|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
model-index: |
|
- name: xlnet-base-cased_fold_9_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_9_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: 1.7204 |
|
- F1: 0.8203 |
|
|
|
## 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 | 291 | 0.4045 | 0.8001 | |
|
| 0.4262 | 2.0 | 582 | 0.3914 | 0.8297 | |
|
| 0.4262 | 3.0 | 873 | 0.5050 | 0.8029 | |
|
| 0.2488 | 4.0 | 1164 | 0.7681 | 0.8007 | |
|
| 0.2488 | 5.0 | 1455 | 0.8349 | 0.8262 | |
|
| 0.1483 | 6.0 | 1746 | 0.9045 | 0.8220 | |
|
| 0.0894 | 7.0 | 2037 | 1.1584 | 0.8165 | |
|
| 0.0894 | 8.0 | 2328 | 1.1818 | 0.8300 | |
|
| 0.0389 | 9.0 | 2619 | 1.3332 | 0.8147 | |
|
| 0.0389 | 10.0 | 2910 | 1.2373 | 0.8285 | |
|
| 0.038 | 11.0 | 3201 | 1.3156 | 0.8234 | |
|
| 0.038 | 12.0 | 3492 | 1.3251 | 0.8341 | |
|
| 0.0211 | 13.0 | 3783 | 1.3144 | 0.8255 | |
|
| 0.0158 | 14.0 | 4074 | 1.5686 | 0.8168 | |
|
| 0.0158 | 15.0 | 4365 | 1.5382 | 0.8185 | |
|
| 0.0165 | 16.0 | 4656 | 1.5203 | 0.8282 | |
|
| 0.0165 | 17.0 | 4947 | 1.5352 | 0.8136 | |
|
| 0.0142 | 18.0 | 5238 | 1.4799 | 0.8243 | |
|
| 0.0062 | 19.0 | 5529 | 1.5030 | 0.8294 | |
|
| 0.0062 | 20.0 | 5820 | 1.6264 | 0.8094 | |
|
| 0.0078 | 21.0 | 6111 | 1.6949 | 0.8122 | |
|
| 0.0078 | 22.0 | 6402 | 1.7106 | 0.8139 | |
|
| 0.0043 | 23.0 | 6693 | 1.7234 | 0.8218 | |
|
| 0.0043 | 24.0 | 6984 | 1.7344 | 0.8208 | |
|
| 0.0028 | 25.0 | 7275 | 1.7204 | 0.8203 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.21.1 |
|
- Pytorch 1.12.0+cu113 |
|
- Datasets 2.4.0 |
|
- Tokenizers 0.12.1 |
|
|
