case class LanguageModelInput(tokens: Constant, maxLength: Option[STen], positions: Option[STen]) extends Product with Serializable
Input to language model
- tokens
batch x sequence, type long
- maxLength
batch x sequence OR batch, see maskedSoftmax. Used to define masking of the attention matrix. Use cases:
- Left-to-right (causal) attention with uniform sequence length. In this case use a batch x sequence 2D matrix with arange(0,sequence) in each row.
- Variable length sequences with bidirectional attention. In this case use a 1D [batch] vector with the real length of each sequence (rest are padded).
- If empty the attention matrix is not masked
- positions
batch x sequence, type long in [0,sequence], selects positions. Final LM logits are computed on the selected positions. If empty then selects all positions.
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- new LanguageModelInput(tokens: Constant, maxLength: Option[STen], positions: Option[STen])
- tokens
batch x sequence, type long
- maxLength
batch x sequence OR batch, see maskedSoftmax. Used to define masking of the attention matrix. Use cases:
- Left-to-right (causal) attention with uniform sequence length. In this case use a batch x sequence 2D matrix with arange(0,sequence) in each row.
- Variable length sequences with bidirectional attention. In this case use a 1D [batch] vector with the real length of each sequence (rest are padded).
- If empty the attention matrix is not masked
- positions
batch x sequence, type long in [0,sequence], selects positions. Final LM logits are computed on the selected positions. If empty then selects all positions.
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- val maxLength: Option[STen]
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- val positions: Option[STen]
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- val tokens: Constant
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(Since version 9)