---
base_model: UBC-NLP/MARBERTv2
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: Model3_Marabertv2_T2_WOS
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Model3_Marabertv2_T2_WOS

This model is a fine-tuned version of [UBC-NLP/MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0816
- F1: 0.8297
- Roc Auc: 0.9146
- Accuracy: 0.7412

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 193  | 0.1523          | 0.5204 | 0.6899  | 0.3743   |
| No log        | 2.0   | 386  | 0.1084          | 0.7070 | 0.7979  | 0.5940   |
| 0.162         | 3.0   | 579  | 0.0896          | 0.7799 | 0.8517  | 0.6872   |
| 0.162         | 4.0   | 772  | 0.0814          | 0.8089 | 0.8834  | 0.7281   |
| 0.162         | 5.0   | 965  | 0.0845          | 0.8037 | 0.8866  | 0.7244   |
| 0.0569        | 6.0   | 1158 | 0.0814          | 0.8112 | 0.8968  | 0.7095   |
| 0.0569        | 7.0   | 1351 | 0.0744          | 0.8253 | 0.9009  | 0.7225   |
| 0.0258        | 8.0   | 1544 | 0.0754          | 0.8313 | 0.9081  | 0.7207   |
| 0.0258        | 9.0   | 1737 | 0.0754          | 0.8418 | 0.9171  | 0.7579   |
| 0.0258        | 10.0  | 1930 | 0.0813          | 0.8264 | 0.9143  | 0.7356   |
| 0.0145        | 11.0  | 2123 | 0.0816          | 0.8297 | 0.9146  | 0.7412   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3