BEE-spoke-data/language-perceiver-title2genre

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

Training Loss	Epoch	Step	Validation Loss	F1
0.3059	1.0	62	0.2893	0.3263
0.2879	2.0	124	0.2795	0.4290
0.2729	3.0	186	0.2730	0.4356
0.2606	4.0	248	0.2722	0.4590
0.2433	5.0	310	0.2747	0.4775
0.227	6.0	372	0.2777	0.4976
0.207	7.0	434	0.2814	0.5088
0.1969	8.0	496	0.2832	0.5108

Framework versions

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