updating examples and doc

examples/run_glue.py

@@ -13,7 +13,7 @@
 # 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.
-""" Finetuning a classification model (Bert, XLM, XLNet,...) on GLUE."""
+""" Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet)."""
 
 from __future__ import absolute_import, division, print_function
 
@@ -230,6 +230,9 @@ def evaluate(args, model, tokenizer, prefix=""):
                 logger.info("  %s = %s", key, str(result[key]))
                 writer.write("%s = %s\n" % (key, str(result[key])))
 
+    if args.local_rank in [-1, 0]:
+        tb_writer.close()
+
     return results
 
 
@@ -242,7 +245,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
         list(filter(None, args.model_name.split('/'))).pop(),
         str(args.max_seq_length),
         str(task)))
-    if os.path.exists(cached_features_file) and not args.overwrite_cache:
+    if os.path.exists(cached_features_file):
         logger.info("Loading features from cached file %s", cached_features_file)
         features = torch.load(cached_features_file)
     else:
@@ -410,7 +413,7 @@ def main():
     if args.local_rank == 0:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
 
-    # Distributed and parrallel training
+    # Distributed and parallel training
     model.to(args.device)
     if args.local_rank != -1:
         model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],