update doc and tests

docs/source/index.rst

@@ -40,6 +40,7 @@ The library currently contains PyTorch implementations, pre-trained model weight
     :maxdepth: 2
     :caption: Package Reference
 
+    model_doc/auto
     model_doc/bert
     model_doc/gpt
     model_doc/transformerxl

docs/source/model_doc/auto.rst

@@ -1,9 +1,15 @@
-AutoModel, AutoConfig and AutoTokenizer - Standard derived classes
----------------------------------------------------------------
+AutoModels
+-----------
 
-In many case, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the ``from_pretrained`` method.
+In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the ``from_pretrained`` method.
+
+AutoClasses are here to do this job for you so that you automatically retreive the relevant model given the name/path to the pretrained weights/config/vocabulary.
+
+There are two types of AutoClasses:
+
+- ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer``: instantiating these ones will directly create a class of the relevant architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of ``BertModel``)
+- All the others (``AutoModelWithLMHead``, ``AutoModelForSequenceClassification``...)  are standardized Auto classes for finetuning. Instantiating these will create instance of the same class (``AutoModelWithLMHead``, ``AutoModelForSequenceClassification``...) comprising (i) the relevant base model class (as mentioned just above) and (ii) a standard fine-tuning head on top, convenient for the task.
 
-Auto classes are here to do this job for you so that you automatically retreive the relevant model given the name/path to the pretrained weights/config/vocabulary.
 
 ``AutoConfig``
 ~~~~~~~~~~~~~~~~~~~~~
@@ -19,6 +25,20 @@ Auto classes are here to do this job for you so that you automatically retreive
     :members:
 
 
+``AutoModelWithLMHead``
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.AutoModelWithLMHead
+    :members:
+
+
+``AutoModelForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: pytorch_transformers.AutoModelForSequenceClassification
+    :members:
+
+
 ``AutoTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

pytorch_transformers/__init__.py

@@ -1,4 +1,5 @@
 __version__ = "1.0.0"
+from .tokenization_auto import AutoTokenizer
 from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
 from .tokenization_openai import OpenAIGPTTokenizer
 from .tokenization_transfo_xl import (TransfoXLTokenizer, TransfoXLCorpus)

pytorch_transformers/tests/tokenization_auto_test.py

@@ -0,0 +1,46 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team 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.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import unittest
+import shutil
+import pytest
+import logging
+
+from pytorch_transformers import AutoTokenizer, BertTokenizer, AutoTokenizer, GPT2Tokenizer
+from pytorch_transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
+from pytorch_transformers.modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_MAP
+
+
+class AutoTokenizerTest(unittest.TestCase):
+    def test_tokenizer_from_pretrained(self):
+        logging.basicConfig(level=logging.INFO)
+        for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            self.assertIsNotNone(tokenizer)
+            self.assertIsInstance(tokenizer, BertTokenizer)
+            self.assertGreater(len(tokenizer), 0)
+
+        for model_name in list(GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            self.assertIsNotNone(tokenizer)
+            self.assertIsInstance(tokenizer, GPT2Tokenizer)
+            self.assertGreater(len(tokenizer), 0)
+
+
+if __name__ == "__main__":
+    unittest.main()