Keep original files for reproduction.

_attic/CLISTER.py

@@ -0,0 +1,159 @@
+import json
+import random
+
+import datasets
+import numpy as np
+import pandas as pd
+
+_CITATION = """\
+@inproceedings{hiebel:cea-03740484,
+  TITLE = {{CLISTER: A corpus for semantic textual similarity in French clinical narratives}},
+  AUTHOR = {Hiebel, Nicolas and Ferret, Olivier and Fort, Kar{\"e}n and N{\'e}v{\'e}ol, Aur{\'e}lie},
+  URL = {https://hal-cea.archives-ouvertes.fr/cea-03740484},
+  BOOKTITLE = {{LREC 2022 - 13th Language Resources and Evaluation Conference}},
+  ADDRESS = {Marseille, France},
+  PUBLISHER = {{European Language Resources Association}},
+  SERIES = {LREC 2022 - Proceedings of the 13th Conference on Language Resources and Evaluation},
+  VOLUME = {2022},
+  PAGES = {4306‑4315},
+  YEAR = {2022},
+  MONTH = Jun,
+  KEYWORDS = {Semantic Similarity ; Corpus Development ; Clinical Text ; French ; Semantic Similarity},
+  PDF = {https://hal-cea.archives-ouvertes.fr/cea-03740484/file/2022.lrec-1.459.pdf},
+  HAL_ID = {cea-03740484},
+  HAL_VERSION = {v1},
+}
+"""
+
+_DESCRIPTION = """\
+Modern Natural Language Processing relies on the availability of annotated corpora for training and \
+evaluating models. Such resources are scarce, especially for specialized domains in languages other \
+than English. In particular, there are very few resources for semantic similarity in the clinical domain \
+in French. This can be useful for many biomedical natural language processing applications, including \
+text generation. We introduce a definition of similarity that is guided by clinical facts and apply it \
+to the development of a new French corpus of 1,000 sentence pairs manually annotated according to \
+similarity scores. This new sentence similarity corpus is made freely available to the community. We \
+further evaluate the corpus through experiments of automatic similarity measurement. We show that a \
+model of sentence embeddings can capture similarity with state of the art performance on the DEFT STS \
+shared task evaluation data set (Spearman=0.8343). We also show that the CLISTER corpus is complementary \
+to DEFT STS. \
+"""
+
+_HOMEPAGE = "https://gitlab.inria.fr/codeine/clister"
+
+_LICENSE = "unknown"
+
+_URL = "data.zip"
+
+
+class CLISTER(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_CONFIG_NAME = "source"
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="source", version="1.0.0", description="The CLISTER corpora"),
+    ]
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "document_1_id": datasets.Value("string"),
+                "document_2_id": datasets.Value("string"),
+                "text_1": datasets.Value("string"),
+                "text_2": datasets.Value("string"),
+                "label": datasets.Value("float"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        data_dir = dl_manager.download_and_extract(_URL).rstrip("/")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "csv_file": data_dir + "/train.csv",
+                    "json_file": data_dir + "/id_to_sentence_train.json",
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "csv_file": data_dir + "/train.csv",
+                    "json_file": data_dir + "/id_to_sentence_train.json",
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "csv_file": data_dir + "/test.csv",
+                    "json_file": data_dir + "/id_to_sentence_test.json",
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, csv_file, json_file, split):
+
+        all_res = []
+
+        key = 0
+
+        # Load JSON file
+        f_json = open(json_file)
+        data_map = json.load(f_json)
+        f_json.close()
+
+        # Load CSV file
+        df = pd.read_csv(csv_file, sep="\t")
+
+        for index, e in df.iterrows():
+
+            all_res.append({
+                "id": str(key),
+                "document_1_id": e["id_1"],
+                "document_2_id": e["id_2"],
+                "text_1": data_map["_".join(e["id_1"].split("_")[0:2])],
+                "text_2": data_map["_".join(e["id_2"].split("_")[0:2])],
+                "label": float(e["sim"]),
+            })
+
+            key += 1
+
+        if split != "test":
+
+            ids = [r["id"] for r in all_res]
+
+            random.seed(4)
+            random.shuffle(ids)
+            random.shuffle(ids)
+            random.shuffle(ids)
+
+            train, validation = np.split(ids, [int(len(ids)*0.8333)])
+
+            if split == "train":
+                allowed_ids = list(train)
+            elif split == "validation":
+                allowed_ids = list(validation)
+
+            for r in all_res:
+                if r["id"] in allowed_ids:
+                    yield r["id"], r
+        else:
+
+            for r in all_res:
+                yield r["id"], r

_attic/data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3cc1341af046106cda69082185bc911076d29a3f18ac28866f72d73a3d97fe85
+size 70776