@@ -442,7 +442,7 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
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# sequence or the second sequence. The embedding vectors for `type=0` and
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# `type=1` were learned during pre-training and are added to the wordpiece
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# embedding vector (and position vector). This is not *strictly* necessary
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# since the [SEP] token unambigiously separates the sequences, but it makes
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# since the [SEP] token unambiguously separates the sequences, but it makes
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# it easier for the model to learn the concept of sequences.
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#
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# For classification tasks, the first vector (corresponding to [CLS]) is
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