Add configuration file

Will do the same for tokenization and modeling
Following the format of: https://huggingface.co/THUDM/chatglm-6b/tree/main

configuration_character_bert.py

@@ -0,0 +1,156 @@
+# coding=utf-8
+# Copyright Hicham EL BOUKKOURI, Olivier FERRET, Thomas LAVERGNE, Hiroshi NOJI,
+# Pierre ZWEIGENBAUM, Junichi TSUJII and The HuggingFace Inc. team.
+# 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.
+""" CharacterBERT model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+CHARACTER_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "helboukkouri/character-bert": "https://huggingface.co/helboukkouri/character-bert/resolve/main/config.json",
+    "helboukkouri/character-bert-medical": "https://huggingface.co/helboukkouri/character-bert-medical/resolve/main/config.json",
+    # See all CharacterBERT models at https://huggingface.co/models?filter=character_bert
+}
+
+
+class CharacterBertConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`CharacterBertModel`]. It is
+    used to instantiate an CharacterBERT 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 CharacterBERT
+    [helboukkouri/character-bert](https://huggingface.co/helboukkouri/character-bert) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model
+    outputs. Read the documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        character_embeddings_dim (`int`, *optional*, defaults to `16`):
+            The size of the character embeddings.
+        cnn_activation (`str`, *optional*, defaults to `"relu"`):
+            The activation function to apply to the cnn representations.
+        cnn_filters (:
+            obj:*list(list(int))*, *optional*, defaults to `[[1, 32], [2, 32], [3, 64], [4, 128], [5, 256], [6, 512], [7, 1024]]`): The list of CNN filters to use in the CharacterCNN module.
+        num_highway_layers (`int`, *optional*, defaults to `2`):
+            The number of Highway layers to apply to the CNNs output.
+        max_word_length (`int`, *optional*, defaults to `50`):
+            The maximum token length in characters (actually, in bytes as any non-ascii characters will be converted to
+            a sequence of utf-8 bytes).
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string,
+            `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (`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).
+        type_vocab_size (`int`, *optional*, defaults to 2):
+            The vocabulary size of the `token_type_ids` passed when calling
+            [`CharacterBertModel`] or [`TFCharacterBertModel`].
+        mlm_vocab_size (`int`, *optional*, defaults to 100000):
+            Size of the output vocabulary for MLM.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+
+    Example:
+
+    ```python
+
+    ```
+
+            >>> from transformers import CharacterBertModel, CharacterBertConfig
+
+            >>> # Initializing a CharacterBERT helboukkouri/character-bert style configuration
+            >>> configuration = CharacterBertConfig()
+
+            >>> # Initializing a model from the helboukkouri/character-bert style configuration
+            >>> model = CharacterBertModel(configuration)
+
+            >>> # Accessing the model configuration
+            >>> configuration = model.config
+    """
+    model_type = "character_bert"
+
+    def __init__(
+        self,
+        character_embeddings_dim=16,
+        cnn_activation="relu",
+        cnn_filters=None,
+        num_highway_layers=2,
+        max_word_length=50,
+        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,
+        mlm_vocab_size=100000,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        is_encoder_decoder=False,
+        use_cache=True,
+        **kwargs
+    ):
+        tie_word_embeddings = kwargs.pop("tie_word_embeddings", False)
+        if tie_word_embeddings:
+            raise ValueError(
+                "Cannot tie word embeddings in CharacterBERT. Please set " "`config.tie_word_embeddings=False`."
+            )
+        super().__init__(
+            type_vocab_size=type_vocab_size,
+            layer_norm_eps=layer_norm_eps,
+            use_cache=use_cache,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        if cnn_filters is None:
+            cnn_filters = [[1, 32], [2, 32], [3, 64], [4, 128], [5, 256], [6, 512], [7, 1024]]
+        self.character_embeddings_dim = character_embeddings_dim
+        self.cnn_activation = cnn_activation
+        self.cnn_filters = cnn_filters
+        self.num_highway_layers = num_highway_layers
+        self.max_word_length = max_word_length
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.mlm_vocab_size = mlm_vocab_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range