---
base_model: SALT-NLP/FLANG-ELECTRA
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: FLANG-ELECTRA_roberta-base
  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_roberta-base

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: 0.4678
- Accuracy: 0.8736
- F1: 0.8728
- Precision: 0.8738
- Recall: 0.8736

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6813        | 1.0   | 181  | 0.5968          | 0.7457   | 0.7326 | 0.7488    | 0.7457 |
| 0.4427        | 2.0   | 362  | 0.5072          | 0.8222   | 0.8200 | 0.8321    | 0.8222 |
| 0.2366        | 3.0   | 543  | 0.4216          | 0.8518   | 0.8509 | 0.8523    | 0.8518 |
| 0.2022        | 4.0   | 724  | 0.5838          | 0.8518   | 0.8501 | 0.8526    | 0.8518 |
| 0.1299        | 5.0   | 905  | 0.4678          | 0.8736   | 0.8728 | 0.8738    | 0.8736 |
| 0.2016        | 6.0   | 1086 | 0.5147          | 0.8362   | 0.8346 | 0.8355    | 0.8362 |
| 0.1255        | 7.0   | 1267 | 0.6612          | 0.8471   | 0.8438 | 0.8549    | 0.8471 |
| 0.1713        | 8.0   | 1448 | 0.8831          | 0.8003   | 0.7992 | 0.8107    | 0.8003 |
| 0.092         | 9.0   | 1629 | 0.6286          | 0.8440   | 0.8434 | 0.8525    | 0.8440 |
| 0.0476        | 10.0  | 1810 | 0.7429          | 0.8690   | 0.8692 | 0.8697    | 0.8690 |


### Framework versions

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