Merge branch 'master' into pr/2189

transformers/configuration_xlm.py

@@ -42,7 +42,7 @@ 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`.
+        vocab_size: 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
@@ -81,7 +81,7 @@ class XLMConfig(PretrainedConfig):
     pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
 
     def __init__(self,
-                 vocab_size_or_config_json_file=30145,
+                 vocab_size=30145,
                  emb_dim=2048,
                  n_layers=12,
                  n_heads=16,
@@ -103,9 +103,6 @@ class XLMConfig(PretrainedConfig):
                  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,
@@ -117,56 +114,43 @@ class XLMConfig(PretrainedConfig):
         """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)")
+        self.vocab_size = vocab_size
+        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.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
 
     @property
-    def vocab_size(self):
-        return self.n_words
+    def n_words(self):  # For backward compatibility
+        return self.vocab_size
 
-    @vocab_size.setter
-    def vocab_size(self, value):
-        self.n_words = value
+    @n_words.setter
+    def n_words(self, value):  # For backward compatibility
+        self.vocab_size = value
 
     @property
     def hidden_size(self):

transformers/configuration_xlnet.py

@@ -35,7 +35,7 @@ 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``.
+        vocab_size: 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
@@ -72,28 +72,22 @@ class XLNetConfig(PretrainedConfig):
     pretrained_config_archive_map = XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
 
     def __init__(self,
-                 vocab_size_or_config_json_file=32000,
+                 vocab_size=32000,
                  d_model=1024,
                  n_layer=24,
                  n_head=16,
                  d_inner=4096,
-                 max_position_embeddings=512,
                  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',
@@ -104,58 +98,45 @@ class XLNetConfig(PretrainedConfig):
         """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():
-                setattr(config, 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)")
+        self.vocab_size = vocab_size
+        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.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
 
     @property
     def max_position_embeddings(self):
         return -1
 
     @property
-    def vocab_size(self):
-        return self.n_token
+    def n_token(self):  # Backward compatibility
+        return self.vocab_size
 
-    @vocab_size.setter
-    def vocab_size(self, value):
-        self.n_token = value
+    @n_token.setter
+    def n_token(self, value):  # Backward compatibility
+        self.vocab_size = value
 
     @property
     def hidden_size(self):

transformers/convert_roberta_original_pytorch_checkpoint_to_pytorch.py

@@ -46,7 +46,7 @@ def convert_roberta_checkpoint_to_pytorch(roberta_checkpoint_path, pytorch_dump_
     roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
     roberta.eval()  # disable dropout
     config = BertConfig(
-        vocab_size_or_config_json_file=50265,
+        vocab_size=50265,
         hidden_size=roberta.args.encoder_embed_dim,
         num_hidden_layers=roberta.args.encoder_layers,
         num_attention_heads=roberta.args.encoder_attention_heads,

transformers/file_utils.py

@@ -72,7 +72,7 @@ WEIGHTS_NAME = "pytorch_model.bin"
 TF2_WEIGHTS_NAME = 'tf_model.h5'
 TF_WEIGHTS_NAME = 'model.ckpt'
 CONFIG_NAME = "config.json"
-
+MODEL_CARD_NAME = "model_card.json"
 
 DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
 DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]

transformers/model_card.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.
+""" 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 .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+from .file_utils import CONFIG_NAME, MODEL_CARD_NAME, cached_path, is_remote_url, hf_bucket_url
+
+
+logger = logging.getLogger(__name__)
+
+
+class ModelCard(object):
+    r""" Model Card class.
+        Store model card as well as methods for loading/downloading/saving model cards.
+
+        Please read the following paper for details and explanation on the sections:
+            "Model Cards for Model Reporting"
+                by Margaret Mitchell, Simone Wu,
+                Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer,
+                Inioluwa Deborah Raji and Timnit Gebru for the proposal behind model cards.
+            Link: https://arxiv.org/abs/1810.03993
+
+        Note:
+            A model card can be loaded and saved to disk.
+
+        Parameters:
+    """
+    def __init__(self, **kwargs):
+        # Recomended attributes from https://arxiv.org/abs/1810.03993 (see papers)
+        self.model_details = kwargs.pop('model_details', {})
+        self.intended_use = kwargs.pop('intended_use', {})
+        self.factors = kwargs.pop('factors', {})
+        self.metrics = kwargs.pop('metrics', {})
+        self.evaluation_data = kwargs.pop('evaluation_data', {})
+        self.training_data = kwargs.pop('training_data', {})
+        self.quantitative_analyses = kwargs.pop('quantitative_analyses', {})
+        self.ethical_considerations = kwargs.pop('ethical_considerations', {})
+        self.caveats_and_recommendations = kwargs.pop('caveats_and_recommendations', {})
+
+        # Open additional attributes
+        for key, value in kwargs.items():
+            try:
+                setattr(self, key, value)
+            except AttributeError as err:
+                logger.error("Can't set {} with value {} for {}".format(key, value, self))
+                raise err
+
+    def save_pretrained(self, save_directory_or_file):
+        """ Save a model card object to the directory or file `save_directory_or_file`.
+        """
+        if os.path.isdir(save_directory_or_file):
+            # If we save using the predefined names, we can load using `from_pretrained`
+            output_model_card_file = os.path.join(save_directory_or_file, MODEL_CARD_NAME)
+        else:
+            output_model_card_file = save_directory_or_file
+
+        self.to_json_file(output_model_card_file)
+        logger.info("Model card saved in {}".format(output_model_card_file))
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        r""" Instantiate a :class:`~transformers.ModelCard` from a pre-trained model model card.
+
+        Parameters:
+            pretrained_model_name_or_path: either:
+
+                - a string with the `shortcut name` of a pre-trained model card to load from cache or download, e.g.: ``bert-base-uncased``.
+                - a string with the `identifier name` of a pre-trained model card that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
+                - a path to a `directory` containing a mode card file saved using the :func:`~transformers.ModelCard.save_pretrained` method, e.g.: ``./my_model_directory/``.
+                - a path or url to a saved model card JSON `file`, e.g.: ``./my_model_directory/model_card.json``.
+
+            cache_dir: (`optional`) string:
+                Path to a directory in which a downloaded pre-trained model
+                card should be cached if the standard cache should not be used.
+
+            kwargs: (`optional`) dict: key/value pairs with which to update the ModelCard object after loading.
+
+                - The values in kwargs of any keys which are model card attributes will be used to override the loaded values.
+                - Behavior concerning key/value pairs whose keys are *not* model card attributes is controlled by the `return_unused_kwargs` keyword parameter.
+
+            force_download: (`optional`) boolean, default False:
+                Force to (re-)download the model card file and override the cached version if it exists.
+
+            resume_download: (`optional`) boolean, default False:
+                Do not delete incompletely recieved file. Attempt to resume the download if such a file 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 model card object.
+                - If True, then this functions returns a tuple `(model card, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not model card attributes: ie the part of kwargs which has not been used to update `ModelCard` and is otherwise ignored.
+
+        Examples::
+
+            model_card = ModelCard.from_pretrained('bert-base-uncased')    # Download model card from S3 and cache.
+            model_card = ModelCard.from_pretrained('./test/saved_model/')  # E.g. model card was saved using `save_pretrained('./test/saved_model/')`
+            model_card = ModelCard.from_pretrained('./test/saved_model/model_card.json')
+            model_card = ModelCard.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
+
+        """
+        cache_dir = kwargs.pop('cache_dir', None)
+        force_download = kwargs.pop('force_download', False)
+        resume_download = kwargs.pop('resume_download', False)
+        proxies = kwargs.pop('proxies', None)
+        return_unused_kwargs = kwargs.pop('return_unused_kwargs', False)
+
+        if pretrained_model_name_or_path in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
+            # For simplicity we use the same pretrained url than the configuration files but with a different suffix (model_card.json)
+            model_card_file = ALL_PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
+            model_card_file = model_card_file.replace(CONFIG_NAME, MODEL_CARD_NAME)
+        elif os.path.isdir(pretrained_model_name_or_path):
+            model_card_file = os.path.join(pretrained_model_name_or_path, MODEL_CARD_NAME)
+        elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
+            model_card_file = pretrained_model_name_or_path
+        else:
+            model_card_file = hf_bucket_url(pretrained_model_name_or_path, postfix=MODEL_CARD_NAME)
+
+        try:
+            # Load from URL or cache if already cached
+            resolved_model_card_file = cached_path(model_card_file, cache_dir=cache_dir, force_download=force_download,
+                                               proxies=proxies, resume_download=resume_download)
+            if resolved_model_card_file == model_card_file:
+                logger.info("loading model card file {}".format(model_card_file))
+            else:
+                logger.info("loading model card file {} from cache at {}".format(
+                    model_card_file, resolved_model_card_file))
+            # Load model card
+            model_card = cls.from_json_file(resolved_model_card_file)
+
+        except EnvironmentError:
+            if pretrained_model_name_or_path in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
+                logger.warning("Couldn't reach server at '{}' to download model card file.".format(
+                        model_card_file))
+            else:
+                logger.warning("Model name '{}' was not found in model name list ({}). " \
+                      "We assumed '{}' was a path or url to a model card file named {} or " \
+                      "a directory containing such a file but couldn't find any such file at this path or url.".format(
+                        pretrained_model_name_or_path,
+                        ', '.join(ALL_PRETRAINED_CONFIG_ARCHIVE_MAP.keys()),
+                        model_card_file, MODEL_CARD_NAME))
+            logger.warning("Creating an empty model card.")
+
+            # We fall back on creating an empty model card
+            model_card = cls()
+
+        except json.JSONDecodeError:
+            logger.warning("Couldn't reach server at '{}' to download model card file or "
+                           "model card file is not a valid JSON file. "
+                           "Please check network or file content here: {}.".format(model_card_file, resolved_model_card_file))
+            logger.warning("Creating an empty model card.")
+
+            # We fall back on creating an empty model card
+            model_card = cls()
+
+        # Update model card with kwargs if needed
+        to_remove = []
+        for key, value in kwargs.items():
+            if hasattr(model_card, key):
+                setattr(model_card, key, value)
+                to_remove.append(key)
+        for key in to_remove:
+            kwargs.pop(key, None)
+
+        logger.info("Model card: %s", str(model_card))
+        if return_unused_kwargs:
+            return model_card, kwargs
+        else:
+            return model_card
+
+    @classmethod
+    def from_dict(cls, json_object):
+        """Constructs a `ModelCard` from a Python dictionary of parameters."""
+        return cls(**json_object)
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        """Constructs a `ModelCard` from a json file of parameters."""
+        with open(json_file, "r", encoding='utf-8') as reader:
+            text = reader.read()
+        dict_obj = json.loads(text)
+        return cls(**dict_obj)
+
+    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())

transformers/modeling_auto.py

@@ -18,18 +18,18 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 
 import logging
 
-from .modeling_bert import BertModel, BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering
-from .modeling_openai import OpenAIGPTModel, OpenAIGPTLMHeadModel
-from .modeling_gpt2 import GPT2Model, GPT2LMHeadModel
-from .modeling_ctrl import CTRLModel, CTRLLMHeadModel
-from .modeling_transfo_xl import TransfoXLModel, TransfoXLLMHeadModel
-from .modeling_xlnet import XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering
-from .modeling_xlm import XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering
-from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification
-from .modeling_distilbert import DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification
-from .modeling_camembert import CamembertModel, CamembertForMaskedLM, CamembertForSequenceClassification, CamembertForMultipleChoice
-from .modeling_albert import AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, AlbertForQuestionAnswering
-from .modeling_t5 import T5Model, T5WithLMHeadModel
+from .modeling_bert import BertModel, BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering, BERT_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_openai import OpenAIGPTModel, OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_gpt2 import GPT2Model, GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_ctrl import CTRLModel, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_transfo_xl import TransfoXLModel, TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_xlnet import XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_xlm import XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering, XLM_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_distilbert import DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_camembert import CamembertModel, CamembertForMaskedLM, CamembertForSequenceClassification, CamembertForMultipleChoice, CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_albert import AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, AlbertForQuestionAnswering, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_t5 import T5Model, T5WithLMHeadModel, T5_PRETRAINED_MODEL_ARCHIVE_MAP
 
 from .modeling_utils import PreTrainedModel, SequenceSummary
 
@@ -38,6 +38,24 @@ from .file_utils import add_start_docstrings
 logger = logging.getLogger(__name__)
 
 
+ALL_PRETRAINED_MODEL_ARCHIVE_MAP = dict((key, value)
+    for pretrained_map in [
+        BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
+        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
+        GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
+        CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
+        XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
+        XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
+        ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+        ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+        CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+        T5_PRETRAINED_MODEL_ARCHIVE_MAP,
+        ]
+    for key, value, in pretrained_map.items())
+
+
 class AutoModel(object):
     r"""
         :class:`~transformers.AutoModel` is a generic model class

transformers/modeling_gpt2.py

@@ -634,6 +634,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
     """
     def __init__(self, config):
         super(GPT2DoubleHeadsModel, self).__init__(config)
+        config.num_labels = 1
         self.transformer = GPT2Model(config)
         self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
         self.multiple_choice_head = SequenceSummary(config)

transformers/modeling_openai.py

@@ -590,6 +590,7 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
     def __init__(self, config):
         super(OpenAIGPTDoubleHeadsModel, self).__init__(config)
 
+        config.num_labels = 1
         self.transformer = OpenAIGPTModel(config)
         self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
         self.multiple_choice_head = SequenceSummary(config)

transformers/modeling_tf_auto.py

@@ -18,22 +18,40 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 
 import logging
 
-from .modeling_tf_bert import TFBertModel, TFBertForMaskedLM, TFBertForSequenceClassification, TFBertForQuestionAnswering
-from .modeling_tf_openai import TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel
-from .modeling_tf_gpt2 import TFGPT2Model, TFGPT2LMHeadModel
-from .modeling_tf_transfo_xl import TFTransfoXLModel, TFTransfoXLLMHeadModel
-from .modeling_tf_xlnet import TFXLNetModel, TFXLNetLMHeadModel, TFXLNetForSequenceClassification, TFXLNetForQuestionAnsweringSimple
-from .modeling_tf_xlm import TFXLMModel, TFXLMWithLMHeadModel, TFXLMForSequenceClassification, TFXLMForQuestionAnsweringSimple
-from .modeling_tf_roberta import TFRobertaModel, TFRobertaForMaskedLM, TFRobertaForSequenceClassification
-from .modeling_tf_distilbert import TFDistilBertModel, TFDistilBertForQuestionAnswering, TFDistilBertForMaskedLM, TFDistilBertForSequenceClassification
-from .modeling_tf_ctrl import TFCTRLModel, TFCTRLLMHeadModel
-from .modeling_tf_t5 import TFT5Model, TFT5WithLMHeadModel
+from .modeling_tf_bert import TFBertModel, TFBertForMaskedLM, TFBertForSequenceClassification, TFBertForQuestionAnswering, TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_tf_openai import TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_tf_gpt2 import TFGPT2Model, TFGPT2LMHeadModel, TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_tf_transfo_xl import TFTransfoXLModel, TFTransfoXLLMHeadModel, TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_tf_xlnet import TFXLNetModel, TFXLNetLMHeadModel, TFXLNetForSequenceClassification, TFXLNetForQuestionAnsweringSimple, TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_tf_xlm import TFXLMModel, TFXLMWithLMHeadModel, TFXLMForSequenceClassification, TFXLMForQuestionAnsweringSimple, TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_tf_roberta import TFRobertaModel, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_tf_distilbert import TFDistilBertModel, TFDistilBertForQuestionAnswering, TFDistilBertForMaskedLM, TFDistilBertForSequenceClassification, TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_tf_ctrl import TFCTRLModel, TFCTRLLMHeadModel, TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_tf_albert import TFAlbertModel, TFAlbertForMaskedLM, TFAlbertForSequenceClassification, TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+from .modeling_tf_t5 import TFT5Model, TFT5WithLMHeadModel, TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP
 
 from .file_utils import add_start_docstrings
 
 logger = logging.getLogger(__name__)
 
 
+TF_ALL_PRETRAINED_MODEL_ARCHIVE_MAP = dict((key, value)
+    for pretrained_map in [
+        TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+        TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
+        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
+        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
+        TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
+        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
+        TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
+        TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+        TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+        TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP,
+        ]
+    for key, value, in pretrained_map.items())
+
+
 class TFAutoModel(object):
     r"""
         :class:`~transformers.TFAutoModel` is a generic model class
@@ -144,6 +162,8 @@ class TFAutoModel(object):
             return TFT5Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'distilbert' in pretrained_model_name_or_path:
             return TFDistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+        elif 'albert' in pretrained_model_name_or_path:
+            return TFAlbertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'roberta' in pretrained_model_name_or_path:
             return TFRobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'bert' in pretrained_model_name_or_path:
@@ -280,6 +300,8 @@ class TFAutoModelWithLMHead(object):
             return TFT5WithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'distilbert' in pretrained_model_name_or_path:
             return TFDistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+        elif 'albert' in pretrained_model_name_or_path:
+            return TFAlbertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'roberta' in pretrained_model_name_or_path:
             return TFRobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'bert' in pretrained_model_name_or_path:
@@ -407,6 +429,8 @@ class TFAutoModelForSequenceClassification(object):
         """
         if 'distilbert' in pretrained_model_name_or_path:
             return TFDistilBertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+        elif 'albert' in pretrained_model_name_or_path:
+            return TFAlbertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'roberta' in pretrained_model_name_or_path:
             return TFRobertaForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'bert' in pretrained_model_name_or_path:

transformers/modeling_tf_gpt2.py

@@ -574,6 +574,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
     """
     def __init__(self, config, *inputs, **kwargs):
         super(TFGPT2DoubleHeadsModel, self).__init__(config, *inputs, **kwargs)
+        config.num_labels = 1
         self.transformer = TFGPT2MainLayer(config, name='transformer')
         self.multiple_choice_head = TFSequenceSummary(config, initializer_range=config.initializer_range, name='multiple_choice_head')
 

transformers/modeling_tf_openai.py

@@ -538,6 +538,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
     """
     def __init__(self, config, *inputs, **kwargs):
         super(TFOpenAIGPTDoubleHeadsModel, self).__init__(config, *inputs, **kwargs)
+        config.num_labels = 1
         self.transformer = TFOpenAIGPTMainLayer(config, name='transformer')
         self.multiple_choice_head = TFSequenceSummary(config, initializer_range=config.initializer_range, name='multiple_choice_head')
 

transformers/modeling_tf_pytorch_utils.py

@@ -143,7 +143,11 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
         name, transpose = convert_tf_weight_name_to_pt_weight_name(sw_name, start_prefix_to_remove=start_prefix_to_remove)
 
         # Find associated numpy array in pytorch model state dict
-        assert name in pt_state_dict, "{} not found in PyTorch model".format(name)
+        if name not in pt_state_dict:
+            if allow_missing_keys:
+                continue
+            raise AttributeError("{} not found in PyTorch model".format(name))
+
         array = pt_state_dict[name].numpy()
 
         if transpose:
@@ -250,6 +254,7 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
 
     all_tf_weights = set(list(tf_weights_map.keys()))
     loaded_pt_weights_data_ptr = {}
+    missing_keys_pt = []
     for pt_weight_name, pt_weight in current_pt_params_dict.items():
         # Handle PyTorch shared weight ()not duplicated in TF 2.0
         if pt_weight.data_ptr() in loaded_pt_weights_data_ptr:
@@ -258,7 +263,10 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
 
         # Find associated numpy array in pytorch model state dict
         if pt_weight_name not in tf_weights_map:
-            raise ValueError("{} not found in TF 2.0 model".format(pt_weight_name))
+            if allow_missing_keys:
+                missing_keys_pt.append(pt_weight_name)
+                continue
+            raise AttributeError("{} not found in TF 2.0 model".format(pt_weight_name))
 
         array, transpose = tf_weights_map[pt_weight_name]
 
@@ -283,6 +291,7 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
         all_tf_weights.discard(pt_weight_name)
 
     missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False)
+    missing_keys += missing_keys_pt
 
     if len(missing_keys) > 0:
         logger.info("Weights of {} not initialized from TF 2.0 model: {}".format(

transformers/modeling_tf_transfo_xl.py

@@ -353,7 +353,7 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
         self.output_attentions = config.output_attentions
         self.output_hidden_states = config.output_hidden_states
 
-        self.n_token = config.n_token
+        self.n_token = config.vocab_size
 
         self.d_embed = config.d_embed
         self.d_model = config.d_model
@@ -361,7 +361,7 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
         self.d_head = config.d_head
         self.untie_r = config.untie_r
 
-        self.word_emb = TFAdaptiveEmbedding(config.n_token, config.d_embed, config.d_model, config.cutoffs, 
+        self.word_emb = TFAdaptiveEmbedding(config.vocab_size, config.d_embed, config.d_model, config.cutoffs, 
                                             div_val=config.div_val, init_std=config.init_std, name='word_emb')
 
         self.drop = tf.keras.layers.Dropout(config.dropout)
@@ -729,7 +729,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
             raise NotImplementedError
         # use adaptive softmax (including standard softmax)
         else:
-            self.crit = TFAdaptiveSoftmaxMask(config.n_token, config.d_embed, config.d_model, 
+            self.crit = TFAdaptiveSoftmaxMask(config.vocab_size, config.d_embed, config.d_model, 
                                               config.cutoffs, div_val=config.div_val, name='crit')
 
     def reset_length(self, tgt_len, ext_len, mem_len):

transformers/modeling_tf_transfo_xl_utilities.py

@@ -25,15 +25,15 @@ import tensorflow as tf
 from .modeling_tf_utils import shape_list
 
 class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
-    def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1,
+    def __init__(self, vocab_size, d_embed, d_proj, cutoffs, div_val=1,
                  keep_order=False, **kwargs):
         super(TFAdaptiveSoftmaxMask, self).__init__(**kwargs)
 
-        self.n_token = n_token
+        self.vocab_size = vocab_size
         self.d_embed = d_embed
         self.d_proj = d_proj
 
-        self.cutoffs = cutoffs + [n_token]
+        self.cutoffs = cutoffs + [vocab_size]
         self.cutoff_ends = [0] + self.cutoffs
         self.div_val = div_val
 
@@ -66,11 +66,11 @@ class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
                     self.out_projs.append(weight)
                 else:
                     self.out_projs.append(None)
-                weight = self.add_weight(shape=(self.n_token, self.d_embed,),
+                weight = self.add_weight(shape=(self.vocab_size, self.d_embed,),
                                          initializer='zeros',
                                          trainable=True,
                                          name='out_layers_._{}_._weight'.format(i))
-                bias = self.add_weight(shape=(self.n_token,),
+                bias = self.add_weight(shape=(self.vocab_size,),
                                          initializer='zeros',
                                          trainable=True,
                                          name='out_layers_._{}_._bias'.format(i))
@@ -114,7 +114,7 @@ class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
         hidden, target = inputs
         head_logprob = 0
         if self.n_clusters == 0:
-            softmax_b = tf.get_variable('bias', [n_token], initializer=tf.zeros_initializer())
+            softmax_b = tf.get_variable('bias', [self.config.vocab_size], initializer=tf.zeros_initializer())
             output = self._logit(hidden, self.out_layers[0][0], self.out_layers[0][1], self.out_projs[0])
             if target is not None:
                 loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output)

transformers/modeling_tf_utils.py

@@ -297,7 +297,7 @@ class TFPreTrainedModel(tf.keras.Model):
 
         if from_pt:
             # Load from a PyTorch checkpoint
-            return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file)
+            return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True)
 
         ret = model(model.dummy_inputs, training=False)  # build the network with dummy inputs
 

transformers/modeling_tf_xlnet.py

@@ -366,7 +366,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
         self.use_bfloat16 = config.use_bfloat16
         self.initializer_range = config.initializer_range
 
-        self.word_embedding = TFSharedEmbeddings(config.n_token, config.d_model, initializer_range=config.initializer_range, name='word_embedding')
+        self.word_embedding = TFSharedEmbeddings(config.vocab_size, config.d_model, initializer_range=config.initializer_range, name='word_embedding')
         self.layer = [TFXLNetLayer(config, name='layer_._{}'.format(i)) for i in range(config.n_layer)]
         self.dropout = tf.keras.layers.Dropout(config.dropout)
 

transformers/modeling_transfo_xl.py

@@ -592,14 +592,14 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
         self.output_attentions = config.output_attentions
         self.output_hidden_states = config.output_hidden_states
 
-        self.n_token = config.n_token
+        self.n_token = config.vocab_size
 
         self.d_embed = config.d_embed
         self.d_model = config.d_model
         self.n_head = config.n_head
         self.d_head = config.d_head
 
-        self.word_emb = AdaptiveEmbedding(config.n_token, config.d_embed, config.d_model, config.cutoffs,
+        self.word_emb = AdaptiveEmbedding(config.vocab_size, config.d_embed, config.d_model, config.cutoffs,
                                           div_val=config.div_val)
 
         self.drop = nn.Dropout(config.dropout)
@@ -836,11 +836,11 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
         self.sample_softmax = config.sample_softmax
         # use sampled softmax
         if config.sample_softmax > 0:
-            self.out_layer = nn.Linear(config.d_model, config.n_token)
-            self.sampler = LogUniformSampler(config.n_token, config.sample_softmax)
+            self.out_layer = nn.Linear(config.d_model, config.vocab_size)
+            self.sampler = LogUniformSampler(config.vocab_size, config.sample_softmax)
         # use adaptive softmax (including standard softmax)
         else:
-            self.crit = ProjectedAdaptiveLogSoftmax(config.n_token, config.d_embed, config.d_model,
+            self.crit = ProjectedAdaptiveLogSoftmax(config.vocab_size, config.d_embed, config.d_model,
                                                     config.cutoffs, div_val=config.div_val)
         self.init_weights()
 

transformers/modeling_xlnet.py

@@ -609,7 +609,7 @@ class XLNetModel(XLNetPreTrainedModel):
         self.clamp_len = config.clamp_len
         self.n_layer = config.n_layer
 
-        self.word_embedding = nn.Embedding(config.n_token, config.d_model)
+        self.word_embedding = nn.Embedding(config.vocab_size, config.d_model)
         self.mask_emb = nn.Parameter(torch.FloatTensor(1, 1, config.d_model))
         self.layer = nn.ModuleList([XLNetLayer(config) for _ in range(config.n_layer)])
         self.dropout = nn.Dropout(config.dropout)
@@ -940,7 +940,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
         self.same_length = config.same_length
 
         self.transformer = XLNetModel(config)
-        self.lm_loss = nn.Linear(config.d_model, config.n_token, bias=True)
+        self.lm_loss = nn.Linear(config.d_model, config.vocab_size, bias=True)
 
         self.init_weights()
 

transformers/tests/configuration_common_test.py

@@ -16,15 +16,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import copy
 import os
-import shutil
 import json
-import random
-import uuid
+import tempfile
 
 import unittest
-import logging
+from .tokenization_tests_commons import TemporaryDirectory
 
 
 class ConfigTester(object):
@@ -48,16 +45,28 @@ class ConfigTester(object):
 
     def create_and_test_config_to_json_file(self):
         config_first = self.config_class(**self.inputs_dict)
-        json_file_path = os.path.join(os.getcwd(), "config_" + str(uuid.uuid4()) + ".json")
-        config_first.to_json_file(json_file_path)
-        config_second = self.config_class.from_json_file(json_file_path)
-        os.remove(json_file_path)
+
+        with TemporaryDirectory() as tmpdirname:
+            json_file_path = os.path.join(tmpdirname, "config.json")
+            config_first.to_json_file(json_file_path)
+            config_second = self.config_class.from_json_file(json_file_path)
+
+        self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
+
+    def create_and_test_config_from_and_save_pretrained(self):
+        config_first = self.config_class(**self.inputs_dict)
+
+        with TemporaryDirectory() as tmpdirname:
+            config_first.save_pretrained(tmpdirname)
+            config_second = self.config_class.from_pretrained(tmpdirname)
+
         self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
 
     def run_common_tests(self):
         self.create_and_test_config_common_properties()
         self.create_and_test_config_to_json_string()
         self.create_and_test_config_to_json_file()
+        self.create_and_test_config_from_and_save_pretrained()
 
 if __name__ == "__main__":
     unittest.main()
\ No newline at end of file

transformers/tests/model_card_test.py

+# coding=utf-8
+# Copyright 2019 HuggingFace 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.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import os
+import json
+import unittest
+
+from transformers.model_card import ModelCard
+from .tokenization_tests_commons import TemporaryDirectory
+
+class ModelCardTester(unittest.TestCase):
+
+    def setUp(self):
+        self.inputs_dict = {'model_details': {
+                                'Organization': 'testing',
+                                'Model date': 'today',
+                                'Model version': 'v2.1, Developed by Test Corp in 2019.',
+                                'Architecture': 'Convolutional Neural Network.',
+                                },
+                            'metrics': 'BLEU and ROUGE-1',
+                            'evaluation_data':{
+                                'Datasets':{
+                                    'BLEU': 'My-great-dataset-v1',
+                                    'ROUGE-1': 'My-short-dataset-v2.1',
+                                },
+                                'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf'
+                            },
+                            'training_data':{
+                                'Dataset': 'English Wikipedia dump dated 2018-12-01',
+                                'Preprocessing': 'Using SentencePiece vocabulary of size 52k tokens. See details on https://arxiv.org/pdf/1810.03993.pdf'
+                            },
+                            'quantitative_analyses': {
+                                'BLEU': 55.1,
+                                'ROUGE-1': 76,
+                            },
+                            }
+
+    def test_model_card_common_properties(self):
+        model_card = ModelCard.from_dict(self.inputs_dict)
+        self.assertTrue(hasattr(model_card, 'model_details'))
+        self.assertTrue(hasattr(model_card, 'intended_use'))
+        self.assertTrue(hasattr(model_card, 'factors'))
+        self.assertTrue(hasattr(model_card, 'metrics'))
+        self.assertTrue(hasattr(model_card, 'evaluation_data'))
+        self.assertTrue(hasattr(model_card, 'training_data'))
+        self.assertTrue(hasattr(model_card, 'quantitative_analyses'))
+        self.assertTrue(hasattr(model_card, 'ethical_considerations'))
+        self.assertTrue(hasattr(model_card, 'caveats_and_recommendations'))
+
+    def test_model_card_to_json_string(self):
+        model_card = ModelCard.from_dict(self.inputs_dict)
+        obj = json.loads(model_card.to_json_string())
+        for key, value in self.inputs_dict.items():
+            self.assertEqual(obj[key], value)
+
+    def test_model_card_to_json_file(self):
+        model_card_first = ModelCard.from_dict(self.inputs_dict)
+
+        with TemporaryDirectory() as tmpdirname:
+            filename = os.path.join(tmpdirname, u"model_card.json")
+            model_card_first.to_json_file(filename)
+            model_card_second = ModelCard.from_json_file(filename)
+
+        self.assertEqual(model_card_second.to_dict(), model_card_first.to_dict())
+
+    def test_model_card_from_and_save_pretrained(self):
+        model_card_first = ModelCard.from_dict(self.inputs_dict)
+
+        with TemporaryDirectory() as tmpdirname:
+            model_card_first.save_pretrained(tmpdirname)
+            model_card_second = ModelCard.from_pretrained(tmpdirname)
+
+        self.assertEqual(model_card_second.to_dict(), model_card_first.to_dict())
+
+if __name__ == "__main__":
+    unittest.main()