metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - clinc_oos
metrics:
  - accuracy
model-index:
  - name: userutterance_classification_ver1
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: clinc_oos
          type: clinc_oos
          config: imbalanced
          split: validation
          args: imbalanced
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9538709677419355

userutterance_classification_ver1

This model is a fine-tuned version of microsoft/deberta-v3-base on the clinc_oos dataset. It achieves the following results on the evaluation set:

Loss: 0.2898
Accuracy: 0.9539

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: 4e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 130
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
4.8334	0.15	200	4.7254	0.0748
3.4798	0.3	400	3.4244	0.2971
2.319	0.45	600	2.4423	0.5184
1.5683	0.6	800	1.7401	0.6310
0.9625	0.75	1000	1.2750	0.7265
0.6922	0.9	1200	0.9717	0.7761
0.5019	1.05	1400	0.8036	0.8284
0.3538	1.2	1600	0.6690	0.8471
0.2413	1.35	1800	0.5585	0.8713
0.2623	1.5	2000	0.4840	0.8874
0.2103	1.66	2200	0.4261	0.9126
0.1456	1.81	2400	0.3872	0.9152
0.1276	1.96	2600	0.3329	0.9290
0.09	2.11	2800	0.2925	0.9432
0.0534	2.26	3000	0.2996	0.9361
0.0588	2.41	3200	0.2951	0.9403
0.044	2.56	3400	0.3324	0.9403
0.0535	2.71	3600	0.3155	0.9432
0.0537	2.86	3800	0.3206	0.9419
0.1325	3.01	4000	0.2945	0.9465
0.0611	3.16	4200	0.2903	0.9442
0.0077	3.31	4400	0.3052	0.9477
0.0187	3.46	4600	0.2774	0.95
0.0125	3.61	4800	0.2851	0.9513
0.0157	3.76	5000	0.2883	0.9523
0.0414	3.91	5200	0.3163	0.9497
0.0025	4.06	5400	0.2998	0.9494
0.0019	4.21	5600	0.2925	0.9513
0.0013	4.36	5800	0.2872	0.9526
0.0014	4.51	6000	0.2906	0.9532
0.0015	4.67	6200	0.2862	0.9529
0.0281	4.82	6400	0.2863	0.9535
0.0287	4.97	6600	0.2898	0.9539

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2