bert-base-multilingual-cased-ner-spanish

SpanMarker with bert-base-multilingual-cased on xtreme/PAN-X.es

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.

Model Details

Model Description

• Model Type: SpanMarker
• Encoder: bert-base-multilingual-cased
• Maximum Sequence Length: 512 tokens
• Maximum Entity Length: 8 words
• Training Dataset: xtreme/PAN-X.es
• Languages: es
• License: cc-by-4.0

Model Sources

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

Model Labels

Uses

Direct Use for Inference

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.")

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Training Details

Training Set Metrics

Training Hyperparameters

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 2

Training Results

Framework Versions

• Python: 3.10.12
• SpanMarker: 1.3.1.dev
• Transformers: 4.33.3
• PyTorch: 2.0.1+cu118
• Datasets: 2.14.5
• Tokenizers: 0.13.3

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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