README.md

---
language:
- en
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
task_categories:
- feature-extraction
- sentence-similarity
pretty_name: S2ORC
tags:
- sentence-transformers
dataset_info:
- config_name: abstract-citation-pair
  features:
  - name: abstract
    dtype: string
  - name: citation
    sequence: string
  splits:
  - name: train
    num_bytes: 233783770553
    num_examples: 26367485
  download_size: 130121093323
  dataset_size: 233783770553
- config_name: abstract-citation-pair-all
  features:
  - name: abstract
    dtype: string
  - name: citation
    sequence: string
  splits:
  - name: train
- config_name: title-abstract-pair
  features:
  - name: title
    dtype: string
  - name: abstract
    dtype: string
  splits:
  - name: train
    num_bytes: 30708996393
    num_examples: 41769185
  download_size: 19187786420
  dataset_size: 30708996393
- config_name: title-citation-pair
  features:
  - name: title
    dtype: string
  - name: citation
    dtype: string
  splits:
  - name: train
    num_bytes: 9567159942
    num_examples: 51030086
  download_size: 7054217221
  dataset_size: 9567159942
- config_name: title-citation-pair-all
  features:
  - name: title
    dtype: string
  - name: citation
    dtype: string
  splits:
  - name: train
configs:
- config_name: abstract-citation-pair
  data_files:
  - split: train
    path: abstract-citation-pair/train-*
- config_name: abstract-citation-pair-all
  data_files:
  - split: train
    path: abstract-citation-pair-all/train-*
- config_name: title-abstract-pair
  data_files:
  - split: train
    path: title-abstract-pair/train-*
  default: true
- config_name: title-citation-pair
  data_files:
  - split: train
    path: title-citation-pair/train-*
- config_name: title-citation-pair-all
  data_files:
  - split: train
    path: title-citation-pair-all/train-*
---

# Dataset Card for S2ORC

This dataset contains titles, abstracts, and citations from scientific papers from the [Semantic Scholar Open Research Corpus (S2ORC)](https://github.com/allenai/s2orc).
This dataset can and has been used to train embedding models, and works out of the box to train or finetune [Sentence Transformer](https://sbert.net/) models.

In our experiments, title-abstract pairs result in the highest performance, followed by titles-citations and then abstract-citations pairs.

## Dataset Subsets

### `title-abstract-pair` subset

* Columns: "title", "abstract"
* Column types: `str`, `str`
* Examples:
    ```python
    {
      "title": "Syntheses, Structures and Properties of Two Transition Metal-Flexible Ligand Coordination Polymers",
      "abstract": "Two coordination polymers based on 3,5-bis(4-carboxyphenylmethyloxy) benzoic acid (H3L), [M(HL)]·2H2O M = Mn(1), Co(2), have been synthesized under hydrothermal conditions. Their structures have been determined by single-crystal X-ray diffraction and further characterized by elemental analysis, IR spectra and TGA. The two complexes possess 3D framework with diamond channels resulting from the trans-configuration of the flexible ligand and three coordination modes, 3(η2, η1), 2(η1, η1), η1, of carboxyl groups in the ligand. The framework can be represented with Schlafli symbol of (48·66)(47·66). The wall of the channel consists of left- or right-handed helical polymeric chains. UV–visible–NIR and photoluminescence spectra, magnetic properties of 1 and 2 have also been discussed.",
    }
    ```
* Collection strategy: Reading the S2ORC titles-abstract dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data).
* Deduplified: No

### `title-citation-pair` subset

* Columns: "title", "citation"
* Column types: `str`, `str`
* Examples:
    ```python
    {
      "title": "An apparent neuroleptic malignant syndrome without extrapyramidal symptoms upon initiation of clozapine therapy: report of a case and results of a clozapine rechallenge.",
      "citation": "Antipsychotic Rechallenge After Neuroleptic Malignant Syndrome with Catatonic Features"
    }
    ```
* Collection strategy: Reading the S2ORC titles-citation dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) and considering each title together with the first citation as a sample.
* Deduplified: No

### `title-citation-pair-all` subset

* Columns: "title", "citation"
* Column types: `str`, `str`
* Examples:
    ```python
    {
      "title": "An apparent neuroleptic malignant syndrome without extrapyramidal symptoms upon initiation of clozapine therapy: report of a case and results of a clozapine rechallenge.",
      "citation": "Antipsychotic Rechallenge After Neuroleptic Malignant Syndrome with Catatonic Features"
    }
    ```
* Collection strategy: Reading the S2ORC titles-citation dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) and considering each title together with each citation as a sample.
* Deduplified: No

### `abstract-citation-pair` subset

* Columns: "abstract", "citation"
* Column types: `str`, `str`
* Examples:
    ```python
    
    ```
* Collection strategy: Reading the S2ORC abstract-citation dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) and considering each citation together with the first abstract as a sample.
* Deduplified: No

### `abstract-citation-pair-all` subset

* Columns: "abstract", "citation"
* Column types: `str`, `str`
* Examples:
    ```python
    
    ```
* Collection strategy: Reading the S2ORC abstract-citation dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) and considering each citation together with each abstract as a sample.
* Deduplified: No