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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- null |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model_index: |
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- name: distilbert-srb-ner-setimes |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9660941783583293 |
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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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# distilbert-srb-ner-setimes |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1522 |
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- Precision: 0.8280 |
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- Recall: 0.8607 |
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- F1: 0.8440 |
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- Accuracy: 0.9661 |
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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: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 207 | 0.2240 | 0.6996 | 0.7200 | 0.7097 | 0.9375 | |
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| No log | 2.0 | 414 | 0.1538 | 0.7501 | 0.7930 | 0.7710 | 0.9546 | |
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| 0.2348 | 3.0 | 621 | 0.1459 | 0.7756 | 0.8115 | 0.7931 | 0.9576 | |
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| 0.2348 | 4.0 | 828 | 0.1465 | 0.7918 | 0.8456 | 0.8178 | 0.9611 | |
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| 0.0782 | 5.0 | 1035 | 0.1310 | 0.7981 | 0.8352 | 0.8162 | 0.9636 | |
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| 0.0782 | 6.0 | 1242 | 0.1466 | 0.8103 | 0.8510 | 0.8301 | 0.9646 | |
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| 0.0782 | 7.0 | 1449 | 0.1441 | 0.8222 | 0.8503 | 0.8360 | 0.9655 | |
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| 0.0343 | 8.0 | 1656 | 0.1493 | 0.8265 | 0.8600 | 0.8429 | 0.9666 | |
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| 0.0343 | 9.0 | 1863 | 0.1524 | 0.8236 | 0.8570 | 0.8400 | 0.9656 | |
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| 0.0169 | 10.0 | 2070 | 0.1522 | 0.8280 | 0.8607 | 0.8440 | 0.9661 | |
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### Framework versions |
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- Transformers 4.9.2 |
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- Pytorch 1.9.0 |
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- Datasets 1.11.0 |
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- Tokenizers 0.10.1 |
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