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