| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - recall |
| model-index: |
| - name: albert_model |
| 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. --> |
|
|
| # albert_model |
| |
| This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6560 |
| - Accuracy: 0.9070 |
| - F1: 0.8852 |
| - Recall: 0.9122 |
| |
| ## 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: 1e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:| |
| | No log | 1.0 | 167 | 0.3571 | 0.8351 | 0.8142 | 0.9198 | |
| | No log | 2.0 | 334 | 0.2670 | 0.8891 | 0.8683 | 0.9313 | |
| | 0.3358 | 3.0 | 501 | 0.2643 | 0.9115 | 0.8885 | 0.8969 | |
| | 0.3358 | 4.0 | 668 | 0.3804 | 0.9130 | 0.8910 | 0.9046 | |
| | 0.3358 | 5.0 | 835 | 0.4376 | 0.9070 | 0.8848 | 0.9084 | |
| | 0.1007 | 6.0 | 1002 | 0.4957 | 0.9100 | 0.8859 | 0.8893 | |
| | 0.1007 | 7.0 | 1169 | 0.6375 | 0.8801 | 0.8601 | 0.9389 | |
| | 0.1007 | 8.0 | 1336 | 0.5978 | 0.8996 | 0.8780 | 0.9198 | |
| | 0.012 | 9.0 | 1503 | 0.6101 | 0.9025 | 0.8816 | 0.9237 | |
| | 0.012 | 10.0 | 1670 | 0.6209 | 0.9085 | 0.8847 | 0.8931 | |
| | 0.012 | 11.0 | 1837 | 0.6485 | 0.9010 | 0.8787 | 0.9122 | |
| | 0.0007 | 12.0 | 2004 | 0.6480 | 0.9070 | 0.8852 | 0.9122 | |
| | 0.0007 | 13.0 | 2171 | 0.6527 | 0.9055 | 0.8835 | 0.9122 | |
| | 0.0007 | 14.0 | 2338 | 0.6557 | 0.9055 | 0.8835 | 0.9122 | |
| | 0.0002 | 15.0 | 2505 | 0.6560 | 0.9070 | 0.8852 | 0.9122 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.29.2 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
| |
