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---
language:
- en
multilinguality:
- monolingual
dataset_info:
- config_name: pair-class
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- config_name: triplet-terms
features:
- name: anchor
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
- config_name: triplet-sentences
features:
- name: anchor
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
configs:
- config_name: pair-class
data_files:
- split: train
path: pair-class/train-*
- split: dev
path: pair-class/dev-*
- split: test
path: pair-class/test-*
- config_name: triplet-terms
data_files:
- split: train
path: triplet-terms/train-*
- split: dev
path: triplet-terms/dev-*
- split: test
path: triplet-terms/test-*
- config_name: triplet-sentences
data_files:
- split: train
path: triplet-sentences/train-*
- split: dev
path: triplet-sentences/dev-*
- split: test
path: triplet-sentences/test-*
---
# Dataset Card for BioTermNLI
This dataset is created with a structure similar to [AllNLI](https://huggingface.co/datasets/sentence-transformers/all-nli). It is intended for fine-tuning embedding models to generate meaningful embeddings of biomedical terms, primarily consisting of alphanumeric identifiers
such as K562, HepG2, and ZNF148.
## Raw Data
From [cellosaurus](https://www.cellosaurus.org/), we collected metadata of 30,001 cell lines that:
* Are human cell lines (NCBI_TaxID=9606)
* Have reference in [BTO](https://www.ebi.ac.uk/ols4/ontologies/bto), [CLO](https://www.ebi.ac.uk/ols4/ontologies/clo), or [EFO](https://www.ebi.ac.uk/ols4/ontologies/efo)
* With at least 1 of those annotations available:
* Diseases: [Prostate Carcinoma](https://www.ebi.ac.uk/ols4/ontologies/ncit/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FNCIT_C4863), [Glioblastoma](https://www.ebi.ac.uk/ols4/ontologies/ordo/classes/http%253A%252F%252Fwww.orpha.net%252FORDO%252FOrphanet_360), etc.
* Derived from site: [lung primordium](https://www.ebi.ac.uk/ols4/ontologies/uberon/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FUBERON_0005597), [corneal epithelium](https://www.ebi.ac.uk/ols4/ontologies/uberon/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FUBERON_0001772), etc.
* Transformant: [N-methyl-N-nitrosourea](https://www.ebi.ac.uk/chebi/chebiOntology.do?chebiId=CHEBI:50102), [Epstein Barr Virus](https://www.ebi.ac.uk/ols4/ontologies/ncbitaxon/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FNCBITaxon_10376), etc.
* Cell type: [kidney epithelial cell](https://www.ebi.ac.uk/ols4/ontologies/cl/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FCL_0002518), [skin fibroblast](https://www.ebi.ac.uk/ols4/ontologies/cl/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FCL_0002620), etc.
## triplet-terms subset
For each annotation type,
## pair-class subset
### Premise
each cell line and each of its available annotation (4 attributes plus category)
```python
if annotation_type == "category":
# Paraphrases for cell line categories (e.g., "Cancer cell line", "Hybrid cell line")
return random.choice(
[
f"{cell_line_term} is a type of {annotation_term}.",
f"{cell_line_term} cell line belongs to the category of {annotation_term}.",
f"The {annotation_term} includes {cell_line_term}.",
]
)
elif annotation_type == "cell_type":
# Paraphrases for cell types (e.g., "aortic endothelial cell", "retinoblast")
return random.choice(
[
f"{cell_line_term} is {annotation_term}.",
f"{annotation_term} is the cell type for {cell_line_term}.",
f"{cell_line_term} represents a {annotation_term}.",
]
)
elif annotation_type == "disease":
# Paraphrases for diseases (e.g., "Cutaneous Melanoma", "Chronic granulomatous disease")
return random.choice(
[
f"{cell_line_term} comes from a patient with {annotation_term}.",
f"A patient of {annotation_term} contributed to {cell_line_term}.",
f"The donor of {cell_line_term} has {annotation_term}.",
]
)
elif annotation_type == "transformant":
# Paraphrases for transformants (e.g., "Ad12-SV40 hybrid virus", "Human papillomavirus 38")
return random.choice(
[
f"The {annotation_term} transformed {cell_line_term}.",
f"{annotation_term} is the transformant in the {cell_line_term} cell line.",
f"The {cell_line_term} cell line contains {annotation_term} as a transformant.",
]
)
elif annotation_type == "site":
# Paraphrases for sites (e.g., "bone marrow", "spleen")
return random.choice(
[
f"{cell_line_term} was derived from {annotation_term}.",
f"{annotation_term} is the origin of the {cell_line_term}.",
f"{cell_line_term} is sourced from {annotation_term}.",
]
)
```
### Hypotheses
##### Entailment
Same cell line and annotation type, replaced them with synonyms if available (from cellosaurus and the ontology where the annotation comes from). Paraphrase by rule-based method.
#### Neutral
Same cell line, different annotation type
#### Contradiction
Different cell line, same annotation type
or:
Same cell line, same annotation type, wrong term?
|