Added CamembertForQuestionAnswering (#2746)

* Added CamembertForQuestionAnswering

* fixed camembert tokenizer case

examples/run_squad.py

@@ -38,6 +38,9 @@ from transformers import (
     BertConfig,
     BertForQuestionAnswering,
     BertTokenizer,
+    CamembertConfig,
+    CamembertForQuestionAnswering,
+    CamembertTokenizer,
     DistilBertConfig,
     DistilBertForQuestionAnswering,
     DistilBertTokenizer,
@@ -70,12 +73,16 @@ except ImportError:
 logger = logging.getLogger(__name__)
 
 ALL_MODELS = sum(
-    (tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, RobertaConfig, XLNetConfig, XLMConfig)),
+    (
+        tuple(conf.pretrained_config_archive_map.keys())
+        for conf in (BertConfig, CamembertConfig, RobertaConfig, XLNetConfig, XLMConfig)
+    ),
     (),
 )
 
 MODEL_CLASSES = {
     "bert": (BertConfig, BertForQuestionAnswering, BertTokenizer),
+    "camembert": (CamembertConfig, CamembertForQuestionAnswering, CamembertTokenizer),
     "roberta": (RobertaConfig, RobertaForQuestionAnswering, RobertaTokenizer),
     "xlnet": (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
     "xlm": (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
@@ -212,7 +219,7 @@ def train(args, train_dataset, model, tokenizer):
                 "end_positions": batch[4],
             }
 
-            if args.model_type in ["xlm", "roberta", "distilbert"]:
+            if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                 del inputs["token_type_ids"]
 
             if args.model_type in ["xlnet", "xlm"]:
@@ -327,7 +334,7 @@ def evaluate(args, model, tokenizer, prefix=""):
                 "token_type_ids": batch[2],
             }
 
-            if args.model_type in ["xlm", "roberta", "distilbert"]:
+            if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                 del inputs["token_type_ids"]
 
             example_indices = batch[3]

src/transformers/__init__.py

@@ -221,6 +221,7 @@ if is_torch_available():
         CamembertModel,
         CamembertForSequenceClassification,
         CamembertForTokenClassification,
+        CamembertForQuestionAnswering,
         CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
     )
     from .modeling_distilbert import (

src/transformers/data/processors/squad.py

@@ -123,7 +123,7 @@ def squad_convert_example_to_features(example, max_seq_length, doc_stride, max_q
     truncated_query = tokenizer.encode(example.question_text, add_special_tokens=False, max_length=max_query_length)
     sequence_added_tokens = (
         tokenizer.max_len - tokenizer.max_len_single_sentence + 1
-        if "roberta" in str(type(tokenizer))
+        if "roberta" in str(type(tokenizer)) or "camembert" in str(type(tokenizer))
         else tokenizer.max_len - tokenizer.max_len_single_sentence
     )
     sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair

src/transformers/modeling_camembert.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 """PyTorch CamemBERT model. """
 
-
 import logging
 
 from .configuration_camembert import CamembertConfig
@@ -23,6 +22,7 @@ from .file_utils import add_start_docstrings
 from .modeling_roberta import (
     RobertaForMaskedLM,
     RobertaForMultipleChoice,
+    RobertaForQuestionAnswering,
     RobertaForSequenceClassification,
     RobertaForTokenClassification,
     RobertaModel,
@@ -37,7 +37,6 @@ CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
     "umberto-wikipedia-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-wikipedia-uncased-v1/pytorch_model.bin",
 }
 
-
 CAMEMBERT_START_DOCSTRING = r"""
 
     This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
@@ -46,7 +45,8 @@ CAMEMBERT_START_DOCSTRING = r"""
 
     Parameters:
         config (:class:`~transformers.CamembertConfig`): Model configuration class with all the parameters of the
-            model. Initializing with a config file does not load the weights associated with the model, only the configuration.
+            model. Initializing with a config file does not load the weights associated with the model, only the
+            configuration.
             Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
@@ -121,3 +121,18 @@ class CamembertForTokenClassification(RobertaForTokenClassification):
 
     config_class = CamembertConfig
     pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+
+
+@add_start_docstrings(
+    """CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD
+    (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits` """,
+    CAMEMBERT_START_DOCSTRING,
+)
+class CamembertForQuestionAnswering(RobertaForQuestionAnswering):
+    """
+    This class overrides :class:`~transformers.RobertaForQuestionAnswering`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = CamembertConfig
+    pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP