add hubconf

hubconf.py

+from pytorch_pretrained_bert.tokenization import BertTokenizer
+from pytorch_pretrained_bert.modeling import (BertForNextSentencePrediction,
+                                              BertForMaskedLM,
+                                              BertForMultipleChoice,
+                                              BertForPreTraining,
+                                              BertForQuestionAnswering,
+                                              BertForSequenceClassification,
+                                              )
+
+dependencies = ['torch', 'tqdm', 'boto3', 'requests', 'regex']
+
+
+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:
+        >>> sentence = 'Hello, World!'
+        >>> tokenizer = torch.hub.load('ailzhang/pytorch-pretrained-BERT:hubconf', 'BertTokenizer', 'bert-base-cased', do_basic_tokenize=False, force_reload=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
+
+
+def bertForNextSentencePrediction(*args, **kwargs):
+    """BERT model with next sentence prediction head.
+    This module comprises the BERT model followed by the next sentence classification head.
+    Params:
+        pretrained_model_name_or_path: either:
+            - a str with the name of a pre-trained model to load selected in the list of:
+                . `bert-base-uncased`
+                . `bert-large-uncased`
+                . `bert-base-cased`
+                . `bert-large-cased`
+                . `bert-base-multilingual-uncased`
+                . `bert-base-multilingual-cased`
+                . `bert-base-chinese`
+            - 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 dictionnary (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)
+    """
+    model = BertForNextSentencePrediction.from_pretrained(*args, **kwargs)
+    return model
+
+
+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.
+    Params:
+        pretrained_model_name_or_path: either:
+            - a str with the name of a pre-trained model to load selected in the list of:
+                . `bert-base-uncased`
+                . `bert-large-uncased`
+                . `bert-base-cased`
+                . `bert-large-cased`
+                . `bert-base-multilingual-uncased`
+                . `bert-base-multilingual-cased`
+                . `bert-base-chinese`
+            - 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 dictionnary (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)
+
+    """
+    model = BertForPreTraining.from_pretrained(*args, **kwargs)
+    return model
+
+
+def bertForMaskedLM(*args, **kwargs):
+    """
+    BertForMaskedLM includes the BertModel Transformer followed by the (possibly)
+    pre-trained masked language modeling head.
+    Params:
+        pretrained_model_name_or_path: either:
+            - a str with the name of a pre-trained model to load selected in the list of:
+                . `bert-base-uncased`
+                . `bert-large-uncased`
+                . `bert-base-cased`
+                . `bert-large-cased`
+                . `bert-base-multilingual-uncased`
+                . `bert-base-multilingual-cased`
+                . `bert-base-chinese`
+            - 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 dictionnary (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)
+    """
+    model = BertForMaskedLM.from_pretrained(*args, **kwargs)
+    return model
+
+
+#def bertForSequenceClassification(*args, **kwargs):
+#    model = BertForSequenceClassification.from_pretrained(*args, **kwargs)
+#    return model
+
+
+#def bertForMultipleChoice(*args, **kwargs):
+#    model = BertForMultipleChoice.from_pretrained(*args, **kwargs)
+#    return model
+
+
+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.
+    Params:
+        pretrained_model_name_or_path: either:
+            - a str with the name of a pre-trained model to load selected in the list of:
+                . `bert-base-uncased`
+                . `bert-large-uncased`
+                . `bert-base-cased`
+                . `bert-large-cased`
+                . `bert-base-multilingual-uncased`
+                . `bert-base-multilingual-cased`
+                . `bert-base-chinese`
+            - 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 dictionnary (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)
+    """
+    model = BertForQuestionAnswering.from_pretrained(*args, **kwargs)
+    return model
+
+
+