metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.942258064516129
distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of distilbert-base-uncased on the clinc_oos dataset. It achieves the following results on the evaluation set:
- Loss: 0.2906
- Accuracy: 0.9423
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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
4.2646 | 1.0 | 318 | 3.2099 | 0.7497 |
2.4701 | 2.0 | 636 | 1.6426 | 0.8526 |
1.2523 | 3.0 | 954 | 0.8519 | 0.9123 |
0.6489 | 4.0 | 1272 | 0.5241 | 0.9303 |
0.3704 | 5.0 | 1590 | 0.3849 | 0.9403 |
0.2407 | 6.0 | 1908 | 0.3246 | 0.9429 |
0.1749 | 7.0 | 2226 | 0.2967 | 0.9426 |
0.1457 | 8.0 | 2544 | 0.2906 | 0.9423 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2