Upload clean.py with huggingface_hub

clean.py

@@ -0,0 +1,105 @@
+"""
+{
+    "document": "",
+    "question": "",
+    "long_answer_candidates": ["", "", ""],
+    "long_answer_candidate_index": 0,
+    "short_answers": ["", "", ""]
+}
+"""
+
+
+import sys
+import jsonlines
+from datasets import load_dataset
+from huggingface_hub import HfApi
+
+
+def clean(raw, path):
+    fp = open(path, "a")
+    writer = jsonlines.Writer(fp)
+
+    count = 0
+    dataset = []
+    for data in raw:
+        try:
+            document = ""
+
+            startmax, endmax = max(data["document"]["tokens"]["start_byte"]), max(data["document"]["tokens"]["end_byte"])
+
+            start2token, end2start = [-1] * (startmax + 1), [-1] * (endmax + 1)
+
+            tokens = data["document"]["tokens"]
+            for i in range(len(tokens["token"])):
+                start2token[tokens["start_byte"][i]] = {
+                    "token": tokens["token"][i],
+                    "is_html": tokens["is_html"][i]
+                }
+
+                end2start[tokens["end_byte"][i]] = tokens["start_byte"][i]
+
+                if not(tokens["is_html"][i]):
+                    document += tokens["token"][i] + " "
+
+            candidates = []
+            for i in range(len(data["long_answer_candidates"]["start_byte"])):
+                candidates.append(" ".join(start2token[j]["token"] for j in range(data["long_answer_candidates"]["start_byte"][i], end2start[data["long_answer_candidates"]["end_byte"][i]]) if (start2token[j] != -1) and not(start2token[j]["is_html"])))
+
+            short_answers = list(map(lambda x: x["text"][0] if x["text"] else "", data["annotations"]["short_answers"]))
+
+            dataset.append({
+                "id": data["id"],
+                "document": document,
+                "question": data["question"]["text"],
+                "long_answer_candidates": candidates,
+                "long_answer_candidate_index": data["annotations"]["long_answer"][0]["candidate_index"],
+                "short_answers": short_answers
+            })
+        except Exception as ex:
+            # raise ex
+            print("Exception: " + str(ex))
+
+        if (count + 1) % 1000 == 0:
+            writer.write_all(dataset)
+            dataset = []
+
+        print("Done: " + str(count), end="\r")
+        count += 1
+
+    if dataset:
+        writer.write_all(dataset)
+
+    writer.close()
+    fp.close()
+
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 1:
+        raise AttributeError("Missing required argument: repository id")
+
+    repo = sys.argv[1]
+
+    api = HfApi()
+
+    train = load_dataset("natural_questions", split="train", streaming=True)
+    train_path = "data/train.jsonl"
+    clean(train, train_path)
+    
+    api.upload_file(
+        path_or_fileobj=train_path,
+        path_in_repo="train.jsonl",
+        repo_id=repo,
+        repo_type="dataset",
+    )
+
+    val = load_dataset("natural_questions", split="validation", streaming=True)
+    val_path = "data/validation.jsonl"
+    clean(val, val_path)
+
+    api.upload_file(
+        path_or_fileobj=val_path,
+        path_in_repo="validation.jsonl",
+        repo_id=repo,
+        repo_type="dataset",
+    )