Source code for transformers.configuration_xlnet

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
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""" XLNet configuration """
from __future__ import absolute_import, division, print_function, unicode_literals

import json
import logging
import sys
from io import open

from .configuration_utils import PretrainedConfig

logger = logging.getLogger(__name__)

XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-config.json",
    'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json",
}


[docs]class XLNetConfig(PretrainedConfig): """Configuration class to store the configuration of a ``XLNetModel``. Args: vocab_size_or_config_json_file: Vocabulary size of ``inputs_ids`` in ``XLNetModel``. d_model: Size of the encoder layers and the pooler layer. n_layer: Number of hidden layers in the Transformer encoder. n_head: Number of attention heads for each attention layer in the Transformer encoder. d_inner: The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. ff_activation: The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu" and "swish" are supported. untie_r: untie relative position biases attn_type: 'bi' for XLNet, 'uni' for Transformer-XL dropout: The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. initializer_range: The sttdev of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps: The epsilon used by LayerNorm. dropout: float, dropout rate. init: str, the initialization scheme, either "normal" or "uniform". init_range: float, initialize the parameters with a uniform distribution in [-init_range, init_range]. Only effective when init="uniform". init_std: float, initialize the parameters with a normal distribution with mean 0 and stddev init_std. Only effective when init="normal". mem_len: int, the number of tokens to cache. reuse_len: int, the number of tokens in the currect batch to be cached and reused in the future. bi_data: bool, whether to use bidirectional input pipeline. Usually set to True during pretraining and False during finetuning. clamp_len: int, clamp all relative distances larger than clamp_len. -1 means no clamping. same_length: bool, whether to use the same attention length for each token. finetuning_task: name of the glue task on which the model was fine-tuned if any """ pretrained_config_archive_map = XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP def __init__(self, vocab_size_or_config_json_file=32000, d_model=1024, n_layer=24, n_head=16, d_inner=4096, max_position_embeddings=512, ff_activation="gelu", untie_r=True, attn_type="bi", initializer_range=0.02, layer_norm_eps=1e-12, dropout=0.1, mem_len=None, reuse_len=None, bi_data=False, clamp_len=-1, same_length=False, finetuning_task=None, num_labels=2, summary_type='last', summary_use_proj=True, summary_activation='tanh', summary_last_dropout=0.1, start_n_top=5, end_n_top=5, **kwargs): """Constructs XLNetConfig. """ super(XLNetConfig, self).__init__(**kwargs) if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2 and isinstance(vocab_size_or_config_json_file, unicode)): with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader: json_config = json.loads(reader.read()) for key, value in json_config.items(): setattr(config, key, value) elif isinstance(vocab_size_or_config_json_file, int): self.n_token = vocab_size_or_config_json_file self.d_model = d_model self.n_layer = n_layer self.n_head = n_head assert d_model % n_head == 0 self.d_head = d_model // n_head self.ff_activation = ff_activation self.d_inner = d_inner self.untie_r = untie_r self.attn_type = attn_type self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.dropout = dropout self.mem_len = mem_len self.reuse_len = reuse_len self.bi_data = bi_data self.clamp_len = clamp_len self.same_length = same_length self.finetuning_task = finetuning_task self.num_labels = num_labels 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.start_n_top = start_n_top self.end_n_top = end_n_top else: raise ValueError("First argument must be either a vocabulary size (int)" " or the path to a pretrained model config file (str)") @property def max_position_embeddings(self): return -1 @property def vocab_size(self): return self.n_token @vocab_size.setter def vocab_size(self, value): self.n_token = value @property def hidden_size(self): return self.d_model @property def num_attention_heads(self): return self.n_head @property def num_hidden_layers(self): return self.n_layer