| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - f1 |
| | model-index: |
| | - name: xlnet-base-cased_fold_2_binary_v1 |
| | 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. --> |
| |
|
| | # xlnet-base-cased_fold_2_binary_v1 |
| |
|
| | This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.8748 |
| | - F1: 0.8066 |
| |
|
| | ## 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: 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: 25 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | | No log | 1.0 | 290 | 0.4803 | 0.7433 | |
| | | 0.434 | 2.0 | 580 | 0.4385 | 0.8099 | |
| | | 0.434 | 3.0 | 870 | 0.5382 | 0.8078 | |
| | | 0.254 | 4.0 | 1160 | 0.6944 | 0.7982 | |
| | | 0.254 | 5.0 | 1450 | 0.9908 | 0.8058 | |
| | | 0.1479 | 6.0 | 1740 | 1.1090 | 0.8062 | |
| | | 0.0874 | 7.0 | 2030 | 1.2405 | 0.8042 | |
| | | 0.0874 | 8.0 | 2320 | 1.3174 | 0.8012 | |
| | | 0.0505 | 9.0 | 2610 | 1.5211 | 0.7909 | |
| | | 0.0505 | 10.0 | 2900 | 1.4014 | 0.8126 | |
| | | 0.0301 | 11.0 | 3190 | 1.4798 | 0.8047 | |
| | | 0.0301 | 12.0 | 3480 | 1.4668 | 0.8091 | |
| | | 0.0279 | 13.0 | 3770 | 1.5286 | 0.8075 | |
| | | 0.0233 | 14.0 | 4060 | 1.6752 | 0.8006 | |
| | | 0.0233 | 15.0 | 4350 | 1.5265 | 0.8132 | |
| | | 0.019 | 16.0 | 4640 | 1.6440 | 0.7949 | |
| | | 0.019 | 17.0 | 4930 | 1.7471 | 0.8097 | |
| | | 0.0096 | 18.0 | 5220 | 1.7329 | 0.8121 | |
| | | 0.0075 | 19.0 | 5510 | 1.7472 | 0.8191 | |
| | | 0.0075 | 20.0 | 5800 | 1.8043 | 0.8161 | |
| | | 0.0052 | 21.0 | 6090 | 1.8102 | 0.8141 | |
| | | 0.0052 | 22.0 | 6380 | 1.7944 | 0.8116 | |
| | | 0.0044 | 23.0 | 6670 | 1.8211 | 0.8141 | |
| | | 0.0044 | 24.0 | 6960 | 1.8741 | 0.8066 | |
| | | 0.0046 | 25.0 | 7250 | 1.8748 | 0.8066 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.21.1 |
| | - Pytorch 1.12.0+cu113 |
| | - Datasets 2.4.0 |
| | - Tokenizers 0.12.1 |
| |
|
