Update testdataset.py

testdataset.py

@@ -24,7 +24,7 @@ testdataset
 ├── testdataset.zip
 """
 
-import json
+import csv
 import os
 from dataclasses import dataclass
 from typing import Dict, List, Tuple
@@ -125,41 +125,42 @@ class MedNLIDataset(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
-        if self.config.data_dir is None:
+        data_dir = self.config.data_dir
+        if data_dir is None:
             raise ValueError(
                 "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
             )
-        else:
-            extract_dir = dl_manager.extract(
-                os.path.join(
-                    self.config.data_dir,
-                    "mednli-a-natural-language-inference-dataset-for-the-clinical-domain-1.0.0.zip",
-                )
-            )
-            data_dir = os.path.join(
-                extract_dir,
-                "mednli-a-natural-language-inference-dataset-for-the-clinical-domain-1.0.0",
-            )
+        # else:
+        #     extract_dir = dl_manager.extract(
+        #         os.path.join(
+        #             self.config.data_dir,
+        #             "mednli-a-natural-language-inference-dataset-for-the-clinical-domain-1.0.0.zip",
+        #         )
+        #     )
+        #     data_dir = os.path.join(
+        #         extract_dir,
+        #         "mednli-a-natural-language-inference-dataset-for-the-clinical-domain-1.0.0",
+        #     )
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "mli_train_v1.jsonl"),
+                    "filepath": os.path.join(data_dir, "train.csv"),
                     "split": "train",
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "mli_test_v1.jsonl"),
+                    "filepath": os.path.join(data_dir, "test.csv"),
                     "split": "test",
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "mli_dev_v1.jsonl"),
+                    "filepath": os.path.join(data_dir, "dev.csv"),
                     "split": "dev",
                 },
             ),
@@ -168,17 +169,17 @@ class MedNLIDataset(datasets.GeneratorBasedBuilder):
     def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
         with open(filepath, "r") as f:
             if self.config.schema == "source":
-                for line in f:
-                    json_line = json.loads(line)
-                    yield json_line["pairID"], json_line
-
-            elif self.config.schema == "bigbio_te":
-                for line in f:
-                    json_line = json.loads(line)
-                    entailment_example = {
-                        "id": json_line["pairID"],
-                        "premise": json_line["sentence1"],
-                        "hypothesis": json_line["sentence2"],
-                        "label": json_line["gold_label"],
-                    }
-                    yield json_line["pairID"], entailment_example
+                test = csv.reader(f)
+                for row in test:
+                    yield row
+
+            # elif self.config.schema == "bigbio_te":
+            #     for line in f:
+            #         json_line = json.loads(line)
+            #         entailment_example = {
+            #             "id": json_line["pairID"],
+            #             "premise": json_line["sentence1"],
+            #             "hypothesis": json_line["sentence2"],
+            #             "label": json_line["gold_label"],
+            #         }
+            #         yield json_line["pairID"], entailment_example