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| 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" |
| 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 |
1from span_marker import SpanMarkerModel
2
3# Download from the 🤗 Hub
4model = SpanMarkerModel.from_pretrained("span-marker-allenai/scibert_scivocab_uncased-me")
5# Run inference
6entities = model.predict("In situ Peak Force Tapping AFM was employed for determining morphology and nano - mechanical properties of the surface layer .")1from span_marker import SpanMarkerModel, Trainer
2
3# Download from the 🤗 Hub
4model = SpanMarkerModel.from_pretrained("span-marker-allenai/scibert_scivocab_uncased-me")
5
6# Specify a Dataset with "tokens" and "ner_tag" columns
7dataset = load_dataset("conll2003") # For example CoNLL2003
8
9# Initialize a Trainer using the pretrained model & dataset
10trainer = Trainer(
11 model=model,
12 train_dataset=dataset["train"],
13 eval_dataset=dataset["validation"],
14)
15trainer.train()
16trainer.save_model("span-marker-allenai/scibert_scivocab_uncased-me-finetuned")| Training set | Min | Median | Max |
|---|---|---|---|
| Sentence length | 3 | 25.6049 | 106 |
| Entities per sentence | 0 | 5.2439 | 22 |
| 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 |
@software{Aarsen_SpanMarker,
author = {Aarsen, Tom},
license = {Apache-2.0},
title = {{SpanMarker for Named Entity Recognition}},
url = {https://github.com/tomaarsen/SpanMarkerNER}
}