Add simple README

README.md

@@ -34,4 +34,107 @@ dataset_info:
 ---
 # Dataset Card for "ner-orgs"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+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()
+```