metadata
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-large-go-emotions_v2
results: []
roberta-large-go-emotions_v2
This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0833
- Accuracy: 0.4548
- Precision: 0.5106
- Recall: 0.5017
- F1: 0.4895
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: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 340 | 0.0922 | 0.4130 | 0.4179 | 0.4257 | 0.4047 |
| 0.1095 | 2.0 | 680 | 0.0838 | 0.4466 | 0.4803 | 0.4888 | 0.4738 |
| 0.1095 | 3.0 | 1020 | 0.0838 | 0.4425 | 0.4785 | 0.4995 | 0.4808 |
| 0.0719 | 4.0 | 1360 | 0.0833 | 0.4548 | 0.5106 | 0.5017 | 0.4895 |
Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.1