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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: anferico/bert-for-patents |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: bert-for-patents-finetuned_ls-sys |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-for-patents-finetuned_ls-sys |
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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" |
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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, |
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identifying patents related to the "Learning and Symbolic Systems" domain. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0529 |
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- Accuracy: 0.981 |
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- Auc: 0.998 |
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- F1: 0.958 |
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- Precision: 0.962 |
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- Recall: 0.955 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:| |
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| 0.3254 | 1.0 | 993 | 0.1486 | 0.947 | 0.987 | 0.88 | 0.922 | 0.841 | |
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| 0.1237 | 2.0 | 1986 | 0.0887 | 0.969 | 0.995 | 0.931 | 0.945 | 0.918 | |
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| 0.0928 | 3.0 | 2979 | 0.0718 | 0.973 | 0.996 | 0.94 | 0.962 | 0.919 | |
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| 0.0811 | 4.0 | 3972 | 0.0635 | 0.977 | 0.997 | 0.949 | 0.96 | 0.939 | |
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| 0.0748 | 5.0 | 4965 | 0.0596 | 0.979 | 0.997 | 0.953 | 0.955 | 0.952 | |
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| 0.0695 | 6.0 | 5958 | 0.0563 | 0.98 | 0.997 | 0.955 | 0.962 | 0.949 | |
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| 0.0682 | 7.0 | 6951 | 0.0552 | 0.98 | 0.997 | 0.957 | 0.958 | 0.956 | |
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| 0.0664 | 8.0 | 7944 | 0.0537 | 0.981 | 0.997 | 0.958 | 0.961 | 0.954 | |
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| 0.0642 | 9.0 | 8937 | 0.0530 | 0.981 | 0.998 | 0.958 | 0.962 | 0.954 | |
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| 0.0647 | 10.0 | 9930 | 0.0529 | 0.981 | 0.998 | 0.958 | 0.962 | 0.955 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.4.1 |
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- Tokenizers 0.21.1 |
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