Upload dataset

README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+paperswithcode_id: maps-token-classification
+pretty_name: Maps Token Classification
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-zoomIn
+          '2': I-zoomIn
+          '3': B-zoomOut
+          '4': I-zoomOut
+          '5': B-panLeft
+          '6': B-panRight
+          '7': B-panUp
+          '8': B-panDown
+          '9': B-goTo
+          '10': B-location
+          '11': I-location
+          '12': B-negation
+          '13': B-layer
+          '14': I-layer
+  config_name: maps-token-classification
+  splits:
+  - name: train
+    num_examples: 146
+  - name: validation
+    num_examples: 20
+  - name: test
+    num_examples: 20
+---
+
+# Dataset Card for "maps-token-classification"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+### Dataset Summary
+
+Maps Token Classification Dataset
+
+This dataset is designed for token classification tasks in the context of maps applications.
+It includes categories for actions, layers, locations, and modifiers.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

generate_dataset_old.py

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+# coding=utf-8
+# Lint as: python3
+"""The Maps Token Classification Dataset."""
+
+import datasets
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+@misc{maps_token_classification,
+    title = {Maps Token Classification Dataset},
+    author = {Your Name},
+    year = {2023},
+    publisher = {Your Institution},
+}
+"""
+
+_DESCRIPTION = """\
+Maps Token Classification Dataset
+
+This dataset is designed for token classification tasks in the context of maps applications.
+It includes categories for actions, layers, locations, and modifiers.
+"""
+
+_URL = "https://raw.githubusercontent.com/aabidk20/mapsVoiceDataset/main/"
+_TRAINING_FILE = "maps_train.conll"
+_DEV_FILE = "maps_dev.conll"
+_TEST_FILE = "maps_test.conll"
+
+class MapsTokenClassificationConfig(datasets.BuilderConfig):
+    """The Maps Token Classification Dataset."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Maps Token Classification.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(MapsTokenClassificationConfig, self).__init__(**kwargs)
+
+class MapsTokenClassification(datasets.GeneratorBasedBuilder):
+    """The Maps Token Classification Dataset."""
+
+    BUILDER_CONFIGS = [
+        MapsTokenClassificationConfig(
+            name="maps_token_classification", version=datasets.Version("1.0.0"), description="The Maps Token Classification Dataset"
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-zoomIn",
+                                "I-zoomIn",
+                                "B-zoomOut",
+                                "I-zoomOut",
+                                "B-panLeft",
+                                "B-panRight",
+                                "B-panUp",
+                                "B-panDown",
+                                "B-goTo",
+                                "B-location",
+                                "I-location",
+                                "B-negation",
+                                "B-layer",
+                                "I-layer",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            current_tokens = []
+            current_labels = []
+            sentence_counter = 0
+            for row in f:
+                row = row.rstrip()
+                if row:
+                    token, label = row.split("\t")
+                    current_tokens.append(token)
+                    current_labels.append(label)
+                else:
+                    # New sentence
+                    if not current_tokens:
+                        # Consecutive empty lines will cause empty sentences
+                        continue
+                    assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
+                    sentence = (
+                        sentence_counter,
+                        {
+                            "id": str(sentence_counter),
+                            "tokens": current_tokens,
+                            "ner_tags": current_labels,
+                        },
+                    )
+                    sentence_counter += 1
+                    current_tokens = []
+                    current_labels = []
+                    yield sentence
+            # Don't forget last sentence in dataset 🧐
+            if current_tokens:
+                yield sentence_counter, {
+                    "id": str(sentence_counter),
+                    "tokens": current_tokens,
+                    "ner_tags": current_labels,
+                }