File size: 2,074 Bytes
3566be9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
---
license: apache-2.0
base_model: jackaduma/SecBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: cyner_secbert
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. -->
# cyner_secbert
This model is a fine-tuned version of [jackaduma/SecBERT](https://huggingface.co/jackaduma/SecBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1212
- Precision: 0.7047
- Recall: 0.5517
- F1: 0.6189
- Accuracy: 0.9723
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.165 | 1.42 | 500 | 0.1212 | 0.7047 | 0.5517 | 0.6189 | 0.9723 |
| 0.04 | 2.84 | 1000 | 0.1647 | 0.6924 | 0.5147 | 0.5905 | 0.9705 |
| 0.0156 | 4.26 | 1500 | 0.1803 | 0.6769 | 0.5351 | 0.5977 | 0.9714 |
| 0.0087 | 5.68 | 2000 | 0.1866 | 0.6574 | 0.5415 | 0.5938 | 0.9713 |
| 0.0036 | 7.1 | 2500 | 0.2020 | 0.6740 | 0.5492 | 0.6052 | 0.9719 |
| 0.0024 | 8.52 | 3000 | 0.2036 | 0.6697 | 0.5568 | 0.6081 | 0.9720 |
| 0.0018 | 9.94 | 3500 | 0.2084 | 0.6682 | 0.5504 | 0.6036 | 0.9715 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|