span-marker-luke-base-conll2003

SpanMarker

This is a SpanMarker model trained on the conll2003 dataset that can be used for Named Entity Recognition.

Model Details

Important Note: I used the Tokenizer from "roberta-base".

from span_marker import SpanMarkerModel
from span_marker.tokenizer import SpanMarkerTokenizer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("lambdavi/span-marker-luke-base-conll2003")
+tokenizer = SpanMarkerTokenizer.from_pretrained("roberta-base", config=model.tokenizer.config)
+model.set_tokenizer(tokenizer)

# Run inference
entities = model.predict("Portsmouth:Middlesex 199 and 426 (J. Pooley 111,M. Ramprakash 108,M. Gatting 83), Hampshire 232 and 109-5.")

Model Description

• Model Type: SpanMarker

<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->

• Maximum Sequence Length: 512 tokens
• Maximum Entity Length: 8 words
• Training Dataset: conll2003

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

Model Sources

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

Model Labels

Uses

Direct Use for Inference

from span_marker import SpanMarkerModel
from span_marker.tokenizer import SpanMarkerTokenizer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("lambdavi/span-marker-luke-base-conll2003")
tokenizer = SpanMarkerTokenizer.from_pretrained("roberta-base", config=model.tokenizer.config)
model.set_tokenizer(tokenizer)

# Run inference
entities = model.predict("Portsmouth:Middlesex 199 and 426 (J. Pooley 111,M. Ramprakash 108,M. Gatting 83), Hampshire 232 and 109-5.")

Downstream Use

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

from span_marker import SpanMarkerModel, Trainer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")

# 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("span_marker_model_id-finetuned")

</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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 5

Training Results

Framework Versions

• Python: 3.10.12
• SpanMarker: 1.5.0
• Transformers: 4.36.0
• PyTorch: 2.0.0
• Datasets: 2.16.1
• 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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