span-marker-xlm-roberta-large-conll-2002-es

SpanMarker with xlm-roberta-large on conll2002

This is a SpanMarker model that can be used for Named Entity Recognition. This SpanMarker model uses xlm-roberta-large as the underlying encoder.

Model Details

Model Description

• Model Type: SpanMarker
• Encoder: xlm-roberta-large
• Maximum Sequence Length: 256 tokens
• Maximum Entity Length: 8 words
• Training Dataset: conll2002
• Language: 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/span-marker-xlm-roberta-large-conll-2002-es")
# Run inference
entities = model.predict("George Washington fue a Washington.")

</details> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Set Metrics

Training Hyperparameters

• learning_rate: 1e-05
• train_batch_size: 16
• eval_batch_size: 8
• 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: 4.33.2
• 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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