BART for summarization training with CNN/DM using pytorch-lightning

examples/summarization/bart/utils.py

@@ -0,0 +1,43 @@
+import os
+
+from torch.utils.data import Dataset
+
+
+class SummarizationDataset(Dataset):
+    def __init__(self, tokenizer, data_dir="./cnn-dailymail/cnn_dm/", type_path="train", block_size=1024):
+        super(SummarizationDataset,).__init__()
+        self.tokenizer = tokenizer
+
+        self.source = []
+        self.target = []
+
+        print("loading " + type_path + " source.")
+
+        with open(os.path.join(data_dir, type_path + ".source"), "r") as f:
+            for text in f.readlines():  # each text is a line and a full story
+                tokenized = tokenizer.batch_encode_plus(
+                    [text], max_length=block_size, pad_to_max_length=True, return_tensors="pt"
+                )
+                self.source.append(tokenized)
+            f.close()
+
+        print("loading " + type_path + " target.")
+
+        with open(os.path.join(data_dir, type_path + ".target"), "r") as f:
+            for text in f.readlines():  # each text is a line and a summary
+                tokenized = tokenizer.batch_encode_plus(
+                    [text], max_length=56, pad_to_max_length=True, return_tensors="pt"
+                )
+                self.target.append(tokenized)
+            f.close()
+
+    def __len__(self):
+        return len(self.source)
+
+    def __getitem__(self, index):
+        source_ids = self.source[index]["input_ids"].squeeze()
+        target_ids = self.target[index]["input_ids"].squeeze()
+
+        src_mask = self.source[index]["attention_mask"].squeeze()  # might need to squeeze
+
+        return {"source_ids": source_ids, "source_mask": src_mask, "target_ids": target_ids}