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---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-ORG
'2': I-ORG
splits:
- name: train
num_bytes: 40381520.59961503
num_examples: 109424
- name: validation
num_bytes: 5782294.96333573
num_examples: 15908
- name: test
num_bytes: 10727120.198367199
num_examples: 28124
download_size: 14938552
dataset_size: 56890935.76131796
---
# Dataset Card for "ner-orgs"
This dataset is a concatenation of subsets of [Few-NERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd), [CoNLL 2003](https://huggingface.co/datasets/conll2003) and [OntoNotes v5](https://huggingface.co/datasets/tner/ontonotes5), but only the "B-ORG" and "I-ORG" labels.
Exactly half of the samples per split contain organisations, while the other half do not contain any.
It was generated using the following script:
```py
import random
from datasets import load_dataset, concatenate_datasets, Features, Sequence, ClassLabel, Value, DatasetDict
FEATURES = Features(
{
"tokens": Sequence(feature=Value(dtype="string")),
"ner_tags": Sequence(feature=ClassLabel(names=["O", "B-ORG", "I-ORG"])),
}
)
def load_fewnerd():
def mapper(sample):
sample["ner_tags"] = [int(tag == 5) for tag in sample["ner_tags"]]
sample["ner_tags"] = [
2 if tag == 1 and idx > 0 and sample["ner_tags"][idx - 1] == 1 else tag
for idx, tag in enumerate(sample["ner_tags"])
]
return sample
dataset = load_dataset("DFKI-SLT/few-nerd", "supervised")
dataset = dataset.map(mapper, remove_columns=["id", "fine_ner_tags"])
dataset = dataset.cast(FEATURES)
return dataset
def load_conll():
label_mapping = {3: 1, 4: 2}
def mapper(sample):
sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
return sample
dataset = load_dataset("conll2003")
dataset = dataset.map(mapper, remove_columns=["id", "pos_tags", "chunk_tags"])
dataset = dataset.cast(FEATURES)
return dataset
def load_ontonotes():
label_mapping = {11: 1, 12: 2}
def mapper(sample):
sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
return sample
dataset = load_dataset("tner/ontonotes5")
dataset = dataset.rename_column("tags", "ner_tags")
dataset = dataset.map(mapper)
dataset = dataset.cast(FEATURES)
return dataset
def has_org(sample):
return bool(sum(sample["ner_tags"]))
def has_no_org(sample):
return not has_org(sample)
def preprocess_raw_dataset(raw_dataset):
# Set the number of sentences without an org equal to the number of sentences with an org
dataset_org = raw_dataset.filter(has_org)
dataset_no_org = raw_dataset.filter(has_no_org)
dataset_no_org = dataset_no_org.select(random.sample(range(len(dataset_no_org)), k=len(dataset_org)))
dataset = concatenate_datasets([dataset_org, dataset_no_org])
return dataset
def main() -> None:
fewnerd_dataset = load_fewnerd()
conll_dataset = load_conll()
ontonotes_dataset = load_ontonotes()
raw_train_dataset = concatenate_datasets([fewnerd_dataset["train"], conll_dataset["train"], ontonotes_dataset["train"]])
raw_eval_dataset = concatenate_datasets([fewnerd_dataset["validation"], conll_dataset["validation"], ontonotes_dataset["validation"]])
raw_test_dataset = concatenate_datasets([fewnerd_dataset["test"], conll_dataset["test"], ontonotes_dataset["test"]])
train_dataset = preprocess_raw_dataset(raw_train_dataset)
eval_dataset = preprocess_raw_dataset(raw_eval_dataset)
test_dataset = preprocess_raw_dataset(raw_test_dataset)
dataset_dict = DatasetDict(
{
"train": train_dataset,
"validation": eval_dataset,
"test": test_dataset,
}
)
dataset_dict.push_to_hub("ner-orgs", private=True)
if __name__ == "__main__":
main()
``` |