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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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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: cybersecurity-ner |
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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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# cybersecurity-ner |
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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: 0.2196 |
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- Precision: 0.7942 |
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- Recall: 0.7925 |
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- F1: 0.7933 |
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- Accuracy: 0.9508 |
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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: 8 |
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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 | 167 | 0.2492 | 0.6870 | 0.7406 | 0.7128 | 0.9293 | |
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| No log | 2.0 | 334 | 0.2026 | 0.7733 | 0.7346 | 0.7534 | 0.9420 | |
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| 0.2118 | 3.0 | 501 | 0.1895 | 0.7735 | 0.7934 | 0.7833 | 0.9493 | |
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| 0.2118 | 4.0 | 668 | 0.1834 | 0.7785 | 0.8189 | 0.7982 | 0.9511 | |
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| 0.2118 | 5.0 | 835 | 0.2060 | 0.8113 | 0.7965 | 0.8039 | 0.9522 | |
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| 0.0507 | 6.0 | 1002 | 0.2153 | 0.7692 | 0.8226 | 0.7950 | 0.9511 | |
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| 0.0507 | 7.0 | 1169 | 0.2141 | 0.7866 | 0.7962 | 0.7914 | 0.9507 | |
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| 0.0507 | 8.0 | 1336 | 0.2196 | 0.7942 | 0.7925 | 0.7933 | 0.9508 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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