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Add TFGPT2ForSequenceClassification based on DialogRPT (#8714)

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* Add TFGPT2ForSequenceClassification based on DialogRPT

* Add TFGPT2ForSequenceClassification based on DialogRPT

* TFGPT2ForSequenceClassification based on DialogRPT-refactored code, implemented review comments and added input processing

* Add TFGPT2ForSequenceClassification based on DialogRPT

* TFGPT2ForSequenceClassification based on DialogRPT-refactored code, implemented review comments and added input processing

* code refactor for latest other TF PR

* code refactor

* code refactor

* Update modeling_tf_gpt2.py
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sandip
2020-12-07 21:28:37 +05:30
committed by GitHub
parent 28c77ddf3b
commit 483e13273f
8 changed files with 250 additions and 3 deletions
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36
src/transformers/modeling_tf_outputs.py
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@@ -557,3 +557,39 @@ class TFSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
encoder_last_hidden_state: Optional[tf.Tensor] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
@dataclass
class TFSequenceClassifierOutputWithPast(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
List of :obj:`tf.Tensor` of length :obj:`config.n_layers`, with each tensor of shape :obj:`(2, batch_size,
num_heads, sequence_length, embed_size_per_head)`).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
``past_key_values`` input) to speed up sequential decoding.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[tf.Tensor] = None
logits: tf.Tensor = None
past_key_values: Optional[List[tf.Tensor]] = None
hidden_states: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[tf.Tensor]] = None