udpated configuration. Test ok

Readme.md

@@ -29,18 +29,93 @@ dataset_info:
   - name: input
     dtype: string
   - name: output
-    dtype: string 
+    dtype: string
   - name: code
-    dtype: string 
+    dtype: string
   - name: res_var
     dtype: string
   - name: operation
     dtype: string
+  - name: id
+    dtype: int32
   splits:
+  - name: train
+    num_bytes: 3222948
+    num_examples: 33939
   - name: test
+    num_bytes: 1392252
     num_examples: 14661
+  download_size: 1178254
+  dataset_size: 4615200
+- config_name: small10
+  features:
+  - name: input
+    dtype: string
+  - name: output
+    dtype: string
+  - name: code
+    dtype: string
+  - name: res_var
+    dtype: string
+  - name: operation
+    dtype: string
+  - name: id
+    dtype: int32
+  splits:
   - name: train
-    num_examples: 33939
+    num_bytes: 956996
+    num_examples: 11313
+  - name: test
+    num_bytes: 413404
+    num_examples: 4887
+  download_size: 312419
+  dataset_size: 1370400
+- config_name: small15
+  features:
+  - name: input
+    dtype: string
+  - name: output
+    dtype: string
+  - name: code
+    dtype: string
+  - name: res_var
+    dtype: string
+  - name: operation
+    dtype: string
+  - name: id
+    dtype: int32
+  splits:
+  - name: train
+    num_bytes: 1074316
+    num_examples: 11313
+  - name: test
+    num_bytes: 464084
+    num_examples: 4887
+  download_size: 393420
+  dataset_size: 1538400
+- config_name: small20
+  features:
+  - name: input
+    dtype: string
+  - name: output
+    dtype: string
+  - name: code
+    dtype: string
+  - name: res_var
+    dtype: string
+  - name: operation
+    dtype: string
+  - name: id
+    dtype: int32
+  splits:
+  - name: train
+    num_bytes: 1191636
+    num_examples: 11313
+  - name: test
+    num_bytes: 514764
+    num_examples: 4887
+  download_size: 472415
+  dataset_size: 1706400
 ---
 
 # Dataset Card for Small String Operations Dataset

SynStOp.py

@@ -35,11 +35,42 @@ year={2023}
 # TODO: Add description of the dataset here
 # You can copy an official description
 _DESCRIPTION = """\
-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.
+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.
+ 
+There are different configurations for the data set.
+
+- `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).
+  - you can further choose different subsets according to either length or the kind of operation
+- `small10`: like small, but only strings to length 10
+- `small15`: like small, but only strings to length 15
+- `small20`: like small, but only strings to length 20
+
+The fields have the following meaning:
+
+ - `input`: input string, i.e. the string and the string operation
+ - `output`: output of the string operation
+ - `code`: code for running the string operation in python,
+ - `res_var`: name of the result variable
+ - `operation`: kind of operation: 
+   - `step_x` for `s[::x]`
+   - `char_at_x` for `s[x]`
+   - `slice_x:y` for `s[x:y]`
+   - `slice_step_x:y:z` for `s[x:y:z]`
+   - `slice_reverse_i:j:k` for `s[i:i+j][::k]`
+
+ Siblings of `data` contain additional metadata information about the dataset.
+
+ - `prompt` describes possible prompts based on that data splitted into input prompts / output prompts
+
+
 """
 
 # TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
+_HOMEPAGE = "https://huggingface.co/PaDaS-Lab/SynStOp"
 
 # TODO: Add the licence for the dataset here if you can find it
 _LICENSE = "Apache 2.0 License"
@@ -48,19 +79,44 @@ _LICENSE = "Apache 2.0 License"
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLS = {
-    "small": ["https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_10_train.json.gz",
-              "https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_10_test.json.gz",
-              "https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_20_train.json.gz",
-              "https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_20_test.json.gz",
-              "https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_15_train.json.gz",
-              "https://github.com/mgrani/StOp-Dataset/raw/main/small/stop_15_test.json.gz",
-              "https://github.com/mgrani/StOp-Dataset/raw/main/small/about.json",
-              "https://github.com/mgrani/StOp-Dataset/raw/main/small/Readme.md"]
+    "small": {
+            "train": ["./small/stop_10_train.json.gz", "./small/stop_20_train.json.gz", "./small/stop_15_train.json.gz",],
+            "test": ["./small/stop_10_test.json.gz", "./small/stop_20_test.json.gz", "./small/stop_15_test.json.gz",]
+              },
+    "small15": {
+            "train": [ "./small/stop_15_train.json.gz",],
+            "test": [ "./small/stop_15_test.json.gz",]
+              },
+    "small10": {
+            "train": ["./small/stop_10_train.json.gz"],
+            "test": ["./small/stop_10_test.json.gz"]
+              },
+    "small20": {
+            "train": [ "./small/stop_20_train.json.gz"],
+            "test": [ "./small/stop_20_test.json.gz"]
+              }
 }
 
 
+class SynStOpDatasetConfig(datasets.BuilderConfig):
+
+    def __init__(self, subset="small", length=(10,15,20), **kwargs):
+        """BuilderConfig for SynStOpDatasetConfig.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SynStOpDatasetConfig, self).__init__(**kwargs)
+        self.subset = subset
+        self.length = length
+        self.files = {
+            "train": ["./{subset}".format(subset=subset) + "/stop_{length}_train.json.gz".format(length=length) for length in length],
+            "test": ["./{subset}".format(subset=subset) + "/stop_{length}_test.json.gz".format(length=length) for length in length],
+        }
+
+
+
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class StopDataset(datasets.GeneratorBasedBuilder):
+class SynStOpDataset(datasets.GeneratorBasedBuilder):
     """TODO: Short description of my dataset."""
 
     VERSION = datasets.Version("0.0.1")
@@ -72,31 +128,24 @@ class StopDataset(datasets.GeneratorBasedBuilder):
     # You will be able to load one or the other configurations in the following list with
     # data = datasets.load_dataset('my_dataset', 'small')
     # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="small", version=VERSION, description="Small string operations dataset with string slices only"),
-        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)"),
-
-    ]
+    BUILDER_CONFIGS = [SynStOpDatasetConfig(name="small", length=(10,15,20),version=VERSION, description="Small set of string operations  with string slices only")] +\
+                      [SynStOpDatasetConfig(name=f"small{l1}", length=(l1,), version=datasets.Version("0.0.1"), description="Small set of string operations  with string slices only") for l1 in [10,15, 20]]
 
     DEFAULT_CONFIG_NAME = "small"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
         # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name.startswith("small"):  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
+        features = datasets.Features(
                 {
                     "input": datasets.Value("string"),
                     "output": datasets.Value("string"),
                     "code": datasets.Value("string"),
                     "res_var": datasets.Value("string"),
-                    "operation": datasets.Value("string")
+                    "operation": datasets.Value("string"),
+                    "id": datasets.Value("int32"),
                     # These are the features of your dataset like images, labels ...
                 }
             )
-            self._init_filters(self.config.name[len("small"):].strip("[]").split(","))
-            self.config.name= self.config.name[:len("small")]
-        else:
-            raise NotImplementedError()
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
@@ -112,18 +161,6 @@ class StopDataset(datasets.GeneratorBasedBuilder):
             # Citation for the dataset
             citation=_CITATION,
         )
-
-    def _init_filters(self, filters):
-        self.filter_operations = []
-        self.filter_len = []
-        for filter in filters:
-            if filter =="": continue
-            k, v = filter.split("=")
-            if k=="l":
-                self.filter_len.append(int(v))
-            elif k=="o":
-                self.filter_operations.append(re.compile(v))
-
     def _split_generators(self, dl_manager):
         # 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
@@ -131,16 +168,14 @@ class StopDataset(datasets.GeneratorBasedBuilder):
         # 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
-        urls = _URLS[self.config.name]
-        if len(self.filter_len)>0:
-            urls = [url for url in urls if any([f"stop_{str(len)}_t" in url for len in self.filter_len])]
+        urls = self.config.files
         data_dir = dl_manager.download_and_extract(urls)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": [data_dir[i] for i, n in enumerate(urls) if "_train.json" in n],
+                    "filepath": data_dir["train"],
                     "split": "train",
                 },
             ),
@@ -149,22 +184,12 @@ class StopDataset(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath":  [data_dir[i] for i, n in enumerate(urls) if "_test.json" in n],
+                    "filepath":  data_dir["test"],
                     "split": "test",
                 },
             ),
         ]
 
-    def _match_operations_filter(self, operation):
-        if self.filter_operations is not None:
-           matches = False
-           for filter in self.filter_operations:
-                if filter.matches(operation):
-                    matches = True
-                    break
-           return matches
-        else: return True
-
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
         # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
@@ -176,10 +201,6 @@ class StopDataset(datasets.GeneratorBasedBuilder):
             for ix, data in enumerate(dataset):
 
                 if self.config.name.startswith("small"):
-
-                    if self._match_operations_filter(data["operation"]):
-                        continue
-
                     # Yields examples as (key, example) tuples
                     id = data["id"] if "id" in data else count
                     count = count + 1
@@ -188,6 +209,7 @@ class StopDataset(datasets.GeneratorBasedBuilder):
                         "output": data["output"],
                         "code": data["code"],
                         "res_var": data["res_var"],
+                        "id": id,
                         "operation": data["operation"]
                     }
                 else:

usage.py

@@ -1,16 +0,0 @@
-import datasets
-
-if __name__=="__main__":
-    # load locally from this repo
-    ds = datasets.load_dataset("./stop.py", "small")
-
-    ds.push_to_hub("PaDaS-Lab/stop-small")
-
-    from datasets import load_dataset
-
-    dataset = load_dataset("PaDaS-Lab/stop-small")
-    print(dataset)
-
-    # load locally from this repo
-    # load locally from this repo
-