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--- |
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license: cc-by-4.0 |
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task_categories: |
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- text-classification |
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- token-classification |
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language: |
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- en |
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tags: |
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- chemistry |
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pretty_name: CLUB |
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size_categories: |
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- 10K<n<100K |
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--- |
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## Table of Contents |
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- [Benchmark Summary](#benchmark-summary) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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<p><h1>🧪🔋 Chemical Language Understanding Benchmark 🛢️🧴</h1></p> |
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<a name="benchmark-summary"></a> |
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Benchmark Summary |
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Chemistry Language Understanding Benchmark is published in ACL2023 industry track to facilitate NLP research in chemical industry [ACL2023 Industry Track](https://aclanthology.org/2023.acl-industry.39/). |
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From our understanding, it is one of the first benchmark datasets with tasks for both patent and literature articles provided by the industrial organization. |
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All the datasets are annotated by professional chemists. |
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<a name="languages"></a> |
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Languages |
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The language of this benchmark is English. |
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<a name="dataset-structure"></a> |
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Data Structure |
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Benchmark has 4 datasets: 2 for text classification and 2 for token classification. |
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| Dataset | Task | # Examples | Avg. Token Length | # Classes / Entity Groups | |
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| ----- | ------ | ---------- | ------------ | ------------------------- | |
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| PETROCHEMICAL | Patent Area Classification | 2,775 | 448.19 | 7 | |
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| RHEOLOGY | Sentence Classification | 2,017 | 55.03 | 5 | |
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| CATALYST | Catalyst Entity Recognition | 4,663 | 42.07 | 5 | |
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| BATTERY | Battery Entity Recognition | 3,750 | 40.73 | 3 | |
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You can refer to the paper for detailed description of the datasets. |
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<a name="data-instances"></a> |
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Data Instances |
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Each example is a paragraph/setence of an academic paper or patent with annotations in a json format. |
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<a name="data-fields"></a> |
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Data Fields |
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The fields for the text classification task are: |
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1) 'id', a unique numbered identifier sequentially assigned. |
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2) 'sentence', the input text. |
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3) 'label', the class for the text. |
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The fields for the token classification task are: |
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1) 'id', a unique numbered identifier sequentially assigned. |
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2) 'tokens', the input text tokenized by BPE tokenizer. |
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3) 'ner_tags', the entity label for the tokens. |
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<a name="data-splits"></a> |
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Data Splits |
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The data is split into 80 (train) / 20 (development). |
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<a name="dataset-creation"></a> |
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Dataset Creation |
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<a name="curation-rationale"></a> |
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Curation Rationale |
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The dataset was created to provide a benchmark in chemical language model for researchers and developers. |
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<a name="source-data"></a> |
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Source Data |
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The dataset consists of open-access chemistry publications and patents annotated by professional chemists. |
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<a name="licensing-information"></a> |
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Licensing Information |
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The manual annotations created for CLUB are licensed under a [Creative Commons Attribution 4.0 International License (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/). |
