+ jElRt^RIHt^RIHt^RIHt]!RR7]!RR]444tR.tR#) zFlaubert configuration)strict)PreTrainedConfig)auto_docstringzflaubert/flaubert_base_uncased) checkpointca]tRt^toRtRtRRRRRRR R R R R RRR/tRtRtRt Rt ^ t ^t Rt RtRtRtRtRt^tRtRtRtRtRt^t^t^t^t^tRtRtRt Rt!Rt"Rt#^t$^t%^t&^t'^t(^t)^t*Rt+V3Rlt,Rt-Vt.R#)FlaubertConfiga pre_norm (`bool`, *optional*, defaults to `False`): Whether to apply the layer normalization before or after the feed forward layer following the attention in each layer (Vaswani et al., Tensor2Tensor for Neural Machine Translation. 2018) emb_dim (`int`, *optional*, defaults to 2048): The dimensionality of embedding layer. gelu_activation (`bool`, *optional*, defaults to True): Whether to use GeLU activation function. 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. embed_init_std (`float`, *optional*, defaults to `2048**-0.5`): Initializer std for embedding layers. bos_index (`int`, *optional*, defaults to 0): The index of the beginning of sentence token in the vocabulary. eos_index (`int`, *optional*, defaults to 1): The index of the end of sentence token in the vocabulary. pad_index (`int`, *optional*, defaults to 2): The index of the padding token in the vocabulary. 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 the model is used as an encoder. 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. flaubert hidden_sizeemb_dimnum_attention_headsn_headsnum_hidden_layersn_layersn_words vocab_size bos_index bos_token_id eos_index eos_token_id pad_index pad_token_idFgiuig?Tig-q=g{Gz?firstNcn<V^8dQh/S[;R&S[S[,;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R&S[S[,;R&S[;R &S[;R &S[;R &S[;R &S[;R &S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[R,;R&S[;R&S[S[,;R&S[;R&S[;R &S[;R!&S[;R"&S[R,;R#&S[R,;R$&S[S[S[,,R,;R%&S[;R&&#)'pre_norm layerdroprr rr dropoutattention_dropoutgelu_activationsinusoidal_embeddingscausalasmn_langs use_lang_embmax_position_embeddingsembed_init_stdlayer_norm_epsinit_stdrrr unk_index mask_index is_encoder summary_typesummary_use_projNsummary_activationsummary_proj_to_labelssummary_first_dropout start_n_top end_n_top mask_token_idlang_idrrrtie_word_embeddings)boolfloatintstrlist)format __classdict__s"t/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/flaubert/configuration_flaubert.py __annotate__FlaubertConfig.__annotate__sYdefs{ ghijklmnopS[qrs{(st uv 'wx yz {|}~@!&AB&CD!EFGHIJKLMNOPQRSTUV!WXd )YZ!'[\!3;,]^_`abcdef* gh* ijS /D(,kl$mg;f?)/__name__ __module__ __qualname____firstlineno____doc__ model_type attribute_maprrrr rr rrrrr r!r"r#r$r%r&r'rrrr(r)r*r+r,r-r.r/r0r1r2r3rrrr4__annotate_func____static_attributes____classdictcell__)r;s@r<rrsBHJyyZ<^^^MH IJGHGG%( O"'FCGL#&&N!NHIIIIJJL!%)#'),KIMG L L+,L $mr?rN) rEhuggingface_hub.dataclassesrconfiguration_utilsrutilsrr__all__r@r?r<rOsK.3#;<t%%t%=t%n  r?