From e1b2949ae6cb34cc39e3934ca87423474f8c8d02 Mon Sep 17 00:00:00 2001 From: drc10723 Date: Thu, 3 Oct 2019 21:22:36 +0530 Subject: [PATCH] DistillBert Documentation Code Example fixes --- transformers/modeling_distilbert.py | 2 +- transformers/modeling_tf_distilbert.py | 6 ++---- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/transformers/modeling_distilbert.py b/transformers/modeling_distilbert.py index 2425ab5f47..ebb89f0f95 100644 --- a/transformers/modeling_distilbert.py +++ b/transformers/modeling_distilbert.py @@ -649,7 +649,7 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel): start_positions = torch.tensor([1]) end_positions = torch.tensor([3]) outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions) - loss, start_scores, end_scores = outputs[:2] + loss, start_scores, end_scores = outputs[:3] """ def __init__(self, config): diff --git a/transformers/modeling_tf_distilbert.py b/transformers/modeling_tf_distilbert.py index 5ce1616bcc..6ed2844567 100644 --- a/transformers/modeling_tf_distilbert.py +++ b/transformers/modeling_tf_distilbert.py @@ -603,7 +603,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel): tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = TFDistilBertForMaskedLM.from_pretrained('distilbert-base-uncased') input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 - outputs = model(input_ids, masked_lm_labels=input_ids) + outputs = model(input_ids) prediction_scores = outputs[0] """ @@ -715,9 +715,7 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel): tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = TFDistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased') input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 - start_positions = tf.constant([1]) - end_positions = tf.constant([3]) - outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions) + outputs = model(input_ids) start_scores, end_scores = outputs[:2] """