---
library_name: transformers
base_model: UBC-NLP/MARBERTv2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoints
  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. -->

# checkpoints

This model is a fine-tuned version of [UBC-NLP/MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1854
- Accuracy: 0.7595
- Balanced Accuracy: 0.7595
- Mcc: 0.6402
- Macro F1: 0.7607
- Macro Precision: 0.7646
- Macro Recall: 0.7595
- Weighted F1: 0.7607
- Weighted Precision: 0.7646
- Weighted Recall: 0.7595
- Class 0 F1: 0.8113
- Class 0 Precision: 0.86
- Class 0 Recall: 0.7679
- Class 1 F1: 0.7235
- Class 1 Precision: 0.7177
- Class 1 Recall: 0.7293
- Class 2 F1: 0.7473
- Class 2 Precision: 0.7162
- Class 2 Recall: 0.7812

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced Accuracy | Mcc    | Macro F1 | Macro Precision | Macro Recall | Weighted F1 | Weighted Precision | Weighted Recall | Class 0 F1 | Class 0 Precision | Class 0 Recall | Class 1 F1 | Class 1 Precision | Class 1 Recall | Class 2 F1 | Class 2 Precision | Class 2 Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:------:|:--------:|:---------------:|:------------:|:-----------:|:------------------:|:---------------:|:----------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|
| 0.5611        | 1.0   | 925  | 0.5775          | 0.7714   | 0.7714            | 0.6584 | 0.7699   | 0.7716          | 0.7714       | 0.7699      | 0.7716             | 0.7714          | 0.8281     | 0.8052            | 0.8523         | 0.7215     | 0.7737            | 0.6759         | 0.7602     | 0.7360            | 0.7861         |
| 0.4367        | 2.0   | 1850 | 0.6233          | 0.7757   | 0.7757            | 0.6641 | 0.7759   | 0.7774          | 0.7757       | 0.7759      | 0.7774             | 0.7757          | 0.8223     | 0.8418            | 0.8036         | 0.7355     | 0.7504            | 0.7212         | 0.7698     | 0.7399            | 0.8023         |
| 0.2949        | 3.0   | 2775 | 0.7892          | 0.7703   | 0.7703            | 0.6573 | 0.7717   | 0.7778          | 0.7703       | 0.7717      | 0.7777             | 0.7703          | 0.8132     | 0.8748            | 0.7597         | 0.7473     | 0.7044            | 0.7958         | 0.7547     | 0.7540            | 0.7553         |
| 0.2141        | 4.0   | 3700 | 1.0209          | 0.7584   | 0.7584            | 0.6389 | 0.7599   | 0.7646          | 0.7584       | 0.7599      | 0.7645             | 0.7584          | 0.8093     | 0.8595            | 0.7646         | 0.7281     | 0.6920            | 0.7682         | 0.7423     | 0.7423            | 0.7423         |
| 0.1405        | 5.0   | 4625 | 1.1854          | 0.7595   | 0.7595            | 0.6402 | 0.7607   | 0.7646          | 0.7595       | 0.7607      | 0.7646             | 0.7595          | 0.8113     | 0.86              | 0.7679         | 0.7235     | 0.7177            | 0.7293         | 0.7473     | 0.7162            | 0.7812         |


### Framework versions

- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2