Initial

.gitignore

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+.idea
+*.txt

_split_train.py

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+import os, json, gzip
+
+def zip_file(lines, dest_file):
+        print(f"Zipping {dest_file} ... ")
+        with gzip.open(dest_file, 'wt') as w:
+            w.writelines(lines)
+        print(f"    .... done.")
+
+def split_train(MB_PER_FILE = 100):
+    print("Splitting TRAIN into shards with {} MB per file:".format(MB_PER_FILE))
+
+    os.makedirs("data", exist_ok=True)
+
+    print("Working ...")
+    cnt = 0
+    lid = 0
+    shard = 0
+    cutoff = MB_PER_FILE * 1000 * 1000
+    accumulator = []
+    line_cnt = 0
+    with open("train.txt", "r", encoding="utf8") as f:
+        for line in f:
+            line_cnt += 1
+            if line_cnt % 1000000 == 0:
+               print(f"\t{line_cnt/1000000:.2f}M lines, at shard {shard} ... ")
+            cnt += len(line)
+
+            data = {
+                 "id": lid,
+                 "text": line.strip()
+            }
+            accumulator.append(json.dumps(data, ensure_ascii=False)+"\n")
+            lid += 1
+
+            if cnt > cutoff:
+                zip_file(accumulator, os.path.join("data", "train-"+str(shard).zfill(3) + ".json.gz"))
+                cnt = 0
+                accumulator = []
+                shard += 1
+
+    # flush buffer
+    zip_file(accumulator, os.path.join("data", "train-"+str(shard).zfill(3) + ".json.gz"))
+    shard += 1
+
+    print("Done, wrote {} files.".format(shard))
+
+def write_valid():
+    # do valid
+    cnt = 0
+    lid = 0
+    accumulator = []
+    with open("valid.txt", "r", encoding="utf8") as f:
+       for line in f:
+           cnt += len(line)
+           data = {
+               "id": lid,
+               "text": line.strip()
+           }
+           accumulator.append(json.dumps(data, ensure_ascii=False)+"\n")
+           lid += 1
+
+    zip_file(accumulator, os.path.join("data", "valid-000.json.gz"))
+
+def split_into_train_and_valid(percent=0.01):
+    import random
+    import ftfy
+    import tqdm
+    train_file = open("train.txt", "w", encoding="utf8")
+    valid_file = open("valid.txt", "w", encoding="utf8")
+    with open("corpus-diac.txt", "r", encoding="utf8") as f:
+         cnt = 0
+         for line in tqdm.tqdm(f):
+             cnt += 1
+             if cnt < 2000000:
+                 continue
+             line = ftfy.fix_text(line.strip())+"\n"
+             if random.random() <= percent:
+                 valid_file.write(line)
+             else:
+                 train_file.write(line)
+    train_file.close()
+    valid_file.close()
+
+#split_into_train_and_valid()
+os.makedirs("data", exist_ok=True)
+write_valid() # do valid
+split_train() # do train

diacritic.py

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+import json
+import datasets
+import gzip
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """\\nRomanian diacritic dataset"""
+_CITATION = """n/a"""
+
+_URL = "https://github.com/dumitrescustefan/diacritic"
+_DATA_URL = "https://huggingface.co/datasets/dumitrescustefan/diacritic/resolve/main/data/{split_suffix}-{index:03d}.json.gz"
+_N_SHARDS_PER_SPLIT = {
+    "train": 78, "validation": 1
+}
+
+class RLM(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="v1", version="1.0.0", description="v1.0 of romanian diacritic corpus"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "v1"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "text": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_urls = {}
+        for split in ["train", "validation"]:
+            data_urls[split] = [
+                _DATA_URL.format(split_suffix=split, index=iindex) for iindex in range(_N_SHARDS_PER_SPLIT[split])
+                    ]
+
+        train_downloaded_files = dl_manager.download(data_urls["train"])
+        validation_downloaded_files = dl_manager.download(data_urls["validation"])
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}),
+               ]
+
+    def _generate_examples(self, filepaths):
+        """This function returns the examples in the raw (text) form by iterating on all the files."""
+        id_ = 0
+        for filepath in filepaths:
+            logger.info("generating examples from = %s", filepath)
+            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
+                for line in f:
+                    if line:
+                        example = json.loads(line)
+                        yield id_, example
+                        id_ += 1