jina-reranker-v1-tiny-en / configuration_bert.py

chore: add model

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	# coding=utf-8
	# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
	# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
	# Copyright (c) 2023 Jina AI GmbH. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" BERT model configuration"""
	from collections import OrderedDict
	from typing import Mapping

	from transformers.configuration_utils import PretrainedConfig
	from transformers.onnx import OnnxConfig
	from transformers.utils import logging


	logger = logging.get_logger(__name__)


	class JinaBertConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`JinaBertModel`]. It is used to
	instantiate a BERT 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 BERT
	[bert-base-uncased](https://huggingface.co/bert-base-uncased) 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 BERT model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`].
	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 [`BertModel`] or [`TFBertModel`].
	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.
	feed_forward_type (`str`, optional, defaults to `"original"`):
	The type of feed forward layer to use in the bert layers.
	Can be one of GLU variants, e.g. `"reglu"`, `"geglu"`
	emb_pooler (`str`, optional, defaults to `None`):
	The function to use for pooling the last layer embeddings to get the sentence embeddings.
	Should be one of `None`, `"mean"`.
	attn_implementation (`str`, optional, defaults to `"torch"`):
	The implementation of the self-attention layer. Can be one of:
	- `None` for the original implementation,
	- `torch` for the PyTorch SDPA implementation,

	Examples:

	```python
	>>> from transformers import JinaBertConfig, JinaBertModel

	>>> # Initializing a JinaBert configuration
	>>> configuration = JinaBertConfig()

	>>> # Initializing a model (with random weights) from the configuration
	>>> model = JinaBertModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config

	>>> # Encode text inputs
	>>> embeddings = model.encode(text_inputs)
	```"""
	model_type = "bert"

	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=0,
	position_embedding_type="absolute",
	use_cache=True,
	classifier_dropout=None,
	feed_forward_type="original",
	emb_pooler=None,
	attn_implementation='torch',
	**kwargs,
	):
	super().__init__(pad_token_id=pad_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
	self.feed_forward_type = feed_forward_type
	self.emb_pooler = emb_pooler
	self.attn_implementation = attn_implementation

	class JinaBertOnnxConfig(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),
	("token_type_ids", dynamic_axis),
	]
	)