metadata
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-sentence-classifier
results: []
roberta-sentence-classifier
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6266
- Accuracy: 0.7990
- Macro F1: 0.7614
- Micro F1: 0.7990
- Qwk: 0.6588
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Micro F1 | Qwk |
|---|---|---|---|---|---|---|---|
| 0.6267 | 1.0 | 27540 | 0.6108 | 0.7818 | 0.7364 | 0.7818 | 0.6352 |
| 0.5539 | 2.0 | 55080 | 0.5939 | 0.7911 | 0.7498 | 0.7911 | 0.6428 |
| 0.475 | 3.0 | 82620 | 0.6021 | 0.7977 | 0.7592 | 0.7977 | 0.6599 |
| 0.4204 | 4.0 | 110160 | 0.6266 | 0.7990 | 0.7614 | 0.7990 | 0.6588 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1