---
base_model: SALT-NLP/FLANG-ELECTRA
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: FLANG-ELECTRA_Synonym-wordnet
  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_Synonym-wordnet

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: 0.3701
- Accuracy: 0.9376
- F1: 0.9374
- Precision: 0.9374
- Recall: 0.9376

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6271        | 1.0   | 181  | 0.4625          | 0.8409   | 0.8408 | 0.8424    | 0.8409 |
| 0.3372        | 2.0   | 362  | 0.3312          | 0.8955   | 0.8959 | 0.8991    | 0.8955 |
| 0.1762        | 3.0   | 543  | 0.3046          | 0.9048   | 0.9040 | 0.9050    | 0.9048 |
| 0.313         | 4.0   | 724  | 0.3908          | 0.8986   | 0.8988 | 0.9018    | 0.8986 |
| 0.2564        | 5.0   | 905  | 0.3268          | 0.9080   | 0.9073 | 0.9078    | 0.9080 |
| 0.3189        | 6.0   | 1086 | 0.6418          | 0.7956   | 0.7933 | 0.8130    | 0.7956 |
| 0.365         | 7.0   | 1267 | 0.7276          | 0.7894   | 0.7889 | 0.7890    | 0.7894 |
| 0.1356        | 8.0   | 1448 | 0.6091          | 0.8814   | 0.8811 | 0.8816    | 0.8814 |
| 0.1139        | 9.0   | 1629 | 0.4184          | 0.8924   | 0.8916 | 0.8948    | 0.8924 |
| 0.1238        | 10.0  | 1810 | 0.3155          | 0.9220   | 0.9213 | 0.9231    | 0.9220 |
| 0.077         | 11.0  | 1991 | 0.4511          | 0.9017   | 0.9025 | 0.9061    | 0.9017 |
| 0.0613        | 12.0  | 2172 | 0.4132          | 0.9142   | 0.9141 | 0.9145    | 0.9142 |
| 0.0514        | 13.0  | 2353 | 0.3735          | 0.9298   | 0.9296 | 0.9321    | 0.9298 |
| 0.057         | 14.0  | 2534 | 0.3701          | 0.9376   | 0.9374 | 0.9374    | 0.9376 |
| 0.0152        | 15.0  | 2715 | 0.3872          | 0.9360   | 0.9357 | 0.9384    | 0.9360 |
| 0.0236        | 16.0  | 2896 | 0.4117          | 0.9314   | 0.9310 | 0.9320    | 0.9314 |
| 0.0277        | 17.0  | 3077 | 0.5325          | 0.9204   | 0.9197 | 0.9208    | 0.9204 |
| 0.0021        | 18.0  | 3258 | 0.4227          | 0.9236   | 0.9229 | 0.9236    | 0.9236 |
| 0.0005        | 19.0  | 3439 | 0.5409          | 0.9314   | 0.9308 | 0.9334    | 0.9314 |


### Framework versions

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