Add load script

murakami.py

@@ -0,0 +1,208 @@
+"""
+Parse all paragraphs from all *.fb2 files in the input directory, create a Huggingface Dataset and push it to the Hub as `vldsavelyev/murakami`.
+"""
+
+
+import os
+from pathlib import Path
+from lxml import etree
+import gdown
+import datasets
+from datasets import Dataset
+from huggingface_hub import create_repo
+import coloredlogs
+
+coloredlogs.install(level="info")
+datasets.logging.set_verbosity_info()
+
+
+_DESCRIPTION = """\
+Russian translations of Murakami novels, to fine-tune a generative language model. Source is FB2 files from http://flibusta.is/a/8570.
+"""
+
+_URL = "https://drive.google.com/open?id=14Uw_efwj70iip1xJD2H2qHSVMuoUDm4z"
+
+
+class Builder(datasets.GeneratorBasedBuilder):
+    """Murakami novels, translated to Russian."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+
+    def _info(self):
+        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features({"text": datasets.Value("string")}),
+        )
+
+    # Number of initial <p> element to take from each fb2, by number. This allows to skip
+    # intros and other junk in the beginning of an fb2. This is built semi-manually using
+    # the `helper_to_find_first_paragraphs` func.
+    START_PARAGRAPHS = {
+        3: 5,
+        6: 27,
+        7: 3,
+        9: 4,
+        10: 3,
+        12: 11,
+        18: 5,
+        20: 3,
+        21: 5,
+    }
+
+    @staticmethod
+    def helper_to_find_first_paragraphs(paragraphs, title, book_number, n=30):
+        """
+        Helps to eyeball first few paragraphs of a book to skip junk paragraphs
+        in the beginning and manually construct the `tart_paragraphs` dict.
+        """
+        found_paragraphs = []
+        skipping = True
+        for i, p in enumerate(list(paragraphs)[:n]):
+            if p.text is None:
+                continue
+            if (
+                book_number in Builder.START_PARAGRAPHS
+                and i >= Builder.START_PARAGRAPHS[book_number]
+            ):
+                skipping = False
+            if skipping and p.text.lower() == title.lower():
+                skipping = False
+            if not skipping:
+                found_paragraphs.append(f"   {i} {p.text}")
+
+        if found_paragraphs:
+            print("✅")
+            print("\n".join(found_paragraphs))
+
+        else:
+            print("❌")
+            for i, p in enumerate(list(paragraphs)[:30]):
+                print(f"   {i} {p.text}")
+
+    def _split_generators(self, dl_manager):
+        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        data_dir = dl_manager.extract(
+            dl_manager.download_custom(
+                _URL,
+                custom_download=lambda src, dst: gdown.download(src, dst, fuzzy=True),
+            )
+        )
+
+        text_by_name = {}
+
+        fb2s = list(Path(data_dir).glob("*.fb2"))
+        if len(fb2s) > 0:
+            print(f"Found {len(fb2s)} fb2 files in {data_dir}")
+        else:
+            raise ValueError(f"No fb2 files found in {data_dir}")
+
+        for bi, path in enumerate(fb2s):
+            print(bi, path)
+
+            # Load the FB2 format file
+            with path.open("rb") as file:
+                fb2_data = file.read()
+
+            # Print structure of the FB2 format file
+            # print(etree.tostring(etree.fromstring(fb2_data), pretty_print=True))
+
+            # Parse the FB2 format file using lxml
+            root = etree.fromstring(fb2_data)
+
+            # Get the title of the book
+            title = root.xpath(
+                "//fb:title-info/fb:book-title",
+                namespaces={"fb": "http://www.gribuser.ru/xml/fictionbook/2.0"},
+            )[0].text
+            print(title)
+
+            # Get all book paragraphs
+            paragraphs = root.xpath(
+                "//fb:p",
+                namespaces={"fb": "http://www.gribuser.ru/xml/fictionbook/2.0"},
+            )
+
+            # UNCOMMENT THE LINE BELOW TO BUILD `START_PARAGRAPHS`:
+            # self.helper_to_find_first_paragraphs(paragraphs, title, bi)
+
+            found_paragraphs = []
+            skipping = True
+            for pi, p in enumerate(paragraphs):
+                if p.text is None:
+                    continue
+                if (
+                    bi in Builder.START_PARAGRAPHS
+                    and pi >= Builder.START_PARAGRAPHS[bi]
+                ):
+                    skipping = False
+                if skipping and p.text.lower() == title.lower():
+                    skipping = False
+                if not skipping:
+                    found_paragraphs.append(p)
+            print(f"Found {len(found_paragraphs)} paragraphs")
+
+            text_by_name[title] = ""
+            for p in found_paragraphs:
+                text_by_name[title] += p.text.replace(" ", " ") + "\n"
+            text_by_name[title] += "\n"
+
+        print("Novel by size:")
+        for title, text in text_by_name.items():
+            print(f"  {title}: {len(text):,} characters")
+
+        smallest_title = min(text_by_name, key=lambda k: len(text_by_name[k]))
+        print(
+            f"Using smallest novel {smallest_title} "
+            f"({len(text_by_name[smallest_title]):,} characters) as a test set"
+        )
+
+        test_titles = [smallest_title]
+        train_titles = [t for t in text_by_name if t not in test_titles]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "titles": train_titles,
+                    "texts": [text_by_name[t] for t in train_titles],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "titles": test_titles,
+                    "texts": [text_by_name[t] for t in test_titles],
+                    "split": "test",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, titles, texts, split):
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        for title, text in zip(titles, texts):
+            yield title, {"text": text}