metadata

tags:
  - generated_from_trainer
datasets:
  - clinc_oos
metrics:
  - accuracy
model-index:
  - name: MiniLMv2-L12-H384-distilled-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: 0.9529032258064516

MiniLMv2-L12-H384-distilled-finetuned-clinc

This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos dataset. It achieves the following results on the evaluation set:

Loss: 0.3058
Accuracy: 0.9529

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: 0.0001
train_batch_size: 64
eval_batch_size: 64
seed: 33
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.9908	1.0	239	1.6816	0.3910
1.5212	2.0	478	1.2365	0.7697
1.129	3.0	717	0.9209	0.8706
0.8462	4.0	956	0.6978	0.9152
0.6497	5.0	1195	0.5499	0.9342
0.5124	6.0	1434	0.4447	0.9445
0.4196	7.0	1673	0.3797	0.9455
0.3587	8.0	1912	0.3358	0.95
0.3228	9.0	2151	0.3133	0.9513
0.3052	10.0	2390	0.3058	0.9529

Framework versions

Transformers 4.17.0
Pytorch 1.10.2+cu113
Datasets 1.18.4
Tokenizers 0.11.6