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---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 20231008-10-distilbert-base-multilingual-cased-new
  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. -->

# 20231008-10-distilbert-base-multilingual-cased-new

This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.6026
- Loss: 1.8350

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 3.0584        | 1.82  | 200  | 0.3419   | 2.8695          |
| 2.5725        | 3.64  | 400  | 0.4167   | 2.5994          |
| 2.3538        | 5.45  | 600  | 0.4714   | 2.3281          |
| 2.2078        | 7.27  | 800  | 0.4617   | 2.4474          |
| 2.1174        | 9.09  | 1000 | 0.5062   | 2.2134          |
| 2.0354        | 10.91 | 1200 | 0.5208   | 2.1015          |
| 2.0026        | 12.73 | 1400 | 0.4932   | 2.0366          |
| 1.9121        | 14.55 | 1600 | 0.5892   | 1.7582          |
| 1.903         | 16.36 | 1800 | 0.5332   | 2.0146          |
| 1.8417        | 18.18 | 2000 | 0.5442   | 1.9231          |
| 1.8791        | 20.0  | 2200 | 0.6026   | 1.8350          |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1