State-of-the-Art NER models - Tagalog
Collection
2 items
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Updated
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This is a SpanMarker model trained on the TLUnified dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-multilingual-cased as the underlying encoder.
Label | Examples |
---|---|
LOC | "Israel", "Batasan", "United States" |
ORG | "MMDA", "International Monitoring Team", "Coordinating Committees for the Cessation of Hostilities" |
PER | "Puno", "Fernando", "Villavicencio" |
Label | Precision | Recall | F1 |
---|---|---|---|
all | 0.8737 | 0.9042 | 0.8887 |
LOC | 0.8830 | 0.9084 | 0.8955 |
ORG | 0.7579 | 0.8587 | 0.8052 |
PER | 0.9264 | 0.9220 | 0.9242 |
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-tlunified")
# Run inference
entities = model.predict("Idinagdag ni South Cotabato Rep Darlene Antonino - Custodio, na illegal na ipagpaliban ang halalan sa ARMM kung ang gagamitin lamang basehan ay ang ipapasang panukala ng Kongreso.")
You can finetune this model on your own dataset.
from span_marker import SpanMarkerModel, Trainer
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-tlunified")
# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003
# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
model=model,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("tomaarsen/span-marker-mbert-base-tlunified-finetuned")
Training set | Min | Median | Max |
---|---|---|---|
Sentence length | 1 | 31.7625 | 150 |
Entities per sentence | 0 | 2.0661 | 38 |
Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
---|---|---|---|---|---|---|
0.6803 | 400 | 0.0074 | 0.8552 | 0.8835 | 0.8691 | 0.9774 |
1.3605 | 800 | 0.0072 | 0.8709 | 0.9034 | 0.8869 | 0.9798 |
2.0408 | 1200 | 0.0070 | 0.8753 | 0.9053 | 0.8900 | 0.9812 |
2.7211 | 1600 | 0.0065 | 0.8876 | 0.9003 | 0.8939 | 0.9807 |
Carbon emissions were measured using CodeCarbon.
@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