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+ small/stop_10_test.json.gz filter=lfs diff=lfs merge=lfs -text
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+ small/stop_15_test.json.gz filter=lfs diff=lfs merge=lfs -text
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+ small/stop_15_train.json.gz filter=lfs diff=lfs merge=lfs -text
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+ small/stop_20_test.json.gz filter=lfs diff=lfs merge=lfs -text
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+ small/stop_20_train.json.gz filter=lfs diff=lfs merge=lfs -text
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Readme.md ADDED
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+ ---
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+ annotations_creators:
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+ - synthetic
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+ language_creators:
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+ - other
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+ language:
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+ - python
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+ license:
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+ - Apache 2.0 Licences
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - 10K<n<100K
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+ source_datasets:
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+ - extended|other
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+ task_categories:
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+ - token-classification
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+ - text-generation
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+ task_ids:
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+ - natural-language-inference
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+ pretty_name: String Operations
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+ tags:
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+ - development
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+ - NLU
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+ - small scale
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+ dataset_info:
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+ - config_name: small
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+ features:
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+ - name: input
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+ dtype: string
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+ - name: output
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+ dtype: string
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+ - name: code
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+ dtype: string
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+ - name: res_var
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+ dtype: string
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+ - name: operation
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+ dtype: string
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+ splits:
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+ - name: test
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+ num_examples: 14661
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+ - name: train
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+ num_examples: 33939
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+ ---
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+
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+ # Dataset Card for Small String Operations Dataset
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** [PaDaS Lab](https://huggingface.co/PaDaS-Lab)
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+ - **Repository:**
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+ - **Paper:**
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+ - **Leaderboard:**
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+ - **Point of Contact:** Michael Granitzer, michael.granitzer@uni-passau.de
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+
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+ ### Other Metadata
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+
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+
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+
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+ ### Dataset Summary
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+
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+ Minimal dataset for intended for LM development and testing using python string operations.
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+ The dataset is created by running different one line python string operations on random strings
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+ The idea is, that transformer implementation can learn the string operations and that this task is a good
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+ proxy tasks for other transformer operations on real languages and real tasks. Consequently, the
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+ data set is small and can be used in the development process without large scale infrastructures.
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ There are different configurations for the data set.
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+
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+ - `small`: contains below 50k instances of various string length and only contains slicing operations, i.e. all python operations expressable with `s[i:j:s]` (which also includes string reversal).
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+ - you can further choose different subsets according to either length or the kind of operation
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+
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+ ### Data Fields
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+
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+ all data instances can be found under the field "data".
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+
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+ - `input`: input string, i.e. the string and the string operation
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+ - `output`: output of the string operation
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+ - `code`: code for running the string operation in python,
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+ - `res_var`: name of the result variable
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+ - `operation`: kind of operation:
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+ - `step_x` for `s[::x]`
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+ - `char_at_x` for `s[x]`
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+ - `slice_x:y` for `s[x:y]`
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+ - `slice_step_x:y:z` for `s[x:y:z]`
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+ - `slice_reverse_i:j:k` for `s[i:i+j][::k]`
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+
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+ Siblings of `data` contain additional metadata information about the dataset.
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+
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+ - `prompt` describes possible prompts based on that data splitted into input prompts / output prompts
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+
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+
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+ ### Data Splits
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+
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+ The dataset is split into a train and test split for different string lengths
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+
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+ ## Dataset Creation
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+
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+ The dataset is synthetically created
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+
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+ ### Licensing Information
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+
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+ MIT License
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+
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+ ### Citation Information
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+
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+ [More Information Needed]
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+
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+ ### Contributions
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+
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+ [Chair of Data Science, University of Passau](https://huggingface.co/PaDaS-Lab)
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+
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+ - Michael Granitzer, University of Passau
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+
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+ Thanks to [@mgrani](https://github.com/mgrani) for adding this dataset.
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+ """
SynStOp.py ADDED
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+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
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+ """String Operations Dataset for fast model development"""
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+
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+
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+ import csv
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+ import json
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+ import os
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+ import re
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+
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+ import datasets
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+ import gzip
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+
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+ # TODO: Add BibTeX citation
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+ # Find for instance the citation on arxiv or on the dataset repo/website
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+ _CITATION = """\
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+ @InProceedings{huggingface:dataset,
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+ title = {String Operations Dataset: A small set of string manipulation tasks for fast model development},
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+ author={Michael Granitzer},
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+ year={2023}
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+ }
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+ """
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+
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+ # TODO: Add description of the dataset here
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+ # You can copy an official description
37
+ _DESCRIPTION = """\
38
+ Minimal dataset for intended for LM development and testing using python string operations. The dataset is created by running different one line python string operations on random strings The idea is, that transformer implementation can learn the string operations and that this task is a good proxy tasks for other transformer operations on real languages and real tasks. Consequently, the data set is small and can be used in the development process without large scale infrastructures.
39
+ """
40
+
41
+ # TODO: Add a link to an official homepage for the dataset here
42
+ _HOMEPAGE = ""
43
+
44
+ # TODO: Add the licence for the dataset here if you can find it
45
+ _LICENSE = "Apache 2.0 License"
46
+
47
+ # TODO: Add link to the official dataset URLs here
48
+ # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
49
+ # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
50
+ _URLS = {
51
+ "small": ["https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_10_train.json.gz",
52
+ "https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_10_test.json.gz",
53
+ "https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_20_train.json.gz",
54
+ "https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_20_test.json.gz",
55
+ "https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_15_train.json.gz",
56
+ "https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_15_test.json.gz",
57
+ "https://github.com/mgrani/StOp-Dataset/raw/main/small/about.json",
58
+ "https://github.com/mgrani/StOp-Dataset/raw/main/small/Readme.md"]
59
+ }
60
+
61
+
62
+ # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
63
+ class StopDataset(datasets.GeneratorBasedBuilder):
64
+ """TODO: Short description of my dataset."""
65
+
66
+ VERSION = datasets.Version("0.0.1")
67
+
68
+ # If you need to make complex sub-parts in the datasets with configurable options
69
+ # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
70
+ # BUILDER_CONFIG_CLASS = MyBuilderConfig
71
+
72
+ # You will be able to load one or the other configurations in the following list with
73
+ # data = datasets.load_dataset('my_dataset', 'small')
74
+ # data = datasets.load_dataset('my_dataset', 'second_domain')
75
+ BUILDER_CONFIGS = [
76
+ datasets.BuilderConfig(name="small", version=VERSION, description="Small string operations dataset with string slices only"),
77
+ datasets.BuilderConfig(name="small[filter]", version=VERSION, description="Small string operations dataset with string slices only. [] allows to specify a comma separated list of filters on the length (i.e. l=X) and operations (i.e. o=y)"),
78
+
79
+ ]
80
+
81
+ DEFAULT_CONFIG_NAME = "small" # It's not mandatory to have a default configuration. Just use one if it make sense.
82
+
83
+ def _info(self):
84
+ # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
85
+ if self.config.name.startswith("small"): # This is the name of the configuration selected in BUILDER_CONFIGS above
86
+ features = datasets.Features(
87
+ {
88
+ "input": datasets.Value("string"),
89
+ "output": datasets.Value("string"),
90
+ "code": datasets.Value("string"),
91
+ "res_var": datasets.Value("string"),
92
+ "operation": datasets.Value("string")
93
+ # These are the features of your dataset like images, labels ...
94
+ }
95
+ )
96
+ self._init_filters(self.config.name[len("small"):].strip("[]").split(","))
97
+ self.config.name= self.config.name[:len("small")]
98
+ else:
99
+ raise NotImplementedError()
100
+ return datasets.DatasetInfo(
101
+ # This is the description that will appear on the datasets page.
102
+ description=_DESCRIPTION,
103
+ # This defines the different columns of the dataset and their types
104
+ features=features, # Here we define them above because they are different between the two configurations
105
+ # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
106
+ # specify them. They'll be used if as_supervised=True in builder.as_dataset.
107
+ # supervised_keys=("sentence", "label"),
108
+ # Homepage of the dataset for documentation
109
+ homepage=_HOMEPAGE,
110
+ # License for the dataset if available
111
+ license=_LICENSE,
112
+ # Citation for the dataset
113
+ citation=_CITATION,
114
+ )
115
+
116
+ def _init_filters(self, filters):
117
+ self.filter_operations = []
118
+ self.filter_len = []
119
+ for filter in filters:
120
+ if filter =="": continue
121
+ k, v = filter.split("=")
122
+ if k=="l":
123
+ self.filter_len.append(int(v))
124
+ elif k=="o":
125
+ self.filter_operations.append(re.compile(v))
126
+
127
+ def _split_generators(self, dl_manager):
128
+ # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
129
+ # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
130
+
131
+ # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
132
+ # 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.
133
+ # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
134
+ urls = _URLS[self.config.name]
135
+ if len(self.filter_len)>0:
136
+ urls = [url for url in urls if any([f"stop_{str(len)}_t" in url for len in self.filter_len])]
137
+ data_dir = dl_manager.download_and_extract(urls)
138
+ return [
139
+ datasets.SplitGenerator(
140
+ name=datasets.Split.TRAIN,
141
+ # These kwargs will be passed to _generate_examples
142
+ gen_kwargs={
143
+ "filepath": [data_dir[i] for i, n in enumerate(urls) if "_train.json" in n],
144
+ "split": "train",
145
+ },
146
+ ),
147
+
148
+ datasets.SplitGenerator(
149
+ name=datasets.Split.TEST,
150
+ # These kwargs will be passed to _generate_examples
151
+ gen_kwargs={
152
+ "filepath": [data_dir[i] for i, n in enumerate(urls) if "_test.json" in n],
153
+ "split": "test",
154
+ },
155
+ ),
156
+ ]
157
+
158
+ def _match_operations_filter(self, operation):
159
+ if self.filter_operations is not None:
160
+ matches = False
161
+ for filter in self.filter_operations:
162
+ if filter.matches(operation):
163
+ matches = True
164
+ break
165
+ return matches
166
+ else: return True
167
+
168
+ # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
169
+ def _generate_examples(self, filepath, split):
170
+ # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
171
+ # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
172
+ count = 0
173
+ for filename in filepath:
174
+ with open(filename, encoding="utf-8") as f:
175
+ dataset = json.load(f)
176
+ for ix, data in enumerate(dataset):
177
+
178
+ if self.config.name.startswith("small"):
179
+
180
+ if self._match_operations_filter(data["operation"]):
181
+ continue
182
+
183
+ # Yields examples as (key, example) tuples
184
+ id = data["id"] if "id" in data else count
185
+ count = count + 1
186
+ yield id, {
187
+ "input": data["input"],
188
+ "output": data["output"],
189
+ "code": data["code"],
190
+ "res_var": data["res_var"],
191
+ "operation": data["operation"]
192
+ }
193
+ else:
194
+ yield "", {
195
+ "sentence": data["sentence"],
196
+ "option2": data["option2"],
197
+ "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
198
+ }
generate.py ADDED
@@ -0,0 +1,292 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ This module generates datasets doing string manipulations
3
+ """
4
+ import enum
5
+ import random
6
+ from collections import deque
7
+ import os
8
+ import string
9
+ import json
10
+
11
+
12
+ class StringOperations(str, enum.Enum):
13
+
14
+ SLICE = "slicing"
15
+ STARTS_ENDS_WITH = "starts_ends_with"
16
+ LEN= "len"
17
+ CONCAT= "concat"
18
+ REPEAT= "repeat"
19
+ UPPER_LOWER_SWAP_CASE= "upper_lower_swap_case"
20
+ IS= "is"
21
+
22
+
23
+ def generate_random_string(length, charset = None):
24
+ """
25
+ Generates a random string of length length
26
+ :param length: the length of the string
27
+ :param alphabet: the alphabet to use
28
+ :return: the string
29
+ """
30
+ if charset is None:
31
+ charset = string.ascii_letters + string.digits
32
+ return ''.join(charset[b % len(charset)] for b in os.urandom(length))
33
+
34
+ def generate_reverse_string_prompt(samples, length=20, rev_op="'{sample}'[::-1]",result_var="res", **kwargs):
35
+ """
36
+ provides samples examples of reversing a random string.
37
+ :param s: the string
38
+ :return: the reversed string
39
+ """
40
+ for i in range(samples):
41
+ s = generate_random_string(length)
42
+ o = rev_op.format(sample=s)
43
+ yield o, s[::-1], f"{result_var}='{o}'[::-1]", result_var, "reverse"
44
+
45
+ def generate_slicing_examples(samples, length=20, #char_at_op="'{sample}'[{pos}]",
46
+ pos_range=(1,2),
47
+ slice_range = (1,1),
48
+ step_size = (1,1),
49
+ result_var="res", **kwargs):
50
+ """
51
+ provides samples examples of reversing a random string.
52
+ :param s: the string
53
+ :return: yields inp, outp, code, res_var, todo_str
54
+ """
55
+ for _ in range(samples):
56
+ s = generate_random_string(length)
57
+ for i in range(*pos_range):
58
+ for j in range(*slice_range):
59
+ if j == 1: # only one character means no slice
60
+ o = f"'{s}'[{i}]"
61
+ yield o, s[i], f"{result_var}={o}", result_var, f"char_at_{i}"
62
+ else:
63
+ for k in range(*step_size):
64
+ if k==0:
65
+ continue
66
+ elif j==0 and i==0: # only step size
67
+ o = f"'{s}'[::{k}]"
68
+ yield o, s[::k], f"{result_var}={o}", result_var, f"step_::{k}"
69
+ elif k==1:
70
+ o = f"'{s}'[{i}:{i+j}]"
71
+ yield o, s[i:i+j], f"{result_var}={o}", result_var, f"slice_{i}:{i+j}"
72
+ elif abs(k)<j: # step size needs to be smaller
73
+ if k>0:
74
+ o = f"'{s}'[{i}:{i+j}:{k}]"
75
+ yield o, s[i:i+j:k], f"{result_var}={o}", result_var, f"slice_step_{i}:{i+j}:{k}"
76
+ else:
77
+ o = f"'{s}'[{i}:{i+j}][::{k}]"
78
+ yield o, s[i:i+j][::k], f"{result_var}={o}", result_var, f"slice_reverse_{i}:{i+j}:{k}"
79
+
80
+
81
+
82
+ class StringOperationGenerator:
83
+ """
84
+
85
+
86
+ """
87
+ data=None
88
+
89
+
90
+ def set_samples(self, equations):
91
+ self.equations = equations
92
+ return self
93
+
94
+ @staticmethod
95
+ def get_prompt(template_name:str = "simple", with_code:bool = False):
96
+ if template_name == "simple":
97
+ returns = StringOperationGenerator._get_simple_string_op_prompt()
98
+ else: returns = StringOperationGenerator._get_plain_prompt()
99
+ if with_code:
100
+ returns[-1].extend( [{"templates": ["###ACTION: exec-python\n{code}\n###/ACTION"],
101
+ "keys": ["code"],
102
+ "component": "action",},
103
+ ])
104
+ return returns
105
+
106
+ @staticmethod
107
+ def _get_plain_prompt():
108
+ """
109
+ :return: template for generating value filled equation, e.g. 1+2=3 and mapping needed for the dataset
110
+ """
111
+ inp, out = [], []
112
+ inp.extend([{"templates": ["{input}="],
113
+ "keys": ["input"],
114
+ "component": "input",
115
+ },
116
+ ])
117
+ out.extend([{"templates": ["{output}\n"],
118
+ "keys": ["output"],
119
+ "component": "output",
120
+ "tags": ["exact"]}])
121
+
122
+ return [inp, out]
123
+
124
+ @staticmethod
125
+ def _get_simple_string_op_prompt():
126
+ inp, out = [], []
127
+ # todo: this is a problem here, since the prompt is context sensitive, i.e. it depends on the data.
128
+ inp.extend([{"templates": ["Conduct the string operation {operation} as follows: {input}.\n"],
129
+ "keys": ["operation", "input"],
130
+ "component": "input",
131
+ },
132
+
133
+ ])
134
+ out.extend([{"templates": ["{res_var}={output}\n"],
135
+ "keys": ["res_var", "output"],
136
+ "component": "output",
137
+ "tags": ["exact"]}])
138
+
139
+ return [inp, out]
140
+
141
+
142
+ def create_data (self, samples=100, operations=(StringOperations.SLICE),
143
+ valid_data_only=True, **kwargs):
144
+ self.data = deque()
145
+
146
+ for op in operations:
147
+ if op==StringOperations.SLICE.value:
148
+ g = generate_slicing_examples(samples, **kwargs)
149
+ else:
150
+ raise NotImplementedError(f"Operation {op} not implemented")
151
+ for inp, outp, code, res_var, todo_str in g:
152
+ if valid_data_only and (outp=="" or outp is None): continue
153
+ self.data.append({"input": inp, "output": outp, "code": code,"res_var": res_var, "operation": todo_str })
154
+
155
+ self.data = list(self.data)
156
+ return self
157
+
158
+ def save(self, filename):
159
+ # load prompts and equations from file
160
+ import json
161
+ with open(filename, "w") as f:
162
+ json.dump(self.data, f)
163
+ return self
164
+
165
+ def load(self, filename):
166
+ # load data and equations from file
167
+ import json
168
+ with open(filename, "r") as f:
169
+ self.data = json.load(f)
170
+ return self
171
+
172
+
173
+ def write_data(dump_dir, file, out, compress, indent=2 ):
174
+ import json, gzip
175
+ filename = os.path.join(dump_dir, file)
176
+ if compress:
177
+ with gzip.open(f'{filename}.gz', 'wt', encoding='utf-8') as f:
178
+ json.dump(out, f, indent=indent)
179
+ else:
180
+ json.dump(out, open(filename, "w"), indent=indent)
181
+ return filename
182
+
183
+
184
+ def generate_data_for_config(dump_dir, about, s_length = (10,25, 5), pos_range = (0,5), slice_range = (0,4),
185
+ step_size = (-1,2), samples_per_config = 10, valid_data_only = True):
186
+
187
+ samples = samples_per_config * (step_size[1]-step_size[0]) \
188
+ * (slice_range[1]-slice_range[0])\
189
+ * (pos_range[1]-pos_range[0])
190
+
191
+ about["data_files"] = {"train": [], "test": []}
192
+
193
+ markdown = ["", "|Length|Set|Group|Amount|File|", "|---|---|---|---|---|" ]
194
+ train_total, test_total, id = 0, 0, 1
195
+ for length in tqdm.tqdm(range(*s_length), desc="Generating data"):
196
+ generator = StringOperationGenerator()
197
+ data = generator.create_data(samples=samples,
198
+ operations=("slicing",),
199
+ length=length,
200
+ pos_range=pos_range,
201
+ slice_range = slice_range,
202
+ step_size = step_size,
203
+ valid_data_only=valid_data_only,
204
+ result_var="res",).data
205
+
206
+ for e in data:
207
+ e["id"] = id
208
+ id=id+1
209
+ cnt = Counter([e["operation"] for e in data])
210
+ test, train = {}, {}
211
+ for d in cnt.keys(): test[d], train[d] = [], []
212
+
213
+ for ix, e in enumerate(data): # not very smart, but it is late
214
+ if len(test[e["operation"]])>cnt[e["operation"]] *(1-split_ratio):
215
+ train[e["operation"]].append(e)
216
+ else:
217
+ test[e["operation"]].append(e)
218
+
219
+ markdown.extend([f"|{length}|train|{k}|{len(v)}|stop_{length}_train.json|" for k, v in train.items()])
220
+ markdown.extend([f"|{length}|test|{k}|{len(v)}|stop_{length}_train.json|" for k, v in test.items()])
221
+
222
+ about["length"]= length
223
+ about["set"] = "train"
224
+ data = [v for value in train.values() for v in value]
225
+ write_data(dump_dir, f"stop_{length}_train.json", data, compress)
226
+ about["data_files"]["train"].append({"length": length,
227
+ "files": [f"stop_{length}_train.json"],
228
+ "entries": len(data),
229
+ "groups": [{"name": k, "amount": len(v)} for k, v in train.items()]})
230
+ train_total+=len(data)
231
+
232
+ data = [v for value in test.values() for v in value]
233
+ write_data(dump_dir, f"stop_{length}_test.json", data, compress)
234
+ about["data_files"]["test"].append({"length": length,
235
+ "files": [f"stop_{length}_test.json"],
236
+ "entries": len(data),
237
+ "groups": [{"name": k, "amount": len(v)} for k, v in test.items()]})
238
+ test_total+=len(data)
239
+
240
+ about["items"] = {"train": train_total, "test": test_total}
241
+ # now add all about key value pairs except data_files to makrdown varialbe as separate table
242
+ pre_md = ["# Metadata", "|Key|Value|", "|---|---|"]
243
+ pre_md.extend([f"|{k}|{v}|" for k, v in about.items() if k!="data_files"])
244
+ markdown = pre_md + markdown
245
+ with open(os.path.join(dump_dir, "about.json"), "w") as f:
246
+ json.dump(about, f, indent=2)
247
+
248
+ with open(os.path.join(dump_dir, "Readme.md"), "w") as f:
249
+ f.write("\n".join(markdown))
250
+
251
+ return about, markdown
252
+
253
+
254
+
255
+ if __name__=="__main__":
256
+ import datetime, os, tqdm
257
+ from collections import Counter
258
+
259
+ split_ratio = 0.7
260
+ compress = True
261
+ about = {
262
+ "dataset_name" : "StOp-small",
263
+ "hfuser":"mgrani",
264
+ "version": "0.0.1",
265
+ # add date today as created field with the date of now
266
+ "created" : datetime.datetime.now().strftime("%Y-%m-%d"),
267
+ "creator" : "Michael Granitzer, michael.granitzer@uni-passau.de",
268
+ "split_ratio" : split_ratio,
269
+ "prompt": {"plain": StringOperationGenerator.get_prompt(template_name="plain"),
270
+ "simple": StringOperationGenerator.get_prompt(template_name="simple"),
271
+ "simple_with_code": StringOperationGenerator.get_prompt(template_name="simple_with_code")}
272
+ }
273
+
274
+ # get the date for today, but nicely formatted as string
275
+
276
+ dump_dir = os.path.expanduser("./small")
277
+ if not os.path.exists(dump_dir): os.mkdir(dump_dir)
278
+ about, markdown = generate_data_for_config(dump_dir, about, s_length=(10,25, 5), pos_range=(0,5),
279
+ slice_range=(0,4), step_size=(-1,2), samples_per_config=10,
280
+ valid_data_only=True)
281
+
282
+
283
+ # with open(os.path.join(dump_dir, "README.md"), "w") as readme:
284
+
285
+ # card = StringOperationGenerator.dataset_card(train_total, test_total,
286
+ # group_stats_table="\n".join(group_stats),
287
+ # metadata= "\n".join([f"{k}={v}" for k,v in about.items()]))
288
+
289
+ # readme.write(card)
290
+ # readme.close()
291
+
292
+
small/Readme.md ADDED
@@ -0,0 +1,178 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Metadata
2
+ |Key|Value|
3
+ |---|---|
4
+ |dataset_name|StOp-small|
5
+ |hfuser|mgrani|
6
+ |version|0.0.1|
7
+ |created|2023-06-28|
8
+ |creator|Michael Granitzer, michael.granitzer@uni-passau.de|
9
+ |split_ratio|0.7|
10
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small/stop_10_test.json.gz ADDED
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+ version https://git-lfs.github.com/spec/v1
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small/stop_20_train.json.gz ADDED
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usage.py ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import datasets
2
+
3
+ if __name__=="__main__":
4
+ # load locally from this repo
5
+ ds = datasets.load_dataset("./stop.py", "small")
6
+
7
+ ds.push_to_hub("PaDaS-Lab/stop-small")
8
+
9
+ from datasets import load_dataset
10
+
11
+ dataset = load_dataset("PaDaS-Lab/stop-small")
12
+ print(dataset)
13
+
14
+ # load locally from this repo
15
+ # load locally from this repo
16
+