---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: UIT-VSFC-Bert-CLSModel-v2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# UIT-VSFC-Bert-CLSModel-v2

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: 0.3111
- Accuracy: 0.8970
- F1: 0.6735
- Precision: 0.8209
- Recall: 0.6603

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 90   | 0.5202          | 0.8023   | 0.5646 | 0.7190    | 0.5744 |
| No log        | 2.0   | 180  | 0.3222          | 0.8844   | 0.6687 | 0.7587    | 0.6550 |
| No log        | 3.0   | 270  | 0.3111          | 0.8970   | 0.6735 | 0.8209    | 0.6603 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1