wip examples

examples/run_bert_squad.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.
-"""Run BERT on SQuAD."""
+""" Finetuning a question-answering Bert model on SQuAD."""
 
 from __future__ import absolute_import, division, print_function
 

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.
-"""BERT finetuning runner."""
+""" Finetuning a classification model (Bert, XLM, XLNet,...) on GLUE."""
 
 from __future__ import absolute_import, division, print_function
 

examples/run_squad.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.
-"""Run BERT on SQuAD."""
+""" Finetuning a question-answering model (Bert, XLM, XLNet,...) on SQuAD."""
 
 from __future__ import absolute_import, division, print_function
 
@@ -21,7 +21,6 @@ import argparse
 import logging
 import os
 import random
-import sys
 from io import open
 
 import numpy as np
@@ -33,31 +32,35 @@ from tqdm import tqdm, trange
 
 from tensorboardX import SummaryWriter
 
-from pytorch_transformers import (BertForQuestionAnswering, XLNetForQuestionAnswering,
+from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
-                                  XLMForQuestionAnswering, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+                                  BertForQuestionAnswering, BertTokenizer,
-                                  XLNET_PRETRAINED_MODEL_ARCHIVE_MAP, XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
+                                  XLMConfig, XLMForQuestionAnswering,
-from pytorch_transformers import (BertTokenizer, XLNetTokenizer,
+                                  XLMTokenizer, XLNetConfig,
-                                  XLMTokenizer)
+                                  XLNetForQuestionAnswering,
+                                  XLNetTokenizer)
+
+from pytorch_transformers import AdamW, WarmupLinearSchedule
 
 from utils_squad import read_squad_examples, convert_examples_to_features, RawResult, write_predictions
 
 logger = logging.getLogger(__name__)
 
-ALL_MODELS = sum((tuple(m.keys()) for m in (BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) \
-                                            XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
+                  for conf in (BertConfig, XLNetConfig, XLMConfig)), ())
-                                            XLM_PRETRAINED_MODEL_ARCHIVE_MAP)), ())
 
 MODEL_CLASSES = {
-    'bert': BertForQuestionAnswering,
+    'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer),
-    'xlnet': XLNetForQuestionAnswering,
+    'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
-    'xlm': XLMForQuestionAnswering,
+    'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
 }
 
-TOKENIZER_CLASSES = {
+def set_seed(args):
-    'bert': BertTokenizer,
+    random.seed(args.seed)
-    'xlnet': XLNetTokenizer,
+    np.random.seed(args.seed)
-    'xlm': XLMTokenizer,
+    torch.manual_seed(args.seed)
-}
+    if args.n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
 
 def train(args, train_dataset, model):
     """ Train the model """