dannyLeo16/ner_model_bert_base

NER for Spanish Emergency Reports (ECU-911)

Author/Maintainer: Danny Paltin (@dannyLeo16) Task: Token Classification (NER) Language: Spanish (es) Finetuned from: dccuchile/bert-base-spanish-wwm-cased Entities (BIO): PER and LOC → ["O","B-PER","I-PER","B-LOC","I-LOC"]

This model is a Spanish BERT fine-tuned to identify persons and locations in short emergency incident descriptions (ECU-911-style). It was developed for the research project:

“Representación del conocimiento para emergencias del ECU-911 mediante PLN, ontologías OWL y reglas SWRL.”

Model Details

• —Architecture: BERT (Whole Word Masking, cased)
• —Tokenizer: dccuchile/bert-base-spanish-wwm-cased
• —Max length: uses base tokenizer model_max_length (padding to max length)
• —Libraries: 🤗 Transformers, 🤗 Datasets, PyTorch
• —Labels: O, B-PER, I-PER, B-LOC, I-LOC

Training Data

• —Source: Custom Spanish emergency reports (Ecuador, ECU-911-style) with token-level BIO annotations.
• —Size: 510 texts; 34,232 tokens (avg 67.12 tokens/text).
• —Entity counts (BIO spans): PER = 421, LOC = 1,643.
• —Token-level label distribution: O=30,132, B-LOC=1,643, I-LOC=1,617, B-PER=421, I-PER=419.
• —Splits: 80% train / 10% validation / 10% test (split aleatorio durante el entrenamiento).

Privacy/Ethics. Data should be anonymized and free of PII. Do not deploy on personal/live data without consent and compliance with local regulation.

Training Procedure

• —Objective: Token classification (cross-entropy); continuation subwords ignored with -100.
• —Hyperparameters:
• —learning_rate = 2e-5
• —num_train_epochs = 3
• —per_device_train_batch_size = 8
• —per_device_eval_batch_size = 8
• —weight_decay = 0.01
• —evaluation_strategy = "epoch", save_strategy = "epoch"
• —load_best_model_at_end = true (por `eval_loss`)
• —Data collator: DataCollatorForTokenClassification (padding a max_length)

Evaluation

Validation (epoch 3):

• —Accuracy: 0.9480
• —Macro F1: 0.7998
• —Macro Precision: 0.7914
• —Macro Recall: 0.8118
• —Eval loss: 0.1458

Test:

• —Accuracy: 0.9740
• —Macro F1: 0.8899
• —Macro Precision: 0.8802
• —Macro Recall: 0.9002
• —Eval loss: 0.0834

(Computed with `sklearn.metrics`, excluding `-100` positions.)

Intended Use

• —NER over Spanish emergency/incident text (ECU-911-like).
• —Downstream knowledge representation (OWL/SWRL).
• —Academic research and prototyping.

Limitations

• —Domain-specific; performance may drop on other domains.
• —Only PER and LOC entities.
• —May struggle with colloquialisms, misspellings, or code-switching.

How to use

from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="dannyLeo16/ner_model_bert_base",
    tokenizer="dannyLeo16/ner_model_bert_base",
    aggregation_strategy="simple"
)
text = "Se reporta accidente en la Av. de las Américas con dos personas heridas."
ner(text)