---
license: mit
base_model: microsoft/MiniLM-L12-H384-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: MiniLM_uncased_classification_tools_classifier-only_fr
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# MiniLM_uncased_classification_tools_classifier-only_fr

This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0829
- Accuracy: 0.075
- Learning Rate: 0.0001

## 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: 24
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Rate   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 7    | 2.0791          | 0.125    | 0.0001 |
| No log        | 2.0   | 14   | 2.0797          | 0.075    | 0.0001 |
| No log        | 3.0   | 21   | 2.0799          | 0.075    | 0.0001 |
| No log        | 4.0   | 28   | 2.0804          | 0.075    | 0.0001 |
| No log        | 5.0   | 35   | 2.0808          | 0.075    | 0.0001 |
| No log        | 6.0   | 42   | 2.0813          | 0.075    | 9e-05  |
| No log        | 7.0   | 49   | 2.0818          | 0.075    | 0.0001 |
| No log        | 8.0   | 56   | 2.0820          | 0.075    | 0.0001 |
| No log        | 9.0   | 63   | 2.0822          | 0.075    | 0.0001 |
| No log        | 10.0  | 70   | 2.0827          | 0.075    | 0.0001 |
| No log        | 11.0  | 77   | 2.0829          | 0.075    | 0.0001 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1