Hugging Face's logo

Cyber-ThreaD
/
CyBERT-our-data

Token Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
CyBERT-our-data / README.md
Anonymous
Upload folder using huggingface_hub
162e8ec
preview code
|
raw
history blame contribute delete
3.66 kB
metadata
base_model: SynamicTechnologies/CYBERT
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: our_data
    results: []

our_data

This model is a fine-tuned version of SynamicTechnologies/CYBERT on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.6376
  • Precision: 0.1972
  • Recall: 0.3585
  • F1: 0.2545
  • Accuracy: 0.6637

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: 2
  • eval_batch_size: 2
  • 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
2.0809 0.4 500 2.0594 0.5 0.0066 0.0131 0.5298
1.8682 0.81 1000 1.8006 0.1043 0.0807 0.0910 0.5529
1.6332 1.21 1500 1.8356 0.1339 0.1495 0.1412 0.5748
1.468 1.61 2000 1.6261 0.1356 0.1706 0.1511 0.5891
1.401 2.01 2500 1.6943 0.1563 0.1693 0.1625 0.5986
1.1878 2.42 3000 1.6740 0.1194 0.2460 0.1608 0.5976
1.1182 2.82 3500 1.6201 0.1589 0.2196 0.1843 0.6227
0.9677 3.22 4000 1.6241 0.1393 0.2196 0.1704 0.6176
0.9055 3.63 4500 1.5932 0.1317 0.2646 0.1758 0.6158
0.8772 4.03 5000 1.5797 0.1654 0.2804 0.2080 0.6254
0.7224 4.43 5500 1.5723 0.1587 0.2976 0.2070 0.6413
0.7498 4.83 6000 1.5957 0.1794 0.2897 0.2215 0.6496
0.6632 5.24 6500 1.6825 0.1864 0.2751 0.2222 0.6427
0.6139 5.64 7000 1.5827 0.1769 0.3479 0.2345 0.6508
0.6212 6.04 7500 1.5537 0.1778 0.3413 0.2338 0.6526
0.5379 6.45 8000 1.5670 0.1792 0.3307 0.2325 0.6536
0.5376 6.85 8500 1.6113 0.1844 0.3386 0.2388 0.6530
0.5 7.25 9000 1.6432 0.1789 0.3214 0.2299 0.6600
0.4928 7.66 9500 1.6422 0.1881 0.3373 0.2415 0.6609
0.4877 8.06 10000 1.6851 0.2042 0.3360 0.254 0.6654
0.4339 8.46 10500 1.6376 0.1972 0.3585 0.2545 0.6637
0.4303 8.86 11000 1.6364 0.1835 0.3452 0.2397 0.6604
0.4509 9.27 11500 1.6448 0.1983 0.3413 0.2509 0.6664
0.4114 9.67 12000 1.6494 0.1956 0.3505 0.2511 0.6658

Framework versions

  • Transformers 4.36.0.dev0
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0