SUMIN/HuggingFace_transformer
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corrected folder creation error for MNLI-MM, verified GLUE results

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Ananya Harsh Jha
2019-03-17 08:16:50 -04:00
parent e0bf01d9a9
commit 8a4e90ff40
1 changed files with 8 additions and 3 deletions
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11
examples/run_classifier.py
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@@ -908,7 +908,7 @@ def main():
with torch.no_grad(): with torch.no_grad():
logits = model(input_ids, segment_ids, input_mask, labels=None) logits = model(input_ids, segment_ids, input_mask, labels=None)
# create eval loss and other metric required by the task # create eval loss and other metric required by the task
if output_mode == "classification": if output_mode == "classification":
loss_fct = CrossEntropyLoss() loss_fct = CrossEntropyLoss()
@@ -944,12 +944,17 @@ def main():
for key in sorted(result.keys()): for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key])) logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key]))) writer.write("%s = %s\n" % (key, str(result[key])))
# hack for MNLI-MM # hack for MNLI-MM
if task_name == "mnli": if task_name == "mnli":
task_name = "mnli-mm" task_name = "mnli-mm"
processor = processors[task_name]() processor = processors[task_name]()
if os.path.exists(args.output_dir + '-MM') and os.listdir(args.output_dir + '-MM') and args.do_train:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
if not os.path.exists(args.output_dir + '-MM'):
os.makedirs(args.output_dir + '-MM')
eval_examples = processor.get_dev_examples(args.data_dir) eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features( eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode) eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
@@ -990,7 +995,7 @@ def main():
else: else:
preds[0] = np.append( preds[0] = np.append(
preds[0], logits.detach().cpu().numpy(), axis=0) preds[0], logits.detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps eval_loss = eval_loss / nb_eval_steps
preds = preds[0] preds = preds[0]
preds = np.argmax(preds, axis=1) preds = np.argmax(preds, axis=1)