Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
License:
Merge branch 'main' of https://huggingface.co/datasets/fgrezes/WIESP2022-NER into main
Browse files- README.md +18 -0
- tag_definitions.txt +100 -0
README.md
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- expert-generated
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language_creators:
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- found
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languages:
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- en
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licenses:
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- cc-by-4.0
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multilinguality:
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- monolingual
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- named-entity-recognition
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---
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# Dataset for the first <a href="https://ui.adsabs.harvard.edu/WIESP/" style="color:blue">Workshop on Information Extraction from Scientific Publications (WIESP/2022)</a>.
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## Dataset Description
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Datasets with text fragments from astrophysics papers, provided by the [NASA Astrophysical Data System](https://ui.adsabs.harvard.edu/) with manually tagged astronomical facilities and other entities of interest (e.g., celestial objects).
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from datasets import Dataset
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wiesp_dev_from_json = Dataset.from_json(path_or_paths="./WIESP2022-NER-DEV.jsonl")
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```
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(NOTE: currently loading from the Huggingface Dataset Hub directly does not work. You need to clone the repository locally)
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How to compute your scores on the training data:
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1. format your predictions as a list of dictionaries, each with the same `"unique_id"` and `"tokens"` keys from the dataset, as well as the list of predicted NER tags under the `"pred_ner_tags"` key (see `WIESP2022-NER-DEV-sample-predictions.jsonl` for an example).
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├── WIESP2022-NER-DEV-sample-predictions.jsonl : an example file with properly formatted predictions on the development data.
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├── WIESP2022-NER-VALIDATION-NO-LABELS.jsonl : 1366 samples for validation without the NER labels. Used for the WIESP2022 workshop.
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├── README.MD: this file.
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└── scoring-scripts/ : scripts used to evaluate submissions.
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├── compute_MCC.py : computes the Matthews correlation coefficient between two datasets.
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└── compute_seqeval.py : computes the seqeval scores (precision, recall, f1, overall and for each class) between two datasets.
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- expert-generated
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language_creators:
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- found
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<<<<<<< HEAD
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languages:
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- en
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licenses:
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=======
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language:
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- en
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license:
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>>>>>>> 297571f844c69c59b0a7d6325ad12c86b64aa523
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- cc-by-4.0
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multilinguality:
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- monolingual
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- named-entity-recognition
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---
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# Dataset for the first <a href="https://ui.adsabs.harvard.edu/WIESP/" style="color:blue">Workshop on Information Extraction from Scientific Publications (WIESP/2022)</a>.
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<<<<<<< HEAD
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=======
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**(NOTE: loading from the Huggingface Dataset Hub directly does not work. You need to clone the repository locally.)**
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>>>>>>> 297571f844c69c59b0a7d6325ad12c86b64aa523
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## Dataset Description
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Datasets with text fragments from astrophysics papers, provided by the [NASA Astrophysical Data System](https://ui.adsabs.harvard.edu/) with manually tagged astronomical facilities and other entities of interest (e.g., celestial objects).
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from datasets import Dataset
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wiesp_dev_from_json = Dataset.from_json(path_or_paths="./WIESP2022-NER-DEV.jsonl")
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```
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+
<<<<<<< HEAD
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(NOTE: currently loading from the Huggingface Dataset Hub directly does not work. You need to clone the repository locally)
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=======
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+
(NOTE: loading from the Huggingface Dataset Hub directly does not work. You need to clone the repository locally.)
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+
>>>>>>> 297571f844c69c59b0a7d6325ad12c86b64aa523
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How to compute your scores on the training data:
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1. format your predictions as a list of dictionaries, each with the same `"unique_id"` and `"tokens"` keys from the dataset, as well as the list of predicted NER tags under the `"pred_ner_tags"` key (see `WIESP2022-NER-DEV-sample-predictions.jsonl` for an example).
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├── WIESP2022-NER-DEV-sample-predictions.jsonl : an example file with properly formatted predictions on the development data.
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├── WIESP2022-NER-VALIDATION-NO-LABELS.jsonl : 1366 samples for validation without the NER labels. Used for the WIESP2022 workshop.
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├── README.MD: this file.
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<<<<<<< HEAD
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=======
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├── tag_definitions.txt: short descriptions and examples of the tags used in the task.
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>>>>>>> 297571f844c69c59b0a7d6325ad12c86b64aa523
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└── scoring-scripts/ : scripts used to evaluate submissions.
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├── compute_MCC.py : computes the Matthews correlation coefficient between two datasets.
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└── compute_seqeval.py : computes the seqeval scores (precision, recall, f1, overall and for each class) between two datasets.
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tag_definitions.txt
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Person
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A named person or their initials
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Example: Andrea M. Ghez, Ghez A.
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Organization
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A named organization that is not an observatory.
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Example: NASA, University of Toledo
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Location
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A named location on Earth.
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Example: Canada
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Entity of Future Interest
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A general catch all for things that may be worth thinking about in the future. Often terms related to gravitational waves.
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Observatory
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A, often similarly located, group of telescopes.
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Example: Keck Observatory, Fermi
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Telescope
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A "bucket" to catch light.
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Example: Hubble Space Telescope, Discovery Channel Telescope
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Mission
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A spacecraft that is not a telescope or observatory that carries multiple instruments
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Example: WIND
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Instrument
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A device, often, but not always, placed on a telescope, to make a measurement.
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Example: Infrared Array Camera, NIRCam
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Wavelength
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A portion of the electromagnetic spectrum. Can be communicated as a particular wavelength, a name, or a particular transition.
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Example: 656.46 nm, H-alpha
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Archive
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A curated collection of the literature or data. Very similar to Database.
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Example: NASA ADS, MAST
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Collaboration
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An organizational entity containing multiple organizations, observatories, and/or countries.
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Example: the Plank Collaboration
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Survey
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An organized search of the sky often dedicated to large scale science projects.
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Example: 2MASS, SDSS
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Grant
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An allocation of money and/or time for a research project.
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Example: grant No. 12345, ADAP grant 12345
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Fellowship
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A grant focused towards students and/or early career researchers.
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Example: Hubble Fellowship
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Database
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A curated set of data. Very similar to Archive.
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Example: Simbad database
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Citation
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A reference to previous work in the literature.
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Example: Allen et al. 2012
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Celestial Object
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A named object in the sky
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Example: ONC, Andromeda galaxy
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Celestial Region
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A defined region projected onto the sky, or celestial coordinates.
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Example: GOODS field, l=2, b=15.
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Celestial Object Region
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A named area on/in a celestial body.
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Example: Inner galaxy
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Event
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A conference, workshop or other event that often brings scientests together.
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Example: Protostars and Planets VI
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Formula
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Mathematical formula or equations.
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Example: F = Gm1m2/r^2, z=2.3
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URL
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A link to a website.
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Example: https://www.astropy.org/
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Identifier
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A unique identifier for data, images, etc.
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Example: ALMA 123.12345
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Tag
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A HTML tag.
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Example: <bold><\bold>
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Text Garbage
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Incorrect text, often multiple punctuation marks with no inner text.
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Example: ,,,
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