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""" Data2VecText configuration""" |
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from collections import OrderedDict |
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from typing import Mapping |
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from ...configuration_utils import PretrainedConfig |
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from ...onnx import OnnxConfig |
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from ...utils import logging |
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logger = logging.get_logger(__name__) |
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DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
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"facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json", |
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} |
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class Data2VecTextConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`Data2VecTextModel`] and [`Data2VecTextModel`]. It |
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is used to instantiate a Data2VecText model according to the specified arguments, defining the model architecture. |
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Instantiating a configuration with the defaults will yield a similar configuration to that of the Data2VecText |
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[facebook/data2vec-text-base](https://huggingface.co/facebook/data2vec-text-base) 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`, *optional*, defaults to 30522): |
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Vocabulary size of the DATA2VEC model. Defines the number of different tokens that can be represented by |
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the `inputs_ids` passed when calling [`Data2VecModel`]. |
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hidden_size (`int`, *optional*, defaults to 768): |
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Dimensionality of the encoder layers and the pooler layer. |
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num_hidden_layers (`int`, *optional*, defaults to 12): |
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Number of hidden layers in the Transformer encoder. |
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num_attention_heads (`int`, *optional*, defaults to 12): |
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Number of attention heads for each attention layer in the Transformer encoder. |
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intermediate_size (`int`, *optional*, defaults to 3072): |
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. |
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hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): |
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, |
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`"relu"`, `"silu"` and `"gelu_new"` are supported. |
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1): |
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. |
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): |
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The dropout ratio for the attention probabilities. |
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max_position_embeddings (`int`, *optional*, defaults to 512): |
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The maximum sequence length that this model might ever be used with. Typically set this to something large |
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just in case (e.g., 512 or 1024 or 2048). |
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type_vocab_size (`int`, *optional*, defaults to 2): |
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The vocabulary size of the `token_type_ids` passed when calling [`Data2VecModel`]. |
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initializer_range (`float`, *optional*, defaults to 0.02): |
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
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layer_norm_eps (`float`, *optional*, defaults to 1e-12): |
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The epsilon used by the layer normalization layers. |
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position_embedding_type (`str`, *optional*, defaults to `"absolute"`): |
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Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For |
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positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to |
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[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). |
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For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models |
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with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). |
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is_decoder (`bool`, *optional*, defaults to `False`): |
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Whether the model is used as a decoder or not. If `False`, the model is used as an encoder. |
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use_cache (`bool`, *optional*, defaults to `True`): |
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Whether or not the model should return the last key/values attentions (not used by all models). Only |
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relevant if `config.is_decoder=True`. |
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classifier_dropout (`float`, *optional*): |
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The dropout ratio for the classification head. |
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Examples: |
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```python |
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>>> from transformers import Data2VecTextConfig, Data2VecTextModel |
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>>> # Initializing a Data2VecText facebook/data2vec-text-base style configuration |
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>>> configuration = Data2VecTextConfig() |
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>>> # Initializing a model (with random weights) from the facebook/data2vec-text-base style configuration |
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>>> model = Data2VecTextModel(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 = "data2vec-text" |
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def __init__( |
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self, |
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vocab_size=30522, |
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hidden_size=768, |
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num_hidden_layers=12, |
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num_attention_heads=12, |
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intermediate_size=3072, |
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hidden_act="gelu", |
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hidden_dropout_prob=0.1, |
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attention_probs_dropout_prob=0.1, |
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max_position_embeddings=512, |
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type_vocab_size=2, |
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initializer_range=0.02, |
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layer_norm_eps=1e-12, |
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pad_token_id=1, |
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bos_token_id=0, |
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eos_token_id=2, |
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position_embedding_type="absolute", |
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use_cache=True, |
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classifier_dropout=None, |
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**kwargs, |
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): |
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_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.hidden_act = hidden_act |
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self.intermediate_size = intermediate_size |
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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.layer_norm_eps = layer_norm_eps |
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self.position_embedding_type = position_embedding_type |
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self.use_cache = use_cache |
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self.classifier_dropout = classifier_dropout |
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class Data2VecTextOnnxConfig(OnnxConfig): |
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@property |
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def inputs(self) -> Mapping[str, Mapping[int, str]]: |
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if self.task == "multiple-choice": |
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dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"} |
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else: |
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dynamic_axis = {0: "batch", 1: "sequence"} |
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return OrderedDict( |
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[ |
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("input_ids", dynamic_axis), |
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("attention_mask", dynamic_axis), |
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] |
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) |
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