Updating documentation and adding requirements.txt

README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-**BERT**, or **B**idirectional **E**mbedding **R**epresentations from
+**BERT**, or **B**idirectional **E**ncoder **R**epresentations from
 **T**ransformers, is a new method of pre-training language representations which
 obtains state-of-the-art results on a wide array of Natural Language Processing
 (NLP) tasks.
@@ -205,7 +205,8 @@ the following flags to `run_classifier.py` or `run_squad.py`:
 
 Please see the
 [Google Cloud TPU tutorial](https://cloud.google.com/tpu/docs/tutorials/mnist)
-for how to use Cloud TPUs.
+for how to use Cloud TPUs. Alternatively, you can use the Codalab notebook
+"[BERT FineTuning with Cloud TPUs](https://colab.sandbox.google.com/github/tensorflow/tpu/blob/master/tools/colab/bert_finetuning_with_cloud_tpus.ipynb)".
 
 On Cloud TPUs, the pretrained model and the output directory will need to be on
 Google Cloud Storage. For example, if you have a bucket named `some_bucket`, you

modeling.py

@@ -12,7 +12,7 @@
 # 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.
-"""Common utility functions related to TensorFlow."""
+"""The main BERT model and related functions."""
 
 from __future__ import absolute_import
 from __future__ import division

requirements.txt

+# This may work with versions of TensorFlow back to 1.7.0, but has not been
+# tested.
+
+tensorflow >= 1.11.0   # CPU Version of TensorFlow.
+# tensorflow-gpu  >= 1.11.0  # GPU version of TensorFlow.