Add first version of the dataset script

README.md

@@ -1,8 +1,47 @@
 ---
 license: other
+dataset_info:
+- config_name: public
+  features:
+  - name: src_tokens
+    sequence: string
+  - name: tgt_tokens
+    sequence: string
+  - name: corrections
+    list:
+    - name: idx_src
+      sequence: int32
+    - name: idx_tgt
+      sequence: int32
+    - name: corr_type
+      dtype: string
+  splits:
+  - name: train
+  download_size: 0
+  dataset_size: 0
+- config_name: private
+  features:
+  - name: src_tokens
+    sequence: string
+  - name: tgt_tokens
+    sequence: string
+  - name: corrections
+    list:
+    - name: idx_src
+      sequence: int32
+    - name: idx_tgt
+      sequence: int32
+    - name: corr_type
+      dtype: string
+  splits:
+  - name: train
+  download_size: 0
+  dataset_size: 0
 ---
 
 **Important**: This is only a script for loading the data, but the data itself is private. The script will only work in case you have access to the data, which you may request for non-commercial purposes [here](https://sterling8.d2.comp.nus.edu.sg/nucle_download/nucle.php).  
 ```python
-data = datasets.load_dataset("matejklemen/nucle", data_dir=<dir-of-private-data>, ignore_verifications=True)"
-```
+data = datasets.load_dataset("matejklemen/nucle", "private", data_dir=<dir-of-private-data>, ignore_verifications=True)"
+```  
+The `ignore_verifications=True` is important as the datasets library initially builds validation statistics that it verifies against, 
+and these cannot be correctly computed when the data is not public.  

nucle.py

@@ -0,0 +1,145 @@
+import logging
+import os
+from copy import deepcopy
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{dahlmeier-etal-2013-building,
+    title = "Building a Large Annotated Corpus of Learner {E}nglish: The {NUS} Corpus of Learner {E}nglish",
+    author = "Dahlmeier, Daniel  and
+      Ng, Hwee Tou  and
+      Wu, Siew Mei",
+    booktitle = "Proceedings of the Eighth Workshop on Innovative Use of {NLP} for Building Educational Applications",
+    month = jun,
+    year = "2013",
+    url = "https://aclanthology.org/W13-1703",
+    pages = "22--31",
+}
+"""
+
+_DESCRIPTION = """\
+The National University of Singapore Corpus of Learner English (NUCLE) consists of 1,400 essays written by mainly Asian undergraduate students at the National University of Singapore
+"""
+
+
+_HOMEPAGE = "https://www.comp.nus.edu.sg/~nlp/corpora.html"
+
+_LICENSE = "other"
+
+_URLS = {
+    "dummy_link": "https://example.com/"
+}
+
+
+class NUCLE(datasets.GeneratorBasedBuilder):
+    """NUCLE dataset for grammatical error correction"""
+
+    VERSION = datasets.Version("3.3.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="public", version=VERSION, description="Dummy public config so that datasets tests pass"),
+        datasets.BuilderConfig(name="private", version=VERSION, description="Actual config used for loading the data")
+    ]
+
+    DEFAULT_CONFIG_NAME = "public"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "src_tokens": datasets.Sequence(datasets.Value("string")),
+                "tgt_tokens": datasets.Sequence(datasets.Value("string")),
+                "corrections": [{
+                    "idx_src": datasets.Sequence(datasets.Value("int32")),
+                    "idx_tgt": datasets.Sequence(datasets.Value("int32")),
+                    "corr_type": datasets.Value("string")
+                }]
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        file_path = f"dummy.m2"
+        if self.config.name == "private":
+            data_dir = dl_manager.manual_dir
+            if data_dir is not None:
+                file_path = os.path.join(data_dir, "nucle.train.gold.bea19.m2")
+            else:
+                logging.warning("Manual data_dir not provided, so the data will not be loaded")
+        else:
+            logging.warning("The default config 'public' is intended to enable passing the tests and loading the "
+                            "private data separately. If you have access to the data, please use the config 'private' "
+                            "and provide the directory to the BEA19-formatted data as `data_dir=...`")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"file_path": file_path}
+            )
+        ]
+
+    def _generate_examples(self, file_path):
+        if not os.path.exists(file_path):
+            return
+
+        skip_edits = {"noop", "UNK", "Um"}
+        with open(file_path, "r", encoding="utf-8") as f:
+            idx_ex = 0
+            src_sent, tgt_sent, corrections, offset = None, None, [], 0
+            for idx_line, _line in enumerate(f):
+                line = _line.strip()
+
+                if len(line) > 0:
+                    prefix, remainder = line[0], line[2:]
+                    if prefix == "S":
+                        src_sent = remainder.split(" ")
+                        tgt_sent = deepcopy(src_sent)
+
+                    elif prefix == "A":
+                        annotation_data = remainder.split("|||")
+                        idx_start, idx_end = map(int, annotation_data[0].split(" "))
+                        edit_type, edit_text = annotation_data[1], annotation_data[2]
+                        if edit_type in skip_edits:
+                            continue
+
+                        formatted_correction = {
+                            "idx_src": list(range(idx_start, idx_end)),
+                            "idx_tgt": [],
+                            "corr_type": edit_type
+                        }
+                        annotator_id = int(annotation_data[-1])
+                        assert annotator_id == 0
+
+                        removal = len(edit_text) == 0 or edit_text == "-NONE-"
+                        if removal:
+                            for idx_to_remove in range(idx_start, idx_end):
+                                del tgt_sent[offset + idx_to_remove]
+                                offset -= 1
+
+                        else:  # replacement/insertion
+                            edit_tokens = edit_text.split(" ")
+                            len_diff = len(edit_tokens) - (idx_end - idx_start)
+
+                            formatted_correction["idx_tgt"] = list(
+                                range(offset + idx_start, offset + idx_end + len_diff))
+                            tgt_sent[offset + idx_start: offset + idx_end] = edit_tokens
+                            offset += len_diff
+
+                        corrections.append(formatted_correction)
+
+                else:  # empty line, indicating end of example
+                    yield idx_ex, {
+                        "src_tokens": src_sent,
+                        "tgt_tokens": tgt_sent,
+                        "corrections": corrections
+                    }
+                    src_sent, tgt_sent, corrections, offset = None, None, [], 0
+                    idx_ex += 1