|
--- |
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language: |
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- en |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 100M<n<1B |
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task_categories: |
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- feature-extraction |
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- sentence-similarity |
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pretty_name: S2ORC |
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tags: |
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- sentence-transformers |
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dataset_info: |
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- config_name: abstract-citation-pair |
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features: |
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- name: abstract |
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dtype: string |
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- name: citation |
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sequence: string |
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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 |
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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: |
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- config_name: abstract-citation-pair |
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data_files: |
|
- split: train |
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path: abstract-citation-pair/train-* |
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- config_name: abstract-citation-pair-all |
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data_files: |
|
- split: train |
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path: abstract-citation-pair-all/train-* |
|
- config_name: title-abstract-pair |
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data_files: |
|
- split: train |
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path: title-abstract-pair/train-* |
|
default: true |
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- config_name: title-citation-pair |
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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-* |
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--- |
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|
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# Dataset Card for S2ORC |
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|
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This dataset contains titles, abstracts, and citations from scientific papers from the [Semantic Scholar Open Research Corpus (S2ORC)](https://github.com/allenai/s2orc). |
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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. |
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In our experiments, title-abstract pairs result in the highest performance, followed by titles-citations and then abstract-citations pairs. |
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|
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## Dataset Subsets |
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|
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### `title-abstract-pair` subset |
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|
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* Columns: "title", "abstract" |
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* Column types: `str`, `str` |
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* Examples: |
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```python |
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{ |
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"title": "Syntheses, Structures and Properties of Two Transition Metal-Flexible Ligand Coordination Polymers", |
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"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.", |
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} |
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``` |
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* Collection strategy: Reading the S2ORC titles-abstract dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data). |
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* Deduplified: No |
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|
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### `title-citation-pair` subset |
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|
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* Columns: "title", "citation" |
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* Column types: `str`, `str` |
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* Examples: |
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```python |
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{ |
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"title": "An apparent neuroleptic malignant syndrome without extrapyramidal symptoms upon initiation of clozapine therapy: report of a case and results of a clozapine rechallenge.", |
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"citation": "Antipsychotic Rechallenge After Neuroleptic Malignant Syndrome with Catatonic Features" |
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} |
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``` |
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* 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. |
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* Deduplified: No |
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|
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### `title-citation-pair-all` subset |
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|
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* Columns: "title", "citation" |
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* Column types: `str`, `str` |
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* Examples: |
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```python |
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{ |
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"title": "An apparent neuroleptic malignant syndrome without extrapyramidal symptoms upon initiation of clozapine therapy: report of a case and results of a clozapine rechallenge.", |
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"citation": "Antipsychotic Rechallenge After Neuroleptic Malignant Syndrome with Catatonic Features" |
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} |
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``` |
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* 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. |
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* Deduplified: No |
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|
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### `abstract-citation-pair` subset |
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|
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* Columns: "abstract", "citation" |
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* Column types: `str`, `str` |
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* Examples: |
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```python |
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|
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``` |
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* 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. |
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* Deduplified: No |
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|
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### `abstract-citation-pair-all` subset |
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|
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* Columns: "abstract", "citation" |
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* Column types: `str`, `str` |
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* Examples: |
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```python |
|
|
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``` |
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* 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. |
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* Deduplified: No |