first version
Browse files- .gitattributes +6 -0
- .gitignore +161 -0
- LICENSE +201 -0
- Readme.md +120 -0
- SynStOp.py +198 -0
- generate.py +292 -0
- small/Readme.md +178 -0
- small/about.json +807 -0
- small/stop_10_test.json.gz +3 -0
- small/stop_10_train.json.gz +3 -0
- small/stop_15_test.json.gz +3 -0
- small/stop_15_train.json.gz +3 -0
- small/stop_20_test.json.gz +3 -0
- small/stop_20_train.json.gz +3 -0
- usage.py +16 -0
.gitattributes
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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small/stop_10_test.json.gz filter=lfs diff=lfs merge=lfs -text
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small/stop_10_train.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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# Byte-compiled / optimized / DLL files
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.idea/
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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dist/
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downloads/
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eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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*.log
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# IPython
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# .python-version
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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#Pipfile.lock
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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venv.bak/
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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#.idea/
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LICENSE
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|
Readme.md
ADDED
@@ -0,0 +1,120 @@
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|
1 |
+
---
|
2 |
+
annotations_creators:
|
3 |
+
- synthetic
|
4 |
+
language_creators:
|
5 |
+
- other
|
6 |
+
language:
|
7 |
+
- python
|
8 |
+
license:
|
9 |
+
- Apache 2.0 Licences
|
10 |
+
multilinguality:
|
11 |
+
- monolingual
|
12 |
+
size_categories:
|
13 |
+
- 10K<n<100K
|
14 |
+
source_datasets:
|
15 |
+
- extended|other
|
16 |
+
task_categories:
|
17 |
+
- token-classification
|
18 |
+
- text-generation
|
19 |
+
task_ids:
|
20 |
+
- natural-language-inference
|
21 |
+
pretty_name: String Operations
|
22 |
+
tags:
|
23 |
+
- development
|
24 |
+
- NLU
|
25 |
+
- small scale
|
26 |
+
dataset_info:
|
27 |
+
- config_name: small
|
28 |
+
features:
|
29 |
+
- name: input
|
30 |
+
dtype: string
|
31 |
+
- name: output
|
32 |
+
dtype: string
|
33 |
+
- name: code
|
34 |
+
dtype: string
|
35 |
+
- name: res_var
|
36 |
+
dtype: string
|
37 |
+
- name: operation
|
38 |
+
dtype: string
|
39 |
+
splits:
|
40 |
+
- name: test
|
41 |
+
num_examples: 14661
|
42 |
+
- name: train
|
43 |
+
num_examples: 33939
|
44 |
+
---
|
45 |
+
|
46 |
+
# Dataset Card for Small String Operations Dataset
|
47 |
+
|
48 |
+
## Dataset Description
|
49 |
+
|
50 |
+
- **Homepage:** [PaDaS Lab](https://huggingface.co/PaDaS-Lab)
|
51 |
+
- **Repository:**
|
52 |
+
- **Paper:**
|
53 |
+
- **Leaderboard:**
|
54 |
+
- **Point of Contact:** Michael Granitzer, michael.granitzer@uni-passau.de
|
55 |
+
|
56 |
+
### Other Metadata
|
57 |
+
|
58 |
+
|
59 |
+
|
60 |
+
### Dataset Summary
|
61 |
+
|
62 |
+
Minimal dataset for intended for LM development and testing using python string operations.
|
63 |
+
The dataset is created by running different one line python string operations on random strings
|
64 |
+
The idea is, that transformer implementation can learn the string operations and that this task is a good
|
65 |
+
proxy tasks for other transformer operations on real languages and real tasks. Consequently, the
|
66 |
+
data set is small and can be used in the development process without large scale infrastructures.
|
67 |
+
|
68 |
+
## Dataset Structure
|
69 |
+
|
70 |
+
### Data Instances
|
71 |
+
|
72 |
+
There are different configurations for the data set.
|
73 |
+
|
74 |
+
- `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).
|
75 |
+
- you can further choose different subsets according to either length or the kind of operation
|
76 |
+
|
77 |
+
### Data Fields
|
78 |
+
|
79 |
+
all data instances can be found under the field "data".
|
80 |
+
|
81 |
+
- `input`: input string, i.e. the string and the string operation
|
82 |
+
- `output`: output of the string operation
|
83 |
+
- `code`: code for running the string operation in python,
|
84 |
+
- `res_var`: name of the result variable
|
85 |
+
- `operation`: kind of operation:
|
86 |
+
- `step_x` for `s[::x]`
|
87 |
+
- `char_at_x` for `s[x]`
|
88 |
+
- `slice_x:y` for `s[x:y]`
|
89 |
+
- `slice_step_x:y:z` for `s[x:y:z]`
|
90 |
+
- `slice_reverse_i:j:k` for `s[i:i+j][::k]`
|
91 |
+
|
92 |
+
Siblings of `data` contain additional metadata information about the dataset.
|
93 |
+
|
94 |
+
- `prompt` describes possible prompts based on that data splitted into input prompts / output prompts
|
95 |
+
|
96 |
+
|
97 |
+
### Data Splits
|
98 |
+
|
99 |
+
The dataset is split into a train and test split for different string lengths
|
100 |
+
|
101 |
+
## Dataset Creation
|
102 |
+
|
103 |
+
The dataset is synthetically created
|
104 |
+
|
105 |
+
### Licensing Information
|
106 |
+
|
107 |
+
MIT License
|
108 |
+
|
109 |
+
### Citation Information
|
110 |
+
|
111 |
+
[More Information Needed]
|
112 |
+
|
113 |
+
### Contributions
|
114 |
+
|
115 |
+
[Chair of Data Science, University of Passau](https://huggingface.co/PaDaS-Lab)
|
116 |
+
|
117 |
+
- Michael Granitzer, University of Passau
|
118 |
+
|
119 |
+
Thanks to [@mgrani](https://github.com/mgrani) for adding this dataset.
|
120 |
+
"""
|
SynStOp.py
ADDED
@@ -0,0 +1,198 @@
|
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|
1 |
+
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# 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.
|
14 |
+
"""String Operations Dataset for fast model development"""
|
15 |
+
|
16 |
+
|
17 |
+
import csv
|
18 |
+
import json
|
19 |
+
import os
|
20 |
+
import re
|
21 |
+
|
22 |
+
import datasets
|
23 |
+
import gzip
|
24 |
+
|
25 |
+
# TODO: Add BibTeX citation
|
26 |
+
# Find for instance the citation on arxiv or on the dataset repo/website
|
27 |
+
_CITATION = """\
|
28 |
+
@InProceedings{huggingface:dataset,
|
29 |
+
title = {String Operations Dataset: A small set of string manipulation tasks for fast model development},
|
30 |
+
author={Michael Granitzer},
|
31 |
+
year={2023}
|
32 |
+
}
|
33 |
+
"""
|
34 |
+
|
35 |
+
# TODO: Add description of the dataset here
|
36 |
+
# 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 @@
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
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|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 @@
|
|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
|prompt|{'plain': [[{'templates': ['{input}='], 'keys': ['input'], 'component': 'input'}], [{'templates': ['{output}\n'], 'keys': ['output'], 'component': 'output', 'tags': ['exact']}]], 'simple': [[{'templates': ['Conduct the string operation {operation} as follows: {input}.\n'], 'keys': ['operation', 'input'], 'component': 'input'}], [{'templates': ['{res_var}={output}\n'], 'keys': ['res_var', 'output'], 'component': 'output', 'tags': ['exact']}]], 'simple_with_code': [[{'templates': ['{input}='], 'keys': ['input'], 'component': 'input'}], [{'templates': ['{output}\n'], 'keys': ['output'], 'component': 'output', 'tags': ['exact']}]]}|
|
11 |
+
|length|20|
|
12 |
+
|set|train|
|
13 |
+
|items|{'train': 33939, 'test': 14661}|
|
14 |
+
|
15 |
+
|Length|Set|Group|Amount|File|
|
16 |
+
|---|---|---|---|---|
|
17 |
+
|10|train|step_::-1|419|stop_10_train.json|
|
18 |
+
|10|train|step_::1|419|stop_10_train.json|
|
19 |
+
|10|train|char_at_0|419|stop_10_train.json|
|
20 |
+
|10|train|slice_reverse_0:2:-1|419|stop_10_train.json|
|
21 |
+
|10|train|slice_0:2|419|stop_10_train.json|
|
22 |
+
|10|train|slice_reverse_0:3:-1|419|stop_10_train.json|
|
23 |
+
|10|train|slice_0:3|419|stop_10_train.json|
|
24 |
+
|10|train|char_at_1|419|stop_10_train.json|
|
25 |
+
|10|train|slice_reverse_1:3:-1|419|stop_10_train.json|
|
26 |
+
|10|train|slice_1:3|419|stop_10_train.json|
|
27 |
+
|10|train|slice_reverse_1:4:-1|419|stop_10_train.json|
|
28 |
+
|10|train|slice_1:4|419|stop_10_train.json|
|
29 |
+
|10|train|char_at_2|419|stop_10_train.json|
|
30 |
+
|10|train|slice_reverse_2:4:-1|419|stop_10_train.json|
|
31 |
+
|10|train|slice_2:4|419|stop_10_train.json|
|
32 |
+
|10|train|slice_reverse_2:5:-1|419|stop_10_train.json|
|
33 |
+
|10|train|slice_2:5|419|stop_10_train.json|
|
34 |
+
|10|train|char_at_3|419|stop_10_train.json|
|
35 |
+
|10|train|slice_reverse_3:5:-1|419|stop_10_train.json|
|
36 |
+
|10|train|slice_3:5|419|stop_10_train.json|
|
37 |
+
|10|train|slice_reverse_3:6:-1|419|stop_10_train.json|
|
38 |
+
|10|train|slice_3:6|419|stop_10_train.json|
|
39 |
+
|10|train|char_at_4|419|stop_10_train.json|
|
40 |
+
|10|train|slice_reverse_4:6:-1|419|stop_10_train.json|
|
41 |
+
|10|train|slice_4:6|419|stop_10_train.json|
|
42 |
+
|10|train|slice_reverse_4:7:-1|419|stop_10_train.json|
|
43 |
+
|10|train|slice_4:7|419|stop_10_train.json|
|
44 |
+
|10|test|step_::-1|181|stop_10_train.json|
|
45 |
+
|10|test|step_::1|181|stop_10_train.json|
|
46 |
+
|10|test|char_at_0|181|stop_10_train.json|
|
47 |
+
|10|test|slice_reverse_0:2:-1|181|stop_10_train.json|
|
48 |
+
|10|test|slice_0:2|181|stop_10_train.json|
|
49 |
+
|10|test|slice_reverse_0:3:-1|181|stop_10_train.json|
|
50 |
+
|10|test|slice_0:3|181|stop_10_train.json|
|
51 |
+
|10|test|char_at_1|181|stop_10_train.json|
|
52 |
+
|10|test|slice_reverse_1:3:-1|181|stop_10_train.json|
|
53 |
+
|10|test|slice_1:3|181|stop_10_train.json|
|
54 |
+
|10|test|slice_reverse_1:4:-1|181|stop_10_train.json|
|
55 |
+
|10|test|slice_1:4|181|stop_10_train.json|
|
56 |
+
|10|test|char_at_2|181|stop_10_train.json|
|
57 |
+
|10|test|slice_reverse_2:4:-1|181|stop_10_train.json|
|
58 |
+
|10|test|slice_2:4|181|stop_10_train.json|
|
59 |
+
|10|test|slice_reverse_2:5:-1|181|stop_10_train.json|
|
60 |
+
|10|test|slice_2:5|181|stop_10_train.json|
|
61 |
+
|10|test|char_at_3|181|stop_10_train.json|
|
62 |
+
|10|test|slice_reverse_3:5:-1|181|stop_10_train.json|
|
63 |
+
|10|test|slice_3:5|181|stop_10_train.json|
|
64 |
+
|10|test|slice_reverse_3:6:-1|181|stop_10_train.json|
|
65 |
+
|10|test|slice_3:6|181|stop_10_train.json|
|
66 |
+
|10|test|char_at_4|181|stop_10_train.json|
|
67 |
+
|10|test|slice_reverse_4:6:-1|181|stop_10_train.json|
|
68 |
+
|10|test|slice_4:6|181|stop_10_train.json|
|
69 |
+
|10|test|slice_reverse_4:7:-1|181|stop_10_train.json|
|
70 |
+
|10|test|slice_4:7|181|stop_10_train.json|
|
71 |
+
|15|train|step_::-1|419|stop_15_train.json|
|
72 |
+
|15|train|step_::1|419|stop_15_train.json|
|
73 |
+
|15|train|char_at_0|419|stop_15_train.json|
|
74 |
+
|15|train|slice_reverse_0:2:-1|419|stop_15_train.json|
|
75 |
+
|15|train|slice_0:2|419|stop_15_train.json|
|
76 |
+
|15|train|slice_reverse_0:3:-1|419|stop_15_train.json|
|
77 |
+
|15|train|slice_0:3|419|stop_15_train.json|
|
78 |
+
|15|train|char_at_1|419|stop_15_train.json|
|
79 |
+
|15|train|slice_reverse_1:3:-1|419|stop_15_train.json|
|
80 |
+
|15|train|slice_1:3|419|stop_15_train.json|
|
81 |
+
|15|train|slice_reverse_1:4:-1|419|stop_15_train.json|
|
82 |
+
|15|train|slice_1:4|419|stop_15_train.json|
|
83 |
+
|15|train|char_at_2|419|stop_15_train.json|
|
84 |
+
|15|train|slice_reverse_2:4:-1|419|stop_15_train.json|
|
85 |
+
|15|train|slice_2:4|419|stop_15_train.json|
|
86 |
+
|15|train|slice_reverse_2:5:-1|419|stop_15_train.json|
|
87 |
+
|15|train|slice_2:5|419|stop_15_train.json|
|
88 |
+
|15|train|char_at_3|419|stop_15_train.json|
|
89 |
+
|15|train|slice_reverse_3:5:-1|419|stop_15_train.json|
|
90 |
+
|15|train|slice_3:5|419|stop_15_train.json|
|
91 |
+
|15|train|slice_reverse_3:6:-1|419|stop_15_train.json|
|
92 |
+
|15|train|slice_3:6|419|stop_15_train.json|
|
93 |
+
|15|train|char_at_4|419|stop_15_train.json|
|
94 |
+
|15|train|slice_reverse_4:6:-1|419|stop_15_train.json|
|
95 |
+
|15|train|slice_4:6|419|stop_15_train.json|
|
96 |
+
|15|train|slice_reverse_4:7:-1|419|stop_15_train.json|
|
97 |
+
|15|train|slice_4:7|419|stop_15_train.json|
|
98 |
+
|15|test|step_::-1|181|stop_15_train.json|
|
99 |
+
|15|test|step_::1|181|stop_15_train.json|
|
100 |
+
|15|test|char_at_0|181|stop_15_train.json|
|
101 |
+
|15|test|slice_reverse_0:2:-1|181|stop_15_train.json|
|
102 |
+
|15|test|slice_0:2|181|stop_15_train.json|
|
103 |
+
|15|test|slice_reverse_0:3:-1|181|stop_15_train.json|
|
104 |
+
|15|test|slice_0:3|181|stop_15_train.json|
|
105 |
+
|15|test|char_at_1|181|stop_15_train.json|
|
106 |
+
|15|test|slice_reverse_1:3:-1|181|stop_15_train.json|
|
107 |
+
|15|test|slice_1:3|181|stop_15_train.json|
|
108 |
+
|15|test|slice_reverse_1:4:-1|181|stop_15_train.json|
|
109 |
+
|15|test|slice_1:4|181|stop_15_train.json|
|
110 |
+
|15|test|char_at_2|181|stop_15_train.json|
|
111 |
+
|15|test|slice_reverse_2:4:-1|181|stop_15_train.json|
|
112 |
+
|15|test|slice_2:4|181|stop_15_train.json|
|
113 |
+
|15|test|slice_reverse_2:5:-1|181|stop_15_train.json|
|
114 |
+
|15|test|slice_2:5|181|stop_15_train.json|
|
115 |
+
|15|test|char_at_3|181|stop_15_train.json|
|
116 |
+
|15|test|slice_reverse_3:5:-1|181|stop_15_train.json|
|
117 |
+
|15|test|slice_3:5|181|stop_15_train.json|
|
118 |
+
|15|test|slice_reverse_3:6:-1|181|stop_15_train.json|
|
119 |
+
|15|test|slice_3:6|181|stop_15_train.json|
|
120 |
+
|15|test|char_at_4|181|stop_15_train.json|
|
121 |
+
|15|test|slice_reverse_4:6:-1|181|stop_15_train.json|
|
122 |
+
|15|test|slice_4:6|181|stop_15_train.json|
|
123 |
+
|15|test|slice_reverse_4:7:-1|181|stop_15_train.json|
|
124 |
+
|15|test|slice_4:7|181|stop_15_train.json|
|
125 |
+
|20|train|step_::-1|419|stop_20_train.json|
|
126 |
+
|20|train|step_::1|419|stop_20_train.json|
|
127 |
+
|20|train|char_at_0|419|stop_20_train.json|
|
128 |
+
|20|train|slice_reverse_0:2:-1|419|stop_20_train.json|
|
129 |
+
|20|train|slice_0:2|419|stop_20_train.json|
|
130 |
+
|20|train|slice_reverse_0:3:-1|419|stop_20_train.json|
|
131 |
+
|20|train|slice_0:3|419|stop_20_train.json|
|
132 |
+
|20|train|char_at_1|419|stop_20_train.json|
|
133 |
+
|20|train|slice_reverse_1:3:-1|419|stop_20_train.json|
|
134 |
+
|20|train|slice_1:3|419|stop_20_train.json|
|
135 |
+
|20|train|slice_reverse_1:4:-1|419|stop_20_train.json|
|
136 |
+
|20|train|slice_1:4|419|stop_20_train.json|
|
137 |
+
|20|train|char_at_2|419|stop_20_train.json|
|
138 |
+
|20|train|slice_reverse_2:4:-1|419|stop_20_train.json|
|
139 |
+
|20|train|slice_2:4|419|stop_20_train.json|
|
140 |
+
|20|train|slice_reverse_2:5:-1|419|stop_20_train.json|
|
141 |
+
|20|train|slice_2:5|419|stop_20_train.json|
|
142 |
+
|20|train|char_at_3|419|stop_20_train.json|
|
143 |
+
|20|train|slice_reverse_3:5:-1|419|stop_20_train.json|
|
144 |
+
|20|train|slice_3:5|419|stop_20_train.json|
|
145 |
+
|20|train|slice_reverse_3:6:-1|419|stop_20_train.json|
|
146 |
+
|20|train|slice_3:6|419|stop_20_train.json|
|
147 |
+
|20|train|char_at_4|419|stop_20_train.json|
|
148 |
+
|20|train|slice_reverse_4:6:-1|419|stop_20_train.json|
|
149 |
+
|20|train|slice_4:6|419|stop_20_train.json|
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150 |
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|20|train|slice_reverse_4:7:-1|419|stop_20_train.json|
|
151 |
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|20|train|slice_4:7|419|stop_20_train.json|
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152 |
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|20|test|step_::-1|181|stop_20_train.json|
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153 |
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154 |
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|20|test|char_at_0|181|stop_20_train.json|
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155 |
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|20|test|slice_reverse_0:2:-1|181|stop_20_train.json|
|
156 |
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|20|test|slice_0:2|181|stop_20_train.json|
|
157 |
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|20|test|slice_reverse_0:3:-1|181|stop_20_train.json|
|
158 |
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|
159 |
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|20|test|char_at_1|181|stop_20_train.json|
|
160 |
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|20|test|slice_reverse_1:3:-1|181|stop_20_train.json|
|
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|
162 |
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|
163 |
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|20|test|slice_1:4|181|stop_20_train.json|
|
164 |
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|20|test|char_at_2|181|stop_20_train.json|
|
165 |
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|20|test|slice_reverse_2:4:-1|181|stop_20_train.json|
|
166 |
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|20|test|slice_2:4|181|stop_20_train.json|
|
167 |
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|20|test|slice_reverse_2:5:-1|181|stop_20_train.json|
|
168 |
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|20|test|slice_2:5|181|stop_20_train.json|
|
169 |
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|20|test|char_at_3|181|stop_20_train.json|
|
170 |
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|20|test|slice_reverse_3:5:-1|181|stop_20_train.json|
|
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|
172 |
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|
173 |
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|
174 |
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|
175 |
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|
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|
177 |
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|20|test|slice_reverse_4:7:-1|181|stop_20_train.json|
|
178 |
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|20|test|slice_4:7|181|stop_20_train.json|
|
small/about.json
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1 |
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{
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"simple": [
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[
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{
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|
647 |
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648 |
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|
649 |
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650 |
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|
651 |
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655 |
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675 |
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676 |
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{
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"name": "slice_4:7",
|
678 |
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|
679 |
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}
|
680 |
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]
|
681 |
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},
|
682 |
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{
|
683 |
+
"length": 20,
|
684 |
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"files": [
|
685 |
+
"stop_20_test.json"
|
686 |
+
],
|
687 |
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"entries": 4887,
|
688 |
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"groups": [
|
689 |
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{
|
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|
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|
692 |
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|
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|
728 |
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|
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|
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|
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|
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|
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{
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750 |
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|
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|
752 |
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|
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|
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|
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|
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|
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|
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|
776 |
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|
777 |
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|
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|
779 |
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|
780 |
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|
781 |
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{
|
782 |
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|
783 |
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|
784 |
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|
785 |
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{
|
786 |
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|
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|
788 |
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|
789 |
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{
|
790 |
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|
791 |
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|
792 |
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|
793 |
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{
|
794 |
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"name": "slice_4:7",
|
795 |
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|
796 |
+
}
|
797 |
+
]
|
798 |
+
}
|
799 |
+
]
|
800 |
+
},
|
801 |
+
"length": 20,
|
802 |
+
"set": "train",
|
803 |
+
"items": {
|
804 |
+
"train": 33939,
|
805 |
+
"test": 14661
|
806 |
+
}
|
807 |
+
}
|
small/stop_10_test.json.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:80c538fc58645fe384222da0679943a9395695429dea6eb2c5ed760f5b2073db
|
3 |
+
size 81715
|
small/stop_10_train.json.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:bb1b3b3f64e31f25c401e9582a65b77beeace02f951627952ca15e0a29355aac
|
3 |
+
size 230704
|
small/stop_15_test.json.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0c420cda9e2c43cfe6203d0d84d2a45c4443ba80b6a4e52e6692bdfbf01bb9e5
|
3 |
+
size 111453
|
small/stop_15_train.json.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:89c4f37b5e5d9fb7e78b199d11e1526a329b41a0095f67b6f2df04f6d7f7c8d4
|
3 |
+
size 281967
|
small/stop_20_test.json.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:978b1dc0fe762b12c5de288167ac9e6da74c91b9c7f0f78c27cc15a3abc62002
|
3 |
+
size 143010
|
small/stop_20_train.json.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6c60391132629154137a68e8b40b0d919960265f9ef0ac87b5272e3babc9329e
|
3 |
+
size 329405
|
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 |
+
|