Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- crowdsourced
+- machine-generated
+language_creators:
+- crowdsourced
+languages:
+- tl
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+---
+
+# Dataset Card for Dengue Dataset in Filipino
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+
+## Dataset Description
+
+- **Homepage: [Dengue Dataset in Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)**
+- **Repository: [Dengue Dataset in Filipino repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)**
+- **Paper: [IEEE paper](https://ieeexplore.ieee.org/document/8459963)**
+- **Leaderboard:**
+- **Point of Contact: [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)**
+
+### Dataset Summary
+
+Benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The dataset is primarily in Filipino, with the addition of some English words commonly used in Filipino vernacular.
+
+## Dataset Structure
+
+### Data Instances
+
+Sample data:
+```
+{
+  "text": "Tapos ang dami pang lamok.",
+  "absent": "0",
+  "dengue": "0",
+  "health": "0",
+  "mosquito": "1",
+  "sick": "0"
+}
+```
+
+### 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
+
+[Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+  @INPROCEEDINGS{8459963,
+    author={E. D. {Livelo} and C. {Cheng}},
+    booktitle={2018 IEEE International Conference on Agents (ICA)},
+    title={Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies},
+    year={2018},
+    volume={},
+    number={},
+    pages={2-7},
+    doi={10.1109/AGENTS.2018.8459963}}
+  }

dataset_infos.json

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+{"default": {"description": "    Benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets.\n", "citation": "    @INPROCEEDINGS{8459963,\n      author={E. D. {Livelo} and C. {Cheng}},\n      booktitle={2018 IEEE International Conference on Agents (ICA)},\n      title={Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies},\n      year={2018},\n      volume={},\n      number={},\n      pages={2-7},\n      doi={10.1109/AGENTS.2018.8459963}}\n    }\n", "homepage": "https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "absent": {"num_classes": 2, "names": ["0", "1"], "names_file": null, "id": null, "_type": "ClassLabel"}, "dengue": {"num_classes": 2, "names": ["0", "1"], "names_file": null, "id": null, "_type": "ClassLabel"}, "health": {"num_classes": 2, "names": ["0", "1"], "names_file": null, "id": null, "_type": "ClassLabel"}, "mosquito": {"num_classes": 2, "names": ["0", "1"], "names_file": null, "id": null, "_type": "ClassLabel"}, "sick": {"num_classes": 2, "names": ["0", "1"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "dengue_filipino", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 428553, "num_examples": 4015, "dataset_name": "dengue_filipino"}, "test": {"name": "test", "num_bytes": 428553, "num_examples": 4015, "dataset_name": "dengue_filipino"}, "validation": {"name": "validation", "num_bytes": 54384, "num_examples": 500, "dataset_name": "dengue_filipino"}}, "download_checksums": {"https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/dengue/dengue_raw.zip": {"num_bytes": 156014, "checksum": "928f7072dec6830c2f18aabc490aec886253e76cf764e06395e7ca66c4a17c4c"}}, "download_size": 156014, "post_processing_size": null, "dataset_size": 911490, "size_in_bytes": 1067504}}

dengue_filipino.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Dengue Dataset Low-Resource Multiclass Text Classification Dataset in Filipino"""
+
+import csv
+import os
+
+import datasets
+
+
+_DESCRIPTION = """\
+    Benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets.
+"""
+
+_CITATION = """\
+    @INPROCEEDINGS{8459963,
+      author={E. D. {Livelo} and C. {Cheng}},
+      booktitle={2018 IEEE International Conference on Agents (ICA)},
+      title={Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies},
+      year={2018},
+      volume={},
+      number={},
+      pages={2-7},
+      doi={10.1109/AGENTS.2018.8459963}}
+    }
+"""
+
+_HOMEPAGE = "https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URL = "https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/dengue/dengue_raw.zip"
+
+
+class DengueFilipino(datasets.GeneratorBasedBuilder):
+    """Dengue Dataset Low-Resource Multiclass Text Classification Dataset in Filipino"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+                "absent": datasets.features.ClassLabel(names=["0", "1"]),
+                "dengue": datasets.features.ClassLabel(names=["0", "1"]),
+                "health": datasets.features.ClassLabel(names=["0", "1"]),
+                "mosquito": datasets.features.ClassLabel(names=["0", "1"]),
+                "sick": datasets.features.ClassLabel(names=["0", "1"]),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URL)
+        train_path = os.path.join(data_dir, "dengue", "train.csv")
+        test_path = os.path.join(data_dir, "dengue", "train.csv")
+        validation_path = os.path.join(data_dir, "dengue", "valid.csv")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_path,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_path,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": validation_path,
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
+            )
+            next(csv_reader)
+            for id_, row in enumerate(csv_reader):
+                try:
+                    text, absent, dengue, health, mosquito, sick = row
+                    payload = {
+                        "text": text,
+                        "absent": absent,
+                        "dengue": dengue,
+                        "health": health,
+                        "mosquito": mosquito,
+                        "sick": sick,
+                    }
+                    yield id_, payload
+                except ValueError:
+                    pass

dummy/1.0.0/dummy_data.zip

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