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---
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
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