FlauBERT documentation

docs/source/index.rst

@@ -97,4 +97,5 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
     model_doc/ctrl
     model_doc/camembert
     model_doc/albert
-    model_doc/xlmroberta
\ No newline at end of file
+    model_doc/xlmroberta
+    model_doc/flaubert
\ No newline at end of file

docs/source/model_doc/flaubert.rst

+FlauBERT
+----------------------------------------------------
+
+The FlauBERT model was proposed in the paper
+`FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le et al.
+It's a transformer pre-trained using a masked language modeling (MLM) objective (BERT-like).
+
+The abstract from the paper is the following:
+
+*Language models have become a key step to achieve state-of-the art results in many different Natural Language
+Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient
+way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their
+contextualization at the sentence level. This has been widely demonstrated for English using contextualized
+representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et
+al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large
+and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre
+for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text
+classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most
+of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified
+evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared
+to the research community for further reproducible experiments in French NLP.*
+
+
+FlaubertConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertConfig
+    :members:
+
+
+FlaubertTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertTokenizer
+    :members:
+
+
+FlaubertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertModel
+    :members:
+
+
+FlaubertWithLMHeadModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertWithLMHeadModel
+    :members:
+
+
+FlaubertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertForSequenceClassification
+    :members:
+
+
+FlaubertForQuestionAnsweringSimple
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertForQuestionAnsweringSimple
+    :members:
+
+
+FlaubertForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertForQuestionAnswering
+    :members:
+
+

src/transformers/configuration_flaubert.py

@@ -31,44 +31,111 @@ FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 
 class FlaubertConfig(XLMConfig):
-    """Configuration class to store the configuration of a `FlaubertModel`.
-
-    Args:
-        vocab_size: Vocabulary size of `inputs_ids` in `FlaubertModel`.
-        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 Flaubert, 'uni' for Transformer-XL
+    """
+        Configuration class to store the configuration of a `FlaubertModel`.
+        This is the configuration class to store the configuration of a :class:`~transformers.XLMModel`.
+        It is used to instantiate an XLM model according to the specified arguments, defining the model
+        architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
+        the `xlm-mlm-en-2048 <https://huggingface.co/xlm-mlm-en-2048>`__ architecture.
 
-        dropout: The dropout probabilitiy for all fully connected
-            layers in the embeddings, encoder, and pooler.
-        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.
+        Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
+        to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
+        for more information.
 
-        dropout: float, dropout rate.
-        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.
+        Args:
+            pre_norm (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether to apply the layer normalization before or after the feed forward layer following the
+                attention in each layer.
+            vocab_size (:obj:`int`, optional, defaults to 30145):
+                Vocabulary size of the XLM model. Defines the different tokens that
+                can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLMModel`.
+            emb_dim (:obj:`int`, optional, defaults to 2048):
+                Dimensionality of the encoder layers and the pooler layer.
+            n_layer (:obj:`int`, optional, defaults to 12):
+                Number of hidden layers in the Transformer encoder.
+            n_head (:obj:`int`, optional, defaults to 16):
+                Number of attention heads for each attention layer in the Transformer encoder.
+            dropout (:obj:`float`, optional, defaults to 0.1):
+                The dropout probability for all fully connected
+                layers in the embeddings, encoder, and pooler.
+            attention_dropout (:obj:`float`, optional, defaults to 0.1):
+                The dropout probability for the attention mechanism
+            gelu_activation (:obj:`boolean`, optional, defaults to :obj:`True`):
+                The non-linear activation function (function or string) in the
+                encoder and pooler. If set to `True`, "gelu" will be used instead of "relu".
+            sinusoidal_embeddings (:obj:`boolean`, optional, defaults to :obj:`False`):
+                Whether to use sinusoidal positional embeddings instead of absolute positional embeddings.
+            causal (:obj:`boolean`, optional, defaults to :obj:`False`):
+                Set this to `True` for the model to behave in a causal manner.
+                Causal models use a triangular attention mask in order to only attend to the left-side context instead
+                if a bidirectional context.
+            asm (:obj:`boolean`, optional, defaults to :obj:`False`):
+                Whether to use an adaptive log softmax projection layer instead of a linear layer for the prediction
+                layer.
+            n_langs (:obj:`int`, optional, defaults to 1):
+                The number of languages the model handles. Set to 1 for monolingual models.
+            use_lang_emb (:obj:`boolean`, optional, defaults to :obj:`True`)
+                Whether to use language embeddings. Some models use additional language embeddings, see
+                `the multilingual models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>`__
+                for information on how to use them.
+            max_position_embeddings (:obj:`int`, optional, defaults to 512):
+                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).
+            embed_init_std (:obj:`float`, optional, defaults to 2048^-0.5):
+                The standard deviation of the truncated_normal_initializer for
+                initializing the embedding matrices.
+            init_std (:obj:`int`, optional, defaults to 50257):
+                The standard deviation of the truncated_normal_initializer for
+                initializing all weight matrices except the embedding matrices.
+            layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
+                The epsilon used by the layer normalization layers.
+            bos_index (:obj:`int`, optional, defaults to 0):
+                The index of the beginning of sentence token in the vocabulary.
+            eos_index (:obj:`int`, optional, defaults to 1):
+                The index of the end of sentence token in the vocabulary.
+            pad_index (:obj:`int`, optional, defaults to 2):
+                The index of the padding token in the vocabulary.
+            unk_index (:obj:`int`, optional, defaults to 3):
+                The index of the unknown token in the vocabulary.
+            mask_index (:obj:`int`, optional, defaults to 5):
+                The index of the masking token in the vocabulary.
+            is_encoder(:obj:`boolean`, optional, defaults to :obj:`True`):
+                Whether the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
+            summary_type (:obj:`string`, optional, defaults to "first"):
+                Argument used when doing sequence summary. Used in for the multiple choice head in
+                :class:`~transformers.XLMForSequenceClassification`.
+                Is one of the following options:
+                    - 'last' => take the last token hidden state (like XLNet)
+                    - 'first' => take the first token hidden state (like Bert)
+                    - 'mean' => take the mean of all tokens hidden states
+                    - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
+                    - 'attn' => Not implemented now, use multi-head attention
+            summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
+                Argument used when doing sequence summary. Used in for the multiple choice head in
+                :class:`~transformers.XLMForSequenceClassification`.
+                Add a projection after the vector extraction
+            summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`):
+                Argument used when doing sequence summary. Used in for the multiple choice head in
+                :class:`~transformers.XLMForSequenceClassification`.
+                'tanh' => add a tanh activation to the output, Other => no activation.
+            summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
+                Argument used when doing sequence summary. Used in for the multiple choice head in
+                :class:`~transformers.XLMForSequenceClassification`.
+                If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
+            summary_first_dropout (:obj:`float`, optional, defaults to 0.1):
+                Argument used when doing sequence summary. Used in for the multiple choice head in
+                :class:`~transformers.XLMForSequenceClassification`.
+                Add a dropout before the projection and activation
+            start_n_top (:obj:`int`, optional, defaults to 5):
+                Used in the SQuAD evaluation script for XLM and XLNet.
+            end_n_top (:obj:`int`, optional, defaults to 5):
+                Used in the SQuAD evaluation script for XLM and XLNet.
+            mask_token_id (:obj:`int`, optional, defaults to 0):
+                Model agnostic parameter to identify masked tokens when generating text in an MLM context.
+            lang_id (:obj:`int`, optional, defaults to 1):
+                The ID of the language used by the model. This parameter is used when generating
+                text in a given language.
     """
 
     pretrained_config_archive_map = FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP