tensorboard safetensors generated_from_trainer base_model:distilbert/distilbert-base-multilingual-cased license:apache-2.0 region:us
---
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: distilbert-base-multilingual-cased-lora-text-classification
  results: []
---

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# distilbert-base-multilingual-cased-lora-text-classification

This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6038
- Precision: 0.7039
- Recall: 0.9283
- F1 and accuracy: {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677}

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy                                            |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log        | 1.0   | 388  | 0.6457          | 0.6452    | 1.0    | {'accuracy': 0.6452442159383034, 'f1': 0.784375}           |
| 0.6427        | 2.0   | 776  | 0.6410          | 0.6452    | 1.0    | {'accuracy': 0.6452442159383034, 'f1': 0.784375}           |
| 0.6298        | 3.0   | 1164 | 0.6290          | 0.6486    | 1.0    | {'accuracy': 0.6503856041131105, 'f1': 0.786833855799373}  |
| 0.6216        | 4.0   | 1552 | 0.6243          | 0.6552    | 0.9841 | {'accuracy': 0.6555269922879178, 'f1': 0.7866242038216561} |
| 0.6216        | 5.0   | 1940 | 0.6148          | 0.6799    | 0.9562 | {'accuracy': 0.6812339331619537, 'f1': 0.7947019867549668} |
| 0.6091        | 6.0   | 2328 | 0.6131          | 0.6791    | 0.9442 | {'accuracy': 0.6760925449871465, 'f1': 0.79}               |
| 0.5892        | 7.0   | 2716 | 0.6102          | 0.6871    | 0.9363 | {'accuracy': 0.6838046272493573, 'f1': 0.7925801011804384} |
| 0.5818        | 8.0   | 3104 | 0.6051          | 0.6997    | 0.9283 | {'accuracy': 0.6966580976863753, 'f1': 0.7979452054794521} |
| 0.5818        | 9.0   | 3492 | 0.6061          | 0.6953    | 0.9363 | {'accuracy': 0.6940874035989717, 'f1': 0.797962648556876}  |
| 0.5766        | 10.0  | 3880 | 0.6038          | 0.7039    | 0.9283 | {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677} |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
