README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - clinc_oos
metrics:
  - accuracy
model-index:
  - name: userutterance_classification_verplus
    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: 0.9619354838709677

userutterance_classification_verplus

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: 0.2270
• Accuracy: 0.9619

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
5.0219	0.21	200	4.9813	0.0077
4.8915	0.42	400	4.5741	0.1155
4.2736	0.63	600	3.5359	0.4719
3.2701	0.84	800	2.4291	0.7429
2.3578	1.05	1000	1.5793	0.8413
1.5695	1.26	1200	1.0029	0.8994
1.0412	1.47	1400	0.6475	0.9187
0.7034	1.68	1600	0.4439	0.9303
0.501	1.89	1800	0.3400	0.9381
0.3187	2.1	2000	0.2793	0.9439
0.2185	2.31	2200	0.2538	0.9490
0.1669	2.52	2400	0.2210	0.9523
0.1081	2.73	2600	0.2225	0.9519
0.1004	2.94	2800	0.2136	0.9555
0.0665	3.14	3000	0.2078	0.9561
0.0509	3.35	3200	0.2155	0.9568
0.05	3.56	3400	0.2107	0.9581
0.0527	3.77	3600	0.2171	0.9568
0.0447	3.98	3800	0.2128	0.9590
0.0259	4.19	4000	0.2099	0.9587
0.0279	4.4	4200	0.2179	0.9577
0.0176	4.61	4400	0.2191	0.9574
0.0288	4.82	4600	0.2216	0.9590
0.0328	5.03	4800	0.2237	0.9606
0.0154	5.24	5000	0.2241	0.9616
0.0157	5.45	5200	0.2265	0.9603
0.023	5.66	5400	0.2276	0.9613
0.0178	5.87	5600	0.2270	0.9619

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3