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Author: daven

data/data_lm.py

@@ -0,0 +1,80 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""AMR dataset."""
+import pickle
+from inspect import EndOfBlock
+import json
+import os
+import gzip
+import datasets
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Any, ClassVar, Dict, List, Optional
+from dataclasses import InitVar, dataclass, field, fields
+from datasets.features.features import string_to_arrow
+import pyarrow as pa
+from tqdm import tqdm
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_DESCRIPTION = """
+
+There are three features:
+  - src: text.
+  - tgt: Linearized AMR.
+"""
+
+_TEXT = "text"
+
+class InnerSpeechData(datasets.GeneratorBasedBuilder):
+    """AMR Dataset."""
+
+    # Version 1.0.0 expands coverage, includes ids, and removes web contents.
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {_TEXT: datasets.Value("string"),}
+            ),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        train_path = self.config.data_files["train"]
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        logger.info("generating examples from = %s", filepath[0])
+        text = []
+        with open(filepath[0], "r") as f:
+            line = f.readline()
+            while line and len(text)<500000:
+                d = json.loads(line)
+                t = d["text"]
+                text.append(t)
+                line = f.readline()
+        print(f"total data num: {len(text)}")
+        for idx in range(len(text)):
+            yield idx, {_TEXT: text[idx]}