NER in Spanish
Collection
Fine-tuned models to perform NER in Spanish using the framework SpanMarker and different encoders and datasets
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3 items
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Updated
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4
This is a SpanMarker model trained on the xtreme/PAN-X.es dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-multilingual-cased as the underlying encoder.
Label | Examples |
---|---|
LOC | "Salamanca", "Paris", "Barcelona (España)" |
ORG | "ONU", "Fútbol Club Barcelona", "Museo Nacional del Prado" |
PER | "Fray Luis de León", "Leo Messi", "Álvaro Bartolomé" |
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("alvarobartt/bert-base-multilingual-cased-ner-spanish")
# Run inference
entities = model.predict("Marie Curie fue profesora en la Universidad de Paris.")
Training set | Min | Median | Max |
---|---|---|---|
Sentence length | 3 | 6.4642 | 64 |
Entities per sentence | 1 | 1.2375 | 24 |
Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
---|---|---|---|---|---|---|
0.3998 | 1000 | 0.0388 | 0.8761 | 0.8641 | 0.8701 | 0.9223 |
0.7997 | 2000 | 0.0326 | 0.8995 | 0.8740 | 0.8866 | 0.9341 |
1.1995 | 3000 | 0.0277 | 0.9076 | 0.9019 | 0.9047 | 0.9424 |
1.5994 | 4000 | 0.0261 | 0.9143 | 0.9113 | 0.9128 | 0.9473 |
1.9992 | 5000 | 0.0234 | 0.9231 | 0.9143 | 0.9187 | 0.9502 |
@software{Aarsen_SpanMarker,
author = {Aarsen, Tom},
license = {Apache-2.0},
title = {{SpanMarker for Named Entity Recognition}},
url = {https://github.com/tomaarsen/SpanMarkerNER}
}
Base model
google-bert/bert-base-multilingual-cased