FM / electra

/usr/local/lib/python3.6/site-packages/transformers/models/electra/

/usr/local/lib/python3.6/site-packages/transformers/models/electra

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
configuration_electra.py	9721	0644	edit dl rm
convert_electra_original_tf_checkpoint_to_pytorch.py	2845	0644	edit dl rm
modeling_electra.py	74529	0644	edit dl rm
modeling_flax_electra.py	44493	0644	edit dl rm
modeling_tf_electra.py	74458	0644	edit dl rm
tokenization_electra.py	2871	0644	edit dl rm
tokenization_electra_fast.py	3897	0644	edit dl rm
__init__.py	4305	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/transformers/models/electra/configuration_electra.py (9721B)

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
""" ELECTRA model 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__)

ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/config.json",
    "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/config.json",
    "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/config.json",
    "google/electra-small-discriminator": "https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json",
    "google/electra-base-discriminator": "https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json",
    "google/electra-large-discriminator": "https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json",
}

class ElectraConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ElectraModel`] or a [`TFElectraModel`]. It is
    used to instantiate a ELECTRA 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 ELECTRA
    [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) 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 ELECTRA model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`ElectraModel`] or [`TFElectraModel`].
        embedding_size (`int`, *optional*, defaults to 128):
            Dimensionality of the encoder layers and the pooler layer.
        hidden_size (`int`, *optional*, defaults to 256):
            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 4):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 1024):
            Dimensionality of the "intermediate" (i.e., 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 [`ElectraModel`] or [`TFElectraModel`].
        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.
        summary_type (`str`, *optional*, defaults to `"first"`):
            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.

Has to be one of the following options:

- `"last"`: Take the last token hidden state (like XLNet).
                - `"first"`: Take the first token hidden state (like BERT).
                - `"mean"`: Take the mean of all tokens hidden states.
                - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
                - `"attn"`: Not implemented now, use multi-head attention.
        summary_use_proj (`bool`, *optional*, defaults to `True`):
            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.

Whether or not to add a projection after the vector extraction.
        summary_activation (`str`, *optional*):
            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.

Pass `"gelu"` for a gelu activation to the output, any other value will result in no activation.
        summary_last_dropout (`float`, *optional*, defaults to 0.0):
            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.

The dropout ratio to be used after the projection and activation.
        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).
        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 ElectraModel, ElectraConfig

>>> # Initializing a ELECTRA electra-base-uncased style configuration
    >>> configuration = ElectraConfig()

>>> # Initializing a model from the electra-base-uncased style configuration
    >>> model = ElectraModel(configuration)

>>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "electra"

def __init__(
        self,
        vocab_size=30522,
        embedding_size=128,
        hidden_size=256,
        num_hidden_layers=12,
        num_attention_heads=4,
        intermediate_size=1024,
        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,
        summary_type="first",
        summary_use_proj=True,
        summary_activation="gelu",
        summary_last_dropout=0.1,
        pad_token_id=0,
        position_embedding_type="absolute",
        use_cache=True,
        classifier_dropout=None,
        **kwargs
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)

self.vocab_size = vocab_size
        self.embedding_size = embedding_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        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.summary_type = summary_type
        self.summary_use_proj = summary_use_proj
        self.summary_activation = summary_activation
        self.summary_last_dropout = summary_last_dropout
        self.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.classifier_dropout = classifier_dropout

class ElectraOnnxConfig(OnnxConfig):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "sequence"}),
                ("attention_mask", {0: "batch", 1: "sequence"}),
                ("token_type_ids", {0: "batch", 1: "sequence"}),
            ]
        )

cmd: