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Datasets:
bluesky333
/
chemical_language_understanding_benchmark

Tasks:
Text Classification
Token Classification
Languages:
English
Size:
10K<n<100K
Tags:
chemistry
License:
Dataset card Data Studio Files
xet
 Community
Dataset Viewer
The dataset viewer is not available for this dataset.
The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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🧪🔋 Chemical Language Understanding Benchmark 🛢️🧴

Benchmark Summary

Chemistry Language Understanding Benchmark is published in ACL2023 industry track to facilitate NLP research in chemical industry ACL2023 Industry Track. From our understanding, it is one of the first benchmark datasets with tasks for both patent and literature articles provided by the industrial organization. All the datasets are annotated by professional chemists.

Languages

The language of this benchmark is English.

Data Structure

Benchmark has 4 datasets: 2 for text classification and 2 for token classification.

Dataset Task # Examples Avg. Token Length # Classes / Entity Groups
PETROCHEMICAL Patent Area Classification 2,775 448.19 7
RHEOLOGY Sentence Classification 2,017 55.03 5
CATALYST Catalyst Entity Recognition 4,663 42.07 5
BATTERY Battery Entity Recognition 3,750 40.73 3

You can refer to the paper for detailed description of the datasets.

Data Instances

Each example is a paragraph/setence of an academic paper or patent with annotations in a json format.

Data Fields

The fields for the text classification task are:

  1. 'id', a unique numbered identifier sequentially assigned.
  2. 'sentence', the input text.
  3. 'label', the class for the text.

The fields for the token classification task are:

  1. 'id', a unique numbered identifier sequentially assigned.
  2. 'tokens', the input text tokenized by BPE tokenizer.
  3. 'ner_tags', the entity label for the tokens.

Data Splits

The data is split into 80 (train) / 20 (development).

Dataset Creation

Curation Rationale

The dataset was created to provide a benchmark in chemical language model for researchers and developers.

Source Data

The dataset consists of open-access chemistry publications and patents annotated by professional chemists.

Licensing Information

The manual annotations created for CLUB are licensed under a Creative Commons Attribution 4.0 International License (CC-BY-4.0).
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