---
base_model: SALT-NLP/FLANG-BERT
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: FLANG-BERT_flang-bert
  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-BERT_flang-bert

This model is a fine-tuned version of [SALT-NLP/FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4908
- Accuracy: 0.8487
- F1: 0.8486
- Precision: 0.8489
- Recall: 0.8487

## 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: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8968        | 1.0   | 91   | 0.8147          | 0.6256   | 0.5775 | 0.6185    | 0.6256 |
| 0.5694        | 2.0   | 182  | 0.4893          | 0.8097   | 0.8101 | 0.8124    | 0.8097 |
| 0.3393        | 3.0   | 273  | 0.4303          | 0.8471   | 0.8472 | 0.8474    | 0.8471 |
| 0.2442        | 4.0   | 364  | 0.5042          | 0.8487   | 0.8462 | 0.8512    | 0.8487 |
| 0.1807        | 5.0   | 455  | 0.4908          | 0.8487   | 0.8486 | 0.8489    | 0.8487 |
| 0.1061        | 6.0   | 546  | 0.5168          | 0.8409   | 0.8396 | 0.8405    | 0.8409 |
| 0.1287        | 7.0   | 637  | 0.6537          | 0.8440   | 0.8438 | 0.8470    | 0.8440 |
| 0.1079        | 8.0   | 728  | 0.6641          | 0.8315   | 0.8319 | 0.8345    | 0.8315 |
| 0.0693        | 9.0   | 819  | 0.8833          | 0.8346   | 0.8344 | 0.8363    | 0.8346 |
| 0.1084        | 10.0  | 910  | 0.8721          | 0.7941   | 0.7947 | 0.7972    | 0.7941 |


### Framework versions

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