---
base_model: SALT-NLP/FLANG-ELECTRA
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: FLANG-ELECTRA_bert-base-uncased
  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. -->

# FLANG-ELECTRA_bert-base-uncased

This model is a fine-tuned version of [SALT-NLP/FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4748
- Accuracy: 0.8705
- F1: 0.8705
- Precision: 0.8705
- Recall: 0.8705

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6775        | 1.0   | 181  | 0.5462          | 0.7972   | 0.7894 | 0.7973    | 0.7972 |
| 0.4966        | 2.0   | 362  | 0.3989          | 0.8612   | 0.8612 | 0.8633    | 0.8612 |
| 0.2509        | 3.0   | 543  | 0.3791          | 0.8612   | 0.8620 | 0.8645    | 0.8612 |
| 0.2241        | 4.0   | 724  | 0.5297          | 0.8471   | 0.8471 | 0.8501    | 0.8471 |
| 0.2248        | 5.0   | 905  | 0.4748          | 0.8705   | 0.8705 | 0.8705    | 0.8705 |
| 1.1108        | 6.0   | 1086 | 1.1042          | 0.3245   | 0.1590 | 0.1053    | 0.3245 |
| 1.1122        | 7.0   | 1267 | 1.1028          | 0.3245   | 0.1590 | 0.1053    | 0.3245 |
| 1.102         | 8.0   | 1448 | 1.0987          | 0.3510   | 0.1824 | 0.1232    | 0.3510 |
| 1.1015        | 9.0   | 1629 | 1.1069          | 0.3245   | 0.1590 | 0.1053    | 0.3245 |
| 1.0908        | 10.0  | 1810 | 1.1022          | 0.3510   | 0.1824 | 0.1232    | 0.3510 |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1