---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
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: 0.0098
- Precision: 0.9760
- Recall: 0.9741
- F1: 0.9750
- Accuracy: 0.9965

## 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: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.03  | 500   | 0.2282          | 0.6818    | 0.7001 | 0.6908 | 0.9246   |
| 0.3487        | 0.06  | 1000  | 0.1214          | 0.8163    | 0.8024 | 0.8093 | 0.9631   |
| 0.3487        | 0.1   | 1500  | 0.0933          | 0.8496    | 0.8544 | 0.8520 | 0.9722   |
| 0.1124        | 0.13  | 2000  | 0.0693          | 0.8845    | 0.8739 | 0.8791 | 0.9786   |
| 0.1124        | 0.16  | 2500  | 0.0540          | 0.8993    | 0.8911 | 0.8952 | 0.9817   |
| 0.0667        | 0.19  | 3000  | 0.0474          | 0.9058    | 0.8929 | 0.8993 | 0.9857   |
| 0.0667        | 0.23  | 3500  | 0.0418          | 0.9221    | 0.9245 | 0.9233 | 0.9865   |
| 0.0492        | 0.26  | 4000  | 0.0294          | 0.9369    | 0.9415 | 0.9392 | 0.9903   |
| 0.0492        | 0.29  | 4500  | 0.0263          | 0.9512    | 0.9446 | 0.9479 | 0.9911   |
| 0.0372        | 0.32  | 5000  | 0.0223          | 0.9495    | 0.9497 | 0.9496 | 0.9915   |
| 0.0372        | 0.35  | 5500  | 0.0212          | 0.9530    | 0.9514 | 0.9522 | 0.9923   |
| 0.0308        | 0.39  | 6000  | 0.0177          | 0.9585    | 0.9560 | 0.9572 | 0.9933   |
| 0.0308        | 0.42  | 6500  | 0.0169          | 0.9619    | 0.9613 | 0.9616 | 0.9936   |
| 0.0261        | 0.45  | 7000  | 0.0140          | 0.9689    | 0.9662 | 0.9676 | 0.9951   |
| 0.0261        | 0.48  | 7500  | 0.0130          | 0.9652    | 0.9629 | 0.9641 | 0.9945   |
| 0.0214        | 0.51  | 8000  | 0.0127          | 0.9676    | 0.9635 | 0.9656 | 0.9953   |
| 0.0214        | 0.55  | 8500  | 0.0109          | 0.9714    | 0.9708 | 0.9711 | 0.9959   |
| 0.0177        | 0.58  | 9000  | 0.0103          | 0.9740    | 0.9727 | 0.9734 | 0.9961   |
| 0.0177        | 0.61  | 9500  | 0.0101          | 0.9768    | 0.9744 | 0.9756 | 0.9963   |
| 0.0159        | 0.64  | 10000 | 0.0098          | 0.9760    | 0.9741 | 0.9750 | 0.9965   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.2