Source code for transformers.configuration_xlm

# coding=utf-8
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""" XLM 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__)

XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json",
    'xlm-mlm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-config.json",
    'xlm-mlm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-config.json",
    'xlm-mlm-enro-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-config.json",
    'xlm-mlm-tlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-config.json",
    'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-config.json",
    'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-config.json",
    'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-config.json",
    'xlm-mlm-17-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-config.json",
    'xlm-mlm-100-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-config.json",
}


[docs]class XLMConfig(PretrainedConfig): """Configuration class to store the configuration of a `XLMModel`. Args: vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `XLMModel`. 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 XLM, 'uni' for Transformer-XL dropout: The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. max_position_embeddings: 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). 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. """ pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP def __init__(self, vocab_size_or_config_json_file=30145, emb_dim=2048, n_layers=12, n_heads=16, dropout=0.1, attention_dropout=0.1, gelu_activation=True, sinusoidal_embeddings=False, causal=False, asm=False, n_langs=1, use_lang_emb=True, max_position_embeddings=512, embed_init_std=2048 ** -0.5, layer_norm_eps=1e-12, init_std=0.02, bos_index=0, eos_index=1, pad_index=2, unk_index=3, mask_index=5, is_encoder=True, finetuning_task=None, num_labels=2, summary_type='first', summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, start_n_top=5, end_n_top=5, **kwargs): """Constructs XLMConfig. """ super(XLMConfig, 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(): self.__dict__[key] = value elif isinstance(vocab_size_or_config_json_file, int): self.n_words = vocab_size_or_config_json_file self.emb_dim = emb_dim self.n_layers = n_layers self.n_heads = n_heads self.dropout = dropout self.attention_dropout = attention_dropout self.gelu_activation = gelu_activation self.sinusoidal_embeddings = sinusoidal_embeddings self.causal = causal self.asm = asm self.n_langs = n_langs self.use_lang_emb = use_lang_emb self.layer_norm_eps = layer_norm_eps self.bos_index = bos_index self.eos_index = eos_index self.pad_index = pad_index self.unk_index = unk_index self.mask_index = mask_index self.is_encoder = is_encoder self.max_position_embeddings = max_position_embeddings self.embed_init_std = embed_init_std self.init_std = init_std 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_proj_to_labels = summary_proj_to_labels self.summary_first_dropout = summary_first_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 vocab_size(self): return self.n_words @vocab_size.setter def vocab_size(self, value): self.n_words = value @property def hidden_size(self): return self.emb_dim @property def num_attention_heads(self): return self.n_heads @property def num_hidden_layers(self): return self.n_layers