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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
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