---
base_model: UBC-NLP/MARBERTv2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: OTE-NoDapt-ABSA-bert-base-MARBERTv2-DefultHp-FineTune
  results: []
---

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# OTE-NoDapt-ABSA-bert-base-MARBERTv2-DefultHp-FineTune

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: 0.6006
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.8892

## 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: 32
- eval_batch_size: 8
- seed: 25
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 0.758         | 1.0   | 121  | 0.6913          | 0.0       | 0.0    | 0.0 | 0.8892   |
| 0.6556        | 2.0   | 242  | 0.6214          | 0.0       | 0.0    | 0.0 | 0.8892   |
| 0.6125        | 3.0   | 363  | 0.6006          | 0.0       | 0.0    | 0.0 | 0.8892   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3