_output_dir
--max_steps=10
--warmup_steps=2
--do_train
--do_eval
--version_2_with_negative
--learning_rate=2e-4
--per_gpu_train_batch_size=2
--per_gpu_eval_batch_size=1
--seed=42
""".split()
with patch.object(sys, "argv", testargs):
result = run_squad.main()
self.assertGreaterEqual(result["f1"], 25)
self.assertGreaterEqual(result["exact"], 21)
def test_run_squad_adapters(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
testargs = """
run_squad.py
--model_type=bert
--model_name_or_path=bert-base-uncased
--data_dir=./tests/fixtures/tests_samples/SQUAD
--model_name=bert-base-uncased
--output_dir=./tests/fixtures/tests_samples/temp_dir
--max_steps=20
--warmup_steps=2
--do_train
--do_eval
--version_2_with_negative
--learning_rate=2e-4
--per_gpu_train_batch_size=2
--per_gpu_eval_batch_size=1
--overwrite_output_dir
--seed=42
--train_adapter
--adapter_config=houlsby
--adapter_reduction_factor=8
""".split()
with patch.object(sys, "argv", testargs):
result = run_squad.main()
self.assertGreaterEqual(result["f1"], 30)
self.assertGreaterEqual(result["exact"], 30)
def test_run_squad(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
testargs = [
"run_squad.py",
"--train_file=./examples/tests_samples/SQUAD/dev-v2.0-small.json",
"--predict_file=./examples/tests_samples/SQUAD/dev-v2.0-small.json",
"--model_name=bert-base-uncased",
"--output_dir=./examples/tests_samples/temp_dir",
"--max_steps=10",
"--warmup_steps=2",
"--do_train",
"--do_eval",
"--version_2_with_negative",
"--learning_rate=1e-4",
"--per_gpu_train_batch_size=2",
"--per_gpu_eval_batch_size=1",
"--overwrite_output_dir",
"--seed=42",
]
model_type, model_name = ("--model_type=bert",
"--model_name_or_path=bert-base-uncased")
with patch.object(sys, "argv", testargs + [model_type, model_name]):
result = run_squad.main()
self.assertGreaterEqual(result["f1"], 30)
self.assertGreaterEqual(result["exact"], 30)
import run_squad as rs rs.main(_)
