Modelpublic

liaad/NER_harem_albertina-100m-portuguese-ptpt-encoder

sourceHugging Facemitupdated 3y agoView on Hugging Face

0likes19downloads

Model Card

NERharemalbertina-100m-portuguese-ptpt-encoder

This model is a fine-tuned version of PORTULAN/albertina-100m-portuguese-ptpt-encoder on the harem dataset. It achieves the following results on the evaluation set:

—Loss: 0.2583
—Precision: 0.6722
—Recall: 0.7254
—F1: 0.6978
—Accuracy: 0.9532

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: 3e-05
—trainbatchsize: 8
—evalbatchsize: 8
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—num_epochs: 300

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	16	0.5322	0.0212	0.0117	0.0151	0.8615
No log	2.0	32	0.3238	0.4230	0.4981	0.4575	0.9110
No log	3.0	48	0.2460	0.5006	0.6007	0.5461	0.9369
No log	4.0	64	0.2240	0.5526	0.6396	0.5930	0.9414
No log	5.0	80	0.2088	0.5498	0.6340	0.5889	0.9492
No log	6.0	96	0.2068	0.5884	0.6645	0.6241	0.9496
No log	7.0	112	0.2253	0.5906	0.6720	0.6287	0.9481
No log	8.0	128	0.2115	0.6245	0.6874	0.6545	0.9516
No log	9.0	144	0.2187	0.6546	0.7062	0.6794	0.9533
No log	10.0	160	0.2398	0.6432	0.7020	0.6713	0.9495
No log	11.0	176	0.2554	0.6653	0.7043	0.6843	0.9526
No log	12.0	192	0.2397	0.6777	0.7212	0.6988	0.9529
No log	13.0	208	0.2565	0.6778	0.7207	0.6986	0.9531
No log	14.0	224	0.2700	0.6586	0.7142	0.6853	0.9506
No log	15.0	240	0.2700	0.7009	0.7259	0.7132	0.9544
No log	16.0	256	0.2688	0.6761	0.7240	0.6993	0.9532
No log	17.0	272	0.2741	0.7132	0.7343	0.7236	0.9558
No log	18.0	288	0.2732	0.6740	0.7132	0.6931	0.9530
No log	19.0	304	0.2745	0.7094	0.7310	0.7201	0.9550
No log	20.0	320	0.2583	0.6722	0.7254	0.6978	0.9532

Framework versions

—Transformers 4.38.2
—Pytorch 2.2.1+cu121
—Datasets 2.18.0
—Tokenizers 0.15.2