Delete configuration_bert.py

configuration_bert.py

@@ -1,168 +0,0 @@
-# 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),
-            ]
-        )