Add first version of dataset script

README.md

@@ -1,3 +1,29 @@
 ---
 license: other
+dataset_info:
+  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
+    num_bytes: 8658209
+    num_examples: 28350
+  - name: validation
+    num_bytes: 668073
+    num_examples: 2191
+  - name: test
+    num_bytes: 823872
+    num_examples: 2695
+  download_size: 2774021
+  dataset_size: 10150154
 ---

clc_fce.py

@@ -0,0 +1,134 @@
+import os
+from copy import deepcopy
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{yannakoudakis-etal-2011-new,
+    title = "A New Dataset and Method for Automatically Grading {ESOL} Texts",
+    author = "Yannakoudakis, Helen  and
+      Briscoe, Ted  and
+      Medlock, Ben",
+    booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
+    month = jun,
+    year = "2011",
+    url = "https://aclanthology.org/P11-1019",
+    pages = "180--189",
+}
+"""
+
+_DESCRIPTION = """\
+The CLC FCE Dataset is a set of 1,244 exam scripts written by candidates sitting the Cambridge ESOL First Certificate 
+in English (FCE) examination in 2000 and 2001. The dataset exposes the sentence-level pre-tokenized M2 version, totaling 
+33236 sentences.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = "Custom, allowed for non-commercial research and educational purposes"
+
+_URLS = {
+    "clc_fce_bea19": "https://www.cl.cam.ac.uk/research/nl/bea2019st/data/fce_v2.1.bea19.tar.gz"
+}
+
+
+class CLCFCE(datasets.GeneratorBasedBuilder):
+    """Cambridge Learner Corpus: First Certificate in English"""
+
+    VERSION = datasets.Version("2.1.0")
+
+    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):
+        urls = _URLS["clc_fce_bea19"]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"file_path": os.path.join(data_dir, "fce", "m2", "fce.train.gold.bea19.m2")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"file_path": os.path.join(data_dir, "fce", "m2", "fce.dev.gold.bea19.m2")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"file_path": os.path.join(data_dir, "fce", "m2", "fce.test.gold.bea19.m2")},
+            ),
+        ]
+
+    def _generate_examples(self, file_path):
+        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
+