Label	Examples
Data	"an overall mitochondrial", "Depth time - series", "defect"
Material	"the subject 's fibroblasts", "COXI , COXII and COXIII subunits", "cross - shore measurement locations"
Method	"an approximation", "EFSA", "in vitro"
Process	"intake", "a significant reduction of synthesis", "translation"

Evaluation

Metrics

Label	Precision	Recall	F1
all	0.6806	0.6618	0.6711
Data	0.5939	0.6190	0.6062
Material	0.765	0.7612	0.7631
Method	0.4667	0.35	0.4
Process	0.6989	0.6341	0.6650

Uses

Direct Use for Inference

python

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span-marker-allenai/specter-me")
# Run inference
entities = model.predict("We established a P fertilizer need map based on integrating results from the two systems .")

Downstream Use

You can finetune this model on your own dataset.

Click to expand

python

from span_marker import SpanMarkerModel, Trainer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span-marker-allenai/specter-me")

# 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-allenai/specter-me-finetuned")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Sentence length	3	25.6049	106
Entities per sentence	0	5.2439	22

Training Hyperparameters

learning_rate: 5e-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: 10

Framework Versions

Python: 3.10.12
SpanMarker: 1.5.0
Transformers: 4.36.2
PyTorch: 2.0.1+cu118
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}
}

zhang19991111/specter-spanmarker-STEM-NER

SpanMarker with allenai/specter on my-data

Model Details

Model Description

Model Sources

Model Labels

Evaluation

Metrics

Uses

Direct Use for Inference

Downstream Use

Training Details

Training Set Metrics

Training Hyperparameters

Framework Versions

Citation

BibTeX

zhang19991111/
specter-spanmarker-STEM-NER