---
library_name: transformers
license: apache-2.0
base_model: anferico/bert-for-patents
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-for-patents-finetuned_ls-sys
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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.

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

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc   | F1    | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|
| 0.3254        | 1.0   | 993  | 0.1486          | 0.947    | 0.987 | 0.88  | 0.922     | 0.841  |
| 0.1237        | 2.0   | 1986 | 0.0887          | 0.969    | 0.995 | 0.931 | 0.945     | 0.918  |
| 0.0928        | 3.0   | 2979 | 0.0718          | 0.973    | 0.996 | 0.94  | 0.962     | 0.919  |
| 0.0811        | 4.0   | 3972 | 0.0635          | 0.977    | 0.997 | 0.949 | 0.96      | 0.939  |
| 0.0748        | 5.0   | 4965 | 0.0596          | 0.979    | 0.997 | 0.953 | 0.955     | 0.952  |
| 0.0695        | 6.0   | 5958 | 0.0563          | 0.98     | 0.997 | 0.955 | 0.962     | 0.949  |
| 0.0682        | 7.0   | 6951 | 0.0552          | 0.98     | 0.997 | 0.957 | 0.958     | 0.956  |
| 0.0664        | 8.0   | 7944 | 0.0537          | 0.981    | 0.997 | 0.958 | 0.961     | 0.954  |
| 0.0642        | 9.0   | 8937 | 0.0530          | 0.981    | 0.998 | 0.958 | 0.962     | 0.954  |
| 0.0647        | 10.0  | 9930 | 0.0529          | 0.981    | 0.998 | 0.958 | 0.962     | 0.955  |


### Framework versions

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