--- 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 args: plus metrics: - name: Accuracy type: accuracy value: 0.9141935483870968 --- # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7816 - Accuracy: 0.9142 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2905 | 1.0 | 318 | 3.2789 | 0.7274 | | 2.6269 | 2.0 | 636 | 1.8737 | 0.8297 | | 1.5487 | 3.0 | 954 | 1.1620 | 0.8910 | | 1.0178 | 4.0 | 1272 | 0.8663 | 0.9061 | | 0.8036 | 5.0 | 1590 | 0.7816 | 0.9142 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.10.3