metadata
license: mit
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.93
- name: F1
type: f1
value: 0.9305878715788028
xlm-roberta-base-finetuned-emotion
This model is a fine-tuned version of xlm-roberta-base on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1597
- Accuracy: 0.93
- F1: 0.9306
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.2299 | 1.0 | 250 | 0.6646 | 0.7735 | 0.7537 |
| 0.4722 | 2.0 | 500 | 0.2553 | 0.9105 | 0.9112 |
| 0.2207 | 3.0 | 750 | 0.1990 | 0.9215 | 0.9221 |
| 0.1559 | 4.0 | 1000 | 0.1537 | 0.931 | 0.9312 |
| 0.129 | 5.0 | 1250 | 0.1597 | 0.93 | 0.9306 |
Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3