| | --- |
| | license: mit |
| | base_model: microsoft/deberta-v3-small |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: ts_tg |
| | 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. --> |
| |
|
| | # ts_tg |
| | |
| | This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0516 |
| | - Accuracy: 0.8517 |
| | - F1: 0.8759 |
| | - Precision: 0.8996 |
| | - Recall: 0.8533 |
| | |
| | ## 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: 128 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 8 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | No log | 1.0 | 202 | 0.1370 | 0.5242 | 0.6378 | 0.8129 | 0.5248 | |
| | | No log | 2.0 | 404 | 0.0857 | 0.6877 | 0.7700 | 0.8749 | 0.6875 | |
| | | 0.1567 | 3.0 | 606 | 0.0667 | 0.7810 | 0.8331 | 0.8929 | 0.7809 | |
| | | 0.1567 | 4.0 | 808 | 0.0593 | 0.8145 | 0.8525 | 0.8947 | 0.8142 | |
| | | 0.0566 | 5.0 | 1010 | 0.0554 | 0.8406 | 0.8668 | 0.8926 | 0.8425 | |
| | | 0.0566 | 6.0 | 1212 | 0.0529 | 0.8437 | 0.8718 | 0.8994 | 0.8459 | |
| | | 0.0566 | 7.0 | 1414 | 0.0522 | 0.8474 | 0.8737 | 0.8992 | 0.8496 | |
| | | 0.0383 | 8.0 | 1616 | 0.0516 | 0.8517 | 0.8759 | 0.8996 | 0.8533 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.42.4 |
| | - Pytorch 2.3.1+cu121 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
| | |
