|
--- |
|
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: [] |
|
--- |
|
|
|
<!-- 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-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: 0.5930 |
|
- Precision: 0.7325 |
|
- Recall: 0.7542 |
|
- F1 and accuracy: {'accuracy': 0.6702412868632708, 'f1': 0.74321503131524} |
|
|
|
## 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 | 372 | 0.6533 | 0.6327 | 1.0 | {'accuracy': 0.6327077747989276, 'f1': 0.7750410509031198} | |
|
| 0.67 | 2.0 | 744 | 0.6432 | 0.6327 | 1.0 | {'accuracy': 0.6327077747989276, 'f1': 0.7750410509031198} | |
|
| 0.6548 | 3.0 | 1116 | 0.6197 | 0.6341 | 0.9915 | {'accuracy': 0.6327077747989276, 'f1': 0.7735537190082644} | |
|
| 0.6548 | 4.0 | 1488 | 0.6020 | 0.6678 | 0.8178 | {'accuracy': 0.6273458445040214, 'f1': 0.7352380952380952} | |
|
| 0.6211 | 5.0 | 1860 | 0.5969 | 0.696 | 0.7373 | {'accuracy': 0.6300268096514745, 'f1': 0.7160493827160493} | |
|
| 0.5929 | 6.0 | 2232 | 0.5954 | 0.6980 | 0.7542 | {'accuracy': 0.6380697050938338, 'f1': 0.7250509164969451} | |
|
| 0.5887 | 7.0 | 2604 | 0.5940 | 0.7412 | 0.7161 | {'accuracy': 0.6621983914209115, 'f1': 0.728448275862069} | |
|
| 0.5887 | 8.0 | 2976 | 0.5937 | 0.7426 | 0.7458 | {'accuracy': 0.675603217158177, 'f1': 0.7441860465116279} | |
|
| 0.5809 | 9.0 | 3348 | 0.5933 | 0.7247 | 0.7585 | {'accuracy': 0.6648793565683646, 'f1': 0.7412008281573499} | |
|
| 0.5726 | 10.0 | 3720 | 0.5930 | 0.7325 | 0.7542 | {'accuracy': 0.6702412868632708, 'f1': 0.74321503131524} | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.17.0 |
|
- Tokenizers 0.15.2 |
|
|
