Struct EmbeddingFromPretrainedOptions¶
Defined in File embedding.h
Page Contents
Struct Documentation¶
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struct EmbeddingFromPretrainedOptions¶
Options for the
Embedding::from_pretrained
function.Public Functions
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inline auto freeze(const bool &new_freeze) -> decltype(*this)¶
If
true
, the tensor does not get updated in the learning process.Equivalent to
embedding.weight.requires_grad_(false)
. Default:true
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inline auto freeze(bool &&new_freeze) -> decltype(*this)¶
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inline const bool &freeze() const noexcept¶
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inline bool &freeze() noexcept¶
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inline auto padding_idx(const std::optional<int64_t> &new_padding_idx) -> decltype(*this)¶
If specified, the entries at
padding_idx
do not contribute to the gradient; therefore, the embedding vector atpadding_idx
is not updated during training, i.e.it remains as a fixed “pad”.
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inline auto padding_idx(std::optional<int64_t> &&new_padding_idx) -> decltype(*this)¶
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inline const std::optional<int64_t> &padding_idx() const noexcept¶
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inline std::optional<int64_t> &padding_idx() noexcept¶
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inline auto max_norm(const std::optional<double> &new_max_norm) -> decltype(*this)¶
If given, each embedding vector with norm larger than
max_norm
is renormalized to have normmax_norm
.
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inline auto max_norm(std::optional<double> &&new_max_norm) -> decltype(*this)¶
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inline const std::optional<double> &max_norm() const noexcept¶
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inline std::optional<double> &max_norm() noexcept¶
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inline auto norm_type(const double &new_norm_type) -> decltype(*this)¶
The p of the p-norm to compute for the
max_norm
option. Default2
.
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inline auto norm_type(double &&new_norm_type) -> decltype(*this)¶
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inline const double &norm_type() const noexcept¶
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inline double &norm_type() noexcept¶
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inline auto scale_grad_by_freq(const bool &new_scale_grad_by_freq) -> decltype(*this)¶
If given, this will scale gradients by the inverse of frequency of the words in the mini-batch.
Default
false
.
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inline auto scale_grad_by_freq(bool &&new_scale_grad_by_freq) -> decltype(*this)¶
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inline const bool &scale_grad_by_freq() const noexcept¶
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inline bool &scale_grad_by_freq() noexcept¶
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inline auto sparse(const bool &new_sparse) -> decltype(*this)¶
If
true
, gradient w.r.t.weight
matrix will be a sparse tensor.
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inline auto sparse(bool &&new_sparse) -> decltype(*this)¶
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inline const bool &sparse() const noexcept¶
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inline bool &sparse() noexcept¶
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inline auto freeze(const bool &new_freeze) -> decltype(*this)¶