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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: uwb_atcc
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. -->
# uwb_atcc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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
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