| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - pv_dataset |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: test_ner3 |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: pv_dataset |
| type: pv_dataset |
| config: PVDatasetCorpus |
| split: train |
| args: PVDatasetCorpus |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.6698151950718686 |
| - name: Recall |
| type: recall |
| value: 0.6499117663801446 |
| - name: F1 |
| type: f1 |
| value: 0.6597133941985438 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9606609586670052 |
| --- |
| |
| <!-- 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. --> |
|
|
| # test_ner3 |
| |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the pv_dataset dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2983 |
| - Precision: 0.6698 |
| - Recall: 0.6499 |
| - F1: 0.6597 |
| - Accuracy: 0.9607 |
|
|
| ## 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: 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: 20 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.1106 | 1.0 | 1813 | 0.1128 | 0.6050 | 0.5949 | 0.5999 | 0.9565 | |
| | 0.0705 | 2.0 | 3626 | 0.1190 | 0.6279 | 0.6122 | 0.6200 | 0.9585 | |
| | 0.0433 | 3.0 | 5439 | 0.1458 | 0.6342 | 0.5983 | 0.6157 | 0.9574 | |
| | 0.0301 | 4.0 | 7252 | 0.1453 | 0.6305 | 0.6818 | 0.6552 | 0.9594 | |
| | 0.0196 | 5.0 | 9065 | 0.1672 | 0.6358 | 0.6871 | 0.6605 | 0.9594 | |
| | 0.0133 | 6.0 | 10878 | 0.1931 | 0.6427 | 0.6138 | 0.6279 | 0.9587 | |
| | 0.0104 | 7.0 | 12691 | 0.1948 | 0.6657 | 0.6511 | 0.6583 | 0.9607 | |
| | 0.0081 | 8.0 | 14504 | 0.2243 | 0.6341 | 0.6574 | 0.6455 | 0.9586 | |
| | 0.0054 | 9.0 | 16317 | 0.2432 | 0.6547 | 0.6318 | 0.6431 | 0.9588 | |
| | 0.0041 | 10.0 | 18130 | 0.2422 | 0.6717 | 0.6397 | 0.6553 | 0.9605 | |
| | 0.0041 | 11.0 | 19943 | 0.2415 | 0.6571 | 0.6420 | 0.6495 | 0.9601 | |
| | 0.0027 | 12.0 | 21756 | 0.2567 | 0.6560 | 0.6590 | 0.6575 | 0.9601 | |
| | 0.0023 | 13.0 | 23569 | 0.2609 | 0.6640 | 0.6495 | 0.6566 | 0.9606 | |
| | 0.002 | 14.0 | 25382 | 0.2710 | 0.6542 | 0.6670 | 0.6606 | 0.9598 | |
| | 0.0012 | 15.0 | 27195 | 0.2766 | 0.6692 | 0.6539 | 0.6615 | 0.9610 | |
| | 0.001 | 16.0 | 29008 | 0.2938 | 0.6692 | 0.6415 | 0.6551 | 0.9603 | |
| | 0.0007 | 17.0 | 30821 | 0.2969 | 0.6654 | 0.6490 | 0.6571 | 0.9604 | |
| | 0.0007 | 18.0 | 32634 | 0.3035 | 0.6628 | 0.6456 | 0.6541 | 0.9601 | |
| | 0.0007 | 19.0 | 34447 | 0.2947 | 0.6730 | 0.6489 | 0.6607 | 0.9609 | |
| | 0.0004 | 20.0 | 36260 | 0.2983 | 0.6698 | 0.6499 | 0.6597 | 0.9607 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.21.0 |
| - Pytorch 1.12.0+cu113 |
| - Datasets 2.4.0 |
| - Tokenizers 0.12.1 |
|
|
