orig_len += len(para["context"])
assert orig_len == len(exp3_source["super_context"]
), "the summed context lengths should line up"
fancyprint("Testing that there are the same number of answers")
orig_ans_count, exp3_ans_count = 0, 0
for article in tqdm(orig_source["data"]):
for para in article["paragraphs"]:
for qas in para["qas"]:
for answer in qas["answers"]:
orig_ans_count += 1
for topic in tqdm(exp3_source["data"]):
for qas in topic["qas"]:
for answer in qas["answers"]:
exp3_ans_count += 1
assert orig_ans_count == exp3_ans_count, "the answer counts should line up"
if __name__ == "__main__":
data = config.data()
test_exp2_data_transform(orig_data=data.train_data_orig,
transformed_data=data.train_data_exp2)
test_exp3_data_transform(orig_data=data.train_data_orig,
transformed_data=data.train_data_exp3)
print()
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
nll_meter.update(loss.item(), batch_size)
# Get F1 and EM scores
p1, p2 = log_p1.exp(), log_p2.exp()
starts, ends = util.discretize(p1, p2, max_len, use_squad_v2)
# Log info
progress_bar.update(batch_size)
progress_bar.set_postfix(NLL=nll_meter.avg)
preds, _ = util.convert_tokens(gold_dict, ids.tolist(),
starts.tolist(), ends.tolist(),
use_squad_v2)
pred_dict.update(preds)
model.train()
results = util.eval_dicts(gold_dict, pred_dict, use_squad_v2)
results_list = [('NLL', nll_meter.avg), ('F1', results['F1']),
('EM', results['EM'])]
if use_squad_v2:
results_list.append(('AvNA', results['AvNA']))
results = OrderedDict(results_list)
return results, pred_dict
if __name__ == '__main__':
main(data=config.data(), flags=sys.argv)
def geConfig(classes, trainPath, validPath, namesPath, batchs):
dataString = config.data(classes, trainPath, validPath, namesPath)
open(join(wd, sets, 'voc.data'), 'w').write(dataString)
cfgString = config.cfg(classes, batchs)
open(join(wd, sets, 'voc.cfg'), 'w').write(cfgString)