span-marker-roberta-large-fewnerd-fine-super

SpanMarker with roberta-large on FewNERD

This is a SpanMarker model trained on the FewNERD dataset that can be used for Named Entity Recognition. This SpanMarker model uses roberta-large as the underlying encoder. See train.py for the training script.

Model Details

Model Description

• Model Type: SpanMarker
• Encoder: roberta-large
• Maximum Sequence Length: 256 tokens
• Maximum Entity Length: 8 words
• Training Dataset: FewNERD
• Language: en
• License: cc-by-sa-4.0

Model Sources

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

Model Labels

Uses

Direct Use

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-roberta-large-fewnerd-fine-super")
# Run inference
entities = model.predict("Most of the Steven Seagal movie ``Under Siege`` (co-starring Tommy Lee Jones) was filmed aboard the Battleship USS Alabama, which is docked on Mobile Bay at Battleship Memorial Park and open to the public.")

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("tomaarsen/span-marker-roberta-large-fewnerd-fine-super")

# 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-roberta-large-fewnerd-fine-super-finetuned")

</details>

Training Details

Training Set Metrics

Training Hyperparameters

• learning_rate: 1e-05
• train_batch_size: 8
• 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: 3

Training Hardware

• On Cloud: No
• GPU Model: 1 x NVIDIA GeForce RTX 3090
• CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
• RAM Size: 31.78 GB

Framework Versions

• Python: 3.9.16
• SpanMarker: 1.3.1.dev
• Transformers : 4.29.2
• PyTorch: 2.0.1+cu118
• Datasets: 2.14.3
• Tokenizers: 0.13.2