from transformers import pipeline

# Load the model
ner_pipeline = pipeline("ner", model="syubraj/spanish_bert_based_ner")

# Example text
text = "Elon Musk vive en Estados Unidos y es dueño de Space X, Tesla y Starlink"

# Perform inference
entities = ner_pipeline(text)
for ent in entities:
  print(f"Word: {ent['word']} | Label: {ent['entity']}")

Training procedure

O: Outside of a named entity.
B-PER: Beginning of a person&#x27;s name.
I-PER: Inside a person&#x27;s name.
B-ORG: Beginning of an organization&#x27;s name.
I-ORG: Inside an organization&#x27;s name.
B-LOC: Beginning of a location name.
I-LOC: Inside a location name.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
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	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	Loc F1	Org F1	Per F1
0.2713	0.8	1000	0.2236	0.8498	0.8672	0.8584	0.9401	0.8834	0.8019	0.8790
0.1537	1.6	2000	0.1909	0.8772	0.8943	0.8857	0.9495	0.9002	0.8369	0.9164
0.1152	2.4	3000	0.2095	0.8848	0.8981	0.8914	0.9523	0.9039	0.8432	0.9220
0.0889	3.2	4000	0.2223	0.8978	0.8998	0.8988	0.9546	0.9080	0.8569	0.9290
0.0701	4.0	5000	0.2152	0.8937	0.9042	0.8989	0.9544	0.9113	0.8565	0.9246
0.0457	4.8	6000	0.2365	0.9017	0.9069	0.9043	0.9563	0.9164	0.8616	0.9310
0.0364	5.6	7000	0.2622	0.9037	0.9086	0.9061	0.9578	0.9148	0.8639	0.9365
0.026	6.4	8000	0.2916	0.9037	0.9159	0.9097	0.9585	0.9183	0.8712	0.9366
0.0215	7.2	9000	0.2985	0.9022	0.9128	0.9074	0.9565	0.9178	0.8676	0.9323
0.0134	8.0	10000	0.3071	0.904	0.9131	0.9085	0.9574	0.9198	0.8671	0.9344
0.0091	8.8	11000	0.3335	0.9056	0.9115	0.9085	0.9573	0.9175	0.8670	0.9373
0.0074	9.6	12000	0.3320	0.9051	0.9121	0.9086	0.9577	0.9190	0.8663	0.9367

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.19.1