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README.md

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----
-annotations_creators:
-- expert-generated
-language_creators:
-- machine-generated
-language:
-- en
-license:
-- unknown
-multilinguality:
-- monolingual
-size_categories:
-- unknown
-source_datasets:
-- original
-task_categories:
-- structure-prediction
-task_ids: []
-pretty_name: SNAP
-tags:
-- word-segmentation
----
-
-# Dataset Card for SNAP
-
-## Dataset Description
-
-- **Repository:** [ardax/hashtag-segmentor](https://github.com/ardax/hashtag-segmentor)
-- **Paper:** [Segmenting hashtags using automatically created training data](http://www.lrec-conf.org/proceedings/lrec2016/pdf/708_Paper.pdf)
-
-### Dataset Summary
-
-Automatically segmented 803K SNAP Twitter Data Set hashtags with the heuristic described in the paper "Segmenting hashtags using automatically created training data".
-
-### Languages
-
-English
-
-## Dataset Structure
-
-### Data Instances
-
-```
-{
-    "index": 0,
-    "hashtag": "BrandThunder",
-    "segmentation": "Brand Thunder"
-}
-```
-
-### Data Fields
-
-- `index`: a numerical index.
-- `hashtag`: the original hashtag.
-- `segmentation`: the gold segmentation for the hashtag.
-
-## Dataset Creation
-
-- All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`.
-
-- The only difference between `hashtag` and `segmentation` or between `identifier` and `segmentation` are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields.
-
-- There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ). 
-
-- If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field.
-
-## Additional Information
-
-### Citation Information
-
-```
-@inproceedings{celebi2016segmenting,
-  title={Segmenting hashtags using automatically created training data},
-  author={Celebi, Arda and {\"O}zg{\"u}r, Arzucan},
-  booktitle={Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)},
-  pages={2981--2985},
-  year={2016}
-}
-```
-
-### Contributions
-
-This dataset was added by [@ruanchaves](https://github.com/ruanchaves) while developing the [hashformers](https://github.com/ruanchaves/hashformers) library.

default/snap-train.parquet

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+oid sha256:2d0171a82d4288977d5437798151907907bd53f46eb1e7c6c1b0626aa26b26cc
+size 28820562

snap.py

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-"""SNAP dataset"""
-
-import datasets
-
-_CITATION = """
-@inproceedings{celebi2016segmenting,
-  title={Segmenting hashtags using automatically created training data},
-  author={Celebi, Arda and {\"O}zg{\"u}r, Arzucan},
-  booktitle={Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)},
-  pages={2981--2985},
-  year={2016}
-}
-"""
-
-_DESCRIPTION = """
-Automatically segmented 803K SNAP Twitter Data Set hashtags with the heuristic described in the paper "Segmenting hashtags using automatically created training data".
-"""
-_URL = "https://raw.githubusercontent.com/ruanchaves/hashformers/master/datasets/SNAP.Hashtags.Segmented.w.Heuristics.txt"
-
-class Snap(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "index": datasets.Value("int32"),
-                    "hashtag": datasets.Value("string"),
-                    "segmentation": datasets.Value("string")
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/ardax/hashtag-segmentor",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download(_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files}),
-        ]
-
-    def _generate_examples(self, filepath):
-
-        with open(filepath, 'r') as f:
-            for idx, line in enumerate(f):
-                yield idx, {
-                    "index": idx,
-                    "hashtag": line.strip().replace(" ", ""),
-                    "segmentation": line.strip()
-                }