metadata

license: mit
tags:
  - generated_from_trainer
  - nlu
  - intent-classification
metrics:
  - accuracy
  - f1
model-index:
  - name: multilingual_minilm-amazon-massive-intent
    results:
      - task:
          name: intent-classification
          type: intent-classification
        dataset:
          name: MASSIVE
          type: AmazonScience/massive
          split: test
        metrics:
          - name: F1
            type: f1
            value: 0.8234
datasets:
  - AmazonScience/massive
language:
  - en
pipeline_tag: text-classification

multilingual_minilm-amazon-massive-intent

This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on the MASSIVE1.1 dataset. It achieves the following results on the evaluation set:

Loss: 0.8941
Accuracy: 0.8234
F1: 0.8234

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
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
3.7961	1.0	720	3.1657	0.3404	0.3404
3.1859	2.0	1440	2.4835	0.4343	0.4343
2.3104	3.0	2160	2.0474	0.5652	0.5652
2.0071	4.0	2880	1.7190	0.6503	0.6503
1.5595	5.0	3600	1.4873	0.6990	0.6990
1.3664	6.0	4320	1.3088	0.7354	0.7354
1.1272	7.0	5040	1.1964	0.7521	0.7521
1.0128	8.0	5760	1.1115	0.7718	0.7718
0.9405	9.0	6480	1.0598	0.7841	0.7841
0.7758	10.0	7200	1.0003	0.7944	0.7944
0.7457	11.0	7920	0.9599	0.8037	0.8037
0.6605	12.0	8640	0.9175	0.8165	0.8165
0.6135	13.0	9360	0.9148	0.8190	0.8190
0.5698	14.0	10080	0.8976	0.8229	0.8229
0.5578	15.0	10800	0.8941	0.8234	0.8234

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.7.1
Tokenizers 0.13.2