metadata
library_name: transformers
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Finetuning_XLNET_Paraphrase_Classification
results: []
Finetuning_XLNET_Paraphrase_Classification
This model is a fine-tuned version of xlnet-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7478
- Accuracy: 0.8775
- F1: 0.8777
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 459 | 0.4274 | 0.8260 | 0.8126 |
| 0.5099 | 2.0 | 918 | 0.3347 | 0.8578 | 0.8547 |
| 0.3271 | 3.0 | 1377 | 0.5607 | 0.875 | 0.8710 |
| 0.2297 | 4.0 | 1836 | 0.7238 | 0.8701 | 0.8702 |
| 0.1346 | 5.0 | 2295 | 0.7478 | 0.8775 | 0.8777 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3