---
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-multilingual-cased_regression_finetuned_mobile01_all
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-multilingual-cased_regression_finetuned_mobile01_all

This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0004
- Mse: 0.0004
- Mae: 0.0183

## 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: 3e-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: cosine
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mse    | Mae    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| No log        | 1.0   | 5    | 0.1000          | 0.1000 | 0.3073 |
| No log        | 2.0   | 10   | 0.0123          | 0.0123 | 0.1057 |
| No log        | 3.0   | 15   | 0.0076          | 0.0076 | 0.0854 |
| No log        | 4.0   | 20   | 0.0042          | 0.0042 | 0.0632 |
| No log        | 5.0   | 25   | 0.0028          | 0.0028 | 0.0516 |
| No log        | 6.0   | 30   | 0.0001          | 0.0001 | 0.0088 |
| No log        | 7.0   | 35   | 0.0012          | 0.0012 | 0.0337 |
| No log        | 8.0   | 40   | 0.0001          | 0.0001 | 0.0078 |
| No log        | 9.0   | 45   | 0.0004          | 0.0004 | 0.0174 |
| 0.0746        | 10.0  | 50   | 0.0004          | 0.0004 | 0.0183 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2