README.md

metadata

library_name: transformers
license: mit
base_model: bert-base-multilingual-cased
tags:
  - generated_from_trainer
model-index:
  - name: fine_tune_bert_output
    results: []
datasets:
  - unimelb-nlp/wikiann
language:
  - es
metrics:
  - recall
  - precision
  - f1
pipeline_tag: token-classification

---

spanish_bert_based_ner

---

fine_tune_bert_output

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

• Loss: 0.3320
• Overall Precision: 0.9051
• Overall Recall: 0.9121
• Overall F1: 0.9086
• Overall Accuracy: 0.9577
• Loc F1: 0.9190
• Org F1: 0.8663
• Per F1: 0.9367

Labels

The following table represents the labels used by the model along with their corresponding indices:

Index	Label
0	O
1	B-PER
2	I-PER
3	B-ORG
4	I-ORG
5	B-LOC
6	I-LOC

Label Descriptions

• O: Outside of a named entity.
• B-PER: Beginning of a person's name.
• I-PER: Inside a person's name.
• B-ORG: Beginning of an organization's name.
• I-ORG: Inside an organization's name.
• B-LOC: Beginning of a location name.
• I-LOC: Inside a location name.

Inference Example

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

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