metadata
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-pos
results: []
xlm-roberta-base-finetuned-pos
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0706
- Precision: 0.9800
- Recall: 0.9808
- F1: 0.9804
- Accuracy: 0.9821
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1627 | 1.0 | 1583 | 0.1289 | 0.9599 | 0.9633 | 0.9616 | 0.9653 |
| 0.1009 | 2.0 | 3166 | 0.0931 | 0.9680 | 0.9716 | 0.9698 | 0.9730 |
| 0.0705 | 3.0 | 4749 | 0.0766 | 0.9758 | 0.9774 | 0.9766 | 0.9786 |
| 0.0536 | 4.0 | 6332 | 0.0697 | 0.9787 | 0.9795 | 0.9791 | 0.9812 |
| 0.0419 | 5.0 | 7915 | 0.0706 | 0.9800 | 0.9808 | 0.9804 | 0.9821 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2