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
          args: plus
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9174193548387096

distilbert-base-uncased-finetuned-clinc

This model is a fine-tuned version of distilbert-base-uncased on the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the NLP with Transformers book. You can find the full code in the accompanying Github repository.

It achieves the following results on the evaluation set:

Loss: 0.7773
Accuracy: 0.9174

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
4.2923	1.0	318	3.2893	0.7423
2.6307	2.0	636	1.8837	0.8281
1.5483	3.0	954	1.1583	0.8968
1.0153	4.0	1272	0.8618	0.9094
0.7958	5.0	1590	0.7773	0.9174

Framework versions

Transformers 4.11.3
Pytorch 1.9.1+cu102
Datasets 1.13.0
Tokenizers 0.10.3