add load script

README.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- sv
+language_creators:
+- found
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: Skolmat
+size_categories: []
+source_datasets:
+- original
+tags: []
+task_categories:
+- text-classification
+task_ids: []
+---
+
+# Dataset Card for Skolmat
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]

skolmat.py

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+import csv
+
+import datasets
+
+_DESCRIPTION = "Swedish school lunches."
+
+_TRAIN_DOWNLOAD_URL = "./meals.csv"
+
+labels = ["r", "fest", "kv", "tb", "cat", "msg", "no", "typ"]
+
+
+class MealsSv(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "meal": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=labels),
+                }
+            ),
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf8") as csv_file:
+            reader = csv.reader(csv_file)
+            next(reader, None)  # skip the headers
+            for i, row in enumerate(reader):
+                (meal, label) = row
+                yield i, {
+                    "meal": meal,
+                    "label": label,
+                }