README.md

---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
- token-classification
language:
- en
multilinguality:
- monolingual
size_categories:
- n<1K
tags:
- causality
pretty_name: BioCause
configs:
- config_name: causality detection
  data_files:
  - split: train
    path: causality-detection/train.parquet
  - split: validation
    path: causality-detection/dev.parquet
  - split: test
    path: causality-detection/test.parquet
  features:
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': uncausal
          '1': causal
- config_name: causal candidate extraction
  data_files:
  - split: train
    path: causal-candidate-extraction/train.parquet
  - split: validation
    path: causal-candidate-extraction/dev.parquet
  - split: test
    path: causal-candidate-extraction/test.parquet
  features:
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: entity
    sequence:
      sequence: int32
- config_name: causality identification
  data_files:
  - split: train
    path: causality-identification/train.parquet
  - split: validation
    path: causality-identification/dev.parquet
  - split: test
    path: causality-identification/test.parquet
  features:
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: relations
    list:
    - name: relationship
      dtype:
        class_label:
          names:
            '0': no-rel
            '1': causal
    - name: first
      dtype: string
    - name: second
      dtype: string
train-eval-index:
- config: causality detection
  task: text-classification
  task_id: text_classification
  splits:
    train_split: train
    eval_split: test
  col_mapping:
    text: text
    label: label
  metrics:
  - type: accuracy
  - type: precision
  - type: recall
  - type: f1
- config: causal candidate extraction
  task: token-classification
  task_id: token_classification
  splits:
    train_split: train
    eval_split: test
  metrics:
  - type: accuracy
  - type: precision
  - type: recall
  - type: f1
- config: causality identification
  task: text-classification
  task_id: text_classification
  splits:
    train_split: train
    eval_split: test
  metrics:
  - type: accuracy
  - type: precision
  - type: recall
  - type: f1
---

> [!NOTE]
> This repository integrates the BioCause corpus into hf datasets. Please find the original dataset
> [here](https://github.com/Luisiglm/BioCause). The data is sourced from the
> [CREST](https://github.com/phosseini/CREST) aggregation. Please see the [citations](#citations) at the end of this README.

## Dataset Description

- **Homepage:** https://github.com/Luisiglm/BioCause
- **Paper:** [BioCause: Annotating and Analysing Causality in the Biomedical Domain](https://doi.org/10.1186/1471-2105-14-2)

# Usage
## Causality Detection
```py
from datasets import load_dataset
dataset = load_dataset("thagen/BioCause", "causality detection")
```

## Causal Candidate Extraction
```py
from datasets import load_dataset
dataset = load_dataset("thagen/BioCause", "causal candidate extraction")
```

## Causality Identification
```py
from datasets import load_dataset
dataset = load_dataset("thagen/BioCause", "causality identification")
```

# Citations

The BioCause paper by [Mihaila et al., 2013](https://doi.org/10.1186/1471-2105-14-2):
```bib
@article{mihaila:2013,
  title = {{{BioCause}}: {{Annotating}} and Analysing Causality in the Biomedical Domain},
  shorttitle = {{{BioCause}}},
  author = {Mihaila, Claudiu and Ohta, Tomoko and Pyysalo, Sampo and Ananiadou, Sophia},
  year = {2013},
  journal = {BMC Bioinform.},
  volume = {14},
  pages = {2},
  doi = {10.1186/1471-2105-14-2}
}
```

CREST by [Hosseini et al., 2021](https://arxiv.org/abs/2103.13606) &mdash; whose aggregation we used to source the BioCause data:
```bib
@article{hosseini:2021,
  title = {Predicting {{Directionality}} in {{Causal Relations}} in {{Text}}},
  author = {Hosseini, Pedram and Broniatowski, David A. and Diab, Mona T.},
  year = {2021},
  journal = {CoRR},
  volume = {abs/2103.13606},
  eprint = {2103.13606},
  archiveprefix = {arXiv}
}
```