metadata
tags:
- spacy
- token-classification
language:
- en
widget:
- text: >-
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of
the coronavirus disease-19 (COVID-19) pandemic, was identified in late
2019 and caused >5 million deaths by February 2022. To date, targeted
antiviral interventions against COVID-19 are limited. The spectrum of
SARS-CoV-2 infection ranges from asymptomatic to fatal disease. However,
the reasons for varying outcomes to SARS-CoV-2 infection are yet to be
elucidated. Here we show that an endogenously activated interferon lambda
(IFN位1) pathway leads to resistance against SARS-CoV-2 infection.
- text: >-
The NHS is offering antibody and antiviral treatments to people with
coronavirus (COVID-19) who are at highest risk of becoming seriously ill.
model-index:
- name: en_covid19_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9139786332
- name: NER Recall
type: recall
value: 0.9362022309
- name: NER F Score
type: f_score
value: 0.9249569618
COVID 19 Bio Annotations
The dataset was taken from https://github.com/davidcampos/covid19-corpus
Dataset The dataset was then split into several datasets each one representing one entity. Namely, Disorder, Species, Chemical or Drug, Gene and Protein, Enzyme, Anatomy, Biological Process, Molecular Function, Cellular Component, Pathway and microRNA. Moreover, another dataset is also created with all those aforementioned that are non-overlapping in nature.
Other Dataset Formats The datasets are available in two formats IOB and Spacy's JSONL format.
IOB: https://github.com/tsantosh7/COVID-19-Named-Entity-Recognition/tree/master/Datasets/BIO
SpaCy JSONL: https://github.com/tsantosh7/COVID-19-Named-Entity-Recognition/tree/master/Datasets/SpaCy
| Feature | Description |
|---|---|
| Name | en_covid19_ner |
| Version | 0.0.0 |
| spaCy | >=3.2.4,<3.3.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | Santosh Tirunagai |
Label Scheme
View label scheme (10 labels for 1 components)
| Component | Labels |
|---|---|
ner |
ANAT, CHED, COMP, DISO, ENZY, FUNC, PATH, PRGE, PROC, SPEC |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
92.50 |
ENTS_P |
91.40 |
ENTS_R |
93.62 |
TRANSFORMER_LOSS |
311768.03 |
NER_LOSS |
371171.50 |