nhankins/es_distilbert_lora_adapter_1.0

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: 0.5734
• Precision: 0.7362
• Recall: 0.8026
• F1 and accuracy: {'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544}

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	0.6573	0.6247	1.0	{'accuracy': 0.6246648793565683, 'f1': 0.768976897689769}
0.6699	2.0	744	0.6452	0.6247	1.0	{'accuracy': 0.6246648793565683, 'f1': 0.768976897689769}
0.6412	3.0	1116	0.6130	0.6602	0.8755	{'accuracy': 0.6407506702412868, 'f1': 0.7527675276752768}
0.6412	4.0	1488	0.5949	0.7413	0.8240	{'accuracy': 0.710455764075067, 'f1': 0.7804878048780487}
0.6158	5.0	1860	0.5860	0.7323	0.8455	{'accuracy': 0.710455764075067, 'f1': 0.7848605577689244}
0.5891	6.0	2232	0.5802	0.7381	0.7983	{'accuracy': 0.6970509383378016, 'f1': 0.7670103092783506}
0.5855	7.0	2604	0.5770	0.7354	0.8112	{'accuracy': 0.6997319034852547, 'f1': 0.7714285714285714}
0.5855	8.0	2976	0.5757	0.7328	0.8240	{'accuracy': 0.7024128686327078, 'f1': 0.7757575757575758}
0.5839	9.0	3348	0.5741	0.7362	0.8026	{'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544}
0.5759	10.0	3720	0.5734	0.7362	0.8026	{'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544}

Framework versions

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