metadata
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
base_model: xlm-roberta-base
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: 0.0743
- Precision: 0.9800
- Recall: 0.9811
- F1: 0.9806
- Accuracy: 0.9817
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 |
|---|---|---|---|---|---|---|---|
| 0.157 | 1.0 | 1583 | 0.1170 | 0.9616 | 0.9641 | 0.9629 | 0.9671 |
| 0.0982 | 2.0 | 3166 | 0.0823 | 0.9740 | 0.9745 | 0.9743 | 0.9766 |
| 0.0666 | 3.0 | 4749 | 0.0778 | 0.9767 | 0.9771 | 0.9769 | 0.9786 |
| 0.0528 | 4.0 | 6332 | 0.0712 | 0.9793 | 0.9800 | 0.9796 | 0.9810 |
| 0.0446 | 5.0 | 7915 | 0.0743 | 0.9800 | 0.9811 | 0.9806 | 0.9817 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3