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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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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_bio_pv_superset |
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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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# distilBERT_bio_pv_superset |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2328 |
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- Precision: 0.5462 |
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- Recall: 0.5325 |
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- F1: 0.5393 |
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- Accuracy: 0.9495 |
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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: 16 |
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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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| 0.0964 | 1.0 | 5467 | 0.1593 | 0.4625 | 0.3682 | 0.4100 | 0.9416 | |
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| 0.1918 | 2.0 | 10934 | 0.1541 | 0.4796 | 0.4658 | 0.4726 | 0.9436 | |
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| 0.0394 | 3.0 | 16401 | 0.1508 | 0.5349 | 0.4744 | 0.5028 | 0.9482 | |
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| 0.1207 | 4.0 | 21868 | 0.1615 | 0.5422 | 0.4953 | 0.5177 | 0.9490 | |
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| 0.0221 | 5.0 | 27335 | 0.1827 | 0.5377 | 0.5018 | 0.5191 | 0.9487 | |
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| 0.0629 | 6.0 | 32802 | 0.1874 | 0.5479 | 0.5130 | 0.5299 | 0.9493 | |
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| 0.0173 | 7.0 | 38269 | 0.2025 | 0.5388 | 0.5323 | 0.5356 | 0.9488 | |
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| 0.2603 | 8.0 | 43736 | 0.2148 | 0.5437 | 0.5397 | 0.5417 | 0.9493 | |
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| 0.0378 | 9.0 | 49203 | 0.2323 | 0.5430 | 0.5194 | 0.5310 | 0.9489 | |
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| 0.031 | 10.0 | 54670 | 0.2328 | 0.5462 | 0.5325 | 0.5393 | 0.9495 | |
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### Framework versions |
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- Transformers 4.21.0 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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