metadata
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-finetuned-emotion
results: []
xlm-roberta-base-finetuned-emotion
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1856
- Accuracy: 0.9395
- F1: 0.9398
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.1374 | 1.0 | 250 | 0.1631 | 0.9335 | 0.9342 |
| 0.1038 | 2.0 | 500 | 0.1772 | 0.9335 | 0.9332 |
| 0.0883 | 3.0 | 750 | 0.1705 | 0.9375 | 0.9384 |
| 0.0788 | 4.0 | 1000 | 0.1524 | 0.9395 | 0.9392 |
| 0.0764 | 5.0 | 1250 | 0.1506 | 0.9375 | 0.9375 |
| 0.0661 | 6.0 | 1500 | 0.1856 | 0.9395 | 0.9398 |
| 0.0578 | 7.0 | 1750 | 0.1819 | 0.934 | 0.9338 |
| 0.0483 | 8.0 | 2000 | 0.1799 | 0.9345 | 0.9344 |
| 0.0417 | 9.0 | 2250 | 0.1854 | 0.9385 | 0.9386 |
| 0.0307 | 10.0 | 2500 | 0.1939 | 0.9375 | 0.9377 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1