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    ngram (`int`, *optional*, defaults to 2):
        Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
        token.
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        The number of buckets to use for each attention layer. This is for relative position calculation. See the
        [T5 paper](see https://huggingface.co/papers/1910.10683) for more details.
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        Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label
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vocab_sizei   Úhidden_sizei   Úencoder_ffn_dimé   Únum_encoder_layersé   Údecoder_ffn_dimÚnum_decoder_layersÚnum_decoder_attention_headsÚattention_dropoutÚdropouti   Úmax_position_embeddingsg{®Gáz”?Úinit_stdTÚis_encoder_decoderÚadd_cross_attentionr   NÚdecoder_start_token_idé   Úngramé    Únum_bucketsé€   Úrelative_max_distanceFÚdisable_ngram_lossg        ÚepsÚ	use_cacheÚpad_token_idé   Úbos_token_idÚeos_token_idÚ
is_decoderÚtie_word_embeddingsÚreturnc                 ó   — | j         S )N)r   )Úselfs    úu/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/prophetnet/configuration_prophetnet.pyÚnum_hidden_layersz"ProphetNetConfig.num_hidden_layersM   s   € àÔ&Ð&ó    c                 ó    — t          d¦  «        ‚)NzyThis model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and `num_decoder_layers`.)ÚNotImplementedError)r2   Úvalues     r3   r4   z"ProphetNetConfig.num_hidden_layersQ   s   € å!ð%ñ
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