---
base_model: SALT-NLP/FLANG-BERT
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: FLANG-BERT_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-BERT_bert-base-uncased

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: 0.7138
- Accuracy: 0.8643
- F1: 0.8645
- Precision: 0.8681
- Recall: 0.8643

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8614        | 1.0   | 91   | 0.8043          | 0.6443   | 0.6279 | 0.6398    | 0.6443 |
| 0.5386        | 2.0   | 182  | 0.4807          | 0.8112   | 0.8113 | 0.8150    | 0.8112 |
| 0.3422        | 3.0   | 273  | 0.4452          | 0.8300   | 0.8304 | 0.8351    | 0.8300 |
| 0.2617        | 4.0   | 364  | 0.5424          | 0.8190   | 0.8177 | 0.8259    | 0.8190 |
| 0.164         | 5.0   | 455  | 0.5162          | 0.8424   | 0.8414 | 0.8424    | 0.8424 |
| 0.1278        | 6.0   | 546  | 0.5737          | 0.8440   | 0.8439 | 0.8440    | 0.8440 |
| 0.0599        | 7.0   | 637  | 0.6869          | 0.8268   | 0.8236 | 0.8311    | 0.8268 |
| 0.1184        | 8.0   | 728  | 0.5331          | 0.8471   | 0.8475 | 0.8493    | 0.8471 |
| 0.1126        | 9.0   | 819  | 0.6979          | 0.8237   | 0.8221 | 0.8332    | 0.8237 |
| 0.0737        | 10.0  | 910  | 0.7481          | 0.8362   | 0.8362 | 0.8381    | 0.8362 |
| 0.1425        | 11.0  | 1001 | 0.7602          | 0.8315   | 0.8308 | 0.8331    | 0.8315 |
| 0.0666        | 12.0  | 1092 | 0.6645          | 0.8612   | 0.8612 | 0.8615    | 0.8612 |
| 0.0523        | 13.0  | 1183 | 0.7138          | 0.8643   | 0.8645 | 0.8681    | 0.8643 |
| 0.0168        | 14.0  | 1274 | 0.7317          | 0.8534   | 0.8525 | 0.8527    | 0.8534 |
| 0.0336        | 15.0  | 1365 | 0.8575          | 0.8456   | 0.8454 | 0.8553    | 0.8456 |
| 0.0424        | 16.0  | 1456 | 0.9331          | 0.8409   | 0.8386 | 0.8423    | 0.8409 |
| 0.0188        | 17.0  | 1547 | 0.7885          | 0.8596   | 0.8595 | 0.8599    | 0.8596 |
| 0.0032        | 18.0  | 1638 | 0.8774          | 0.8596   | 0.8584 | 0.8592    | 0.8596 |


### Framework versions

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