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---
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-multilingual-cased_regression_finetuned_news_all
results: []
---
<!-- 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. -->
# distilbert-base-multilingual-cased_regression_finetuned_news_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: 0.8565
- Mse: 0.8565
- Mae: 0.5566
- Rmse: 0.9255
- Mape: inf
- R Squared: 0.5011
## 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: 2175
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | Rmse | Mape | R Squared |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:----:|:---------:|
| 0.9354 | 1.0 | 21755 | 0.9298 | 0.9298 | 0.5985 | 0.9643 | inf | 0.4584 |
| 0.8432 | 2.0 | 43510 | 0.8988 | 0.8988 | 0.5756 | 0.9481 | inf | 0.4764 |
| 0.8033 | 3.0 | 65265 | 0.8810 | 0.8810 | 0.5685 | 0.9386 | inf | 0.4868 |
| 0.8119 | 4.0 | 87020 | 0.8778 | 0.8778 | 0.5623 | 0.9369 | inf | 0.4887 |
| 0.7401 | 5.0 | 108775 | 0.8565 | 0.8565 | 0.5566 | 0.9255 | inf | 0.5011 |
| 0.6964 | 6.0 | 130530 | 0.8877 | 0.8877 | 0.5587 | 0.9422 | inf | 0.4829 |
| 0.6213 | 7.0 | 152285 | 0.8918 | 0.8918 | 0.5607 | 0.9444 | inf | 0.4805 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
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