bert-for-patents-finetuned_ls-sys

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bert-for-patents-finetuned_ls-sys

This model is a fine-tuned version of anferico/bert-for-patents on a unique dataset consisting of 45392 patent applications, of which 10392 were defined as "LS-SYS-related" and 35000 as "Not LS-SYS-related". The fine-tuning is performed on patents' titles and abstracts. The base model was fine-tuned to perform a binary classification task, identifying patents related to the "Learning and Symbolic Systems" domain.

Read the working paper here: https://dipartimenti.unicatt.it/politica-economica-DIPE0050.pdf

It achieves the following results on the evaluation set:

• Loss: 0.0529
• Accuracy: 0.981
• Auc: 0.998
• F1: 0.958
• Precision: 0.962
• Recall: 0.955

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.49.0
• Pytorch 2.6.0+cu124
• Datasets 3.4.1
• Tokenizers 0.21.1