metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: uwb_atcc
    results: []

uwb_atcc

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6191
Accuracy: 0.9103
Precision: 0.9239
Recall: 0.9161
F1: 0.9200
Report: precision recall f1-score support
```
     0       0.89      0.90      0.90       463
     1       0.92      0.92      0.92       596
```
accuracy 0.91 1059 macro avg 0.91 0.91 0.91 1059

weighted avg 0.91 0.91 0.91 1059

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: 5e-05
train_batch_size: 32
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 3000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Report
No log	3.36	500	0.2346	0.9207	0.9197	0.9413	0.9303	precision recall f1-score support

       0       0.92      0.89      0.91       463
       1       0.92      0.94      0.93       596

accuracy                           0.92      1059

macro avg 0.92 0.92 0.92 1059 weighted avg 0.92 0.92 0.92 1059 | | 0.2212 | 6.71 | 1000 | 0.3161 | 0.9046 | 0.9260 | 0.9027 | 0.9142 | precision recall f1-score support

       0       0.88      0.91      0.89       463
       1       0.93      0.90      0.91       596

accuracy                           0.90      1059

macro avg 0.90 0.90 0.90 1059 weighted avg 0.91 0.90 0.90 1059 | | 0.2212 | 10.07 | 1500 | 0.4337 | 0.9065 | 0.9191 | 0.9144 | 0.9167 | precision recall f1-score support

       0       0.89      0.90      0.89       463
       1       0.92      0.91      0.92       596

accuracy                           0.91      1059

macro avg 0.90 0.91 0.91 1059 weighted avg 0.91 0.91 0.91 1059 | | 0.0651 | 13.42 | 2000 | 0.4743 | 0.9178 | 0.9249 | 0.9295 | 0.9272 | precision recall f1-score support

       0       0.91      0.90      0.91       463
       1       0.92      0.93      0.93       596

accuracy                           0.92      1059

macro avg 0.92 0.92 0.92 1059 weighted avg 0.92 0.92 0.92 1059 | | 0.0651 | 16.78 | 2500 | 0.5538 | 0.9103 | 0.9196 | 0.9211 | 0.9204 | precision recall f1-score support

       0       0.90      0.90      0.90       463
       1       0.92      0.92      0.92       596

accuracy                           0.91      1059

macro avg 0.91 0.91 0.91 1059 weighted avg 0.91 0.91 0.91 1059 | | 0.0296 | 20.13 | 3000 | 0.6191 | 0.9103 | 0.9239 | 0.9161 | 0.9200 | precision recall f1-score support

       0       0.89      0.90      0.90       463
       1       0.92      0.92      0.92       596

accuracy                           0.91      1059

macro avg 0.91 0.91 0.91 1059 weighted avg 0.91 0.91 0.91 1059 |

Framework versions

Transformers 4.24.0
Pytorch 1.13.0+cu117
Datasets 2.7.0
Tokenizers 0.13.2