metadata
license: apache-2.0
base_model: distilbert-base-uncased
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: 0.8903225806451613
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: 1.0392
- Accuracy: 0.8903
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 318 | 3.5092 | 0.6306 |
| 3.9506 | 2.0 | 636 | 2.1778 | 0.8058 |
| 3.9506 | 3.0 | 954 | 1.4469 | 0.8648 |
| 2.0031 | 4.0 | 1272 | 1.1542 | 0.8797 |
| 1.2402 | 5.0 | 1590 | 1.0392 | 0.8903 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.2.0.dev20231129
- Datasets 2.15.0
- Tokenizers 0.15.0