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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: RuntimeError
Message: Dataset scripts are no longer supported, but found weak_labelled_en_ner_corpus.py
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
path=dataset,
token=hf_token,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
path,
...<4 lines>...
**download_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1215, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1175, in dataset_module_factory
raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
RuntimeError: Dataset scripts are no longer supported, but found weak_labelled_en_ner_corpus.pyNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for "weak_labelled_ner_corpus"
Dataset Summary
The dataset is generated using AWS Comprehend service to annotate:
- Bloomberg news dataset: https://github.com/philipperemy/financial-news-dataset*
- Leipzig sentence corpus: https://wortschatz.uni-leipzig.de/en/download/English**
- Leipzig wiki corpus: https://wortschatz.uni-leipzig.de/en/download/English***
* Bloomberg news was splitted into sentences using nltk.PunktSentenceTokenizer before the annotation
** subset of English corpus 2020 with 1M sentences was used for Leipzig sentence corpus
*** subset of English wiki corpus 2016 with 1M sentences was used for Leipzig wiki corpus
Usage
from datasets import load_dataset
bloomberg_dataset = load_dataset("imvladikon/weak_labelled_en_ner_corpus", "bloomberg_corpus")
leipzig_dataset = load_dataset("imvladikon/weak_labelled_en_ner_corpus", "leipzig_sentence_corpus")
leipzig_wiki_dataset = load_dataset("imvladikon/weak_labelled_en_ner_corpus", "leipzig_wiki_corpus")
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
- Size of downloaded dataset files: 770.6 MB
- Size of the generated dataset: 4 GB
- Total amount of disk used: 4 GB
Data Fields
The data fields are the same among all splits with different fields metadata that are related to the given dataset.
Fields
{"id",
"text",
"entities"
{
"Score",
"Type",
"Text",
"BeginOffset",
"EndOffset",
}
),
"tags": {
"tokens",
"raw_tags",
"ner_tags",
}
}
where
- entities - AWS Comprehend predictions (spans/offsets)
- tags - offsets that converted to conll2003 format for using it in the training (raw_tags is string representation of the ner_tags, which is ClassLabel, read the documentation about datasets.ClassLabel.str2int and ClassLabel.int2str)
NER tags
Tags description:
- O Outside of a named entity
- PER Person
- LOC Location
- ORG Organization
- MISC Miscellaneous
- DATE Date and time expression
- QTY Quantity
- EVE Event
- TTL Title
- DUC Commercial item
Bloomberg
Tags:
['B-DATE', 'I-DATE', 'L-DATE', 'U-DATE', 'B-DUC', 'I-DUC', 'L-DUC', 'U-DUC', 'B-EVE', 'I-EVE', 'L-EVE', 'U-EVE', 'B-LOC', 'I-LOC', 'L-LOC', 'U-LOC', 'B-MISC', 'I-MISC', 'L-MISC', 'U-MISC', 'B-ORG', 'I-ORG', 'L-ORG', 'U-ORG', 'B-PER', 'I-PER', 'L-PER', 'U-PER', 'B-QTY', 'I-QTY', 'L-QTY', 'U-QTY', 'B-TTL', 'I-TTL', 'L-TTL', 'U-TTL', 'O']
Tags statistics:
{
"O": 281586813,
"B-QTY": 2675754,
"L-QTY": 2675754,
"I-QTY": 2076724,
"U-ORG": 1459628,
"I-ORG": 1407875,
"B-ORG": 1318711,
"L-ORG": 1318711,
"B-PER": 1254037,
"L-PER": 1254037,
"U-MISC": 1195204,
"U-LOC": 1084052,
"U-DATE": 1010118,
"B-DATE": 919815,
"L-DATE": 919815,
"I-DATE": 650064,
"U-PER": 607212,
"U-QTY": 559523,
"B-LOC": 425431,
"L-LOC": 425431,
"I-PER": 262887,
"I-LOC": 201532,
"I-MISC": 190576,
"B-MISC": 162978,
"L-MISC": 162978,
"I-TTL": 64641,
"B-TTL": 53330,
"L-TTL": 53330,
"B-EVE": 43329,
"L-EVE": 43329,
"U-TTL": 41568,
"I-EVE": 35316,
"U-DUC": 33457,
"U-EVE": 19103,
"I-DUC": 15622,
"B-DUC": 15580,
"L-DUC": 15580
}
Leipzig
Tags:
['B-DATE', 'I-DATE', 'L-DATE', 'U-DATE', 'B-DUC', 'I-DUC', 'L-DUC', 'U-DUC', 'B-EVE', 'I-EVE', 'L-EVE', 'U-EVE', 'B-LOC', 'I-LOC', 'L-LOC', 'U-LOC', 'B-MISC', 'I-MISC', 'L-MISC', 'U-MISC', 'B-ORG', 'I-ORG', 'L-ORG', 'U-ORG', 'B-PER', 'I-PER', 'L-PER', 'U-PER', 'B-QTY', 'I-QTY', 'L-QTY', 'U-QTY', 'B-TTL', 'I-TTL', 'L-TTL', 'U-TTL', 'O']
Tags statistics:
{
"O": 80548295,
"B-QTY": 376043,
"L-QTY": 376043,
"I-QTY": 274133,
"U-PER": 246431,
"B-PER": 208376,
"L-PER": 208376,
"U-ORG": 197315,
"U-MISC": 165578,
"B-ORG": 158643,
"L-ORG": 158643,
"U-LOC": 128848,
"I-ORG": 124848,
"U-QTY": 116041,
"B-DATE": 116024,
"L-DATE": 116024,
"U-DATE": 103259,
"B-LOC": 87310,
"L-LOC": 87310,
"I-DATE": 71875,
"I-TTL": 55814,
"I-PER": 44467,
"B-TTL": 40233,
"L-TTL": 40233,
"I-LOC": 39452,
"U-TTL": 28557,
"B-EVE": 24288,
"L-EVE": 24288,
"U-EVE": 22354,
"B-MISC": 21389,
"L-MISC": 21389,
"I-EVE": 15315,
"I-MISC": 12625,
"U-DUC": 8853,
"B-DUC": 8295,
"L-DUC": 8295,
"I-DUC": 4779
}
Sample:
{
"uid": "806fe637ed51e03d9ef7a8889fc84f63f8fc8569",
"sent_num": 10,
"text": "Earnings from Mexico, which contributed 37 percent of\nearnings last year, probably rose 23 percent to 428 million\neuros, Peixoto estimated.",
"date": "2011-05-04",
"filename": "bbva-may-post-lower-first-quarter-profit-hurt-by-spain-decline",
"article_date": "",
"author": "BBVA May Post Lower First-Quarter Profit, Hurt by Spain Decline",
"url": " B y C h a r l e s P e n t y",
"entities": {
"Score": [
0.9865140318870544,
0.9992061853408813,
0.9937268495559692,
0.9993883967399597,
0.8999369144439697,
0.9975314140319824
],
"Type": [
"LOCATION",
"QUANTITY",
"DATE",
"QUANTITY",
"QUANTITY",
"PERSON"
],
"Text": [
"Mexico",
"37 percent",
"last year",
"23 percent",
"428 million\neuros",
"Peixoto"
],
"BeginOffset": [
14,
40,
63,
88,
102,
121
],
"EndOffset": [
20,
50,
72,
98,
119,
128
]
},
"tags": {
"tokens": [
"Earnings",
"from",
"Mexico",
"Earnings",
"from",
"Mexico",
",",
"which",
"contributed",
"37",
"percent",
"Earnings",
"from",
"Mexico",
",",
"which",
"contributed",
"37",
"percent",
"of",
"\n",
"earnings",
"last",
"year",
"Earnings",
"from",
"Mexico",
",",
"which",
"contributed",
"37",
"percent",
"of",
"\n",
"earnings",
"last",
"year",
",",
"probably",
"rose",
"23",
"percent",
"Earnings",
"from",
"Mexico",
",",
"which",
"contributed",
"37",
"percent",
"of",
"\n",
"earnings",
"last",
"year",
",",
"probably",
"rose",
"23",
"percent",
"to",
"428",
"million",
"\n",
"euros",
"Earnings",
"from",
"Mexico",
",",
"which",
"contributed",
"37",
"percent",
"of",
"\n",
"earnings",
"last",
"year",
",",
"probably",
"rose",
"23",
"percent",
"to",
"428",
"million",
"\n",
"euros",
",",
"Peixoto",
" ",
"estimated",
"."
],
"raw_tags": [
"O",
"O",
"U-LOC",
"O",
"O",
"O",
"O",
"O",
"O",
"B-QTY",
"L-QTY",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-DATE",
"L-DATE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-QTY",
"L-QTY",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-QTY",
"I-QTY",
"I-QTY",
"L-QTY",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"U-PER",
"O",
"O",
"O"
],
"ner_tags": [
36,
36,
15,
36,
36,
36,
36,
36,
36,
28,
30,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
0,
2,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
28,
30,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
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36,
36,
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28,
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30,
36,
36,
36,
36,
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36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
27,
36,
36,
36
]
}
}
Data Splits
| name | train |
|---|---|
| bloomberg_corpus | 3515149 |
| leipzig_sentence_corpus | 860192 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
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