| --- |
| 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. |
