span-marker-alephbert-small-nemo-mt-he

SpanMarker

This is a SpanMarker model trained on the imvladikon/nemo_corpus dataset that can be used for Named Entity Recognition.

Model Details

Model Description

• Model Type: SpanMarker

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• Maximum Sequence Length: 512 tokens
• Maximum Entity Length: 100 words
• Training Dataset: imvladikon/nemo_corpus

<!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Repository: SpanMarker on GitHub
• Thesis: SpanMarker For Named Entity Recognition

Model Labels

Evaluation

Metrics

Uses

Direct Use for Inference

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("iahlt/span-marker-alephbert-small-nemo-mt-he")
# Run inference
entities = model.predict("יו\"ר ועדת ה נוער נתן סלובטיק אמר ש ה שחקנים של אנחנו לא משתלבים ב אירופה.")
entities

Using spacy

pip install spacy_udpipe

import spacy
from spacy.lang.he import Hebrew
import spacy_udpipe

spacy_udpipe.download("he") # download public udpipe model, but possible to use any your spacy model
nlp = spacy_udpipe.load("he")
nlp.add_pipe("span_marker", config={"model": "iahlt/span-marker-alephbert-small-nemo-mt-he"})

text = "יו\"ר ועדת הנוער נתן סלובטיק אמר שהשחקנים של אנחנו לא משתלבים באירופה."
doc = nlp(text)
print([(entity, entity.label_) for entity in doc.ents])
# [(ועדת הנוער, 'ORG'), (נתן סלובטיק, 'PER'), (אירופה, 'GPE')]

Training Details

Training Set Metrics

Training Hyperparameters

• learning_rate: 1e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 4
• mixed_precision_training: Native AMP

Evaluation results

Test results

Framework Versions

• Python: 3.10.12
• SpanMarker: 1.5.0
• Transformers: 4.35.2
• PyTorch: 2.1.0+cu118
• Datasets: 2.15.0
• Tokenizers: 0.15.0

Citation

BibTeX

@software{Aarsen_SpanMarker,
    author = {Aarsen, Tom},
    license = {Apache-2.0},
    title = {{SpanMarker for Named Entity Recognition}},
    url = {https://github.com/tomaarsen/SpanMarkerNER}
}

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