spa-eng-pos-tagging-v6

This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3128
• Accuracy: 0.9056
• Precision: 0.9032
• Recall: 0.8293
• F1: 0.8345
• Hamming Loss: 0.0944

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 14

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Hamming Loss
1.0141	1.0	1744	0.7804	0.7158	0.7328	0.6183	0.6345	0.2842
0.6292	2.0	3488	0.5384	0.7973	0.8111	0.7029	0.7213	0.2027
0.4438	3.0	5232	0.4236	0.8462	0.8346	0.7762	0.7732	0.1538
0.3626	4.0	6976	0.3856	0.8651	0.8524	0.7933	0.7903	0.1349
0.3141	5.0	8720	0.3697	0.8712	0.8688	0.7998	0.8028	0.1288
0.2575	6.0	10464	0.3689	0.8751	0.8758	0.8003	0.8058	0.1249
0.2117	7.0	12208	0.3329	0.8890	0.8832	0.8169	0.8184	0.1110
0.1864	8.0	13952	0.3235	0.9010	0.8946	0.8278	0.8293	0.0990
0.1555	9.0	15696	0.3128	0.9056	0.9032	0.8293	0.8345	0.0944
0.1322	10.0	17440	0.3311	0.9088	0.9010	0.8376	0.8377	0.0912
0.1111	11.0	19184	0.3394	0.9101	0.9081	0.8319	0.8383	0.0899
0.0874	12.0	20928	0.3472	0.9148	0.9100	0.8407	0.8440	0.0852
0.0659	13.0	22672	0.3635	0.9131	0.9072	0.8400	0.8422	0.0869
0.0608	14.0	24416	0.3560	0.9187	0.9140	0.8452	0.8482	0.0813

Framework versions

• Transformers 4.32.0
• Pytorch 2.0.1+cu118
• Tokenizers 0.13.3