| | --- |
| | license: apache-2.0 |
| | base_model: bert-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - anno_ctr |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: annoctr_bert_uncased |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: anno_ctr |
| | type: anno_ctr |
| | config: all_tags |
| | split: test |
| | args: all_tags |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.7928388746803069 |
| | - name: Recall |
| | type: recall |
| | value: 0.7809920945182869 |
| | - name: F1 |
| | type: f1 |
| | value: 0.7868708971553611 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.936522196415268 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # annoctr_bert_uncased |
| |
|
| | This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the anno_ctr dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.3322 |
| | - Precision: 0.7928 |
| | - Recall: 0.7810 |
| | - F1: 0.7869 |
| | - Accuracy: 0.9365 |
| | |
| | ## 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: 1e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.54 | 1.0 | 474 | 0.3452 | 0.6983 | 0.6601 | 0.6786 | 0.9137 | |
| | | 0.3013 | 2.0 | 948 | 0.3466 | 0.7774 | 0.7018 | 0.7376 | 0.9240 | |
| | | 0.0392 | 3.0 | 1422 | 0.3071 | 0.7851 | 0.7517 | 0.7680 | 0.9303 | |
| | | 0.5695 | 4.0 | 1896 | 0.2941 | 0.7810 | 0.7617 | 0.7712 | 0.9334 | |
| | | 0.0021 | 5.0 | 2370 | 0.3109 | 0.7928 | 0.7720 | 0.7823 | 0.9351 | |
| | | 0.0419 | 6.0 | 2844 | 0.3020 | 0.7772 | 0.7796 | 0.7784 | 0.9341 | |
| | | 0.2979 | 7.0 | 3318 | 0.3169 | 0.8019 | 0.7814 | 0.7915 | 0.9374 | |
| | | 0.0017 | 8.0 | 3792 | 0.3260 | 0.7972 | 0.7778 | 0.7874 | 0.9365 | |
| | | 0.0166 | 9.0 | 4266 | 0.3349 | 0.7935 | 0.7789 | 0.7861 | 0.9364 | |
| | | 0.0685 | 10.0 | 4740 | 0.3322 | 0.7928 | 0.7810 | 0.7869 | 0.9365 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.40.1 |
| | - Pytorch 2.3.0+cu121 |
| | - Datasets 2.19.1 |
| | - Tokenizers 0.19.1 |
| | |
