241 lines
11 KiB
Python
241 lines
11 KiB
Python
# coding=utf-8
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# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" XLNet configuration"""
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import warnings
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class XLNetConfig(PretrainedConfig):
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"""
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This is the configuration class to store the configuration of a [`XLNetModel`] or a [`TFXLNetModel`]. It is used to
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instantiate a XLNet 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
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[xlnet/xlnet-large-cased](https://huggingface.co/xlnet/xlnet-large-cased) 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 32000):
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Vocabulary size of the XLNet model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`XLNetModel`] or [`TFXLNetModel`].
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d_model (`int`, *optional*, defaults to 1024):
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Dimensionality of the encoder layers and the pooler layer.
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n_layer (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer encoder.
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n_head (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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d_inner (`int`, *optional*, defaults to 4096):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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ff_activation (`str` or `Callable`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the If string, `"gelu"`, `"relu"`, `"silu"` and
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`"gelu_new"` are supported.
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untie_r (`bool`, *optional*, defaults to `True`):
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Whether or not to untie relative position biases
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attn_type (`str`, *optional*, defaults to `"bi"`):
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The attention type used by the model. Set `"bi"` for XLNet, `"uni"` for Transformer-XL.
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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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dropout (`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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mem_len (`int` or `None`, *optional*):
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The number of tokens to cache. The key/value pairs that have already been pre-computed in a previous
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forward pass won't be re-computed. See the
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[quickstart](https://huggingface.co/transformers/quickstart.html#using-the-past) for more information.
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reuse_len (`int`, *optional*):
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The number of tokens in the current batch to be cached and reused in the future.
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bi_data (`bool`, *optional*, defaults to `False`):
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Whether or not to use bidirectional input pipeline. Usually set to `True` during pretraining and `False`
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during finetuning.
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clamp_len (`int`, *optional*, defaults to -1):
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Clamp all relative distances larger than clamp_len. Setting this attribute to -1 means no clamping.
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same_length (`bool`, *optional*, defaults to `False`):
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Whether or not to use the same attention length for each token.
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summary_type (`str`, *optional*, defaults to "last"):
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
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Has to be one of the following options:
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- `"last"`: Take the last token hidden state (like XLNet).
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- `"first"`: Take the first token hidden state (like BERT).
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- `"mean"`: Take the mean of all tokens hidden states.
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- `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
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- `"attn"`: Not implemented now, use multi-head attention.
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summary_use_proj (`bool`, *optional*, defaults to `True`):
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
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Whether or not to add a projection after the vector extraction.
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summary_activation (`str`, *optional*):
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
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Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
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summary_proj_to_labels (`boo`, *optional*, defaults to `True`):
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Used in the sequence classification and multiple choice models.
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Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
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summary_last_dropout (`float`, *optional*, defaults to 0.1):
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Used in the sequence classification and multiple choice models.
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The dropout ratio to be used after the projection and activation.
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start_n_top (`int`, *optional*, defaults to 5):
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Used in the SQuAD evaluation script.
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end_n_top (`int`, *optional*, defaults to 5):
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Used in the SQuAD evaluation script.
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use_mems_eval (`bool`, *optional*, defaults to `True`):
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Whether or not the model should make use of the recurrent memory mechanism in evaluation mode.
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use_mems_train (`bool`, *optional*, defaults to `False`):
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Whether or not the model should make use of the recurrent memory mechanism in train mode.
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<Tip>
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For pretraining, it is recommended to set `use_mems_train` to `True`. For fine-tuning, it is recommended to
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set `use_mems_train` to `False` as discussed
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[here](https://github.com/zihangdai/xlnet/issues/41#issuecomment-505102587). If `use_mems_train` is set to
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`True`, one has to make sure that the train batches are correctly pre-processed, *e.g.* `batch_1 = [[This
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line is], [This is the]]` and `batch_2 = [[ the first line], [ second line]]` and that all batches are of
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equal size.
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</Tip>
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Examples:
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```python
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>>> from transformers import XLNetConfig, XLNetModel
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>>> # Initializing a XLNet configuration
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>>> configuration = XLNetConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = XLNetModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "xlnet"
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keys_to_ignore_at_inference = ["mems"]
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attribute_map = {
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"n_token": "vocab_size", # Backward compatibility
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"hidden_size": "d_model",
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"num_attention_heads": "n_head",
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"num_hidden_layers": "n_layer",
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}
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def __init__(
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self,
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vocab_size=32000,
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d_model=1024,
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n_layer=24,
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n_head=16,
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d_inner=4096,
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ff_activation="gelu",
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untie_r=True,
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attn_type="bi",
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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dropout=0.1,
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mem_len=512,
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reuse_len=None,
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use_mems_eval=True,
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use_mems_train=False,
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bi_data=False,
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clamp_len=-1,
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same_length=False,
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summary_type="last",
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summary_use_proj=True,
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summary_activation="tanh",
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summary_last_dropout=0.1,
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start_n_top=5,
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end_n_top=5,
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pad_token_id=5,
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bos_token_id=1,
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eos_token_id=2,
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**kwargs,
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):
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"""Constructs XLNetConfig."""
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self.vocab_size = vocab_size
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self.d_model = d_model
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self.n_layer = n_layer
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self.n_head = n_head
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if d_model % n_head != 0:
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raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0")
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if "d_head" in kwargs:
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if kwargs["d_head"] != d_model // n_head:
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raise ValueError(
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f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"
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)
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self.d_head = d_model // n_head
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self.ff_activation = ff_activation
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self.d_inner = d_inner
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self.untie_r = untie_r
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self.attn_type = attn_type
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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.dropout = dropout
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self.mem_len = mem_len
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self.reuse_len = reuse_len
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self.bi_data = bi_data
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self.clamp_len = clamp_len
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self.same_length = same_length
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self.summary_type = summary_type
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self.summary_use_proj = summary_use_proj
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self.summary_activation = summary_activation
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self.summary_last_dropout = summary_last_dropout
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self.start_n_top = start_n_top
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self.end_n_top = end_n_top
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self.bos_token_id = bos_token_id
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self.pad_token_id = pad_token_id
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self.eos_token_id = eos_token_id
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if "use_cache" in kwargs:
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warnings.warn(
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"The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
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" instead.",
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FutureWarning,
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)
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use_mems_eval = kwargs["use_cache"]
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self.use_mems_eval = use_mems_eval
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self.use_mems_train = use_mems_train
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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@property
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def max_position_embeddings(self):
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logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.")
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return -1
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@max_position_embeddings.setter
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def max_position_embeddings(self, value):
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# Message copied from Transformer-XL documentation
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raise NotImplementedError(
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f"The model {self.model_type} is one of the few models that has no sequence length limit."
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)
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