metadata
license: mit
base_model: xlm-roberta-base
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: 0.929
- name: F1
type: f1
value: 0.9300165528214905
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: 0.1727
- Accuracy: 0.929
- F1: 0.9300
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: 32
- eval_batch_size: 32
- seed: 254
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.9321 | 1.0 | 500 | 0.3098 | 0.895 | 0.8961 |
| 0.2468 | 2.0 | 1000 | 0.1798 | 0.932 | 0.9326 |
| 0.1506 | 3.0 | 1500 | 0.1727 | 0.929 | 0.9300 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.0