metadata
library_name: transformers
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta_large_len256
results: []
roberta_large_len256
This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3063
- Accuracy: 0.9503
- F1: 0.9503
- Precision: 0.9503
- Recall: 0.9503
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.3137 | 1.0 | 4000 | 0.3495 | 0.9193 | 0.9195 | 0.9217 | 0.9193 |
| 0.1578 | 2.0 | 8000 | 0.2891 | 0.9484 | 0.9484 | 0.9491 | 0.9484 |
| 0.0851 | 3.0 | 12000 | 0.3063 | 0.9503 | 0.9503 | 0.9503 | 0.9503 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.1.2
- Datasets 3.5.1
- Tokenizers 0.21.1