---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bertweet-base-Twitter_Sentiment_Analysis_v3
  results: []
license: mit
---

# bertweet-base-Twitter_Sentiment_Analysis_v3

This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5141
- Accuracy: 0.8552
- Weighted f1: 0.8541
- Micro f1: 0.8552
- Macro f1: 0.8178
- Weighted recall: 0.8552
- Micro recall: 0.8552
- Macro recall: 0.8207
- Weighted precision: 0.8541
- Micro precision: 0.8552
- Macro precision: 0.8171

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.4404        | 1.0   | 149  | 0.2815          | 0.8314   | 0.8153      | 0.8314   | 0.7660   | 0.8314          | 0.8314       | 0.7560       | 0.8331             | 0.8314          | 0.8244          |
| 0.2493        | 2.0   | 298  | 0.2558          | 0.8588   | 0.8523      | 0.8588   | 0.8151   | 0.8588          | 0.8588       | 0.7961       | 0.8554             | 0.8588          | 0.8463          |
| 0.1905        | 3.0   | 447  | 0.2734          | 0.8580   | 0.8534      | 0.8580   | 0.8164   | 0.8580          | 0.8580       | 0.8094       | 0.8553             | 0.8580          | 0.8326          |
| 0.1504        | 4.0   | 596  | 0.3150          | 0.8631   | 0.8607      | 0.8631   | 0.8247   | 0.8631          | 0.8631       | 0.8291       | 0.8627             | 0.8631          | 0.8279          |
| 0.112         | 5.0   | 745  | 0.3451          | 0.8564   | 0.8522      | 0.8564   | 0.8145   | 0.8564          | 0.8564       | 0.8136       | 0.8544             | 0.8564          | 0.8254          |
| 0.0885        | 6.0   | 894  | 0.3929          | 0.8576   | 0.8532      | 0.8576   | 0.8158   | 0.8576          | 0.8576       | 0.8123       | 0.8554             | 0.8576          | 0.8293          |
| 0.0735        | 7.0   | 1043 | 0.4233          | 0.8564   | 0.8541      | 0.8564   | 0.8164   | 0.8564          | 0.8564       | 0.8128       | 0.8535             | 0.8564          | 0.8225          |
| 0.0642        | 8.0   | 1192 | 0.4454          | 0.8525   | 0.8495      | 0.8525   | 0.8106   | 0.8525          | 0.8525       | 0.8055       | 0.8491             | 0.8525          | 0.8192          |
| 0.0512        | 9.0   | 1341 | 0.5098          | 0.8537   | 0.8543      | 0.8537   | 0.8194   | 0.8537          | 0.8537       | 0.8261       | 0.8552             | 0.8537          | 0.8133          |
| 0.0448        | 10.0  | 1490 | 0.5268          | 0.8537   | 0.8538      | 0.8537   | 0.8170   | 0.8537          | 0.8537       | 0.8256       | 0.8549             | 0.8537          | 0.8101          |
| 0.038         | 11.0  | 1639 | 0.5076          | 0.8564   | 0.8555      | 0.8564   | 0.8195   | 0.8564          | 0.8564       | 0.8209       | 0.8551             | 0.8564          | 0.8191          |
| 0.0357        | 12.0  | 1788 | 0.5141          | 0.8552   | 0.8541      | 0.8552   | 0.8178   | 0.8552          | 0.8552       | 0.8207       | 0.8541             | 0.8552          | 0.8171          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1


## License Notice
This model is a fine-tuned derivative of a pretrained model.
Users must comply with the original model license.


## Dataset Notice
This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.