metadata
license: mit
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: roberta-large-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9741935483870968
roberta-large-finetuned-clinc
This model is a fine-tuned version of roberta-large on the clinc_oos dataset. It achieves the following results on the evaluation set:
- Loss: 0.1594
- Accuracy: 0.9742
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: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 5.0651 | 1.0 | 120 | 5.0213 | 0.0065 |
| 4.2482 | 2.0 | 240 | 2.5682 | 0.7997 |
| 1.694 | 3.0 | 360 | 0.6019 | 0.9445 |
| 0.4594 | 4.0 | 480 | 0.2330 | 0.9655 |
| 0.1599 | 5.0 | 600 | 0.1594 | 0.9742 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6