---
language:
- en
license: mit
tags:
- generated_from_trainer
- nlu
- intent-classification
datasets:
- AmazonScience/massive
metrics:
- accuracy
- f1
pipeline_tag: text-classification
base_model: microsoft/Multilingual-MiniLM-L12-H384
model-index:
- name: multilingual_minilm-amazon-massive-intent
  results:
  - task:
      type: intent-classification
      name: intent-classification
    dataset:
      name: MASSIVE
      type: AmazonScience/massive
      split: test
    metrics:
    - type: f1
      value: 0.8234
      name: F1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# multilingual_minilm-amazon-massive-intent

This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) 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