SpanMarker with allenai/specter2_base on my-data
This is a SpanMarker model that can be used for Named Entity Recognition. This SpanMarker model uses allenai/specter2_base as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: allenai/specter2_base
- Maximum Sequence Length: 256 tokens
- Maximum Entity Length: 8 words
- Language: en
- License: cc-by-sa-4.0
Model Sources
- Repository: SpanMarker on GitHub
- Thesis: SpanMarker For Named Entity Recognition
Model Labels
Label | Examples |
---|---|
Data | "Depth time - series", "defect", "an overall mitochondrial" |
Material | "cross - shore measurement locations", "the subject 's fibroblasts", "COXI , COXII and COXIII subunits" |
Method | "an approximation", "EFSA", "in vitro" |
Process | "intake", "a significant reduction of synthesis", "translation" |
Evaluation
Metrics
Label | Precision | Recall | F1 |
---|---|---|---|
all | 0.7108 | 0.6715 | 0.6906 |
Data | 0.6591 | 0.6138 | 0.6356 |
Material | 0.795 | 0.7910 | 0.7930 |
Method | 0.5 | 0.45 | 0.4737 |
Process | 0.6898 | 0.6293 | 0.6582 |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span-marker-allenai/specter2_base-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
from span_marker import SpanMarkerModel, Trainer
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span-marker-allenai/specter2_base-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/specter2_base-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}
}
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Model tree for zhang19991111/specter2-spanmarker-STEM-NER
Base model
allenai/specter2_baseEvaluation results
- F1 on my-datatest set self-reported0.691
- Precision on my-datatest set self-reported0.711
- Recall on my-datatest set self-reported0.672