|
--- |
|
license: apache-2.0 |
|
base_model: distilbert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: cybersecurity-ner |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# cybersecurity-ner |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2398 |
|
- Precision: 0.7853 |
|
- Recall: 0.7984 |
|
- F1: 0.7918 |
|
- Accuracy: 0.9504 |
|
|
|
## 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: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 1.0 | 167 | 0.2454 | 0.8038 | 0.7664 | 0.7846 | 0.9489 | |
|
| No log | 2.0 | 334 | 0.2225 | 0.7697 | 0.8230 | 0.7954 | 0.9512 | |
|
| 0.0449 | 3.0 | 501 | 0.2229 | 0.7883 | 0.8022 | 0.7952 | 0.9521 | |
|
| 0.0449 | 4.0 | 668 | 0.2311 | 0.7819 | 0.8116 | 0.7965 | 0.9517 | |
|
| 0.0449 | 5.0 | 835 | 0.2398 | 0.7853 | 0.7984 | 0.7918 | 0.9504 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.15.0 |
|
|
