README.md

metadata

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: []

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

This model is a fine-tuned version of 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