SpanMarker with allenai/specter on my-data
This is a SpanMarker model that can be used for Named Entity Recognition. This SpanMarker model uses allenai/specter as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: allenai/specter
- Maximum Sequence Length: 256 tokens
- Maximum Entity Length: 8 words
- Language: en
- License: cc-by-sa-4.0
Model Sources
Model Labels
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
from span_marker import SpanMarkerModel
model = SpanMarkerModel.from_pretrained("span-marker-allenai/specter-me")
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
from span_marker import SpanMarkerModel, Trainer
model = SpanMarkerModel.from_pretrained("span-marker-allenai/specter-me")
dataset = load_dataset("conll2003")
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}
}