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| # coding=utf-8 | |
| # Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team. | |
| # | |
| # 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. | |
| """ XLM configuration """ | |
| import logging | |
| 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", | |
| } | |
| class XLMConfig(PretrainedConfig): | |
| """ | |
| This is the configuration class to store the configuration of a :class:`~transformers.XLMModel`. | |
| It is used to instantiate an XLM 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 `xlm-mlm-en-2048 <https://huggingface.co/xlm-mlm-en-2048>`__ architecture. | |
| Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used | |
| to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` | |
| for more information. | |
| Args: | |
| vocab_size (:obj:`int`, optional, defaults to 30145): | |
| Vocabulary size of the XLM model. Defines the different tokens that | |
| can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLMModel`. | |
| emb_dim (:obj:`int`, optional, defaults to 2048): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| n_layer (:obj:`int`, optional, defaults to 12): | |
| Number of hidden layers in the Transformer encoder. | |
| n_head (:obj:`int`, optional, defaults to 16): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| dropout (:obj:`float`, optional, defaults to 0.1): | |
| The dropout probability for all fully connected | |
| layers in the embeddings, encoder, and pooler. | |
| attention_dropout (:obj:`float`, optional, defaults to 0.1): | |
| The dropout probability for the attention mechanism | |
| gelu_activation (:obj:`boolean`, optional, defaults to :obj:`True`): | |
| The non-linear activation function (function or string) in the | |
| encoder and pooler. If set to `True`, "gelu" will be used instead of "relu". | |
| sinusoidal_embeddings (:obj:`boolean`, optional, defaults to :obj:`False`): | |
| Whether to use sinusoidal positional embeddings instead of absolute positional embeddings. | |
| causal (:obj:`boolean`, optional, defaults to :obj:`False`): | |
| Set this to `True` for the model to behave in a causal manner. | |
| Causal models use a triangular attention mask in order to only attend to the left-side context instead | |
| if a bidirectional context. | |
| asm (:obj:`boolean`, optional, defaults to :obj:`False`): | |
| Whether to use an adaptive log softmax projection layer instead of a linear layer for the prediction | |
| layer. | |
| n_langs (:obj:`int`, optional, defaults to 1): | |
| The number of languages the model handles. Set to 1 for monolingual models. | |
| use_lang_emb (:obj:`boolean`, optional, defaults to :obj:`True`) | |
| Whether to use language embeddings. Some models use additional language embeddings, see | |
| `the multilingual models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>`__ | |
| for information on how to use them. | |
| max_position_embeddings (:obj:`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). | |
| embed_init_std (:obj:`float`, optional, defaults to 2048^-0.5): | |
| The standard deviation of the truncated_normal_initializer for | |
| initializing the embedding matrices. | |
| init_std (:obj:`int`, optional, defaults to 50257): | |
| The standard deviation of the truncated_normal_initializer for | |
| initializing all weight matrices except the embedding matrices. | |
| layer_norm_eps (:obj:`float`, optional, defaults to 1e-12): | |
| The epsilon used by the layer normalization layers. | |
| bos_index (:obj:`int`, optional, defaults to 0): | |
| The index of the beginning of sentence token in the vocabulary. | |
| eos_index (:obj:`int`, optional, defaults to 1): | |
| The index of the end of sentence token in the vocabulary. | |
| pad_index (:obj:`int`, optional, defaults to 2): | |
| The index of the padding token in the vocabulary. | |
| unk_index (:obj:`int`, optional, defaults to 3): | |
| The index of the unknown token in the vocabulary. | |
| mask_index (:obj:`int`, optional, defaults to 5): | |
| The index of the masking token in the vocabulary. | |
| is_encoder(:obj:`boolean`, optional, defaults to :obj:`True`): | |
| Whether the initialized model should be a transformer encoder or decoder as seen in Vaswani et al. | |
| summary_type (:obj:`string`, optional, defaults to "first"): | |
| Argument used when doing sequence summary. Used in for the multiple choice head in | |
| :class:`~transformers.XLMForSequenceClassification`. | |
| Is 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 (GPT/GPT-2) | |
| - 'attn' => Not implemented now, use multi-head attention | |
| summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`): | |
| Argument used when doing sequence summary. Used in for the multiple choice head in | |
| :class:`~transformers.XLMForSequenceClassification`. | |
| Add a projection after the vector extraction | |
| summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`): | |
| Argument used when doing sequence summary. Used in for the multiple choice head in | |
| :class:`~transformers.XLMForSequenceClassification`. | |
| 'tanh' => add a tanh activation to the output, Other => no activation. | |
| summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`): | |
| Argument used when doing sequence summary. Used in for the multiple choice head in | |
| :class:`~transformers.XLMForSequenceClassification`. | |
| If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False. | |
| summary_first_dropout (:obj:`float`, optional, defaults to 0.1): | |
| Argument used when doing sequence summary. Used in for the multiple choice head in | |
| :class:`~transformers.XLMForSequenceClassification`. | |
| Add a dropout before the projection and activation | |
| start_n_top (:obj:`int`, optional, defaults to 5): | |
| Used in the SQuAD evaluation script for XLM and XLNet. | |
| end_n_top (:obj:`int`, optional, defaults to 5): | |
| Used in the SQuAD evaluation script for XLM and XLNet. | |
| mask_token_id (:obj:`int`, optional, defaults to 0): | |
| Model agnostic parameter to identify masked tokens when generating text in an MLM context. | |
| lang_id (:obj:`int`, optional, defaults to 1): | |
| The ID of the language used by the model. This parameter is used when generating | |
| text in a given language. | |
| Example:: | |
| from transformers import XLMConfig, XLMModel | |
| # Initializing a XLM configuration | |
| configuration = XLMConfig() | |
| # Initializing a model from the configuration | |
| model = XLMModel(configuration) | |
| # Accessing the model configuration | |
| configuration = model.config | |
| Attributes: | |
| pretrained_config_archive_map (Dict[str, str]): | |
| A dictionary containing all the available pre-trained checkpoints. | |
| """ | |
| pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP | |
| model_type = "xlm" | |
| def __init__( | |
| self, | |
| vocab_size=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, | |
| 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, | |
| mask_token_id=0, | |
| lang_id=0, | |
| **kwargs | |
| ): | |
| """Constructs XLMConfig. | |
| """ | |
| super().__init__(**kwargs) | |
| self.vocab_size = vocab_size | |
| 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.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 | |
| self.mask_token_id = mask_token_id | |
| self.lang_id = lang_id | |
| if "n_words" in kwargs: | |
| self.n_words = kwargs["n_words"] | |
| def n_words(self): # For backward compatibility | |
| return self.vocab_size | |
| def n_words(self, value): # For backward compatibility | |
| self.vocab_size = value | |
| def hidden_size(self): | |
| return self.emb_dim | |
| def num_attention_heads(self): | |
| return self.n_heads | |
| def num_hidden_layers(self): | |
| return self.n_layers | |