dannyLeo16
/

ner_model_bert_base

@@ -1,199 +1,139 @@
 ---
 library_name: transformers
-tags: []
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 ---
+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`)
+- **Hardware:** <especifica aquí, p. ej. “Google Colab – GPU NVIDIA T4”>
+- **Seed:** <si fijaste semilla, indícala>
+---
 ## 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)