Datasets:

adsabs
/

WIESP2022-NER

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- found
	language:
	- en
	license:
	- cc-by-4.0
	multilinguality:
	- monolingual
	pretty_name: 'WIESP2022-NER'
	size_categories:
	- 1K<n<10K
	source_datasets: []
	task_categories:
	- token-classification
	task_ids:
	- named-entity-recognition
	---
	# 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>.
	(NOTE: loading from the Huggingface Dataset Hub directly does not work. You need to clone the repository locally.)

	## Dataset Description
	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).
	Datasets are in JSON Lines format (each line is a json dictionary).
	The datasets are formatted similarly to the CONLL2003 format. Each token is associated with an NER tag. The tags follow the "B-" and "I-" convention from the [IOB2 syntax]("https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)")

	Each entry consists of a dictionary with the following keys:
	- `"unique_id"`: a unique identifier for this data sample. Must be included in the predictions.
	- `"tokens"`: the list of tokens (strings) that form the text of this sample. Must be included in the predictions.
	- `"ner_tags"`: the list of NER tags (in IOB2 format)

	The following keys are not strictly needed by the participants:
	- `"ner_ids"`: the pre-computed list of ids corresponding ner_tags, as given by the dictionary in ner_tags.json
	- `"label_studio_id"`, `"section"`, `"bibcode"`: references for internal NASA/ADS use.

	## Instructions for Workshop participants:
	How to load the data:
	(assuming `./WIESP2022-NER-DEV.jsonl` is in the current directory, change as needed)
	- python (as list of dictionaries):
	```
	import json
	with open("./WIESP2022-NER-DEV.jsonl", 'r') as f:
	wiesp_dev_json = [json.loads(l) for l in list(f)]
	```
	- into Huggingface (as a Huggingface Dataset):
	```
	from datasets import Dataset
	wiesp_dev_from_json = Dataset.from_json(path_or_paths="./WIESP2022-NER-DEV.jsonl")
	```
	(NOTE: loading from the Huggingface Dataset Hub directly does not work. You need to clone the repository locally.)

	How to compute your scores on the training data:
	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).
	2. pass the references and predictions datasets to the `compute_MCC()` and `compute_seqeval()` functions (from the `.py` files with the same names).

	Requirement to run the scoring scripts:
	[NumPy](https://numpy.org/install/)
	[scikit-learn](https://scikit-learn.org/stable/install.html)
	[seqeval](https://github.com/chakki-works/seqeval#installation)

	To get scores on the validation data, zip your predictions file (a single `.jsonl' file formatted following the same instructions as above) and upload the `.zip` file to the [Codalabs](https://codalab.lisn.upsaclay.fr/competitions/5062) competition.

	## File list
	```
	├── WIESP2022-NER-TRAINING.jsonl : 1753 samples for training.
	├── WIESP2022-NER-DEV.jsonl : 20 samples for development.
	├── WIESP2022-NER-DEV-sample-predictions.jsonl : an example file with properly formatted predictions on the development data.
	├── WIESP2022-NER-VALIDATION-NO-LABELS.jsonl : 1366 samples for validation without the NER labels. Used for the WIESP2022 workshop.
	├── README.MD: this file.
	├── tag_definitions.txt: short descriptions and examples of the tags used in the task.
	└── scoring-scripts/ : scripts used to evaluate submissions.
	├── compute_MCC.py : computes the Matthews correlation coefficient between two datasets.
	└── compute_seqeval.py : computes the seqeval scores (precision, recall, f1, overall and for each class) between two datasets.
	```