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README.md
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### Transfer-learning Pretraining
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The model was trained on a single TPU Pod V3-8 for
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It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
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The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
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### Fine-tuning
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This model was then fine-tuned on a single TPU Pod V3-8 for
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## Evaluation results
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### Transfer-learning Pretraining
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The model was trained on a single TPU Pod V3-8 for 240,000 steps in total, using sequence length 512 (batch size 4096).
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It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
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The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
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### Fine-tuning
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This model was then fine-tuned on a single TPU Pod V3-8 for 1,400,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing api recommendation generation data.
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## Evaluation results
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