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    langs (`list[str]`):
        A list with source language and target_language (e.g., ['en', 'ru']).
    src_vocab_size (`int`):
        Vocabulary size of the encoder. Defines the number of different tokens that can be represented by the
        `inputs_ids` passed to the forward method in the encoder.
    tgt_vocab_size (`int`):
        Vocabulary size of the decoder. Defines the number of different tokens that can be represented by the
        `inputs_ids` passed to the forward method in the decoder.
    max_length (`int`, *optional*, defaults to 200):
        Maximum length to generate.
    num_beams (`int`, *optional*, defaults to 5):
        Number of beams for beam search that will be used by default in the `generate` method of the model. 1 means
        no beam search.
    length_penalty (`float`, *optional*, defaults to 1):
        Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
        the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
        likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
        `length_penalty` < 0.0 encourages shorter sequences.
    early_stopping (`bool`, *optional*, defaults to `False`):
        Flag that will be used by default in the `generate` method of the model. Whether to stop the beam search
        when at least `num_beams` sentences are finished per batch or not.

    Examples:

    ```python
    >>> from transformers import FSMTConfig, FSMTModel

    >>> # Initializing a FSMT facebook/wmt19-en-ru style configuration
    >>> config = FSMTConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = FSMTModel(config)

    >>> # Accessing the model configuration
    >>> configuration = model.config
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