tjKdZddlmZddlmZddlmZedeGdd eZd gZd S) zXLM configuration)strict)PreTrainedConfig)auto_docstringzFacebookAI/xlm-mlm-en-2048) checkpointc.eZdZUdZdZddddddd d Zd Zeed<d Z eed<d Z eed<dZ eed<dZ e ezed<dZe ezed<dZeed<dZeed<dZeed<dZeed<dZeed<dZeed<dZeed<dZe ed<dZe ed <d!Ze ed"<d#Zeed$<d%Zeed&<dZeed'<d(Zeed)<dZeed*<d+Z ed+zed,<dZ!eed-<dZ"e ezed.<d%Z#eed/<d%Z$eed0<d1Z%ed+zed2<d1Z&eed3<d4Z'ed+zed <d1Z(ed+zed<dZ)ee*ezd+zed<dZ+eed5<d+S)6 XLMConfigaw gelu_activation (`bool`, *optional*, defaults to `True`): Whether or not to use *gelu* for the activations instead of *relu*. sinusoidal_embeddings (`bool`, *optional*, defaults to `False`): Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings. causal (`bool`, *optional*, defaults to `False`): Whether or not the model should 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 (`bool`, *optional*, defaults to `False`): Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction layer. n_langs (`int`, *optional*, defaults to 1): The number of languages the model handles. Set to 1 for monolingual models. use_lang_emb (`bool`, *optional*, defaults to `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. embed_init_std (`float`, *optional*, defaults to 2048^-0.5): The standard deviation of the truncated_normal_initializer for initializing the embedding matrices. unk_index (`int`, *optional*, defaults to 3): The index of the unknown token in the vocabulary. mask_index (`int`, *optional*, defaults to 5): The index of the masking token in the vocabulary. is_encoder (`bool`, *optional*, defaults to `True`): Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al. summary_type (`string`, *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 `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`bool`, *optional*, defaults to `True`): Used in the sequence classification and multiple choice models. Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_first_dropout (`float`, *optional*, defaults to 0.1): Used in the sequence classification and multiple choice models. The dropout ratio to be used after the projection and activation. start_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. end_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. mask_token_id (`int`, *optional*, defaults to 0): Model agnostic parameter to identify masked tokens when generating text in an MLM context. lang_id (`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. Examples: ```python >>> from transformers import XLMConfig, XLMModel >>> # Initializing a XLM configuration >>> configuration = XLMConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = XLMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```xlmemb_dimn_headsn_layers vocab_size bos_token_id eos_token_id pad_token_id) hidden_sizenum_attention_headsnum_hidden_layersn_words bos_index eos_index pad_indexiui g?dropoutattention_dropoutTgelu_activationFsinusoidal_embeddingscausalasmn_langs use_lang_embimax_position_embeddingsg;f?embed_init_stdg-q=layer_norm_epsg{Gz?init_stdr unk_index mask_index is_encoderfirst summary_typesummary_use_projNsummary_activationsummary_proj_to_labelssummary_first_dropout start_n_top end_n_topr mask_token_idlang_idtie_word_embeddings),__name__ __module__ __qualname____doc__ model_type attribute_maprint__annotations__r r r rfloatrrboolrrr r"r#r$r%r&r'r(r*r+r-strr.r/r0r1r2r3r4r5rrrlistr7g/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/xlm/configuration_xlm.pyr r szC C JJ ('###MJGSHcGSGUS[%(us{((( OT   "'4'''FDCGSL$#&S&&&&NE&&&!NE!!!HeIsJJL#!d!!!%)d )))#'D'''),53;,,,KIs !M3:!!!GS L#*    L#*   +,L#S /D(,,, $$$$$$rEr N) r;huggingface_hub.dataclassesrconfiguration_utilsrutilsrr __all__rDrErFrKs......333333######7888p%p%p%p%p% p%p%98p%f -rE