---
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: 0.8877
- Mse: 0.8877
- Mae: 0.5973
- Rmse: 0.9422
- Mape: inf
- R Squared: 0.5145

## 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
- lr_scheduler_warmup_steps: 778
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mse    | Mae    | Rmse   | Mape | R Squared |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:----:|:---------:|
| 1.0906        | 1.0   | 7789  | 1.0449          | 1.0449 | 0.7032 | 1.0222 | inf  | 0.4285    |
| 0.9156        | 2.0   | 15578 | 0.9369          | 0.9369 | 0.6340 | 0.9679 | inf  | 0.4875    |
| 0.6858        | 3.0   | 23367 | 0.9153          | 0.9153 | 0.6189 | 0.9567 | inf  | 0.4993    |
| 1.0272        | 4.0   | 31156 | 0.8877          | 0.8877 | 0.5973 | 0.9422 | inf  | 0.5145    |
| 0.7273        | 5.0   | 38945 | 0.8928          | 0.8928 | 0.6004 | 0.9449 | inf  | 0.5117    |
| 0.8211        | 6.0   | 46734 | 0.8880          | 0.8880 | 0.5936 | 0.9423 | inf  | 0.5143    |


### Framework versions

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