README.md

---
annotations_creators:
- expert-generated
language_creators:
- other
languages:
- en
licenses:
- 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 Workshop on Information Extraction from Scientific Publications (WIESP/2022)

## Dataset Description
Datasets are in JSON Lines format (each line is a json dictionary).
The datasets are formatted similarly to the CONLL2003 format in that they associate each token with an NER tag. The tags follow the "B-" and "I-" convention from the IOB2 syntax. 

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:
Predictions must be given in the same JSON Lines format, must include 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.

### How-To
How to compute your scores on the training data:
1. If not already done, convert your predictions to a Huggingface dataset with the format described above.
2. pass the references and predictions datasets to the `compute_MCC()` and `compute_seqeval()` function (from the `.py` files with the same names).

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 Hugginface Dataset):
```
from datasets import Dataset
wiesp_dev_from_json = Dataset.from_json(path_or_paths="./WIESP2022-NER-DEV.jsonl")
```

## File list
```
├── WIESP2022-NER-TRAINING.jsonl : 1753 samples for training.
├── WIESP2022-NER-DEV.jsonl : 20 samples for development.
├── WIESP2022-NER-VALIDATION-NO-LABELS.jsonl : 1366 samples for validation without the NER labels. Used for the WIESP2022 workshop.
├── README.MD: this file.
└── 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.
```