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@@ -41,9 +41,7 @@ We use the pretrained [`sentence-transformers/all-mpnet-base-v2`](https://huggin
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  We use the initial model without modifying its architecture or pre-trained model parameters.
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  However, we reduce the processed sequence length to 384 to reduce the training time of the model.
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- Regarding other hyperparameters, we use the same interaction data batch size of 1024; we use the negative sampling parameter m = 10000.
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- We use constant learning rate of 1e-5, and we train the model for five epochs.
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  ### Dataset
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  We finetuned our model on the Goodbooks-10k dataset with item descriptions generated with [`meta-llama/Meta-Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) model. For details please see the dataset page [`beeformer/recsys-goodbooks-10k`](https://huggingface.co/datasets/beeformer/recsys-goodbooks-10k).
 
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  We use the initial model without modifying its architecture or pre-trained model parameters.
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  However, we reduce the processed sequence length to 384 to reduce the training time of the model.
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  ### Dataset
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  We finetuned our model on the Goodbooks-10k dataset with item descriptions generated with [`meta-llama/Meta-Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) model. For details please see the dataset page [`beeformer/recsys-goodbooks-10k`](https://huggingface.co/datasets/beeformer/recsys-goodbooks-10k).