Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
License:
Commit
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49d1ede
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Parent(s):
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Update parquet files
Browse files- .gitattributes +0 -38
- README.md +0 -85
- WIESP2022-NER-DEV-sample-predictions.jsonl +0 -0
- WIESP2022-NER-DEV.jsonl +0 -0
- WIESP2022-NER-TESTING-NO-LABELS.jsonl +0 -0
- WIESP2022-NER-TESTING.jsonl +0 -3
- WIESP2022-NER-TRAINING.jsonl +0 -0
- WIESP2022-NER-VALIDATION-NO-LABELS.jsonl +0 -0
- WIESP2022-NER-VALIDATION.jsonl +0 -0
- dataset_infos.json +0 -1
- data/test-00000-of-00001.parquet → fgrezes--WIESP2022-NER/parquet-test.parquet +2 -2
- data/train-00000-of-00001.parquet → fgrezes--WIESP2022-NER/parquet-train.parquet +2 -2
- data/validation-00000-of-00001.parquet → fgrezes--WIESP2022-NER/parquet-validation.parquet +2 -2
- ner_tags.json +0 -1
- scoring-scripts/compute_MCC.py +0 -31
- scoring-scripts/compute_seqeval.py +0 -49
- tag_definitions.md +0 -38
- tag_definitions.txt +0 -38
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README.md
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---
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annotations_creators:
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- expert-generated
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language_creators:
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- found
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language:
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- en
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license:
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- cc-by-4.0
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multilinguality:
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- monolingual
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pretty_name: 'WIESP2022-NER'
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size_categories:
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- 1K<n<10K
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source_datasets: []
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task_categories:
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- token-classification
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task_ids:
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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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Datasets are in JSON Lines format (each line is a json dictionary).
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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)")
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Each entry consists of a dictionary with the following keys:
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- `"unique_id"`: a unique identifier for this data sample. Must be included in the predictions.
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- `"tokens"`: the list of tokens (strings) that form the text of this sample. Must be included in the predictions.
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- `"ner_tags"`: the list of NER tags (in IOB2 format)
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The following keys are not strictly needed by the participants:
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- `"ner_ids"`: the pre-computed list of ids corresponding ner_tags, as given by the dictionary in ner_tags.json
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- `"label_studio_id"`, `"section"`, `"bibcode"`: references for internal NASA/ADS use.
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## Instructions for Workshop participants:
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How to load the data using the Huggingface library:
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```python
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from datasets import load_dataset
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dataset = load_dataset("adsabs/WIESP2022-NER")
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```
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How to load the data if you cloned the repository locally:
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(assuming `./WIESP2022-NER-DEV.jsonl` is in the current directory, change as needed)
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- python (as list of dictionaries):
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```python
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import json
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with open("./WIESP2022-NER-DEV.jsonl", 'r') as f:
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wiesp_dev_json = [json.loads(l) for l in list(f)]
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```
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- into Huggingface (as a Huggingface Dataset):
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```python
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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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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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2. pass the references and predictions datasets to the `compute_MCC()` and `compute_seqeval()` functions (from the `.py` files with the same names).
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Requirement to run the scoring scripts:
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[NumPy](https://numpy.org/install/)
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[scikit-learn](https://scikit-learn.org/stable/install.html)
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[seqeval](https://github.com/chakki-works/seqeval#installation)
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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.
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## File list
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```
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├── WIESP2022-NER-TRAINING.jsonl : 1753 samples for training.
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├── WIESP2022-NER-DEV.jsonl : 20 samples for development.
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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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├── WIESP2022-NER-VALIDATION.jsonl : 1366 samples for validation
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├── WIESP2022-NER-TESTING-NO-LABELS.jsonl : 2505 samples for testing without the NER labels. Used for the WIESP2022 workshop.
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├── WIESP2022-NER-TESTING.jsonl : 2505 samples for testing
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├── README.MD : this file.
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├── tag_definitions.md : short descriptions and examples of the tags used in the task.
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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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```
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WIESP2022-NER-DEV-sample-predictions.jsonl
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WIESP2022-NER-DEV.jsonl
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WIESP2022-NER-TESTING-NO-LABELS.jsonl
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WIESP2022-NER-TESTING.jsonl
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WIESP2022-NER-VALIDATION-NO-LABELS.jsonl
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dataset_infos.json
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{"fgrezes--WIESP2022-NER": {"description": "", "citation": "", "homepage": "", "license": "", "features": {"bibcode": {"dtype": "string", "id": null, "_type": "Value"}, "label_studio_id": {"dtype": "int64", "id": null, "_type": "Value"}, "ner_ids": {"feature": {"dtype": "int64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "section": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "unique_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "json", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 13805842, "num_examples": 1753, "dataset_name": "WIESP2022-NER"}, "validation": {"name": "validation", "num_bytes": 10803621, "num_examples": 1366, "dataset_name": "WIESP2022-NER"}, "test": {"name": "test", "num_bytes": 19270355, "num_examples": 2505, "dataset_name": "WIESP2022-NER"}}, "download_checksums": null, "download_size": 5518829, "post_processing_size": null, "dataset_size": 43879818, "size_in_bytes": 49398647}}
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ner_tags.json
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{"B-Archive": 0, "B-CelestialObject": 1, "B-CelestialObjectRegion": 2, "B-CelestialRegion": 3, "B-Citation": 4, "B-Collaboration": 5, "B-ComputingFacility": 6, "B-Database": 7, "B-Dataset": 8, "B-EntityOfFutureInterest": 9, "B-Event": 10, "B-Fellowship": 11, "B-Formula": 12, "B-Grant": 13, "B-Identifier": 14, "B-Instrument": 15, "B-Location": 16, "B-Mission": 17, "B-Model": 18, "B-ObservationalTechniques": 19, "B-Observatory": 20, "B-Organization": 21, "B-Person": 22, "B-Proposal": 23, "B-Software": 24, "B-Survey": 25, "B-Tag": 26, "B-Telescope": 27, "B-TextGarbage": 28, "B-URL": 29, "B-Wavelength": 30, "I-Archive": 31, "I-CelestialObject": 32, "I-CelestialObjectRegion": 33, "I-CelestialRegion": 34, "I-Citation": 35, "I-Collaboration": 36, "I-ComputingFacility": 37, "I-Database": 38, "I-Dataset": 39, "I-EntityOfFutureInterest": 40, "I-Event": 41, "I-Fellowship": 42, "I-Formula": 43, "I-Grant": 44, "I-Identifier": 45, "I-Instrument": 46, "I-Location": 47, "I-Mission": 48, "I-Model": 49, "I-ObservationalTechniques": 50, "I-Observatory": 51, "I-Organization": 52, "I-Person": 53, "I-Proposal": 54, "I-Software": 55, "I-Survey": 56, "I-Tag": 57, "I-Telescope": 58, "I-TextGarbage": 59, "I-URL": 60, "I-Wavelength": 61, "O": 62}
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scoring-scripts/compute_MCC.py
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from sklearn.metrics import matthews_corrcoef
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import numpy as np
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def compute_MCC_jsonl(references_jsonl, predictions_jsonl, ref_col='ner_tags', pred_col='pred_ner_tags'):
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'''
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Computes the Matthews correlation coeff between two datasets in jsonl format (list of dicts each with same keys).
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Sorts the datasets by 'unique_id' and verifies that the tokens match.
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'''
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# reverse the dict
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ref_dict = {k:[e[k] for e in references_jsonl] for k in references_jsonl[0].keys()}
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pred_dict = {k:[e[k] for e in predictions_jsonl] for k in predictions_jsonl[0].keys()}
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# sort by unique_id
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ref_idx = np.argsort(ref_dict['unique_id'])
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pred_idx = np.argsort(pred_dict['unique_id'])
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ref_ner_tags = np.array(ref_dict[ref_col], dtype=object)[ref_idx]
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pred_ner_tags = np.array(pred_dict[pred_col], dtype=object)[pred_idx]
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ref_tokens = np.array(ref_dict['tokens'], dtype=object)[ref_idx]
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pred_tokens = np.array(pred_dict['tokens'], dtype=object)[pred_idx]
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# check that tokens match
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for t1,t2 in zip(ref_tokens, pred_tokens):
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assert(t1==t2)
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# the lists have to be flattened
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flat_ref_tags = np.concatenate(ref_ner_tags)
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flat_pred_tags = np.concatenate(pred_ner_tags)
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mcc_score = matthews_corrcoef(y_true=flat_ref_tags,
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y_pred=flat_pred_tags)
|
| 30 |
-
|
| 31 |
-
return(mcc_score)
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scoring-scripts/compute_seqeval.py
DELETED
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@@ -1,49 +0,0 @@
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|
| 1 |
-
from seqeval.metrics import classification_report, f1_score, precision_score, recall_score, accuracy_score
|
| 2 |
-
from seqeval.scheme import IOB2
|
| 3 |
-
import numpy as np
|
| 4 |
-
def compute_seqeval_jsonl(references_jsonl, predictions_jsonl, ref_col='ner_tags', pred_col='pred_ner_tags'):
|
| 5 |
-
'''
|
| 6 |
-
Computes the seqeval scores between two datasets loaded from jsonl (list of dicts with same keys).
|
| 7 |
-
Sorts the datasets by 'unique_id' and verifies that the tokens match.
|
| 8 |
-
'''
|
| 9 |
-
# extract the tags and reverse the dict
|
| 10 |
-
ref_dict = {k:[e[k] for e in references_jsonl] for k in references_jsonl[0].keys()}
|
| 11 |
-
pred_dict = {k:[e[k] for e in predictions_jsonl] for k in predictions_jsonl[0].keys()}
|
| 12 |
-
|
| 13 |
-
# sort by unique_id
|
| 14 |
-
ref_idx = np.argsort(ref_dict['unique_id'])
|
| 15 |
-
pred_idx = np.argsort(pred_dict['unique_id'])
|
| 16 |
-
ref_ner_tags = np.array(ref_dict[ref_col], dtype=object)[ref_idx]
|
| 17 |
-
pred_ner_tags = np.array(pred_dict[pred_col], dtype=object)[pred_idx]
|
| 18 |
-
ref_tokens = np.array(ref_dict['tokens'], dtype=object)[ref_idx]
|
| 19 |
-
pred_tokens = np.array(pred_dict['tokens'], dtype=object)[pred_idx]
|
| 20 |
-
|
| 21 |
-
# check that tokens match
|
| 22 |
-
assert((ref_tokens==pred_tokens).all())
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
# get report
|
| 26 |
-
report = classification_report(y_true=ref_ner_tags, y_pred=pred_ner_tags,
|
| 27 |
-
scheme=IOB2, output_dict=True,
|
| 28 |
-
)
|
| 29 |
-
|
| 30 |
-
# extract values we care about
|
| 31 |
-
report.pop("macro avg")
|
| 32 |
-
report.pop("weighted avg")
|
| 33 |
-
overall_score = report.pop("micro avg")
|
| 34 |
-
|
| 35 |
-
seqeval_results = {
|
| 36 |
-
type_name: {
|
| 37 |
-
"precision": score["precision"],
|
| 38 |
-
"recall": score["recall"],
|
| 39 |
-
"f1": score["f1-score"],
|
| 40 |
-
"suport": score["support"],
|
| 41 |
-
}
|
| 42 |
-
for type_name, score in report.items()
|
| 43 |
-
}
|
| 44 |
-
seqeval_results["overall_precision"] = overall_score["precision"]
|
| 45 |
-
seqeval_results["overall_recall"] = overall_score["recall"]
|
| 46 |
-
seqeval_results["overall_f1"] = overall_score["f1-score"]
|
| 47 |
-
seqeval_results["overall_accuracy"] = accuracy_score(y_true=ref_ner_tags, y_pred=pred_ner_tags)
|
| 48 |
-
|
| 49 |
-
return(seqeval_results)
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tag_definitions.md
DELETED
|
@@ -1,38 +0,0 @@
|
|
| 1 |
-
## Definitions of the Labels used in the DEAL Shared Task
|
| 2 |
-
Below you will find the full list of labels used in the DEAL shared task, along with their definitions and some examples.
|
| 3 |
-
In the dataset, tokens are labeled using the [IOB2 format](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)) (ex: B-Archive, I-Archive).
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
| **Label** (abbreviation) | **Definition** | **Example** |
|
| 7 |
-
| ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
|
| 8 |
-
| Archive (Arc) | A curated collection of the literature or data. Very similar to Database | NASA ADS, MAST |
|
| 9 |
-
| CelestialObject (COb) | A named object in the sky | ONC, Andromeda galaxy |
|
| 10 |
-
| CelestialObjectRegion (COR) | A named area on/in a celestial body | Inner galaxy |
|
| 11 |
-
| CelestialRegion (CeR) | A defined region projected onto the sky, or celestial coordinates | GOODS field, l=2, b=15 |
|
| 12 |
-
| Citation (Cit) | A reference to previous work in the literature | Allen et al. 2012 |
|
| 13 |
-
| Collaboration (Col) | An organizational entity containing multiple organizations, observatories, and/or countries | the Plank Collaboration |
|
| 14 |
-
| ComputingFacility (CoF) | A facility whose primary purpose is to operate computational resources. | Example: Australian National Computational Infrastructure high-performance computing centre, CINECA supercomputing Centre |
|
| 15 |
-
| Database (DaB) | A curated and searchable set of related data tables (very similar to Archive) | Simbad |
|
| 16 |
-
| Dataset (DaS) | A curated set of data (essentially a single data table) | Gaia EDR3 |
|
| 17 |
-
| EntityOfFutureInterest (EFI) | A general catch all for things that we think may be worth thinking about in the future (often terms related to gravitational waves) | |
|
| 18 |
-
| Event (Eve) | A conference, workshop or other event that often brings scientists together | Protostars and Planets VI |
|
| 19 |
-
| Fellowship (Fel) | A grant focused towards students and/or early career researchers | Hubble Fellowship |
|
| 20 |
-
| Formula (For) | Mathematical formula or equations | F = Gm1m2/r^2, z=2.3 |
|
| 21 |
-
| Grant (Gra) | An allocation of money and/or time for a research project | grant No. 12345, ADAP grant 12345 |
|
| 22 |
-
| Identifier (Ide) | A unique identifier for data, images, etc | ALMA 123.12345 |
|
| 23 |
-
| Instrument (Ins) | A device, often, but not always, placed on a telescope, to make a measurement | Infrared Array Camera, NIRCam |
|
| 24 |
-
| Location (Loc) | A named location on Earth | Canada |
|
| 25 |
-
| Mission (Mis) | A spacecraft that is not a telescope or observatory that carries multiple instruments | WIND |
|
| 26 |
-
| Model (Mod) | A named scientific or computational model. | TNG100, Salpeter IMF |
|
| 27 |
-
| ObservationalTechniques (ObT) | A method used to observe celestial objects | Resolved Long-slit Spectroscopy, adaptive optics imaging |
|
| 28 |
-
| Observatory (Obs) | A, often similarly located, group of telescopes | Keck Observatory, Fermi |
|
| 29 |
-
| Organization (Org) | A named organization that is not an observatory | NASA, University of Toledo |
|
| 30 |
-
| Person (Per) | A named person or their initials | A. Einstein |
|
| 31 |
-
| Proposal (Pro) | A request for telescope time or funding | 74.C-0209(A), GN-2014B-Q-26 |
|
| 32 |
-
| Software (Sof) | Named computer code or language | Python, Numpy, ClumpFind |
|
| 33 |
-
| Survey (Sur) | An organized search of the sky often dedicated to large scale science projects | 2MASS, SDSS |
|
| 34 |
-
| Tag (Tag) | A HTML tag | <bold><\bold> |
|
| 35 |
-
| Telescope (Tel) | A "bucket" to catch light | Hubble Space Telescope, Discovery Channel Telescope |
|
| 36 |
-
| TextGarbage (TeG) | Incorrect text, often multiple punctuation marks with no inner text | ',,,,,' |
|
| 37 |
-
| URL (URL) | A link to a website | https://www.astropy.org/ |
|
| 38 |
-
| Wavelength (Wav) | A portion of the electromagnetic spectrum (can be communicated as a particular wavelength, a name, or a particular transition) | 656.46 nm, H-alpha |
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|
tag_definitions.txt
DELETED
|
@@ -1,38 +0,0 @@
|
|
| 1 |
-
## Definitions of the Labels used in the DEAL Shared Task
|
| 2 |
-
Below you will find the full list of labels used in the DEAL shared task, along with their definitions and some examples.
|
| 3 |
-
In the dataset, tokens are labeled using the [IOB2 format](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)) (ex: B-Archive, I-Archive).
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
| **Label** (abbreviation) | **Definition** | **Example** |
|
| 7 |
-
| ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
|
| 8 |
-
| Archive (Arc) | A curated collection of the literature or data. Very similar to Database | NASA ADS, MAST |
|
| 9 |
-
| CelestialObject (COb) | A named object in the sky | ONC, Andromeda galaxy |
|
| 10 |
-
| CelestialObjectRegion (COR) | A named area on/in a celestial body | Inner galaxy |
|
| 11 |
-
| CelestialRegion (CeR) | A defined region projected onto the sky, or celestial coordinates | GOODS field, l=2, b=15 |
|
| 12 |
-
| Citation (Cit) | A reference to previous work in the literature | Allen et al. 2012 |
|
| 13 |
-
| Collaboration (Col) | An organizational entity containing multiple organizations, observatories, and/or countries | the Plank Collaboration |
|
| 14 |
-
| ComputingFacility (CoF) | A facility whose primary purpose is to operate computational resources. | Example: Australian National Computational Infrastructure high-performance computing centre, CINECA supercomputing Centre |
|
| 15 |
-
| Database (DaB) | A curated and searchable set of related data tables (very similar to Archive) | Simbad |
|
| 16 |
-
| Dataset (DaS) | A curated set of data (essentially a single data table) | Gaia EDR3 |
|
| 17 |
-
| EntityOfFutureInterest (EFI) | A general catch all for things that we think may be worth thinking about in the future (often terms related to gravitational waves) | |
|
| 18 |
-
| Event (Eve) | A conference, workshop or other event that often brings scientists together | Protostars and Planets VI |
|
| 19 |
-
| Fellowship (Fel) | A grant focused towards students and/or early career researchers | Hubble Fellowship |
|
| 20 |
-
| Formula (For) | Mathematical formula or equations | F = Gm1m2/r^2, z=2.3 |
|
| 21 |
-
| Grant (Gra) | An allocation of money and/or time for a research project | grant No. 12345, ADAP grant 12345 |
|
| 22 |
-
| Identifier (Ide) | A unique identifier for data, images, etc | ALMA 123.12345 |
|
| 23 |
-
| Instrument (Ins) | A device, often, but not always, placed on a telescope, to make a measurement | Infrared Array Camera, NIRCam |
|
| 24 |
-
| Location (Loc) | A named location on Earth | Canada |
|
| 25 |
-
| Mission (Mis) | A spacecraft that is not a telescope or observatory that carries multiple instruments | WIND |
|
| 26 |
-
| Model (Mod) | A named scientific or computational model. | TNG100, Salpeter IMF |
|
| 27 |
-
| ObservationalTechniques (ObT) | A method used to observe celestial objects | Resolved Long-slit Spectroscopy, adaptive optics imaging |
|
| 28 |
-
| Observatory (Obs) | A, often similarly located, group of telescopes | Keck Observatory, Fermi |
|
| 29 |
-
| Organization (Org) | A named organization that is not an observatory | NASA, University of Toledo |
|
| 30 |
-
| Person (Per) | A named person or their initials | A. Einstein |
|
| 31 |
-
| Proposal (Pro) | A request for telescope time or funding | 74.C-0209(A), GN-2014B-Q-26 |
|
| 32 |
-
| Software (Sof) | Named computer code or language | Python, Numpy, ClumpFind |
|
| 33 |
-
| Survey (Sur) | An organized search of the sky often dedicated to large scale science projects | 2MASS, SDSS |
|
| 34 |
-
| Tag (Tag) | A HTML tag | <bold><\bold> |
|
| 35 |
-
| Telescope (Tel) | A "bucket" to catch light | Hubble Space Telescope, Discovery Channel Telescope |
|
| 36 |
-
| TextGarbage (TeG) | Incorrect text, often multiple punctuation marks with no inner text | ',,,,,' |
|
| 37 |
-
| URL (URL) | A link to a website | https://www.astropy.org/ |
|
| 38 |
-
| Wavelength (Wav) | A portion of the electromagnetic spectrum (can be communicated as a particular wavelength, a name, or a particular transition) | 656.46 nm, H-alpha |
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