Add optional HuggingFace dataset support

tfkit/utility/dataset.py

@@ -7,6 +7,11 @@
 from torch.utils import data
 from tqdm.contrib.concurrent import process_map
 
+try:
+    from datasets import load_dataset
+except Exception:  # pragma: no cover - optional dependency
+    load_dataset = None
+
 
 def get_dataset(file_path, task_class, tokenizer, parameter):
     panel = nlp2.Panel()
@@ -18,6 +23,14 @@ def get_dataset(file_path, task_class, tokenizer, parameter):
     #         panel.add_element(k=missarg, v=all_arg[missarg], msg=missarg, default=all_arg[missarg])
     #     filled_arg = panel.get_result_dict()
     #     parameter.update(filled_arg)
+    if load_dataset is not None and not os.path.isfile(file_path):
+        try:
+            hf_ds = load_dataset(file_path, split=parameter.get('split', 'train'))
+            return HFDataset(hf_ds, tokenizer=tokenizer,
+                             preprocessor=task_class.Preprocessor,
+                             preprocessing_arg=parameter)
+        except Exception:
+            pass
     ds = TFKitDataset(fpath=file_path, tokenizer=tokenizer,
                       preprocessor=task_class.Preprocessor,
                       preprocessing_arg=parameter)
@@ -76,3 +89,33 @@ def __getitem__(self, idx):
             {**{'task_dict': self.task_dict}, **{key: self.sample[key][idx] for key in self.sample.keys()}},
             self.tokenizer,
             maxlen=self.preprocessor.parameters['maxlen'])
+
+
+class HFDataset(data.Dataset):
+    """Dataset wrapper for the HuggingFace datasets library."""
+
+    def __init__(self, hf_dataset, tokenizer, preprocessor, preprocessing_arg=None):
+        preprocessing_arg = preprocessing_arg or {}
+        self.task_dict = {}
+        self.sample = defaultdict(list)
+        self.preprocessor = preprocessor(tokenizer, kwargs=preprocessing_arg)
+        self.tokenizer = tokenizer
+
+        print("Start preprocessing with HuggingFace dataset...")
+        length = 0
+        for raw_item in hf_dataset:
+            for items in self.preprocessor.preprocess(raw_item):
+                length += 1
+                for k, v in items.items():
+                    self.sample[k].append(v)
+        self.length = length
+        self.task = self.task_dict
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, idx):
+        return self.preprocessor.postprocess(
+            {**{'task_dict': self.task_dict}, **{key: self.sample[key][idx] for key in self.sample.keys()}},
+            self.tokenizer,
+            maxlen=self.preprocessor.parameters['maxlen'])