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---
license: mit
tags:
- generated_from_trainer
- nlu
- text-classification
- intent-classification
metrics:
- accuracy
- f1
model-index:
- name: multilingual_minilm-amazon_massive-intent_eu_noen
results:
- task:
name: intent-classification
type: intent-classification
dataset:
name: MASSIVE
type: AmazonScience/massive
split: test
metrics:
- name: F1
type: f1
value: 0.8551
datasets:
- AmazonScience/massive
language:
- de
- fr
- it
- pt
- es
- pl
---
<!-- 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_eu_noen
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: 0.7794
- Accuracy: 0.8551
- F1: 0.8551
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.7624 | 1.0 | 4318 | 1.5462 | 0.6331 | 0.6331 |
| 0.9535 | 2.0 | 8636 | 0.9628 | 0.7698 | 0.7698 |
| 0.6849 | 3.0 | 12954 | 0.8034 | 0.8097 | 0.8097 |
| 0.5163 | 4.0 | 17272 | 0.7444 | 0.8290 | 0.8290 |
| 0.3973 | 5.0 | 21590 | 0.7346 | 0.8383 | 0.8383 |
| 0.331 | 6.0 | 25908 | 0.7369 | 0.8453 | 0.8453 |
| 0.2876 | 7.0 | 30226 | 0.7325 | 0.8510 | 0.8510 |
| 0.2319 | 8.0 | 34544 | 0.7726 | 0.8496 | 0.8496 |
| 0.2098 | 9.0 | 38862 | 0.7803 | 0.8543 | 0.8543 |
| 0.1863 | 10.0 | 43180 | 0.7794 | 0.8551 | 0.8551 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2 |