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---
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
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