metadata
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. 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, we collected metadata of 30,001 cell lines that:
- Are human cell lines (NCBI_TaxID=9606)
- Have reference in BTO, CLO, or EFO
- With at least 1 of those annotations available:
- Diseases: Prostate Carcinoma, Glioblastoma, etc.
- Derived from site: lung primordium, corneal epithelium, etc.
- Transformant: N-methyl-N-nitrosourea, Epstein Barr Virus, etc.
- Cell type: kidney epithelial cell, skin fibroblast, 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)
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?