---
base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wisesight_sentiment
  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. -->

# wisesight_sentiment

This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4140
- Accuracy: 0.9025
- F1 Micro: 0.9025
- Precision Micro: 0.9025
- Recall Micro: 0.9025
- F1 Macro: 0.9010
- Precision Macro: 0.9006
- Recall Macro: 0.9015
- Nb Samples: 1015

## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 1412
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2031
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1 Micro | Precision Micro | Recall Micro | F1 Macro | Precision Macro | Recall Macro | Nb Samples |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:----------:|
| 0.587         | 1.0   | 2031  | 0.5865          | 0.8532   | 0.8532   | 0.8532          | 0.8532       | 0.8492   | 0.8550          | 0.8460       | 1015       |
| 0.5212        | 2.0   | 4062  | 0.7026          | 0.8158   | 0.8158   | 0.8158          | 0.8158       | 0.8156   | 0.8380          | 0.8322       | 1015       |
| 0.4554        | 3.0   | 6093  | 0.4360          | 0.8768   | 0.8768   | 0.8768          | 0.8768       | 0.8731   | 0.8813          | 0.8690       | 1015       |
| 0.3752        | 4.0   | 8124  | 0.4121          | 0.8897   | 0.8897   | 0.8897          | 0.8897       | 0.8867   | 0.8929          | 0.8831       | 1015       |
| 0.382         | 5.0   | 10155 | 0.4709          | 0.8916   | 0.8916   | 0.8916          | 0.8916       | 0.8894   | 0.8917          | 0.8876       | 1015       |
| 0.3465        | 6.0   | 12186 | 0.3929          | 0.8975   | 0.8975   | 0.8975          | 0.8975       | 0.8957   | 0.8964          | 0.8952       | 1015       |
| 0.3246        | 7.0   | 14217 | 0.4801          | 0.8897   | 0.8897   | 0.8897          | 0.8897       | 0.8873   | 0.8899          | 0.8854       | 1015       |
| 0.2808        | 8.0   | 16248 | 0.5070          | 0.8897   | 0.8897   | 0.8897          | 0.8897       | 0.8881   | 0.8874          | 0.8889       | 1015       |
| 0.2786        | 9.0   | 18279 | 0.4098          | 0.8995   | 0.8995   | 0.8995          | 0.8995       | 0.8975   | 0.8993          | 0.8962       | 1015       |
| 0.2151        | 10.0  | 20310 | 0.4140          | 0.9025   | 0.9025   | 0.9025          | 0.9025       | 0.9010   | 0.9006          | 0.9015       | 1015       |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1