self-attention mechanism
mekanismo ng self-attention
using self-attention
paggamit ng self-attention
self-attention layer
layer ng self-attention
apply self-attention
ilapat ang self-attention
self-attention weights
timbang ng self-attention
with self-attention
kasama ang self-attention
self-attention scores
iskor ng self-attention
self-attention model
modelo ng self-attention
self-attention network
network ng self-attention
self-attention improved
pinabuti ang self-attention
the model utilizes self-attention to weigh the importance of different words in the input sequence.
Ginagamit ng modelo ang self-attention upang timbangin ang kahalagahan ng iba't ibang salita sa input sequence.
self-attention allows the transformer to capture long-range dependencies effectively.
Pinahihintulutan ng self-attention ang transformer na makuha ang mahahabang dependencies nang epektibo.
we fine-tuned the pre-trained model with self-attention on a new dataset.
Fine-tune namin ang pre-trained model na may self-attention sa isang bagong dataset.
the self-attention mechanism significantly improved the model's performance on the task.
Malaki ang naitulong ng self-attention mechanism sa pagpapabuti ng performance ng modelo sa gawain.
visualizing self-attention weights provides insights into the model's reasoning process.
Ang pag-visualize ng self-attention weights ay nagbibigay ng mga pananaw sa proseso ng pagdadahilan ng modelo.
multi-head self-attention enables the model to attend to different aspects of the input.
Pinahihintulutan ng multi-head self-attention ang modelo na bigyang-pansin ang iba't ibang aspeto ng input.
self-attention layers are crucial for understanding context in natural language processing.
Mahalaga ang mga layer ng self-attention para sa pag-unawa sa konteksto sa natural language processing.
the self-attention mechanism helps the model resolve ambiguity in the sentence.
Tinutulungan ng self-attention mechanism ang modelo na lutasin ang ambiguity sa pangungusap.
we compared self-attention with traditional recurrent neural networks.
Kinumpara namin ang self-attention sa tradisyonal na recurrent neural networks.
the effectiveness of self-attention is well-established in the field of nlp.
Ang pagiging epektibo ng self-attention ay kilala na sa larangan ng NLP.
self-attention contributes to better machine translation quality.
Nag-aambag ang self-attention sa mas mahusay na kalidad ng machine translation.
self-attention mechanism
mekanismo ng self-attention
using self-attention
paggamit ng self-attention
self-attention layer
layer ng self-attention
apply self-attention
ilapat ang self-attention
self-attention weights
timbang ng self-attention
with self-attention
kasama ang self-attention
self-attention scores
iskor ng self-attention
self-attention model
modelo ng self-attention
self-attention network
network ng self-attention
self-attention improved
pinabuti ang self-attention
the model utilizes self-attention to weigh the importance of different words in the input sequence.
Ginagamit ng modelo ang self-attention upang timbangin ang kahalagahan ng iba't ibang salita sa input sequence.
self-attention allows the transformer to capture long-range dependencies effectively.
Pinahihintulutan ng self-attention ang transformer na makuha ang mahahabang dependencies nang epektibo.
we fine-tuned the pre-trained model with self-attention on a new dataset.
Fine-tune namin ang pre-trained model na may self-attention sa isang bagong dataset.
the self-attention mechanism significantly improved the model's performance on the task.
Malaki ang naitulong ng self-attention mechanism sa pagpapabuti ng performance ng modelo sa gawain.
visualizing self-attention weights provides insights into the model's reasoning process.
Ang pag-visualize ng self-attention weights ay nagbibigay ng mga pananaw sa proseso ng pagdadahilan ng modelo.
multi-head self-attention enables the model to attend to different aspects of the input.
Pinahihintulutan ng multi-head self-attention ang modelo na bigyang-pansin ang iba't ibang aspeto ng input.
self-attention layers are crucial for understanding context in natural language processing.
Mahalaga ang mga layer ng self-attention para sa pag-unawa sa konteksto sa natural language processing.
the self-attention mechanism helps the model resolve ambiguity in the sentence.
Tinutulungan ng self-attention mechanism ang modelo na lutasin ang ambiguity sa pangungusap.
we compared self-attention with traditional recurrent neural networks.
Kinumpara namin ang self-attention sa tradisyonal na recurrent neural networks.
the effectiveness of self-attention is well-established in the field of nlp.
Ang pagiging epektibo ng self-attention ay kilala na sa larangan ng NLP.
self-attention contributes to better machine translation quality.
Nag-aambag ang self-attention sa mas mahusay na kalidad ng machine translation.
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