| --- |
| language: |
| - es |
| license: apache-2.0 |
| library_name: transformers |
| base_model: dccuchile/bert-base-spanish-wwm-cased |
| pipeline_tag: token-classification |
| tags: |
| - ner |
| - token-classification |
| - spanish |
| - bert |
| - emergencies |
| - ecu-911 |
| datasets: |
| - custom-ecu911 |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: ner_model_bert_base |
| results: |
| - task: |
| type: token-classification |
| name: Named Entity Recognition |
| dataset: |
| name: custom-ecu911 |
| type: custom |
| split: test |
| metrics: |
| - type: accuracy |
| value: 0.9739766081871345 |
| - type: f1 |
| name: Macro F1 |
| value: 0.8898766824816503 |
| - type: precision |
| name: Macro Precision |
| value: 0.8801934151701145 |
| - type: recall |
| name: Macro Recall |
| value: 0.9001920589792443 |
| --- |
| |
| # NER for Spanish Emergency Reports (ECU-911) |
|
|
| **Author/Maintainer:** Danny Paltin ([@dannyLeo16](https://huggingface.co/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 |
|
|
| ```python |
| 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) |
| |
