language-perceiver-title2genre

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language-perceiver for title-genre classification

This model is a fine-tuned version of deepmind/language-perceiver on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2832
• F1: 0.5108

Model description

This classifies one or more genre labels in a multi-label setting for a given book title.

The 'standard' way of interpreting the predictions is that the predicted labels for a given example are only the ones with a greater than 50% probability.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 8.0

Training results

Framework versions

• Transformers 4.33.3
• Pytorch 2.2.0.dev20231001+cu121
• Datasets 2.14.5
• Tokenizers 0.13.3