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""" BERT model configuration""" |
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from collections import OrderedDict |
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from typing import Mapping |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.onnx import OnnxConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class JinaBertConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`JinaBertModel`]. It is used to |
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instantiate a BERT 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 BERT |
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[bert-base-uncased](https://huggingface.co/bert-base-uncased) 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 BERT model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`]. |
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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 [`BertModel`] or [`TFBertModel`]. |
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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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feed_forward_type (`str`, *optional*, defaults to `"original"`): |
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The type of feed forward layer to use in the bert layers. |
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Can be one of GLU variants, e.g. `"reglu"`, `"geglu"` |
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emb_pooler (`str`, *optional*, defaults to `None`): |
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The function to use for pooling the last layer embeddings to get the sentence embeddings. |
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Should be one of `None`, `"mean"`. |
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attn_implementation (`str`, *optional*, defaults to `"torch"`): |
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The implementation of the self-attention layer. Can be one of: |
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- `None` for the original implementation, |
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- `torch` for the PyTorch SDPA implementation, |
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Examples: |
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```python |
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>>> from transformers import JinaBertConfig, JinaBertModel |
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>>> # Initializing a JinaBert configuration |
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>>> configuration = JinaBertConfig() |
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>>> # Initializing a model (with random weights) from the configuration |
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>>> model = JinaBertModel(configuration) |
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>>> # Accessing the model configuration |
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>>> configuration = model.config |
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>>> # Encode text inputs |
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>>> embeddings = model.encode(text_inputs) |
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```""" |
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model_type = "bert" |
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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=0, |
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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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feed_forward_type="original", |
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emb_pooler=None, |
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attn_implementation='torch', |
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**kwargs, |
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): |
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super().__init__(pad_token_id=pad_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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self.feed_forward_type = feed_forward_type |
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self.emb_pooler = emb_pooler |
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self.attn_implementation = attn_implementation |
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class JinaBertOnnxConfig(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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("token_type_ids", dynamic_axis), |
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] |
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) |
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