wehub-resource-sync
217 lines
8.4 KiB
Python
217 lines
8.4 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Albert model configuration"""
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from __future__ import annotations
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from paddlenlp.transformers.configuration_utils import PretrainedConfig
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__all__ = ["PRETRAINED_INIT_CONFIGURATION", "RobertaConfig"]
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PRETRAINED_INIT_CONFIGURATION = {
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"hfl/roberta-wwm-ext": {
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"max_position_embeddings": 512,
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"type_vocab_size": 2,
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"vocab_size": 21128,
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"pad_token_id": 0,
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},
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"hfl/roberta-wwm-ext-large": {
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"max_position_embeddings": 512,
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"type_vocab_size": 2,
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"vocab_size": 21128,
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"pad_token_id": 0,
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},
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"hfl/rbt6": {
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"max_position_embeddings": 512,
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"num_attention_heads": 12,
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"num_hidden_layers": 6,
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"type_vocab_size": 2,
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"vocab_size": 21128,
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"pad_token_id": 0,
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},
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"hfl/rbt4": {
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"max_position_embeddings": 512,
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"num_attention_heads": 12,
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"num_hidden_layers": 4,
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"type_vocab_size": 2,
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"vocab_size": 21128,
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"pad_token_id": 0,
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},
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"hfl/rbt3": {
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"max_position_embeddings": 512,
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"num_attention_heads": 12,
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"num_hidden_layers": 3,
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"type_vocab_size": 2,
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"vocab_size": 21128,
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"pad_token_id": 0,
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},
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"hfl/rbtl3": {
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"max_position_embeddings": 512,
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"num_attention_heads": 16,
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"num_hidden_layers": 3,
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"type_vocab_size": 2,
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"vocab_size": 21128,
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"pad_token_id": 0,
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},
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}
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class RobertaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`RobertaModel`]. It is used to
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instantiate a ALBERT model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the ALBERT
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albert-base-v1 architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (int):
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Vocabulary size of `inputs_ids` in `RobertaModel`. Also is the vocab size of token embedding matrix.
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Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `RobertaModel`.
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hidden_size (int, optional):
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Dimensionality of the embedding layer, encoder layers and pooler layer. Defaults to `768`.
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num_hidden_layers (int, optional):
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Number of hidden layers in the Transformer encoder. Defaults to `12`.
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num_attention_heads (int, optional):
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Number of attention heads for each attention layer in the Transformer encoder.
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Defaults to `12`.
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intermediate_size (int, optional):
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Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors
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to ff layers are firstly projected from `hidden_size` to `intermediate_size`,
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and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`.
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Defaults to `3072`.
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hidden_act (str, optional):
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The non-linear activation function in the feed-forward layer.
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``"gelu"``, ``"relu"`` and any other paddle supported activation functions
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are supported. Defaults to ``"gelu"``.
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hidden_dropout_prob (float, optional):
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The dropout probability for all fully connected layers in the embeddings and encoder.
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Defaults to `0.1`.
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attention_probs_dropout_prob (float, optional):
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The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target.
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Defaults to `0.1`.
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max_position_embeddings (int, optional):
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The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input
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sequence. Defaults to `512`.
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type_vocab_size (int, optional):
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The vocabulary size of the `token_type_ids` passed when calling `~transformers.RobertaModel`.
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Defaults to `2`.
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initializer_range (float, optional):
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The standard deviation of the normal initializer. Defaults to 0.02.
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.. note::
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A normal_initializer initializes weight matrices as normal distributions.
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See :meth:`RobertaPretrainedModel._init_weights()` for how weights are initialized in `RobertaModel`.
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pad_token_id(int, optional):
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The index of padding token in the token vocabulary.
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Defaults to `0`.
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cls_token_id(int, optional):
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The index of cls token in the token vocabulary.
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Defaults to `101`.
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Examples:
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```python
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>>> from paddlenlp.transformers import RobertaModel, AlbertConfig
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>>> # Initializing a ALBERT albert-base-v1 style configuration
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>>> configuration = AlbertConfig()
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>>> # Initializing a model from the albert-base-v1 style configuration
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>>> model = RobertaModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "roberta"
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pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
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def __init__(
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self,
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vocab_size: int = 21128,
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hidden_size: int = 768,
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num_hidden_layers: int = 12,
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num_attention_heads: int = 12,
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intermediate_size: int = 3072,
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hidden_act: str = "gelu",
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hidden_dropout_prob: float = 0.1,
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attention_probs_dropout_prob: float = 0.1,
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max_position_embeddings: int = 512,
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type_vocab_size: int = 16,
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initializer_range: float = 0.02,
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pad_token_id: int = 0,
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layer_norm_eps: float = 1e-12,
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cls_token_id: int = 101,
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**kwargs
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):
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super().__init__(pad_token_id=pad_token_id, cls_token_id=cls_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.pad_token_id = pad_token_id
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self.layer_norm_eps = layer_norm_eps
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self.cls_token_id = cls_token_id
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