metadata
license: mit
base_model: xlnet-large-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: task1_xlnet-large-cased_3_4_2e-05_0.01
results: []
task1_xlnet-large-cased_3_4_2e-05_0.01
This model is a fine-tuned version of xlnet-large-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7090
- Accuracy: 0.8147
- F1: 0.0
- Precision: 0.0
- Recall: 0.0
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.6754 | 1.0 | 1629 | 0.5660 | 0.8147 | 0.0 | 0.0 | 0.0 |
| 0.7117 | 2.0 | 3258 | 0.6926 | 0.8147 | 0.0 | 0.0 | 0.0 |
| 0.6359 | 3.0 | 4887 | 0.7090 | 0.8147 | 0.0 | 0.0 | 0.0 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3