add problems, conll2002_es_ner and conll2002_nl_ner (tensorflow#1253)

Author: yuwen-yan

tensor2tensor/data_generators/conll_ner.py

@@ -0,0 +1,84 @@
+# coding=utf-8
+# Copyright 2018 The Tensor2Tensor 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.
+
+"""Data generators for CoNLL dataset."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import zipfile
+
+from tensor2tensor.data_generators import generator_utils
+from tensor2tensor.data_generators import problem
+from tensor2tensor.data_generators import text_problems
+from tensor2tensor.utils import registry
+import tensorflow as tf
+
+@registry.register_problem
+class Conll2002Ner(text_problems.Text2textTmpdir):
+  """Base class for CoNLL2002 problems."""
+  def source_data_files(self, dataset_split):
+    """Files to be passed to generate_samples."""
+    raise NotImplementedError()
+
+  def generate_samples(self, data_dir, tmp_dir, dataset_split):
+    del data_dir
+
+    url = 'https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/packages/corpora/conll2002.zip' # pylint: disable=line-too-long
+    compressed_filename = os.path.basename(url)
+    compressed_filepath = os.path.join(tmp_dir, compressed_filename)
+    generator_utils.maybe_download(tmp_dir, compressed_filename, url)
+
+    compressed_dir = compressed_filepath.strip(".zip")
+
+    filenames = self.source_data_files(dataset_split)
+    for filename in filenames:
+      filepath = os.path.join(compressed_dir, filename)
+      if not tf.gfile.Exists(filepath):
+        with zipfile.ZipFile(compressed_filepath, 'r') as corpus_zip:
+          corpus_zip.extractall(tmp_dir)
+      with tf.gfile.GFile(filepath, mode="r") as cur_file:
+        words, tags = [], []
+        for line in cur_file:
+          line_split = line.strip().split()
+          if len(line_split) == 0:
+            yield {"inputs": str.join(" ", words),
+                   "targets": str.join(" ", tags)}
+            words, tags = [], []
+            continue
+          words.append(line_split[0])
+          tags.append(line_split[2])
+        if len(words) != 0:
+          yield {"inputs": str.join(" ", words), "targets": str.join(" ", tags)}
+
+@registry.register_problem
+class Conll2002EsNer(Conll2002Ner):
+  """Problem spec for CoNLL2002 Spanish named entity task."""
+  TRAIN_FILES = ["esp.train"]
+  EVAL_FILES = ["esp.testa", "esp.testb"]
+  def source_data_files(self, dataset_split):
+    is_training = dataset_split == problem.DatasetSplit.TRAIN
+    return self.TRAIN_FILES if is_training else self.EVAL_FILES
+
+@registry.register_problem
+class Conll2002NlNer(Conll2002Ner):
+  """Problem spec for CoNLL2002 Dutch named entity task."""
+  TRAIN_FILES = ["ned.train"]
+  EVAL_FILES = ["ned.testa", "ned.testb"]
+  def source_data_files(self, dataset_split):
+    is_training = dataset_split == problem.DatasetSplit.TRAIN
+    return self.TRAIN_FILES if is_training else self.EVAL_FILES