README.md

---
annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
paperswithcode_id: crossner
pretty_name: CrossNER-SCIENCE
dataset_info:
  features:
  - name: tokens
    sequence: string
  - name: ner_tags
    sequence:
      class_label:
        names:
            "0": O
            "1": B-scientist
            "2": I-scientist
            "3": B-person
            "4": I-person
            "5": B-university
            "6": I-university
            "7": B-organisation
            "8": I-organisation
            "9": B-country
            "10": I-country
            "11": B-location
            "12": I-location
            "13": B-discipline
            "14": I-discipline
            "15": B-enzyme
            "16": I-enzyme
            "17": B-protein
            "18": I-protein
            "19": B-chemicalelement
            "20": I-chemicalelement
            "21": B-chemicalcompound
            "22": I-chemicalcompound
            "23": B-astronomicalobject
            "24": I-astronomicalobject
            "25": B-academicjournal
            "26": I-academicjournal
            "27": B-event
            "28": I-event
            "29": B-theory
            "30": I-theory
            "31": B-award
            "32": I-award
            "33": B-misc
            "34": I-misc
  splits:
  - name: train
    num_bytes: 20000
    num_examples: 200
  - name: validation
    num_bytes: 45000
    num_examples: 450
  - name: test
    num_bytes: 54300
    num_examples: 543
---

# CrossNER SCIENCE Dataset

An NER dataset for cross-domain evaluation, [read more](https://arxiv.org/abs/2012.04373).  
This split contains labeled data from the SCIENCE domain.

## Features

- **tokens**: A list of words in the sentence
- **ner_tags**: A list of NER labels (as integers) corresponding to each token

## Label Mapping

The dataset uses the following 35 labels:

| Index | Label |
|-------|-------|
| 0 | O |
| 1 | B-scientist |
| 2 | I-scientist |
| 3 | B-person |
| 4 | I-person |
| 5 | B-university |
| 6 | I-university |
| 7 | B-organisation |
| 8 | I-organisation |
| 9 | B-country |
| 10 | I-country |
| 11 | B-location |
| 12 | I-location |
| 13 | B-discipline |
| 14 | I-discipline |
| 15 | B-enzyme |
| 16 | I-enzyme |
| 17 | B-protein |
| 18 | I-protein |
| 19 | B-chemicalelement |
| 20 | I-chemicalelement |
| 21 | B-chemicalcompound |
| 22 | I-chemicalcompound |
| 23 | B-astronomicalobject |
| 24 | I-astronomicalobject |
| 25 | B-academicjournal |
| 26 | I-academicjournal |
| 27 | B-event |
| 28 | I-event |
| 29 | B-theory |
| 30 | I-theory |
| 31 | B-award |
| 32 | I-award |
| 33 | B-misc |
| 34 | I-misc |

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("eesuhn/crossner-science")
```