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improve docstring (#4422)

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Patrick von Platen
2020-05-18 17:31:35 +02:00
committed by GitHub
parent 026a5d0888
commit 590adb130b
1 changed files with 6 additions and 6 deletions
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src/transformers/configuration_t5.py
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@@ -39,10 +39,10 @@ class T5Config(PretrainedConfig):
Arguments:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `T5Model`.
hidden_size: Size of the encoder layers and the pooler layer.
num_hidden_layers: Number of hidden layers in the Transformer encoder.
num_attention_heads: Number of attention heads for each attention layer in
the Transformer encoder.
d_model: Size of the encoder layers and the pooler layer. `d_model` can also accesed via the property `hidden_size`.
num_layers: Number of hidden layers in the Transformer encoder. `num_layers` can also be accessed via the property `num_hidden_layers`.
num_heads: Number of attention heads for each attention layer in
the Transformer encoder. `num_heads` can also be accessed via the property `num_attention_heads`.
intermediate_size: The size of the "intermediate" (i.e., feed-forward)
layer in the Transformer encoder.
hidden_act: The non-linear activation function (function or string) in the
@@ -51,9 +51,9 @@ class T5Config(PretrainedConfig):
layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob: The dropout ratio for the attention
probabilities.
max_position_embeddings: The maximum sequence length that this model might
n_positions: The maximum sequence length that this model might
ever be used with. Typically set this to something large just in case
(e.g., 512 or 1024 or 2048).
(e.g., 512 or 1024 or 2048). `n_positions` can also be accessed via the property `max_position_embeddings'.
type_vocab_size: The vocabulary size of the `token_type_ids` passed into
`T5Model`.
initializer_factor: A factor for initializing all weight matrices (should be kept to 1.0, used for initialization testing).