Datasets:
EMBO's picture EMBO
/
biolang

Task Categories: sequence-modeling
Languages: en
Multilinguality: monolingual
Size Categories: n>1M
Licenses: cc-by-4.0
Language Creators: expert-generated
Annotations Creators: machine-generated

Dataset Card for BioLang

Dataset Summary

BioLang is a dataset is based on abstracts from the open access section of EuropePubMed Central to train language models in the domain of biology. The dataset can be used for random masked language modeling or for language modeling using only specific part-of-speech maksing. More details on generation and use of the dataset at https://github.com/source-data/soda-roberta .

Supported Tasks and Leaderboards

MLM: masked language modeling DET: part-of-speach masked language model, with determinants (DET) tagged SMALL: part-of-speech masked language model, with "small" words (DET, CCONJ, SCONJ, ADP, PRON) tagged VERB: part-of-speach masked language model, with verbs (VERB) tagged

Languages

English

Dataset Structure

Data Instances

{
    "input_ids":[
        0, 2444, 6997, 46162, 7744, 35, 20632, 20862, 3457, 36, 500, 23858, 29, 43, 32, 3919, 716, 15, 49, 4476, 4, 1398, 6, 52, 1118, 5, 20862, 819, 9, 430, 23305, 248, 23858, 29, 4, 256, 40086, 104, 35, 1927, 1069, 459, 1484, 58, 4776, 13, 23305, 634, 16706, 493, 2529, 8954, 14475, 73, 34263, 6, 4213, 718, 833, 12, 24291, 4473, 22500, 14475, 73, 510, 705, 73, 34263, 6, 5143, 4313, 2529, 8954, 14475, 73, 34263, 6, 8, 5143, 4313, 2529, 8954, 14475, 248, 23858, 29, 23, 4448, 225, 4722, 2392, 11, 9341, 261, 4, 49043, 35, 96, 746, 6, 5962, 9, 38415, 4776, 408, 36, 3897, 4, 398, 8871, 56, 23305, 4, 20, 15608, 21, 8061, 6164, 207, 13, 70, 248, 23858, 29, 6, 150, 5, 42561, 21, 8061, 5663, 207, 13, 80, 3457, 4, 509, 1296, 5129, 21567, 3457, 36, 398, 23528, 8748, 22065, 11654, 35, 7253, 15, 49, 4476, 6, 70, 3457, 4682, 65, 189, 28, 5131, 13, 23305, 9726, 4, 2
    ], 
    "label_ids": [
        "X", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "ADJ", "ADJ", "NOUN", "PUNCT", "PROPN", "PROPN", "PROPN", "PUNCT", "AUX", "VERB", "VERB", "ADP", "DET", "NOUN", "PUNCT", "ADV", "PUNCT", "PRON", "VERB", "DET", "ADJ", "NOUN", "ADP", "ADJ", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "ADJ", "ADJ", "ADJ", "PUNCT", "NOUN", "NOUN", "NOUN", "NOUN", "AUX", "VERB", "ADP", "NOUN", "VERB", "PROPN", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "CCONJ", "ADJ", "PROPN", "PROPN", "PROPN", "PROPN", "NOUN", "NOUN", "NOUN", "ADP", "PROPN", "PROPN", "PROPN", "PROPN", "ADP", "PROPN", "PROPN", "PUNCT", "PROPN", "PUNCT", "ADP", "NOUN", "PUNCT", "NUM", "ADP", "NUM", "VERB", "NOUN", "PUNCT", "NUM", "NUM", "NUM", "NOUN", "AUX", "NOUN", "PUNCT", "DET", "NOUN", "AUX", "X", "NUM", "NOUN", "ADP", "DET", "NOUN", "NOUN", "NOUN", "PUNCT", "SCONJ", "DET", "NOUN", "AUX", "X", "NUM", "NOUN", "ADP", "NUM", "NOUN", "PUNCT", "NUM", "NOUN", "VERB", "ADJ", "NOUN", "PUNCT", "NUM", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "VERB", "ADP", "DET", "NOUN", "PUNCT", "DET", "NOUN", "SCONJ", "PRON", "VERB", "AUX", "VERB", "ADP", "NOUN", "NOUN", "PUNCT", "X"
    ], 
    "special_tokens_mask": [
        1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
    ]
}

Data Fields

MLM:

  • input_ids: a list of int32 features.
  • special_tokens_mask: a list of int8 features.

DET, VERB, SMALL:

  • input_ids: a list of int32 features.
  • tag_mask: a list of int8 features.

Data Splits

  • train:
    • features: ['input_ids', 'special_tokens_mask'],
    • num_rows: 12_005_390
  • test:
    • features: ['input_ids', 'special_tokens_mask'],
    • num_rows: 37_112
  • validation:
    • features: ['input_ids', 'special_tokens_mask'],
    • num_rows: 36_713

Dataset Creation

Curation Rationale

The dataset was assembled to train language models in the field of cell and molecular biology. To expand the size of the dataset and to include many examples with highly technical language, abstracts were complemented with figure legends (or figure 'captions').

Source Data

Initial Data Collection and Normalization

The xml content of papers were downloaded in January 2021 from the open access section of EuropePMC. Figure legends and abstracts were extracted from the JATS XML, tokenized with the roberta-base tokenizer and part-of-speech tagged with Spacy's en_core_web_sm model (https://spacy.io).

More details at https://github.com/source-data/soda-roberta

Who are the source language producers?

Experts scientists.

Annotations

Annotation process

Part-of-speech was tagged automatically.

Who are the annotators?

Spacy's en_core_web_sm model (https://spacy.io) was used for part-of-speech tagging.

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Thomas Lemberger

Licensing Information

CC-BY 4.0

Citation Information

[More Information Needed]

Contributions

Thanks to @tlemberger for adding this dataset.

Models trained or fine-tuned on EMBO/biolang