Add new csvs for training, testing and validation models and python script for working with hf datasets lib

completeformer-masked.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the Semeru Lab and SEART research group.
+#
+# 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.
+"""TODO: Add a description here."""
+
+
+import csv
+import glob
+import os
+
+import datasets
+
+import numpy as np
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+    title = {A great new dataset},
+    author={huggingface, Inc.
+    },
+    year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_DATA_URLs = {
+    "tokenizer": "https://cdn-lfs.huggingface.co/datasets/semeru/completeformer-masked/30668967d62b849f48db64ff26c0d2b92bd08d940471b80b80ce2ff39bd8358e",
+    "all": {
+        "train": "https://cdn-lfs.huggingface.co/datasets/semeru/completeformer-masked/30668967d62b849f48db64ff26c0d2b92bd08d940471b80b80ce2ff39bd8358e",
+        "valid": "https://cdn-lfs.huggingface.co/datasets/semeru/completeformer-masked/30668967d62b849f48db64ff26c0d2b92bd08d940471b80b80ce2ff39bd8358e",
+        "test": "https://cdn-lfs.huggingface.co/datasets/semeru/completeformer-masked/30668967d62b849f48db64ff26c0d2b92bd08d940471b80b80ce2ff39bd8358e",
+    },
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class CSNCHumanJudgementDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="all",
+            version=VERSION,
+            description="",
+        ),
+        datasets.BuilderConfig(
+            name="tokenizer",
+            version=VERSION,
+            description="",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        if self.config.name == "tokenizer":
+            features = datasets.Features(
+                { 
+                    "function": datasets.Value("string"),
+                }
+            )
+        else:
+            features = datasets.Features(
+                { 
+                    "method": datasets.Value("string"),
+                    "block": datasets.Value("string"),
+                    "complex_masked_block": datasets.Value("string"),
+                    "complex_input": datasets.Value("string"),
+                    "complex_target": datasets.Value("string"),
+                    "medium_masked_block": datasets.Value("string"),
+                    "medium_input": datasets.Value("string"),
+                    "medium_target": datasets.Value("string"),
+                    "simple_masked_block": datasets.Value("string"),
+                    "simple_input": datasets.Value("string"),
+                    "simple_target": datasets.Value("string"),
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        my_urls = _DATA_URLs[self.config.name]
+        if self.config.name == "tokenizer":
+            data_dir = dl_manager.download_and_extract(my_urls)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": data_dir},
+                ),
+            ]
+        else:
+            data_dirs = {}
+            for k, v in my_urls.items():
+                data_dirs[k] = dl_manager.download_and_extract(v)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "file_path": data_dirs["train"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "file_path": data_dirs["valid"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "file_path": data_dirs["test"],
+                    },
+                ),
+            ]
+
+    def _generate_examples(
+        self,
+        file_path,
+    ):
+        """Yields examples as (key, example) tuples."""
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+        with open(file_path, encoding="utf-8") as f:
+            csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
+            next(csv_reader, None) # skip header
+
+            for row_id, row in enumerate(csv_reader):
+                if self.config.name == "tokenizer":
+                    yield row_id, {
+                        "function": row[1],
+                    }
+                else:
+                    _, method, block, complex_masked_block, complex_input, complex_target, medium_masked_block, medium_input, medium_target, simple_masked_block, simple_input, simple_target = row
+                    yield row_id, {
+                        "method": method,
+                        "block": block,
+                        "complex_masked_block": complex_masked_block,
+                        "complex_input": complex_input,
+                        "complex_target": complex_target,
+                        "medium_masked_block": medium_masked_block,
+                        "medium_input": medium_input,
+                        "medium_target": medium_target,
+                        "simple_masked_block": simple_masked_block,
+                        "simple_input": simple_input,
+                        "simple_target": simple_target,
+                    }

test_clean.csv

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+size 533318567

training_clean.csv

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+oid sha256:f39db3b2843aa34c2956fae91a379c30953cd06dde8b783e245f3325cca906ee
+size 4270744191

validation_clean.csv

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+size 535985056