Datasets:
nkjp
/

Modalities:
Text
Formats:
parquet
Languages:
Polish
Libraries:
Datasets
pandas
License:
system HF staff commited on
Commit
a5983a1
1 Parent(s): 87ee898

Update files from the datasets library (from 1.8.0)

Browse files

Release notes: https://github.com/huggingface/datasets/releases/tag/1.8.0

Files changed (2) hide show
  1. dataset_infos.json +1 -1
  2. nkjp-ner.py +2 -0
dataset_infos.json CHANGED
@@ -1 +1 @@
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- {"default": {"description": "The NKJP-NER is based on a human-annotated part of National Corpus of Polish (NKJP). We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity.\n", "citation": "@book{przepiorkowski2012narodowy,\ntitle={Narodowy korpus j{\\k{e}}zyka polskiego},\nauthor={Przepi{'o}rkowski, Adam},\nyear={2012},\npublisher={Naukowe PWN}\n}\n", "homepage": "https://klejbenchmark.com/tasks/", "license": "GNU GPL v.3", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"num_classes": 6, "names": ["geogName", "noEntity", "orgName", "persName", "placeName", "time"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "nkjp_ner", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1612125, "num_examples": 15794, "dataset_name": "nkjp_ner"}, "test": {"name": "test", "num_bytes": 221092, "num_examples": 2058, "dataset_name": "nkjp_ner"}, "validation": {"name": "validation", "num_bytes": 196652, "num_examples": 1941, "dataset_name": "nkjp_ner"}}, "download_checksums": {"https://klejbenchmark.com/static/data/klej_nkjp-ner.zip": {"num_bytes": 821629, "checksum": "4b4573219731b198d43958e347dcd3e83654c89daa980c88de3bec8d628044ac"}}, "download_size": 821629, "post_processing_size": null, "dataset_size": 2029869, "size_in_bytes": 2851498}}
 
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+ {"default": {"description": "The NKJP-NER is based on a human-annotated part of National Corpus of Polish (NKJP). We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity.\n", "citation": "@book{przepiorkowski2012narodowy,\ntitle={Narodowy korpus jezyka polskiego},\nauthor={Przepi{'o}rkowski, Adam},\nyear={2012},\npublisher={Naukowe PWN}\n}\n", "homepage": "https://klejbenchmark.com/tasks/", "license": "GNU GPL v.3", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"num_classes": 6, "names": ["geogName", "noEntity", "orgName", "persName", "placeName", "time"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "text-classification", "text_column": "sentence", "label_column": "target", "labels": ["geogName", "noEntity", "orgName", "persName", "placeName", "time"]}], "builder_name": "nkjp_ner", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1612125, "num_examples": 15794, "dataset_name": "nkjp_ner"}, "test": {"name": "test", "num_bytes": 221092, "num_examples": 2058, "dataset_name": "nkjp_ner"}, "validation": {"name": "validation", "num_bytes": 196652, "num_examples": 1941, "dataset_name": "nkjp_ner"}}, "download_checksums": {"https://klejbenchmark.com/static/data/klej_nkjp-ner.zip": {"num_bytes": 821629, "checksum": "4b4573219731b198d43958e347dcd3e83654c89daa980c88de3bec8d628044ac"}}, "download_size": 821629, "post_processing_size": null, "dataset_size": 2029869, "size_in_bytes": 2851498}}
nkjp-ner.py CHANGED
@@ -19,6 +19,7 @@ import csv
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  import os
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  import datasets
 
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  _CITATION = """\
@@ -68,6 +69,7 @@ class NkjpNer(datasets.GeneratorBasedBuilder):
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  homepage=_HOMEPAGE,
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  license=_LICENSE,
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  citation=_CITATION,
 
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  )
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  def _split_generators(self, dl_manager):
 
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  import os
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  import datasets
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+ from datasets.tasks import TextClassification
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  _CITATION = """\
 
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  homepage=_HOMEPAGE,
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  license=_LICENSE,
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  citation=_CITATION,
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+ task_templates=[TextClassification(text_column="sentence", label_column="target")],
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  )
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  def _split_generators(self, dl_manager):