SpanMarker with allenai/scibert_scivocab_uncased on my-data
This is a SpanMarker model that can be used for Named Entity Recognition. This SpanMarker model uses allenai/scibert_scivocab_uncased as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: allenai/scibert_scivocab_uncased
- 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 | "an overall mitochondrial", "defect", "Depth time - series" |
Material | "cross - shore measurement locations", "the subject 's fibroblasts", "COXI , COXII and COXIII subunits" |
Method | "EFSA", "an approximation", "in vitro" |
Process | "translation", "intake", "a significant reduction of synthesis" |
Evaluation
Metrics
Label | Precision | Recall | F1 |
---|---|---|---|
all | 0.6981 | 0.6732 | 0.6854 |
Data | 0.6269 | 0.6402 | 0.6335 |
Material | 0.8085 | 0.7562 | 0.7815 |
Method | 0.4211 | 0.4 | 0.4103 |
Process | 0.6891 | 0.6488 | 0.6683 |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span-marker-allenai/scibert_scivocab_uncased-me")
# Run inference
entities = model.predict("In situ Peak Force Tapping AFM was employed for determining morphology and nano - mechanical properties of the surface layer .")
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/scibert_scivocab_uncased-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/scibert_scivocab_uncased-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
Training Results
Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
---|---|---|---|---|---|---|
2.0134 | 300 | 0.0476 | 0.7297 | 0.5821 | 0.6476 | 0.7880 |
4.0268 | 600 | 0.0532 | 0.7537 | 0.6775 | 0.7136 | 0.8281 |
6.0403 | 900 | 0.0655 | 0.7162 | 0.7080 | 0.7121 | 0.8357 |
8.0537 | 1200 | 0.0761 | 0.7143 | 0.7061 | 0.7102 | 0.8251 |
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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Base model
allenai/scibert_scivocab_uncasedEvaluation results
- F1 on my-datatest set self-reported0.685
- Precision on my-datatest set self-reported0.698
- Recall on my-datatest set self-reported0.673