Delete loading script

CLISTER.py

@@ -1,159 +0,0 @@
-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