Merge pull request #1 from huggingface/master

merege from original repo

examples/run_lm_finetuning.py

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-Fine-tuning the library models for language modeling on WikiText-2 (GPT, GPT-2, BERT, RoBERTa).
+Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
 GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
 using a masked language modeling (MLM) loss.
 """
@@ -247,7 +247,6 @@ def evaluate(args, model, tokenizer, prefix=""):
     # Loop to handle MNLI double evaluation (matched, mis-matched)
     eval_output_dir = args.output_dir
 
-    results = {}
     eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True)
 
     if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
@@ -289,7 +288,7 @@ def evaluate(args, model, tokenizer, prefix=""):
             logger.info("  %s = %s", key, str(result[key]))
             writer.write("%s = %s\n" % (key, str(result[key])))
 
-    return results
+    return result
 
 
 def main():

hubconf.py

-dependencies = ['torch', 'tqdm', 'boto3', 'requests', 'regex']
-
-from hubconfs.bert_hubconf import (
-    bertTokenizer,
-    bertModel,
-    bertForNextSentencePrediction,
-    bertForPreTraining,
-    bertForMaskedLM,
-    bertForSequenceClassification,
-    bertForMultipleChoice,
-    bertForQuestionAnswering,
-    bertForTokenClassification
-)
-from hubconfs.gpt_hubconf import (
-    openAIGPTTokenizer,
-    openAIGPTModel,
-    openAIGPTLMHeadModel,
-    openAIGPTDoubleHeadsModel
-)
-from hubconfs.gpt2_hubconf import (
-    gpt2Tokenizer,
-    gpt2Model,
-    gpt2LMHeadModel,
-    gpt2DoubleHeadsModel
-)
-from hubconfs.transformer_xl_hubconf import (
-    transformerXLTokenizer,
-    transformerXLModel,
-    transformerXLLMHeadModel
+from pytorch_transformers import (
+    AutoTokenizer, AutoConfig, AutoModel, AutoModelWithLMHead, AutoModelForSequenceClassification, AutoModelForQuestionAnswering
 )
+from pytorch_transformers.file_utils import add_start_docstrings
+
+dependencies = ['torch', 'tqdm', 'boto3', 'requests', 'regex', 'sentencepiece', 'sacremoses']
+
+@add_start_docstrings(AutoConfig.__doc__)
+def config(*args, **kwargs):
+    r""" 
+                # Using torch.hub !
+                import torch
+
+                config = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased')  # Download configuration from S3 and cache.
+                config = torch.hub.load('huggingface/pytorch-transformers', 'config', './test/bert_saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
+                config = torch.hub.load('huggingface/pytorch-transformers', 'config', './test/bert_saved_model/my_configuration.json')
+                config = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False)
+                assert config.output_attention == True
+                config, unused_kwargs = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False, return_unused_kwargs=True)
+                assert config.output_attention == True
+                assert unused_kwargs == {'foo': False}
+
+            """
+
+    return AutoConfig.from_pretrained(*args, **kwargs)
+
+
+@add_start_docstrings(AutoTokenizer.__doc__)
+def tokenizer(*args, **kwargs):
+    r""" 
+        # Using torch.hub !
+        import torch
+
+        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', 'bert-base-uncased')    # Download vocabulary from S3 and cache.
+        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', './test/bert_saved_model/')  # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
+
+    """
+
+    return AutoTokenizer.from_pretrained(*args, **kwargs)
+
+
+@add_start_docstrings(AutoModel.__doc__)
+def model(*args, **kwargs):
+    r"""
+            # Using torch.hub !
+            import torch
+
+            model = torch.hub.load('huggingface/pytorch-transformers', 'model', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+            model = torch.hub.load('huggingface/pytorch-transformers', 'model', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+            model = torch.hub.load('huggingface/pytorch-transformers', 'model', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
+            assert model.config.output_attention == True
+            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
+            config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
+            model = torch.hub.load('huggingface/pytorch-transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+
+        """
+
+    return AutoModel.from_pretrained(*args, **kwargs)
+
+@add_start_docstrings(AutoModelWithLMHead.__doc__)
+def modelWithLMHead(*args, **kwargs):
+    r"""
+        # Using torch.hub !
+        import torch
+
+        model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+        model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+        model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
+        assert model.config.output_attention == True
+        # Loading from a TF checkpoint file instead of a PyTorch model (slower)
+        config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
+        model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+
+    """
+    return AutoModelWithLMHead.from_pretrained(*args, **kwargs)
+
+
+@add_start_docstrings(AutoModelForSequenceClassification.__doc__)
+def modelForSequenceClassification(*args, **kwargs):
+    r"""
+            # Using torch.hub !
+            import torch
+
+            model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+            model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+            model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
+            assert model.config.output_attention == True
+            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
+            config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
+            model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+
+        """
+
+    return AutoModelForSequenceClassification.from_pretrained(*args, **kwargs)
+
+
+@add_start_docstrings(AutoModelForQuestionAnswering.__doc__)
+def modelForQuestionAnswering(*args, **kwargs):
+    r"""
+        # Using torch.hub !
+        import torch
+
+        model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+        model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+        model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
+        assert model.config.output_attention == True
+        # Loading from a TF checkpoint file instead of a PyTorch model (slower)
+        config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
+        model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+
+    """
+    return AutoModelForQuestionAnswering.from_pretrained(*args, **kwargs)

hubconfs/bert_hubconf.py

-from pytorch_transformers.tokenization_bert import BertTokenizer
-from pytorch_transformers.modeling_bert import (
-        BertModel,
-        BertForNextSentencePrediction,
-        BertForMaskedLM,
-        BertForMultipleChoice,
-        BertForPreTraining,
-        BertForQuestionAnswering,
-        BertForSequenceClassification,
-        BertForTokenClassification,
-        )
-
-# A lot of models share the same param doc. Use a decorator
-# to save typing
-bert_docstring = """
-    Params:
-        pretrained_model_name_or_path: either:
-            - a str with the name of a pre-trained model to load
-                . `bert-base-uncased`
-                . `bert-large-uncased`
-                . `bert-base-cased`
-                . `bert-large-cased`
-                . `bert-base-multilingual-uncased`
-                . `bert-base-multilingual-cased`
-                . `bert-base-chinese`
-                . `bert-base-german-cased`
-                . `bert-large-uncased-whole-word-masking`
-                . `bert-large-cased-whole-word-masking`
-            - a path or url to a pretrained model archive containing:
-                . `bert_config.json` a configuration file for the model
-                . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining
-                  instance
-            - a path or url to a pretrained model archive containing:
-                . `bert_config.json` a configuration file for the model
-                . `model.chkpt` a TensorFlow checkpoint
-        from_tf: should we load the weights from a locally saved TensorFlow
-                 checkpoint
-        cache_dir: an optional path to a folder in which the pre-trained models
-                   will be cached.
-        state_dict: an optional state dictionary
-                    (collections.OrderedDict object) to use instead of Google
-                    pre-trained models
-        *inputs, **kwargs: additional input for the specific Bert class
-            (ex: num_labels for BertForSequenceClassification)
-"""
-
-
-def _append_from_pretrained_docstring(docstr):
-    def docstring_decorator(fn):
-        fn.__doc__ = fn.__doc__ + docstr
-        return fn
-    return docstring_decorator
-
-
-def bertTokenizer(*args, **kwargs):
-    """
-    Instantiate a BertTokenizer from a pre-trained/customized vocab file
-    Args:
-    pretrained_model_name_or_path: Path to pretrained model archive
-                                   or one of pre-trained vocab configs below.
-                                       * bert-base-uncased
-                                       * bert-large-uncased
-                                       * bert-base-cased
-                                       * bert-large-cased
-                                       * bert-base-multilingual-uncased
-                                       * bert-base-multilingual-cased
-                                       * bert-base-chinese
-    Keyword args:
-    cache_dir: an optional path to a specific directory to download and cache
-               the pre-trained model weights.
-               Default: None
-    do_lower_case: Whether to lower case the input.
-                   Only has an effect when do_wordpiece_only=False
-                   Default: True
-    do_basic_tokenize: Whether to do basic tokenization before wordpiece.
-                       Default: True
-    max_len: An artificial maximum length to truncate tokenized sequences to;
-             Effective maximum length is always the minimum of this
-             value (if specified) and the underlying BERT model's
-             sequence length.
-             Default: None
-    never_split: List of tokens which will never be split during tokenization.
-                 Only has an effect when do_wordpiece_only=False
-                 Default: ["[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]"]
-
-    Example:
-        import torch
-        sentence = 'Hello, World!'
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
-        toks = tokenizer.tokenize(sentence)
-        ['Hello', '##,', 'World', '##!']
-        ids = tokenizer.convert_tokens_to_ids(toks)
-        [8667, 28136, 1291, 28125]
-    """
-    tokenizer = BertTokenizer.from_pretrained(*args, **kwargs)
-    return tokenizer
-
-
-@_append_from_pretrained_docstring(bert_docstring)
-def bertModel(*args, **kwargs):
-    """
-    BertModel is the basic BERT Transformer model with a layer of summed token,
-    position and sequence embeddings followed by a series of identical
-    self-attention blocks (12 for BERT-base, 24 for BERT-large).
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
-        #  Prepare tokenized input
-        text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-        tokenized_text = tokenizer.tokenize(text)
-        indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-        segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
-        tokens_tensor = torch.tensor([indexed_tokens])
-        segments_tensors = torch.tensor([segments_ids])
-        # Load bertModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'bertModel', 'bert-base-cased')
-        model.eval()
-        # Predict hidden states features for each layer
-        with torch.no_grad():
-                encoded_layers, _ = model(tokens_tensor, segments_tensors)
-    """
-    model = BertModel.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(bert_docstring)
-def bertForNextSentencePrediction(*args, **kwargs):
-    """
-    BERT model with next sentence prediction head.
-    This module comprises the BERT model followed by the next sentence
-    classification head.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
-        #  Prepare tokenized input
-        text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-        tokenized_text = tokenizer.tokenize(text)
-        indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-        segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
-        tokens_tensor = torch.tensor([indexed_tokens])
-        segments_tensors = torch.tensor([segments_ids])
-        # Load bertForNextSentencePrediction
-        model = torch.hub.load('huggingface/pytorch-transformers', 'bertForNextSentencePrediction', 'bert-base-cased')
-        model.eval()
-        # Predict the next sentence classification logits
-        with torch.no_grad():
-                next_sent_classif_logits = model(tokens_tensor, segments_tensors)
-    """
-    model = BertForNextSentencePrediction.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(bert_docstring)
-def bertForPreTraining(*args, **kwargs):
-    """
-    BERT model with pre-training heads.
-    This module comprises the BERT model followed by the two pre-training heads
-        - the masked language modeling head, and
-        - the next sentence classification head.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
-        #  Prepare tokenized input
-        text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-        tokenized_text = tokenizer.tokenize(text)
-        segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
-        tokens_tensor = torch.tensor([indexed_tokens])
-        segments_tensors = torch.tensor([segments_ids])
-        # Load bertForPreTraining
-        model = torch.hub.load('huggingface/pytorch-transformers', 'bertForPreTraining', 'bert-base-cased')
-        masked_lm_logits_scores, seq_relationship_logits = model(tokens_tensor, segments_tensors)
-    """
-    model = BertForPreTraining.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(bert_docstring)
-def bertForMaskedLM(*args, **kwargs):
-    """
-    BertForMaskedLM includes the BertModel Transformer followed by the
-    (possibly) pre-trained masked language modeling head.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
-        #  Prepare tokenized input
-        text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-        tokenized_text = tokenizer.tokenize(text)
-        masked_index = 8
-        tokenized_text[masked_index] = '[MASK]'
-        indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-        segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
-        tokens_tensor = torch.tensor([indexed_tokens])
-        segments_tensors = torch.tensor([segments_ids])
-        # Load bertForMaskedLM
-        model = torch.hub.load('huggingface/pytorch-transformers', 'bertForMaskedLM', 'bert-base-cased')
-        model.eval()
-        # Predict all tokens
-        with torch.no_grad():
-                predictions = model(tokens_tensor, segments_tensors)
-        predicted_index = torch.argmax(predictions[0, masked_index]).item()
-        predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
-        'henson'
-    """
-    model = BertForMaskedLM.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(bert_docstring)
-def bertForSequenceClassification(*args, **kwargs):
-    """
-    BertForSequenceClassification is a fine-tuning model that includes
-    BertModel and a sequence-level (sequence or pair of sequences) classifier
-    on top of the BertModel. Note that the classification head is only initialized
-    and has to be trained.
-
-    The sequence-level classifier is a linear layer that takes as input the
-    last hidden state of the first character in the input sequence
-    (see Figures 3a and 3b in the BERT paper).
-
-    Args:
-    num_labels: the number (>=2) of classes for the classifier.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
-        #  Prepare tokenized input
-        text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-        tokenized_text = tokenizer.tokenize(text)
-        indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-        segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
-        tokens_tensor = torch.tensor([indexed_tokens])
-        segments_tensors = torch.tensor([segments_ids])
-        # Load bertForSequenceClassification
-        model = torch.hub.load('huggingface/pytorch-transformers', 'bertForSequenceClassification', 'bert-base-cased', num_labels=2)
-        model.eval()
-        # Predict the sequence classification logits
-        with torch.no_grad():
-                seq_classif_logits = model(tokens_tensor, segments_tensors)
-        # Or get the sequence classification loss
-        labels = torch.tensor([1])
-        seq_classif_loss = model(tokens_tensor, segments_tensors, labels=labels) # set model.train() before if training this loss
-    """
-    model = BertForSequenceClassification.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(bert_docstring)
-def bertForMultipleChoice(*args, **kwargs):
-    """
-    BertForMultipleChoice is a fine-tuning model that includes BertModel and a
-    linear layer on top of the BertModel. Note that the multiple choice head is
-    only initialized and has to be trained.
-
-    Args:
-    num_choices: the number (>=2) of classes for the classifier.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
-        #  Prepare tokenized input
-        text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-        tokenized_text = tokenizer.tokenize(text)
-        indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-        segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
-        tokens_tensor = torch.tensor([indexed_tokens, indexed_tokens]).unsqueeze(0)
-        segments_tensors = torch.tensor([segments_ids, segments_ids]).unsqueeze(0)
-        # Load bertForMultipleChoice
-        model = torch.hub.load('huggingface/pytorch-transformers', 'bertForMultipleChoice', 'bert-base-cased', num_choices=2)
-        model.eval()
-        # Predict the multiple choice logits
-        with torch.no_grad():
-                multiple_choice_logits = model(tokens_tensor, segments_tensors)
-        # Or get the multiple choice loss
-        labels = torch.tensor([1])
-        multiple_choice_loss = model(tokens_tensor, segments_tensors, labels=labels) # set model.train() before if training this loss
-    """
-    model = BertForMultipleChoice.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(bert_docstring)
-def bertForQuestionAnswering(*args, **kwargs):
-    """
-    BertForQuestionAnswering is a fine-tuning model that includes BertModel
-    with a token-level classifiers on top of the full sequence of last hidden
-    states. Note that the classification head is only initialized
-    and has to be trained.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
-        #  Prepare tokenized input
-        text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-        tokenized_text = tokenizer.tokenize(text)
-        indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-        segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
-        tokens_tensor = torch.tensor([indexed_tokens])
-        segments_tensors = torch.tensor([segments_ids])
-        # Load bertForQuestionAnswering
-        model = torch.hub.load('huggingface/pytorch-transformers', 'bertForQuestionAnswering', 'bert-base-cased')
-        model.eval()
-        # Predict the start and end positions logits
-        with torch.no_grad():
-                start_logits, end_logits = model(tokens_tensor, segments_tensors)
-        # Or get the total loss which is the sum of the CrossEntropy loss for the start and end token positions
-        start_positions, end_positions = torch.tensor([12]), torch.tensor([14])
-        # set model.train() before if training this loss
-        multiple_choice_loss = model(tokens_tensor, segments_tensors, start_positions=start_positions, end_positions=end_positions)
-    """
-    model = BertForQuestionAnswering.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(bert_docstring)
-def bertForTokenClassification(*args, **kwargs):
-    """
-    BertForTokenClassification is a fine-tuning model that includes BertModel
-    and a token-level classifier on top of the BertModel. Note that the classification
-    head is only initialized and has to be trained.
-
-    The token-level classifier is a linear layer that takes as input the last
-    hidden state of the sequence.
-
-    Args:
-    num_labels: the number (>=2) of classes for the classifier.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
-        #  Prepare tokenized input
-        text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-        tokenized_text = tokenizer.tokenize(text)
-        indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-        segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
-        tokens_tensor = torch.tensor([indexed_tokens])
-        segments_tensors = torch.tensor([segments_ids])
-        # Load bertForTokenClassification
-        model = torch.hub.load('huggingface/pytorch-transformers', 'bertForTokenClassification', 'bert-base-cased', num_labels=2)
-        model.eval()
-        # Predict the token classification logits
-        with torch.no_grad():
-                classif_logits = model(tokens_tensor, segments_tensors)
-        # Or get the token classification loss
-        labels = torch.tensor([[0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0]])
-        classif_loss = model(tokens_tensor, segments_tensors, labels=labels) # set model.train() before if training this loss
-    """
-    model = BertForTokenClassification.from_pretrained(*args, **kwargs)
-    return model

hubconfs/gpt2_hubconf.py

-from pytorch_transformers.tokenization_gpt2 import GPT2Tokenizer
-from pytorch_transformers.modeling_gpt2 import (
-    GPT2Model,
-    GPT2LMHeadModel,
-    GPT2DoubleHeadsModel
-)
-
-# A lot of models share the same param doc. Use a decorator
-# to save typing
-gpt2_docstring = """
-    Params:
-        pretrained_model_name_or_path: either:
-            - a str with the name of a pre-trained model to load selected in the list of:
-                . `gpt2`, `gpt2-medium`
-            - a path or url to a pretrained model archive containing:
-                . `gpt2_config.json` a configuration file for the model
-                . `pytorch_model.bin` a PyTorch dump of a GPT2Model instance
-            - a path or url to a pretrained model archive containing:
-                . `gpt2_config.json` a configuration file for the model
-                . a TensorFlow checkpoint with trained weights
-        from_tf: should we load the weights from a locally saved TensorFlow checkpoint
-        cache_dir: an optional path to a folder in which the pre-trained models will be cached.
-        state_dict: an optional state dictionary (collections.OrderedDict object) to use instead of pre-trained models
-        *inputs, **kwargs: additional input for the specific GPT-2 class
-"""
-
-
-def _append_from_pretrained_docstring(docstr):
-    def docstring_decorator(fn):
-        fn.__doc__ = fn.__doc__ + docstr
-        return fn
-    return docstring_decorator
-
-
-def gpt2Tokenizer(*args, **kwargs):
-    """
-    Instantiate a GPT-2 BPE tokenizer for OpenAI GPT-2 from a pre-trained/customized vocab file.
-    Peculiarities:
-        - Byte-level BPE
-
-    Args:
-    pretrained_model_name_or_path: Path to pretrained model archive
-                                   or one of pre-trained vocab configs below.
-                                       * gpt2
-    Keyword args:
-    special_tokens: Special tokens in vocabulary that are not pretrained ([SEP], [CLS]...)
-                    Default: None
-    max_len: An artificial maximum length to truncate tokenized sequences to;
-             Effective maximum length is always the minimum of this
-             value (if specified) and the underlying BERT model's
-             sequence length.
-             Default: None
-
-    Example:
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
-
-        text = "Who was Jim Henson ?"
-        indexed_tokens = tokenizer.encode(tokenized_text)
-    """
-    tokenizer = GPT2Tokenizer.from_pretrained(*args, **kwargs)
-    return tokenizer
-
-
-@_append_from_pretrained_docstring(gpt2_docstring)
-def gpt2Model(*args, **kwargs):
-    """
-    gpt2Model is the basic OpenAI GPT-2 Transformer model based on
-    identical stacked masked self-attention blocks and pre-trained
-    on large scale dataset using language modeling signal.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
-
-        #  Prepare tokenized input
-        text_1 = "Who was Jim Henson ?"
-        text_2 = "Jim Henson was a puppeteer"
-        indexed_tokens_1 = tokenizer.encode(text_1)
-        indexed_tokens_2 = tokenizer.encode(text_2)
-        tokens_tensor_1 = torch.tensor([indexed_tokens_1])
-        tokens_tensor_2 = torch.tensor([indexed_tokens_2])
-
-        # Load gpt2Model
-        model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Model', 'gpt2')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        # past can be used to reuse precomputed hidden state in a subsequent predictions
-        with torch.no_grad():
-                hidden_states_1, past = model(tokens_tensor_1)
-                hidden_states_2, past = model(tokens_tensor_2, past=past)
-    """
-    model = GPT2Model.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(gpt2_docstring)
-def gpt2LMHeadModel(*args, **kwargs):
-    """
-    gpt2LMHeadModel is the OpenAI GPT-2 Transformer model with the
-    tied (pre-trained) language modeling head on top.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
-
-        #  Prepare tokenized input
-        text_1 = "Who was Jim Henson ?"
-        text_2 = "Jim Henson was a puppeteer"
-        indexed_tokens_1 = tokenizer.encode(text_1)
-        indexed_tokens_2 = tokenizer.encode(text_2)
-        tokens_tensor_1 = torch.tensor([indexed_tokens_1])
-        tokens_tensor_2 = torch.tensor([indexed_tokens_2])
-
-        # Load gpt2LMHeadModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2LMHeadModel', 'gpt2')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        # past can be used to reuse precomputed hidden state in a subsequent predictions
-        with torch.no_grad():
-                predictions_1, past = model(tokens_tensor_1)
-                predictions_2, past = model(tokens_tensor_2, past=past)
-
-        # Get the predicted last token
-        predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
-        predicted_token = tokenizer.decode([predicted_index])
-        assert predicted_token == ' who'
-    """
-    model = GPT2LMHeadModel.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(gpt2_docstring)
-def gpt2DoubleHeadsModel(*args, **kwargs):
-    """
-    gpt2DoubleHeadsModel is the OpenAI GPT-2 Transformer model with the
-    tied (pre-trained) language modeling head and a multiple choice
-    classification head (only initialized, not pre-trained).
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
-
-        #  Prepare tokenized input
-        text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
-        text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
-        tokenized_text1 = tokenizer.tokenize(text1)
-        tokenized_text2 = tokenizer.tokenize(text2)
-        indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
-        indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
-        tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
-        mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
-
-        # Load gpt2DoubleHeadsModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2DoubleHeadsModel', 'gpt2')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        with torch.no_grad():
-                lm_logits, multiple_choice_logits, presents = model(tokens_tensor, mc_token_ids)
-    """
-    model = GPT2DoubleHeadsModel.from_pretrained(*args, **kwargs)
-    return model

hubconfs/gpt_hubconf.py

-from pytorch_transformers.tokenization_openai import OpenAIGPTTokenizer
-from pytorch_transformers.modeling_openai import (
-	OpenAIGPTModel,
-	OpenAIGPTLMHeadModel,
-	OpenAIGPTDoubleHeadsModel
-)
-
-# Dependecies that are not specified in global hubconf.py
-specific_dependencies = ['spacy', 'ftfy']
-
-# A lot of models share the same param doc. Use a decorator
-# to save typing
-gpt_docstring = """
-    OpenAI GPT use a single embedding matrix to store the word and special embeddings.
-    Special tokens embeddings are additional tokens that are not pre-trained: [SEP], [CLS]...
-    Special tokens need to be trained during the fine-tuning if you use them.
-    The number of special embeddings can be controled using the `set_num_special_tokens(num_special_tokens)` function.
-
-    The embeddings are ordered as follow in the token embeddings matrice:
-        [0,                                                         ----------------------
-         ...                                                        -> word embeddings
-         config.vocab_size - 1,                                     ______________________
-         config.vocab_size,
-         ...                                                        -> special embeddings
-         config.vocab_size + config.n_special - 1]                  ______________________
-
-    where total_tokens_embeddings can be obtained as config.total_tokens_embeddings and is:
-        total_tokens_embeddings = config.vocab_size + config.n_special
-    You should use the associate indices to index the embeddings.
-
-    Params:
-		pretrained_model_name_or_path: either:
-			- a str with the name of a pre-trained model to load selected in the list of:
-				. `openai-gpt`
-			- a path or url to a pretrained model archive containing:
-				. `openai_gpt_config.json` a configuration file for the model
-				. `pytorch_model.bin` a PyTorch dump of a OpenAIGPTModel instance
-			- a path or url to a pretrained model archive containing:
-				. `openai-gpt-config.json` a configuration file for the model
-				. a series of NumPy files containing OpenAI TensorFlow trained weights
-		from_tf: should we load the weights from a locally saved TensorFlow checkpoint
-		cache_dir: an optional path to a folder in which the pre-trained models will be cached.
-		state_dict: an optional state dictionary (collections.OrderedDict object)
-		        	to use instead of pre-trained models
-		*inputs, **kwargs: additional input for the specific OpenAI-GPT class
-"""
-
-
-def _append_from_pretrained_docstring(docstr):
-    def docstring_decorator(fn):
-        fn.__doc__ = fn.__doc__ + docstr
-        return fn
-    return docstring_decorator
-
-
-def openAIGPTTokenizer(*args, **kwargs):
-    """
-    Instantiate a BPE tokenizer for OpenAI GPT from a pre-trained/customized vocab file.
-	Peculiarities:
-        - lower case all inputs
-        - uses SpaCy tokenizer ('en' model) and ftfy for pre-BPE tokenization if they are installed, fallback to BERT's BasicTokenizer if not.
-        - argument special_tokens and function set_special_tokens:
-            can be used to add additional symbols (ex: "__classify__") to a vocabulary.
-
-    Args:
-    pretrained_model_name_or_path: Path to pretrained model archive
-                                   or one of pre-trained vocab configs below.
-                                       * openai-gpt
-    Keyword args:
-	special_tokens: Special tokens in vocabulary that are not pretrained ([SEP], [CLS]...)
-					Default: None
-	max_len: An artificial maximum length to truncate tokenized sequences to;
-        	 Effective maximum length is always the minimum of this
-             value (if specified) and the underlying BERT model's
-             sequence length.
-			 Default: None
-
-    Example:
-		import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
-		
-		text = "Who was Jim Henson ? Jim Henson was a puppeteer"
-        tokenized_text = tokenizer.tokenize(text)
-        indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-        [763, 509, 4265, 2298, 945, 257, 4265, 2298, 945, 509, 246, 10148, 39041, 483]
-    """
-    tokenizer = OpenAIGPTTokenizer.from_pretrained(*args, **kwargs)
-    return tokenizer
-
-
-@_append_from_pretrained_docstring(gpt_docstring)
-def openAIGPTModel(*args, **kwargs):
-    """
-    OpenAIGPTModel is the basic OpenAI GPT Transformer model based on
-	identical stacked masked self-attention blocks and pre-trained
-	on large scale dataset using language modeling signal.
-
-    Example:
-        # Load the tokenizer
-		import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
-
-        #  Prepare tokenized input
-        text = "Who was Jim Henson ? Jim Henson was a puppeteer"
-        tokenized_text = tokenizer.tokenize(text)
-        indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-        tokens_tensor = torch.tensor([indexed_tokens])
-
-        # Load openAIGPTModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTModel', 'openai-gpt')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        with torch.no_grad():
-                hidden_states = model(tokens_tensor)
-    """
-    model = OpenAIGPTModel.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(gpt_docstring)
-def openAIGPTLMHeadModel(*args, **kwargs):
-    """
-    OpenAIGPTLMHeadModel is the OpenAI GPT Transformer model with the
-	tied (pre-trained) language modeling head on top.
-
-	Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
-
-        #  Prepare tokenized input
-        text = "Who was Jim Henson ? Jim Henson was a puppeteer"
-        tokenized_text = tokenizer.tokenize(text)
-        indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-        tokens_tensor = torch.tensor([indexed_tokens])
-
-        # Load openAIGPTLMHeadModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTLMHeadModel', 'openai-gpt')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        with torch.no_grad():
-                predictions = model(tokens_tensor)
-
-		# Get the predicted last token
-		predicted_index = torch.argmax(predictions[0, -1, :]).item()
-		predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
-        '.</w>'
-    """
-    model = OpenAIGPTLMHeadModel.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(gpt_docstring)
-def openAIGPTDoubleHeadsModel(*args, **kwargs):
-    """
-    OpenAIGPTDoubleHeadsModel is the OpenAI GPT Transformer model with the
-	tied (pre-trained) language modeling head and a multiple choice
-	classification head (only initialized, not pre-trained).
-
-	Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTTokenizer', 'openai-gpt')
-
-        #  Prepare tokenized input
-        text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
-        text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
-        tokenized_text1 = tokenizer.tokenize(text1)
-        tokenized_text2 = tokenizer.tokenize(text2)
-        indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
-        indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
-        tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
-        mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
-
-        # Load openAIGPTDoubleHeadsModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'openAIGPTDoubleHeadsModel', 'openai-gpt')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        with torch.no_grad():
-                lm_logits, multiple_choice_logits = model(tokens_tensor, mc_token_ids)
-    """
-    model = OpenAIGPTDoubleHeadsModel.from_pretrained(*args, **kwargs)
-    return model

hubconfs/transformer_xl_hubconf.py

-from pytorch_transformers.tokenization_transfo_xl import TransfoXLTokenizer
-from pytorch_transformers.modeling_transfo_xl import (
-    TransfoXLModel,
-    TransfoXLLMHeadModel
-)
-
-# A lot of models share the same param doc. Use a decorator
-# to save typing
-transformer_xl_docstring = """
-    Transformer XL use a relative positioning (with sinusiodal patterns) and adaptive softmax inputs which means that:
-    - you don't need to specify positioning embeddings indices
-    - the tokens in the vocabulary have to be sorted to decreasing frequency.
-
-    Params:
-        pretrained_model_name_or_path: either:
-            - a str with the name of a pre-trained model to load selected in the list of:
-                . `transfo-xl-wt103`
-            - a path or url to a pretrained model archive containing:
-                . `transfo_xl_config.json` a configuration file for the model
-                . `pytorch_model.bin` a PyTorch dump of a TransfoXLModel instance
-            - a path or url to a pretrained model archive containing:
-                . `transfo_xl_config.json` a configuration file for the model
-                . `model.chkpt` a TensorFlow checkpoint
-        from_tf: should we load the weights from a locally saved TensorFlow checkpoint
-        cache_dir: an optional path to a folder in which the pre-trained models will be cached.
-        state_dict: an optional state dictionary (collections.OrderedDict object) to use instead of pre-trained models
-        *inputs, **kwargs: additional input for the specific TransformerXL class
-"""
-
-
-def _append_from_pretrained_docstring(docstr):
-    def docstring_decorator(fn):
-        fn.__doc__ = fn.__doc__ + docstr
-        return fn
-    return docstring_decorator
-
-
-def transformerXLTokenizer(*args, **kwargs):
-    """
-    Instantiate a Transformer-XL tokenizer adapted from Vocab class in https://github.com/kimiyoung/transformer-xl
-
-    Args:
-    pretrained_model_name_or_path: Path to pretrained model archive
-                                   or one of pre-trained vocab configs below.
-                                       * transfo-xl-wt103
-
-    Example:
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
-        
-        text = "Who was Jim Henson ?"
-        tokenized_text = tokenizer.tokenize(tokenized_text)
-        indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
-    """
-    tokenizer = TransfoXLTokenizer.from_pretrained(*args, **kwargs)
-    return tokenizer
-
-
-@_append_from_pretrained_docstring(transformer_xl_docstring)
-def transformerXLModel(*args, **kwargs):
-    """
-    transformerXLModel is the basic Transformer XL model.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
-
-        #  Prepare tokenized input
-        text_1 = "Who was Jim Henson ?"
-        text_2 = "Jim Henson was a puppeteer"
-        tokenized_text_1 = tokenizer.tokenize(text_1)
-        tokenized_text_2 = tokenizer.tokenize(text_2)
-        indexed_tokens_1 = tokenizer.convert_tokens_to_ids(tokenized_text_1)
-        indexed_tokens_2 = tokenizer.convert_tokens_to_ids(tokenized_text_2)
-        tokens_tensor_1 = torch.tensor([indexed_tokens_1])
-        tokens_tensor_2 = torch.tensor([indexed_tokens_2])
-
-        # Load transformerXLModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLModel', 'transfo-xl-wt103')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        # We can re-use the memory cells in a subsequent call to attend a longer context
-        with torch.no_grad():
-                hidden_states_1, mems_1 = model(tokens_tensor_1)
-                hidden_states_2, mems_2 = model(tokens_tensor_2, mems=mems_1)
-    """
-    model = TransfoXLModel.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(transformer_xl_docstring)
-def transformerXLLMHeadModel(*args, **kwargs):
-    """
-    transformerXLModel is the basic Transformer XL model with the
-    tied (pre-trained) language modeling head on top.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
-
-        #  Prepare tokenized input
-        text_1 = "Who was Jim Henson ?"
-        text_2 = "Jim Henson was a puppeteer"
-        tokenized_text_1 = tokenizer.tokenize(text_1)
-        tokenized_text_2 = tokenizer.tokenize(text_2)
-        indexed_tokens_1 = tokenizer.convert_tokens_to_ids(tokenized_text_1)
-        indexed_tokens_2 = tokenizer.convert_tokens_to_ids(tokenized_text_2)
-        tokens_tensor_1 = torch.tensor([indexed_tokens_1])
-        tokens_tensor_2 = torch.tensor([indexed_tokens_2])
-
-        # Load transformerXLLMHeadModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLLMHeadModel', 'transfo-xl-wt103')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        # We can re-use the memory cells in a subsequent call to attend a longer context
-        with torch.no_grad():
-                predictions_1, mems_1 = model(tokens_tensor_1)
-                predictions_2, mems_2 = model(tokens_tensor_2, mems=mems_1)
-
-        # Get the predicted last token
-        predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
-        predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
-        assert predicted_token == 'who'
-    """
-    model = TransfoXLLMHeadModel.from_pretrained(*args, **kwargs)
-    return model

hubconfs/xlm_hubconf.py

-from pytorch_transformers.tokenization_xlm import XLMTokenizer
-from pytorch_transformers.modeling_xlm import (
-    XLMConfig,
-    XLMModel,
-    XLMWithLMHeadModel,
-    XLMForSequenceClassification,
-    XLMForQuestionAnswering
-)
-
-# A lot of models share the same param doc. Use a decorator
-# to save typing
-xlm_start_docstring = """
-    Model class adapted from the XLM Transformer model of
-        "Cross-lingual Language Model Pretraining" by Guillaume Lample, Alexis Conneau
-        Paper: https://arxiv.org/abs/1901.07291
-        Original code: https://github.com/facebookresearch/XLM
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlmTokenizer', 'xlm-mlm-en-2048')
-
-        #  Prepare tokenized input
-        text_1 = "Who was Jim Henson ?"
-        text_2 = "Jim Henson was a puppeteer"
-        indexed_tokens_1 = tokenizer.encode(text_1)
-        indexed_tokens_2 = tokenizer.encode(text_2)
-        tokens_tensor_1 = torch.tensor([indexed_tokens_1])
-        tokens_tensor_2 = torch.tensor([indexed_tokens_2])
-"""
-
-# A lot of models share the same param doc. Use a decorator
-# to save typing
-xlm_end_docstring = """
-    Params:
-        pretrained_model_name_or_path: either:
-            - a str with the name of a pre-trained model to load selected in the list of:
-                . `xlm-mlm-en-2048`
-            - a path or url to a pretrained model archive containing:
-                . `config.json` a configuration file for the model
-                . `pytorch_model.bin` a PyTorch dump created using the `convert_xlm_checkpoint_to_pytorch` conversion script
-        cache_dir: an optional path to a folder in which the pre-trained models will be cached.
-        state_dict: an optional state dictionary (collections.OrderedDict object) to use instead of pre-trained models
-        *inputs, **kwargs: additional input for the specific XLM class
-"""
-
-
-def _begin_with_docstring(docstr):
-    def docstring_decorator(fn):
-        fn.__doc__ = fn.__doc__ + docstr
-        return fn
-    return docstring_decorator
-
-def _end_with_docstring(docstr):
-    def docstring_decorator(fn):
-        fn.__doc__ = fn.__doc__ + docstr
-        return fn
-    return docstring_decorator
-
-
-def xlmTokenizer(*args, **kwargs):
-    """
-    Instantiate a XLM BPE tokenizer for XLM from a pre-trained vocab file.
-
-    Args:
-    pretrained_model_name_or_path: Path to pretrained model archive
-                                   or one of pre-trained vocab configs below.
-                                       * xlm-mlm-en-2048
-    Keyword args:
-    special_tokens: Special tokens in vocabulary that are not pretrained
-                    Default: None
-    max_len: An artificial maximum length to truncate tokenized sequences to;
-             Effective maximum length is always the minimum of this
-             value (if specified) and the underlying model's
-             sequence length.
-             Default: None
-
-    Example:
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlmTokenizer', 'xlm-mlm-en-2048')
-
-        text = "Who was Jim Henson ?"
-        indexed_tokens = tokenizer.encode(tokenized_text)
-    """
-    tokenizer = XLMTokenizer.from_pretrained(*args, **kwargs)
-    return tokenizer
-
-
-@_begin_with_docstring(xlm_start_docstring)
-@_end_with_docstring(xlm_end_docstring)
-def xlmModel(*args, **kwargs):
-    """
-        # Load xlmModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'xlmModel', 'xlm-mlm-en-2048')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        with torch.no_grad():
-                hidden_states_1, mems = model(tokens_tensor_1)
-                hidden_states_2, mems = model(tokens_tensor_2, past=mems)
-    """
-    model = XLMModel.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_begin_with_docstring(xlm_start_docstring)
-@_end_with_docstring(xlm_end_docstring)
-def xlmLMHeadModel(*args, **kwargs):
-    """
-        #  Prepare tokenized input
-        text_1 = "Who was Jim Henson ?"
-        text_2 = "Jim Henson was a puppeteer"
-        indexed_tokens_1 = tokenizer.encode(text_1)
-        indexed_tokens_2 = tokenizer.encode(text_2)
-        tokens_tensor_1 = torch.tensor([indexed_tokens_1])
-        tokens_tensor_2 = torch.tensor([indexed_tokens_2])
-
-        # Load xlnetLMHeadModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetLMHeadModel', 'xlm-mlm-en-2048')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        with torch.no_grad():
-                predictions_1, mems = model(tokens_tensor_1)
-                predictions_2, mems = model(tokens_tensor_2, mems=mems)
-
-        # Get the predicted last token
-        predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
-        predicted_token = tokenizer.decode([predicted_index])
-        assert predicted_token == ' who'
-    """
-    model = XLMWithLMHeadModel.from_pretrained(*args, **kwargs)
-    return model
-
-
-# @_end_with_docstring(xlnet_docstring)
-# def xlnetForSequenceClassification(*args, **kwargs):
-#     """
-#     xlnetModel is the basic XLNet Transformer model from
-#         "XLNet: Generalized Autoregressive Pretraining for Language Understanding"
-#         by Zhilin Yang, Zihang Dai1, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
-
-#     Example:
-#         # Load the tokenizer
-#         import torch
-#         tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlm-mlm-en-2048')
-
-#         #  Prepare tokenized input
-#         text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
-#         text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
-#         tokenized_text1 = tokenizer.tokenize(text1)
-#         tokenized_text2 = tokenizer.tokenize(text2)
-#         indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
-#         indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
-#         tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
-#         mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
-
-#         # Load xlnetForSequenceClassification
-#         model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetForSequenceClassification', 'xlm-mlm-en-2048')
-#         model.eval()
-
-#         # Predict sequence classes logits
-#         with torch.no_grad():
-#                 lm_logits, mems = model(tokens_tensor)
-#     """
-#     model = XLNetForSequenceClassification.from_pretrained(*args, **kwargs)
-#     return model

hubconfs/xlnet_hubconf.1.py

-from pytorch_transformers.tokenization_xlnet import XLNetTokenizer
-from pytorch_transformers.modeling_xlnet import (
-    XLNetConfig,
-    XLNetModel,
-    XLNetLMHeadModel,
-    # XLNetForSequenceClassification
-)
-
-# A lot of models share the same param doc. Use a decorator
-# to save typing
-xlnet_docstring = """
-    Params:
-        pretrained_model_name_or_path: either:
-            - a str with the name of a pre-trained model to load selected in the list of:
-                . `xlnet-large-cased`
-            - a path or url to a pretrained model archive containing:
-                . `config.json` a configuration file for the model
-                . `pytorch_model.bin` a PyTorch dump of a XLNetForPreTraining instance
-            - a path or url to a pretrained model archive containing:
-                . `xlnet_config.json` a configuration file for the model
-                . `model.chkpt` a TensorFlow checkpoint
-        from_tf: should we load the weights from a locally saved TensorFlow checkpoint
-        cache_dir: an optional path to a folder in which the pre-trained models will be cached.
-        state_dict: an optional state dictionary (collections.OrderedDict object) to use instead of pre-trained models
-        *inputs, **kwargs: additional input for the specific XLNet class
-"""
-
-
-def _append_from_pretrained_docstring(docstr):
-    def docstring_decorator(fn):
-        fn.__doc__ = fn.__doc__ + docstr
-        return fn
-    return docstring_decorator
-
-
-def xlnetTokenizer(*args, **kwargs):
-    """
-    Instantiate a XLNet sentencepiece tokenizer for XLNet from a pre-trained vocab file.
-    Peculiarities:
-        - require Google sentencepiece (https://github.com/google/sentencepiece)
-
-    Args:
-    pretrained_model_name_or_path: Path to pretrained model archive
-                                   or one of pre-trained vocab configs below.
-                                       * xlnet-large-cased
-    Keyword args:
-    special_tokens: Special tokens in vocabulary that are not pretrained
-                    Default: None
-    max_len: An artificial maximum length to truncate tokenized sequences to;
-             Effective maximum length is always the minimum of this
-             value (if specified) and the underlying model's
-             sequence length.
-             Default: None
-
-    Example:
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
-
-        text = "Who was Jim Henson ?"
-        indexed_tokens = tokenizer.encode(tokenized_text)
-    """
-    tokenizer = XLNetTokenizer.from_pretrained(*args, **kwargs)
-    return tokenizer
-
-
-@_append_from_pretrained_docstring(xlnet_docstring)
-def xlnetModel(*args, **kwargs):
-    """
-    xlnetModel is the basic XLNet Transformer model from
-        "XLNet: Generalized Autoregressive Pretraining for Language Understanding"
-        by Zhilin Yang, Zihang Dai1, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
-
-        #  Prepare tokenized input
-        text_1 = "Who was Jim Henson ?"
-        text_2 = "Jim Henson was a puppeteer"
-        indexed_tokens_1 = tokenizer.encode(text_1)
-        indexed_tokens_2 = tokenizer.encode(text_2)
-        tokens_tensor_1 = torch.tensor([indexed_tokens_1])
-        tokens_tensor_2 = torch.tensor([indexed_tokens_2])
-
-        # Load xlnetModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetModel', 'xlnet-large-cased')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        with torch.no_grad():
-                hidden_states_1, mems = model(tokens_tensor_1)
-                hidden_states_2, mems = model(tokens_tensor_2, past=mems)
-    """
-    model = XLNetModel.from_pretrained(*args, **kwargs)
-    return model
-
-
-@_append_from_pretrained_docstring(xlnet_docstring)
-def xlnetLMHeadModel(*args, **kwargs):
-    """
-    xlnetModel is the basic XLNet Transformer model from
-        "XLNet: Generalized Autoregressive Pretraining for Language Understanding"
-        by Zhilin Yang, Zihang Dai1, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
-    with a tied (pre-trained) language modeling head on top.
-
-    Example:
-        # Load the tokenizer
-        import torch
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
-
-        #  Prepare tokenized input
-        text_1 = "Who was Jim Henson ?"
-        text_2 = "Jim Henson was a puppeteer"
-        indexed_tokens_1 = tokenizer.encode(text_1)
-        indexed_tokens_2 = tokenizer.encode(text_2)
-        tokens_tensor_1 = torch.tensor([indexed_tokens_1])
-        tokens_tensor_2 = torch.tensor([indexed_tokens_2])
-
-        # Load xlnetLMHeadModel
-        model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetLMHeadModel', 'xlnet-large-cased')
-        model.eval()
-
-        # Predict hidden states features for each layer
-        with torch.no_grad():
-                predictions_1, mems = model(tokens_tensor_1)
-                predictions_2, mems = model(tokens_tensor_2, mems=mems)
-
-        # Get the predicted last token
-        predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
-        predicted_token = tokenizer.decode([predicted_index])
-        assert predicted_token == ' who'
-    """
-    model = XLNetLMHeadModel.from_pretrained(*args, **kwargs)
-    return model
-
-
-# @_append_from_pretrained_docstring(xlnet_docstring)
-# def xlnetForSequenceClassification(*args, **kwargs):
-#     """
-#     xlnetModel is the basic XLNet Transformer model from
-#         "XLNet: Generalized Autoregressive Pretraining for Language Understanding"
-#         by Zhilin Yang, Zihang Dai1, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
-
-#     Example:
-#         # Load the tokenizer
-#         import torch
-#         tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
-
-#         #  Prepare tokenized input
-#         text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
-#         text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
-#         tokenized_text1 = tokenizer.tokenize(text1)
-#         tokenized_text2 = tokenizer.tokenize(text2)
-#         indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
-#         indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
-#         tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
-#         mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
-
-#         # Load xlnetForSequenceClassification
-#         model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetForSequenceClassification', 'xlnet-large-cased')
-#         model.eval()
-
-#         # Predict sequence classes logits
-#         with torch.no_grad():
-#                 lm_logits, mems = model(tokens_tensor)
-#     """
-#     model = XLNetForSequenceClassification.from_pretrained(*args, **kwargs)
-#     return model

pytorch_transformers/__init__.py

-__version__ = "1.1.0"
+__version__ = "1.2.0"
+# Work around to update TensorFlow's absl.logging threshold which alters the
+# default Python logging output behavior when present.
+# see: https://github.com/abseil/abseil-py/issues/99
+# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493
+try:
+    import absl.logging
+    absl.logging.set_verbosity('info')
+    absl.logging.set_stderrthreshold('info')
+    absl.logging._warn_preinit_stderr = False
+except:
+    pass
+
+# Tokenizer
+from .tokenization_utils import (PreTrainedTokenizer)
 from .tokenization_auto import AutoTokenizer
 from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
 from .tokenization_openai import OpenAIGPTTokenizer
@@ -9,45 +23,53 @@ from .tokenization_xlm import XLMTokenizer
 from .tokenization_roberta import RobertaTokenizer
 from .tokenization_distilbert import DistilBertTokenizer
 
-from .tokenization_utils import (PreTrainedTokenizer)
+# Configurations
+from .configuration_utils import PretrainedConfig
+from .configuration_auto import AutoConfig
+from .configuration_bert import BertConfig, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
+from .configuration_openai import OpenAIGPTConfig, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
+from .configuration_transfo_xl import TransfoXLConfig, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
+from .configuration_gpt2 import GPT2Config, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
+from .configuration_xlnet import XLNetConfig, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
+from .configuration_xlm import XLMConfig, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
+from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
+from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
 
-from .modeling_auto import (AutoConfig, AutoModel)
+# Modeling
+from .modeling_utils import (PreTrainedModel, prune_layer, Conv1D)
+from .modeling_auto import (AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering,
+                            AutoModelWithLMHead)
 
-from .modeling_bert import (BertConfig, BertPreTrainedModel, BertModel, BertForPreTraining,
+from .modeling_bert import (BertPreTrainedModel, BertModel, BertForPreTraining,
                             BertForMaskedLM, BertForNextSentencePrediction,
                             BertForSequenceClassification, BertForMultipleChoice,
                             BertForTokenClassification, BertForQuestionAnswering,
-                            load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
-                            BERT_PRETRAINED_CONFIG_ARCHIVE_MAP)
-from .modeling_openai import (OpenAIGPTConfig, OpenAIGPTPreTrainedModel, OpenAIGPTModel,
+                            load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
+from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel,
                               OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
-                              load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-                              OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
-from .modeling_transfo_xl import (TransfoXLConfig, TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel,
-                                  load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
-                                  TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
-from .modeling_gpt2 import (GPT2Config, GPT2PreTrainedModel, GPT2Model,
+                              load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
+from .modeling_transfo_xl import (TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel,
+                                  load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
+from .modeling_gpt2 import (GPT2PreTrainedModel, GPT2Model,
                             GPT2LMHeadModel, GPT2DoubleHeadsModel,
-                            load_tf_weights_in_gpt2, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
-                            GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
-from .modeling_xlnet import (XLNetConfig,
-                             XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
+                            load_tf_weights_in_gpt2, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
+from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
                              XLNetForSequenceClassification, XLNetForQuestionAnswering,
-                             load_tf_weights_in_xlnet, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
-                             XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
-from .modeling_xlm import (XLMConfig, XLMPreTrainedModel , XLMModel,
+                             load_tf_weights_in_xlnet, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
+from .modeling_xlm import (XLMPreTrainedModel , XLMModel,
                            XLMWithLMHeadModel, XLMForSequenceClassification,
-                           XLMForQuestionAnswering, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
-                           XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
-from .modeling_roberta import (RobertaConfig, RobertaForMaskedLM, RobertaModel, RobertaForSequenceClassification,
-                               ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
-from .modeling_distilbert import (DistilBertConfig, DistilBertForMaskedLM, DistilBertModel,
+                           XLMForQuestionAnswering, XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
+from .modeling_roberta import (RobertaForMaskedLM, RobertaModel, RobertaForSequenceClassification,
+                               ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
+from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
                                DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
-                               DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
-from .modeling_utils import (WEIGHTS_NAME, CONFIG_NAME, TF_WEIGHTS_NAME,
-                          PretrainedConfig, PreTrainedModel, prune_layer, Conv1D)
+                               DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
 
+# Optimization
 from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,
                            WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
 
-from .file_utils import (PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE, cached_path)
+# Files and general utilities
+from .file_utils import (PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
+                         cached_path, add_start_docstrings, add_end_docstrings,
+                         WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME)

pytorch_transformers/configuration_auto.py

+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team.
+#
+# 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.
+""" Auto Model class. """
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+from .configuration_bert import BertConfig
+from .configuration_openai import OpenAIGPTConfig
+from .configuration_gpt2 import GPT2Config
+from .configuration_transfo_xl import TransfoXLConfig
+from .configuration_xlnet import XLNetConfig
+from .configuration_xlm import XLMConfig
+from .configuration_roberta import RobertaConfig
+from .configuration_distilbert import DistilBertConfig
+
+logger = logging.getLogger(__name__)
+
+
+class AutoConfig(object):
+    r""":class:`~pytorch_transformers.AutoConfig` is a generic configuration class
+        that will be instantiated as one of the configuration classes of the library
+        when created with the `AutoConfig.from_pretrained(pretrained_model_name_or_path)`
+        class method.
+
+        The `from_pretrained()` method take care of returning the correct model class instance
+        using pattern matching on the `pretrained_model_name_or_path` string.
+
+        The base model class to instantiate is selected as the first pattern matching
+        in the `pretrained_model_name_or_path` string (in the following order):
+            - contains `distilbert`: DistilBertConfig (DistilBERT model)
+            - contains `bert`: BertConfig (Bert model)
+            - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
+            - contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
+            - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
+            - contains `xlnet`: XLNetConfig (XLNet model)
+            - contains `xlm`: XLMConfig (XLM model)
+            - contains `roberta`: RobertaConfig (RoBERTa model)
+
+        This class cannot be instantiated using `__init__()` (throw an error).
+    """
+    def __init__(self):
+        raise EnvironmentError("AutoConfig is designed to be instantiated "
+            "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method.")
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        r""" Instantiate a one of the configuration classes of the library
+        from a pre-trained model configuration.
+
+        The configuration class to instantiate is selected as the first pattern matching
+        in the `pretrained_model_name_or_path` string (in the following order):
+            - contains `distilbert`: DistilBertConfig (DistilBERT model)
+            - contains `bert`: BertConfig (Bert model)
+            - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
+            - contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
+            - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
+            - contains `xlnet`: XLNetConfig (XLNet model)
+            - contains `xlm`: XLMConfig (XLM model)
+            - contains `roberta`: RobertaConfig (RoBERTa model)
+
+        Params:
+            pretrained_model_name_or_path: either:
+
+                - a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
+                - a path to a `directory` containing a configuration file saved using the :func:`~pytorch_transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
+                - a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
+
+            cache_dir: (`optional`) string:
+                Path to a directory in which a downloaded pre-trained model
+                configuration should be cached if the standard cache should not be used.
+
+            kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading.
+
+                - The values in kwargs of any keys which are configuration attributes will be used to override the loaded values.
+                - Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.
+
+            force_download: (`optional`) boolean, default False:
+                Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
+
+            proxies: (`optional`) dict, default None:
+                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
+                The proxies are used on each request.
+
+            return_unused_kwargs: (`optional`) bool:
+
+                - If False, then this function returns just the final configuration object.
+                - If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
+
+        Examples::
+
+            config = AutoConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
+            config = AutoConfig.from_pretrained('./test/bert_saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
+            config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json')
+            config = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
+            assert config.output_attention == True
+            config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True,
+                                                               foo=False, return_unused_kwargs=True)
+            assert config.output_attention == True
+            assert unused_kwargs == {'foo': False}
+
+        """
+        if 'distilbert' in pretrained_model_name_or_path:
+            return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        elif 'roberta' in pretrained_model_name_or_path:
+            return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        elif 'bert' in pretrained_model_name_or_path:
+            return BertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        elif 'openai-gpt' in pretrained_model_name_or_path:
+            return OpenAIGPTConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        elif 'gpt2' in pretrained_model_name_or_path:
+            return GPT2Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        elif 'transfo-xl' in pretrained_model_name_or_path:
+            return TransfoXLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        elif 'xlnet' in pretrained_model_name_or_path:
+            return XLNetConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        elif 'xlm' in pretrained_model_name_or_path:
+            return XLMConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+
+        raise ValueError("Unrecognized model identifier in {}. Should contains one of "
+                         "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
+                         "'xlm', 'roberta'".format(pretrained_model_name_or_path))

pytorch_transformers/configuration_bert.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+""" BERT model configuration """
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import sys
+from io import open
+
+from .configuration_utils import PretrainedConfig
+
+logger = logging.getLogger(__name__)
+
+BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json",
+    'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-config.json",
+    'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json",
+    'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-config.json",
+    'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-config.json",
+    'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-config.json",
+    'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json",
+    'bert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-config.json",
+    'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-config.json",
+    'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-config.json",
+    'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-config.json",
+    'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-config.json",
+    'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-config.json",
+}
+
+
+class BertConfig(PretrainedConfig):
+    r"""
+        :class:`~pytorch_transformers.BertConfig` is the configuration class to store the configuration of a
+        `BertModel`.
+
+
+        Arguments:
+            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
+            hidden_size: Size of the encoder layers and the pooler layer.
+            num_hidden_layers: Number of hidden layers in the Transformer encoder.
+            num_attention_heads: Number of attention heads for each attention layer in
+                the Transformer encoder.
+            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
+                layer in the Transformer encoder.
+            hidden_act: The non-linear activation function (function or string) in the
+                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
+            hidden_dropout_prob: The dropout probabilitiy for all fully connected
+                layers in the embeddings, encoder, and pooler.
+            attention_probs_dropout_prob: The dropout ratio for the attention
+                probabilities.
+            max_position_embeddings: The maximum sequence length that this model might
+                ever be used with. Typically set this to something large just in case
+                (e.g., 512 or 1024 or 2048).
+            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
+                `BertModel`.
+            initializer_range: The sttdev of the truncated_normal_initializer for
+                initializing all weight matrices.
+            layer_norm_eps: The epsilon used by LayerNorm.
+    """
+    pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+    def __init__(self,
+                 vocab_size_or_config_json_file=30522,
+                 hidden_size=768,
+                 num_hidden_layers=12,
+                 num_attention_heads=12,
+                 intermediate_size=3072,
+                 hidden_act="gelu",
+                 hidden_dropout_prob=0.1,
+                 attention_probs_dropout_prob=0.1,
+                 max_position_embeddings=512,
+                 type_vocab_size=2,
+                 initializer_range=0.02,
+                 layer_norm_eps=1e-12,
+                 **kwargs):
+        super(BertConfig, self).__init__(**kwargs)
+        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
+                        and isinstance(vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.vocab_size = vocab_size_or_config_json_file
+            self.hidden_size = hidden_size
+            self.num_hidden_layers = num_hidden_layers
+            self.num_attention_heads = num_attention_heads
+            self.hidden_act = hidden_act
+            self.intermediate_size = intermediate_size
+            self.hidden_dropout_prob = hidden_dropout_prob
+            self.attention_probs_dropout_prob = attention_probs_dropout_prob
+            self.max_position_embeddings = max_position_embeddings
+            self.type_vocab_size = type_vocab_size
+            self.initializer_range = initializer_range
+            self.layer_norm_eps = layer_norm_eps
+        else:
+            raise ValueError("First argument must be either a vocabulary size (int)"
+                             " or the path to a pretrained model config file (str)")

pytorch_transformers/configuration_distilbert.py

+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
+#
+# 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.
+""" DistilBERT model configuration """
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import sys
+import json
+import logging
+from io import open
+
+from .configuration_utils import PretrainedConfig
+
+logger = logging.getLogger(__name__)
+
+DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json",
+    'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json"
+}
+
+
+class DistilBertConfig(PretrainedConfig):
+    pretrained_config_archive_map = DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+    def __init__(self,
+                 vocab_size_or_config_json_file=30522,
+                 max_position_embeddings=512,
+                 sinusoidal_pos_embds=True,
+                 n_layers=6,
+                 n_heads=12,
+                 dim=768,
+                 hidden_dim=4*768,
+                 dropout=0.1,
+                 attention_dropout=0.1,
+                 activation='gelu',
+                 initializer_range=0.02,
+                 tie_weights_=True,
+                 qa_dropout=0.1,
+                 seq_classif_dropout=0.2,
+                 **kwargs):
+        super(DistilBertConfig, self).__init__(**kwargs)
+
+        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
+                        and isinstance(vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.vocab_size = vocab_size_or_config_json_file
+            self.max_position_embeddings = max_position_embeddings
+            self.sinusoidal_pos_embds = sinusoidal_pos_embds
+            self.n_layers = n_layers
+            self.n_heads = n_heads
+            self.dim = dim
+            self.hidden_dim = hidden_dim
+            self.dropout = dropout
+            self.attention_dropout = attention_dropout
+            self.activation = activation
+            self.initializer_range = initializer_range
+            self.tie_weights_ = tie_weights_
+            self.qa_dropout = qa_dropout
+            self.seq_classif_dropout = seq_classif_dropout
+        else:
+            raise ValueError("First argument must be either a vocabulary size (int)"
+                             " or the path to a pretrained model config file (str)")
+    @property
+    def hidden_size(self):
+        return self.dim
+
+    @property
+    def num_attention_heads(self):
+        return self.n_heads
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layers

pytorch_transformers/configuration_gpt2.py

+# coding=utf-8
+# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+""" OpenAI GPT-2 configuration """
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import sys
+from io import open
+
+from .configuration_utils import PretrainedConfig
+
+logger = logging.getLogger(__name__)
+
+GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
+                                      "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json",
+                                      "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-config.json"}
+
+class GPT2Config(PretrainedConfig):
+    """Configuration class to store the configuration of a `GPT2Model`.
+
+    Args:
+        vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `GPT2Model` or a configuration json file.
+        n_positions: Number of positional embeddings.
+        n_ctx: Size of the causal mask (usually same as n_positions).
+        n_embd: Dimensionality of the embeddings and hidden states.
+        n_layer: Number of hidden layers in the Transformer encoder.
+        n_head: Number of attention heads for each attention layer in
+            the Transformer encoder.
+        layer_norm_epsilon: epsilon to use in the layer norm layers
+        resid_pdrop: The dropout probabilitiy for all fully connected
+            layers in the embeddings, encoder, and pooler.
+        attn_pdrop: The dropout ratio for the attention
+            probabilities.
+        embd_pdrop: The dropout ratio for the embeddings.
+        initializer_range: The sttdev of the truncated_normal_initializer for
+            initializing all weight matrices.
+    """
+    pretrained_config_archive_map = GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+    def __init__(
+        self,
+        vocab_size_or_config_json_file=50257,
+        n_positions=1024,
+        n_ctx=1024,
+        n_embd=768,
+        n_layer=12,
+        n_head=12,
+        resid_pdrop=0.1,
+        embd_pdrop=0.1,
+        attn_pdrop=0.1,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+
+        num_labels=1,
+        summary_type='cls_index',
+        summary_use_proj=True,
+        summary_activation=None,
+        summary_proj_to_labels=True,
+        summary_first_dropout=0.1,
+        **kwargs
+    ):
+        """Constructs GPT2Config.
+
+        Args:
+            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `GPT2Model` or a configuration json file.
+            n_positions: Number of positional embeddings.
+            n_ctx: Size of the causal mask (usually same as n_positions).
+            n_embd: Dimensionality of the embeddings and hidden states.
+            n_layer: Number of hidden layers in the Transformer encoder.
+            n_head: Number of attention heads for each attention layer in
+                the Transformer encoder.
+            layer_norm_epsilon: epsilon to use in the layer norm layers
+            resid_pdrop: The dropout probabilitiy for all fully connected
+                layers in the embeddings, encoder, and pooler.
+            attn_pdrop: The dropout ratio for the attention
+                probabilities.
+            embd_pdrop: The dropout ratio for the embeddings.
+            initializer_range: The sttdev of the truncated_normal_initializer for
+                initializing all weight matrices.
+        """
+        super(GPT2Config, self).__init__(**kwargs)
+
+        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
+                        and isinstance(vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.vocab_size = vocab_size_or_config_json_file
+            self.n_ctx = n_ctx
+            self.n_positions = n_positions
+            self.n_embd = n_embd
+            self.n_layer = n_layer
+            self.n_head = n_head
+            self.resid_pdrop = resid_pdrop
+            self.embd_pdrop = embd_pdrop
+            self.attn_pdrop = attn_pdrop
+            self.layer_norm_epsilon = layer_norm_epsilon
+            self.initializer_range = initializer_range
+
+            self.num_labels = num_labels
+            self.summary_type = summary_type
+            self.summary_use_proj = summary_use_proj
+            self.summary_activation = summary_activation
+            self.summary_first_dropout = summary_first_dropout
+            self.summary_proj_to_labels = summary_proj_to_labels
+        else:
+            raise ValueError(
+                "First argument must be either a vocabulary size (int)"
+                "or the path to a pretrained model config file (str)"
+            )
+
+    @property
+    def max_position_embeddings(self):
+        return self.n_positions
+
+    @property
+    def hidden_size(self):
+        return self.n_embd
+
+    @property
+    def num_attention_heads(self):
+        return self.n_head
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layer

pytorch_transformers/configuration_openai.py

+# coding=utf-8
+# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+""" OpenAI GPT configuration """
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import sys
+from io import open
+
+from .configuration_utils import PretrainedConfig
+
+logger = logging.getLogger(__name__)
+
+OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-config.json"
+}
+
+class OpenAIGPTConfig(PretrainedConfig):
+    """
+    Configuration class to store the configuration of a `OpenAIGPTModel`.
+
+    Args:
+        vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `OpenAIGPTModel` or a configuration json file.
+        n_special: The number of special tokens to learn during fine-tuning ('[SEP]', '[CLF]', ...)
+        n_positions: Number of positional embeddings.
+        n_ctx: Size of the causal mask (usually same as n_positions).
+        n_embd: Dimensionality of the embeddings and hidden states.
+        n_layer: Number of hidden layers in the Transformer encoder.
+        n_head: Number of attention heads for each attention layer in
+            the Transformer encoder.
+        afn: The non-linear activation function (function or string) in the
+            encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
+        resid_pdrop: The dropout probabilitiy for all fully connected
+            layers in the embeddings, encoder, and pooler.
+        attn_pdrop: The dropout ratio for the attention
+            probabilities.
+        embd_pdrop: The dropout ratio for the embeddings.
+        layer_norm_epsilon: epsilon to use in the layer norm layers
+        initializer_range: The sttdev of the truncated_normal_initializer for
+            initializing all weight matrices.
+        predict_special_tokens: should we predict special tokens (when the model has a LM head)
+    """
+    pretrained_config_archive_map = OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+    def __init__(
+        self,
+        vocab_size_or_config_json_file=40478,
+        n_positions=512,
+        n_ctx=512,
+        n_embd=768,
+        n_layer=12,
+        n_head=12,
+        afn="gelu",
+        resid_pdrop=0.1,
+        embd_pdrop=0.1,
+        attn_pdrop=0.1,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        predict_special_tokens=True,
+
+        num_labels=1,
+        summary_type='cls_index',
+        summary_use_proj=True,
+        summary_activation=None,
+        summary_proj_to_labels=True,
+        summary_first_dropout=0.1,
+        **kwargs
+    ):
+        """Constructs OpenAIGPTConfig.
+        """
+        super(OpenAIGPTConfig, self).__init__(**kwargs)
+
+        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
+                        and isinstance(vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.vocab_size = vocab_size_or_config_json_file
+            self.n_ctx = n_ctx
+            self.n_positions = n_positions
+            self.n_embd = n_embd
+            self.n_layer = n_layer
+            self.n_head = n_head
+            self.afn = afn
+            self.resid_pdrop = resid_pdrop
+            self.embd_pdrop = embd_pdrop
+            self.attn_pdrop = attn_pdrop
+            self.layer_norm_epsilon = layer_norm_epsilon
+            self.initializer_range = initializer_range
+            self.predict_special_tokens = predict_special_tokens
+
+            self.num_labels = num_labels
+            self.summary_type = summary_type
+            self.summary_use_proj = summary_use_proj
+            self.summary_activation = summary_activation
+            self.summary_first_dropout = summary_first_dropout
+            self.summary_proj_to_labels = summary_proj_to_labels
+        else:
+            raise ValueError(
+                "First argument must be either a vocabulary size (int)"
+                "or the path to a pretrained model config file (str)"
+            )
+
+    @property
+    def max_position_embeddings(self):
+        return self.n_positions
+
+    @property
+    def hidden_size(self):
+        return self.n_embd
+
+    @property
+    def num_attention_heads(self):
+        return self.n_head
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layer

pytorch_transformers/configuration_roberta.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+""" RoBERTa configuration """
+
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import logging
+
+from .configuration_bert import BertConfig
+
+logger = logging.getLogger(__name__)
+
+ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-config.json",
+    'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-config.json",
+    'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-config.json",
+}
+
+
+class RobertaConfig(BertConfig):
+    pretrained_config_archive_map = ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP

pytorch_transformers/configuration_transfo_xl.py

+# coding=utf-8
+# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+""" Transformer XL configuration """
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import sys
+from io import open
+
+from .configuration_utils import PretrainedConfig
+
+logger = logging.getLogger(__name__)
+
+TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-config.json",
+}
+
+class TransfoXLConfig(PretrainedConfig):
+    """Configuration class to store the configuration of a `TransfoXLModel`.
+
+        Args:
+            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `TransfoXLModel` or a configuration json file.
+            cutoffs: cutoffs for the adaptive softmax
+            d_model: Dimensionality of the model's hidden states.
+            d_embed: Dimensionality of the embeddings
+            d_head: Dimensionality of the model's heads.
+            div_val: divident value for adapative input and softmax
+            pre_lnorm: apply LayerNorm to the input instead of the output
+            d_inner: Inner dimension in FF
+            n_layer: Number of hidden layers in the Transformer encoder.
+            n_head: Number of attention heads for each attention layer in
+                the Transformer encoder.
+            tgt_len: number of tokens to predict
+            ext_len: length of the extended context
+            mem_len: length of the retained previous heads
+            same_length: use the same attn length for all tokens
+            proj_share_all_but_first: True to share all but first projs, False not to share.
+            attn_type: attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al.
+            clamp_len: use the same pos embeddings after clamp_len
+            sample_softmax: number of samples in sampled softmax
+            adaptive: use adaptive softmax
+            tie_weight: tie the word embedding and softmax weights
+            dropout: The dropout probabilitiy for all fully connected
+                layers in the embeddings, encoder, and pooler.
+            dropatt: The dropout ratio for the attention probabilities.
+            untie_r: untie relative position biases
+            embd_pdrop: The dropout ratio for the embeddings.
+            init: parameter initializer to use
+            init_range: parameters initialized by U(-init_range, init_range).
+            proj_init_std: parameters initialized by N(0, init_std)
+            init_std: parameters initialized by N(0, init_std)
+    """
+    pretrained_config_archive_map = TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+    def __init__(self,
+                 vocab_size_or_config_json_file=267735,
+                 cutoffs=[20000, 40000, 200000],
+                 d_model=1024,
+                 d_embed=1024,
+                 n_head=16,
+                 d_head=64,
+                 d_inner=4096,
+                 div_val=4,
+                 pre_lnorm=False,
+                 n_layer=18,
+                 tgt_len=128,
+                 ext_len=0,
+                 mem_len=1600,
+                 clamp_len=1000,
+                 same_length=True,
+                 proj_share_all_but_first=True,
+                 attn_type=0,
+                 sample_softmax=-1,
+                 adaptive=True,
+                 tie_weight=True,
+                 dropout=0.1,
+                 dropatt=0.0,
+                 untie_r=True,
+                 init="normal",
+                 init_range=0.01,
+                 proj_init_std=0.01,
+                 init_std=0.02,
+                 **kwargs):
+        """Constructs TransfoXLConfig.
+        """
+        super(TransfoXLConfig, self).__init__(**kwargs)
+
+        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
+                        and isinstance(vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.n_token = vocab_size_or_config_json_file
+            self.cutoffs = []
+            self.cutoffs.extend(cutoffs)
+            self.tie_weight = tie_weight
+            if proj_share_all_but_first:
+                self.tie_projs = [False] + [True] * len(self.cutoffs)
+            else:
+                self.tie_projs = [False] + [False] * len(self.cutoffs)
+            self.d_model = d_model
+            self.d_embed = d_embed
+            self.d_head = d_head
+            self.d_inner = d_inner
+            self.div_val = div_val
+            self.pre_lnorm = pre_lnorm
+            self.n_layer = n_layer
+            self.n_head = n_head
+            self.tgt_len = tgt_len
+            self.ext_len = ext_len
+            self.mem_len = mem_len
+            self.same_length = same_length
+            self.attn_type = attn_type
+            self.clamp_len = clamp_len
+            self.sample_softmax = sample_softmax
+            self.adaptive = adaptive
+            self.dropout = dropout
+            self.dropatt = dropatt
+            self.untie_r = untie_r
+            self.init = init
+            self.init_range = init_range
+            self.proj_init_std = proj_init_std
+            self.init_std = init_std
+        else:
+            raise ValueError("First argument must be either a vocabulary size (int)"
+                             " or the path to a pretrained model config file (str)")
+
+    @property
+    def max_position_embeddings(self):
+        return self.tgt_len + self.ext_len + self.mem_len
+
+    @property
+    def vocab_size(self):
+        return self.n_token
+
+    @vocab_size.setter
+    def vocab_size(self, value):
+        self.n_token = value
+
+    @property
+    def hidden_size(self):
+        return self.d_model
+
+    @property
+    def num_attention_heads(self):
+        return self.n_head
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layer

pytorch_transformers/configuration_utils.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+""" Configuration base class and utilities."""
+
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import copy
+import json
+import logging
+import os
+from io import open
+
+from .file_utils import cached_path, CONFIG_NAME
+
+logger = logging.getLogger(__name__)
+
+class PretrainedConfig(object):
+    r""" Base class for all configuration classes.
+        Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving configurations.
+
+        Note:
+            A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to initialize a model does **not** load the model weights.
+            It only affects the model's configuration.
+
+        Class attributes (overridden by derived classes):
+            - ``pretrained_config_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained model configurations as values.
+
+        Parameters:
+            ``finetuning_task``: string, default `None`. Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.
+            ``num_labels``: integer, default `2`. Number of classes to use when the model is a classification model (sequences/tokens)
+            ``output_attentions``: boolean, default `False`. Should the model returns attentions weights.
+            ``output_hidden_states``: string, default `False`. Should the model returns all hidden-states.
+            ``torchscript``: string, default `False`. Is the model used with Torchscript.
+    """
+    pretrained_config_archive_map = {}
+
+    def __init__(self, **kwargs):
+        self.finetuning_task = kwargs.pop('finetuning_task', None)
+        self.num_labels = kwargs.pop('num_labels', 2)
+        self.output_attentions = kwargs.pop('output_attentions', False)
+        self.output_hidden_states = kwargs.pop('output_hidden_states', False)
+        self.torchscript = kwargs.pop('torchscript', False)
+        self.pruned_heads = kwargs.pop('pruned_heads', {})
+
+    def save_pretrained(self, save_directory):
+        """ Save a configuration object to the directory `save_directory`, so that it
+            can be re-loaded using the :func:`~pytorch_transformers.PretrainedConfig.from_pretrained` class method.
+        """
+        assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
+
+        # If we save using the predefined names, we can load using `from_pretrained`
+        output_config_file = os.path.join(save_directory, CONFIG_NAME)
+
+        self.to_json_file(output_config_file)
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        r""" Instantiate a :class:`~pytorch_transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
+
+        Parameters:
+            pretrained_model_name_or_path: either:
+
+                - a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
+                - a path to a `directory` containing a configuration file saved using the :func:`~pytorch_transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
+                - a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
+
+            cache_dir: (`optional`) string:
+                Path to a directory in which a downloaded pre-trained model
+                configuration should be cached if the standard cache should not be used.
+
+            kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading.
+
+                - The values in kwargs of any keys which are configuration attributes will be used to override the loaded values.
+                - Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.
+
+            force_download: (`optional`) boolean, default False:
+                Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
+
+            proxies: (`optional`) dict, default None:
+                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
+                The proxies are used on each request.
+
+            return_unused_kwargs: (`optional`) bool:
+
+                - If False, then this function returns just the final configuration object.
+                - If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
+
+        Examples::
+
+            # We can't instantiate directly the base class `PretrainedConfig` so let's show the examples on a
+            # derived class: BertConfig
+            config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
+            config = BertConfig.from_pretrained('./test/saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
+            config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
+            config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
+            assert config.output_attention == True
+            config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True,
+                                                               foo=False, return_unused_kwargs=True)
+            assert config.output_attention == True
+            assert unused_kwargs == {'foo': False}
+
+        """
+        cache_dir = kwargs.pop('cache_dir', None)
+        force_download = kwargs.pop('force_download', False)
+        proxies = kwargs.pop('proxies', None)
+        return_unused_kwargs = kwargs.pop('return_unused_kwargs', False)
+
+        if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
+            config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path]
+        elif os.path.isdir(pretrained_model_name_or_path):
+            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
+        else:
+            config_file = pretrained_model_name_or_path
+        # redirect to the cache, if necessary
+        try:
+            resolved_config_file = cached_path(config_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
+        except EnvironmentError as e:
+            if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
+                logger.error(
+                    "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
+                        config_file))
+            else:
+                logger.error(
+                    "Model name '{}' was not found in model name list ({}). "
+                    "We assumed '{}' was a path or url but couldn't find any file "
+                    "associated to this path or url.".format(
+                        pretrained_model_name_or_path,
+                        ', '.join(cls.pretrained_config_archive_map.keys()),
+                        config_file))
+            raise e
+        if resolved_config_file == config_file:
+            logger.info("loading configuration file {}".format(config_file))
+        else:
+            logger.info("loading configuration file {} from cache at {}".format(
+                config_file, resolved_config_file))
+
+        # Load config
+        config = cls.from_json_file(resolved_config_file)
+
+        if hasattr(config, 'pruned_heads'):
+            config.pruned_heads = dict((int(key), set(value)) for key, value in config.pruned_heads.items())
+
+        # Update config with kwargs if needed
+        to_remove = []
+        for key, value in kwargs.items():
+            if hasattr(config, key):
+                setattr(config, key, value)
+                to_remove.append(key)
+        for key in to_remove:
+            kwargs.pop(key, None)
+
+        logger.info("Model config %s", config)
+        if return_unused_kwargs:
+            return config, kwargs
+        else:
+            return config
+
+    @classmethod
+    def from_dict(cls, json_object):
+        """Constructs a `Config` from a Python dictionary of parameters."""
+        config = cls(vocab_size_or_config_json_file=-1)
+        for key, value in json_object.items():
+            config.__dict__[key] = value
+        return config
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        """Constructs a `BertConfig` from a json file of parameters."""
+        with open(json_file, "r", encoding='utf-8') as reader:
+            text = reader.read()
+        return cls.from_dict(json.loads(text))
+
+    def __eq__(self, other):
+        return self.__dict__ == other.__dict__
+
+    def __repr__(self):
+        return str(self.to_json_string())
+
+    def to_dict(self):
+        """Serializes this instance to a Python dictionary."""
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self):
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+    def to_json_file(self, json_file_path):
+        """ Save this instance to a json file."""
+        with open(json_file_path, "w", encoding='utf-8') as writer:
+            writer.write(self.to_json_string())

pytorch_transformers/configuration_xlm.py

+# coding=utf-8
+# Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
+#
+# 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.
+""" XLM configuration """
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import sys
+from io import open
+
+from .configuration_utils import PretrainedConfig
+
+logger = logging.getLogger(__name__)
+
+XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json",
+    'xlm-mlm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-config.json",
+    'xlm-mlm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-config.json",
+    'xlm-mlm-enro-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-config.json",
+    'xlm-mlm-tlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-config.json",
+    'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-config.json",
+    'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-config.json",
+    'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-config.json",
+    'xlm-mlm-17-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-config.json",
+    'xlm-mlm-100-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-config.json",
+}
+
+
+class XLMConfig(PretrainedConfig):
+    """Configuration class to store the configuration of a `XLMModel`.
+
+    Args:
+        vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `XLMModel`.
+        d_model: Size of the encoder layers and the pooler layer.
+        n_layer: Number of hidden layers in the Transformer encoder.
+        n_head: Number of attention heads for each attention layer in
+            the Transformer encoder.
+        d_inner: The size of the "intermediate" (i.e., feed-forward)
+            layer in the Transformer encoder.
+        ff_activation: The non-linear activation function (function or string) in the
+            encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
+        untie_r: untie relative position biases
+        attn_type: 'bi' for XLM, 'uni' for Transformer-XL
+
+        dropout: The dropout probabilitiy for all fully connected
+            layers in the embeddings, encoder, and pooler.
+        dropatt: The dropout ratio for the attention
+            probabilities.
+        max_position_embeddings: The maximum sequence length that this model might
+            ever be used with. Typically set this to something large just in case
+            (e.g., 512 or 1024 or 2048).
+        initializer_range: The sttdev of the truncated_normal_initializer for
+            initializing all weight matrices.
+        layer_norm_eps: The epsilon used by LayerNorm.
+
+        dropout: float, dropout rate.
+        dropatt: float, dropout rate on attention probabilities.
+        init: str, the initialization scheme, either "normal" or "uniform".
+        init_range: float, initialize the parameters with a uniform distribution
+            in [-init_range, init_range]. Only effective when init="uniform".
+        init_std: float, initialize the parameters with a normal distribution
+            with mean 0 and stddev init_std. Only effective when init="normal".
+        mem_len: int, the number of tokens to cache.
+        reuse_len: int, the number of tokens in the currect batch to be cached
+            and reused in the future.
+        bi_data: bool, whether to use bidirectional input pipeline.
+            Usually set to True during pretraining and False during finetuning.
+        clamp_len: int, clamp all relative distances larger than clamp_len.
+            -1 means no clamping.
+        same_length: bool, whether to use the same attention length for each token.
+    """
+    pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+    def __init__(self,
+                 vocab_size_or_config_json_file=30145,
+                 emb_dim=2048,
+                 n_layers=12,
+                 n_heads=16,
+                 dropout=0.1,
+                 attention_dropout=0.1,
+                 gelu_activation=True,
+                 sinusoidal_embeddings=False,
+                 causal=False,
+                 asm=False,
+                 n_langs=1,
+                 use_lang_emb=True,
+                 max_position_embeddings=512,
+                 embed_init_std=2048 ** -0.5,
+                 layer_norm_eps=1e-12,
+                 init_std=0.02,
+                 bos_index=0,
+                 eos_index=1,
+                 pad_index=2,
+                 unk_index=3,
+                 mask_index=5,
+                 is_encoder=True,
+
+                 finetuning_task=None,
+                 num_labels=2,
+                 summary_type='first',
+                 summary_use_proj=True,
+                 summary_activation=None,
+                 summary_proj_to_labels=True,
+                 summary_first_dropout=0.1,
+                 start_n_top=5,
+                 end_n_top=5,
+                 **kwargs):
+        """Constructs XLMConfig.
+        """
+        super(XLMConfig, self).__init__(**kwargs)
+
+        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
+                        and isinstance(vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.n_words = vocab_size_or_config_json_file
+            self.emb_dim = emb_dim
+            self.n_layers = n_layers
+            self.n_heads = n_heads
+            self.dropout = dropout
+            self.attention_dropout = attention_dropout
+            self.gelu_activation = gelu_activation
+            self.sinusoidal_embeddings = sinusoidal_embeddings
+            self.causal = causal
+            self.asm = asm
+            self.n_langs = n_langs
+            self.use_lang_emb = use_lang_emb
+            self.layer_norm_eps = layer_norm_eps
+            self.bos_index = bos_index
+            self.eos_index = eos_index
+            self.pad_index = pad_index
+            self.unk_index = unk_index
+            self.mask_index = mask_index
+            self.is_encoder = is_encoder
+            self.max_position_embeddings = max_position_embeddings
+            self.embed_init_std = embed_init_std
+            self.init_std = init_std
+            self.finetuning_task = finetuning_task
+            self.num_labels = num_labels
+            self.summary_type = summary_type
+            self.summary_use_proj = summary_use_proj
+            self.summary_activation = summary_activation
+            self.summary_proj_to_labels = summary_proj_to_labels
+            self.summary_first_dropout = summary_first_dropout
+            self.start_n_top = start_n_top
+            self.end_n_top = end_n_top
+        else:
+            raise ValueError("First argument must be either a vocabulary size (int)"
+                             " or the path to a pretrained model config file (str)")
+
+    @property
+    def vocab_size(self):
+        return self.n_words
+
+    @vocab_size.setter
+    def vocab_size(self, value):
+        self.n_words = value
+
+    @property
+    def hidden_size(self):
+        return self.emb_dim
+
+    @property
+    def num_attention_heads(self):
+        return self.n_heads
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layers

pytorch_transformers/configuration_xlnet.py

+# coding=utf-8
+# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+""" XLNet configuration """
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import sys
+from io import open
+
+from .configuration_utils import PretrainedConfig
+
+logger = logging.getLogger(__name__)
+
+XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-config.json",
+    'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json",
+}
+
+
+class XLNetConfig(PretrainedConfig):
+    """Configuration class to store the configuration of a ``XLNetModel``.
+
+    Args:
+        vocab_size_or_config_json_file: Vocabulary size of ``inputs_ids`` in ``XLNetModel``.
+        d_model: Size of the encoder layers and the pooler layer.
+        n_layer: Number of hidden layers in the Transformer encoder.
+        n_head: Number of attention heads for each attention layer in
+            the Transformer encoder.
+        d_inner: The size of the "intermediate" (i.e., feed-forward)
+            layer in the Transformer encoder.
+        ff_activation: The non-linear activation function (function or string) in the
+            encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
+        untie_r: untie relative position biases
+        attn_type: 'bi' for XLNet, 'uni' for Transformer-XL
+
+        dropout: The dropout probabilitiy for all fully connected
+            layers in the embeddings, encoder, and pooler.
+        dropatt: The dropout ratio for the attention
+            probabilities.
+        initializer_range: The sttdev of the truncated_normal_initializer for
+            initializing all weight matrices.
+        layer_norm_eps: The epsilon used by LayerNorm.
+
+        dropout: float, dropout rate.
+        dropatt: float, dropout rate on attention probabilities.
+        init: str, the initialization scheme, either "normal" or "uniform".
+        init_range: float, initialize the parameters with a uniform distribution
+            in [-init_range, init_range]. Only effective when init="uniform".
+        init_std: float, initialize the parameters with a normal distribution
+            with mean 0 and stddev init_std. Only effective when init="normal".
+        mem_len: int, the number of tokens to cache.
+        reuse_len: int, the number of tokens in the currect batch to be cached
+            and reused in the future.
+        bi_data: bool, whether to use bidirectional input pipeline.
+            Usually set to True during pretraining and False during finetuning.
+        clamp_len: int, clamp all relative distances larger than clamp_len.
+            -1 means no clamping.
+        same_length: bool, whether to use the same attention length for each token.
+        finetuning_task: name of the glue task on which the model was fine-tuned if any
+    """
+    pretrained_config_archive_map = XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+    def __init__(self,
+                 vocab_size_or_config_json_file=32000,
+                 d_model=1024,
+                 n_layer=24,
+                 n_head=16,
+                 d_inner=4096,
+                 ff_activation="gelu",
+                 untie_r=True,
+                 attn_type="bi",
+
+                 initializer_range=0.02,
+                 layer_norm_eps=1e-12,
+
+                 dropout=0.1,
+                 mem_len=None,
+                 reuse_len=None,
+                 bi_data=False,
+                 clamp_len=-1,
+                 same_length=False,
+
+                 finetuning_task=None,
+                 num_labels=2,
+                 summary_type='last',
+                 summary_use_proj=True,
+                 summary_activation='tanh',
+                 summary_last_dropout=0.1,
+                 start_n_top=5,
+                 end_n_top=5,
+                 **kwargs):
+        """Constructs XLNetConfig.
+        """
+        super(XLNetConfig, self).__init__(**kwargs)
+
+        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
+                        and isinstance(vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.n_token = vocab_size_or_config_json_file
+            self.d_model = d_model
+            self.n_layer = n_layer
+            self.n_head = n_head
+            assert d_model % n_head == 0
+            self.d_head = d_model // n_head
+            self.ff_activation = ff_activation
+            self.d_inner = d_inner
+            self.untie_r = untie_r
+            self.attn_type = attn_type
+
+            self.initializer_range = initializer_range
+            self.layer_norm_eps = layer_norm_eps
+
+            self.dropout = dropout
+            self.mem_len = mem_len
+            self.reuse_len = reuse_len
+            self.bi_data = bi_data
+            self.clamp_len = clamp_len
+            self.same_length = same_length
+
+            self.finetuning_task = finetuning_task
+            self.num_labels = num_labels
+            self.summary_type = summary_type
+            self.summary_use_proj = summary_use_proj
+            self.summary_activation = summary_activation
+            self.summary_last_dropout = summary_last_dropout
+            self.start_n_top = start_n_top
+            self.end_n_top = end_n_top
+        else:
+            raise ValueError("First argument must be either a vocabulary size (int)"
+                             " or the path to a pretrained model config file (str)")
+
+    @property
+    def max_position_embeddings(self):
+        return -1
+
+    @property
+    def vocab_size(self):
+        return self.n_token
+
+    @vocab_size.setter
+    def vocab_size(self, value):
+        self.n_token = value
+
+    @property
+    def hidden_size(self):
+        return self.d_model
+
+    @property
+    def num_attention_heads(self):
+        return self.n_head
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layer

pytorch_transformers/convert_gpt2_checkpoint_to_pytorch.py

@@ -21,7 +21,7 @@ from io import open
 
 import torch
 
-from pytorch_transformers.modeling_gpt2 import (CONFIG_NAME, WEIGHTS_NAME,
+from pytorch_transformers import (CONFIG_NAME, WEIGHTS_NAME,
                                                      GPT2Config,
                                                      GPT2Model,
                                                      load_tf_weights_in_gpt2)