Update config.json

config.json

@@ -31,4 +31,125 @@
   "transformers_version": "4.25.1",
   "use_cache": true,
   "vocab_size": 250100
-}
+}# coding=utf-8
+# Copyright 2020 Mesh TensorFlow authors, T5 Authors and 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.
+""" PyTorch mT5 model. """
+
+from ...utils import logging
+from ..t5.modeling_t5 import T5EncoderModel, T5ForConditionalGeneration, T5Model
+from .configuration_mt5 import MT5Config
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "T5Config"
+_TOKENIZER_FOR_DOC = "T5Tokenizer"
+
+
+
+[DOCS]
+class MT5Model(T5Model):
+    r"""
+    This class overrides :class:`~transformers.T5Model`. Please check the superclass for the appropriate documentation
+    alongside usage examples.
+
+    Examples::
+
+        >>> from transformers import MT5Model, T5Tokenizer
+        >>> model = MT5Model.from_pretrained("google/mt5-small")
+        >>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
+        >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+        >>> summary = "Weiter Verhandlung in Syrien."
+        >>> inputs = tokenizer(article, return_tensors="pt")
+        >>> with tokenizer.as_target_tokenizer():
+        ...     labels = tokenizer(summary, return_tensors="pt")
+
+        >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"])
+        >>> hidden_states = outputs.last_hidden_state
+    """
+    model_type = "mt5"
+    config_class = MT5Config
+    _keys_to_ignore_on_load_missing = [
+        r"encoder\.embed_tokens\.weight",
+        r"decoder\.embed_tokens\.weight",
+        r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight",
+    ]
+    _keys_to_ignore_on_save = [
+        r"encoder\.embed_tokens\.weight",
+        r"decoder\.embed_tokens\.weight",
+    ]
+
+
+
+
+[DOCS]
+class MT5ForConditionalGeneration(T5ForConditionalGeneration):
+    r"""
+    This class overrides :class:`~transformers.T5ForConditionalGeneration`. Please check the superclass for the
+    appropriate documentation alongside usage examples.
+
+    Examples::
+
+        >>> from transformers import MT5ForConditionalGeneration, T5Tokenizer
+        >>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
+        >>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
+        >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+        >>> summary = "Weiter Verhandlung in Syrien."
+        >>> inputs = tokenizer(article, return_tensors="pt")
+        >>> with tokenizer.as_target_tokenizer():
+        ...     labels = tokenizer(summary, return_tensors="pt")
+
+        >>> outputs = model(**inputs,labels=labels["input_ids"])
+        >>> loss = outputs.loss
+    """
+
+    model_type = "mt5"
+    config_class = MT5Config
+    _keys_to_ignore_on_load_missing = [
+        r"encoder\.embed_tokens\.weight",
+    ]
+    _keys_to_ignore_on_save = [
+        r"encoder\.embed_tokens\.weight",
+    ]
+
+
+
+
+[DOCS]
+class MT5EncoderModel(T5EncoderModel):
+    r"""
+    This class overrides :class:`~transformers.T5EncoderModel`. Please check the superclass for the appropriate
+    documentation alongside usage examples.
+
+    Examples::
+
+        >>> from transformers import MT5EncoderModel, T5Tokenizer
+        >>> model = MT5EncoderModel.from_pretrained("google/mt5-small")
+        >>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
+        >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+        >>> input_ids = tokenizer(article, return_tensors="pt").input_ids
+        >>> outputs = model(input_ids)
+        >>> hidden_state = outputs.last_hidden_state
+    """
+
+    model_type = "mt5"
+    config_class = MT5Config
+    _keys_to_ignore_on_load_missing = [
+        r"encoder\.embed_tokens\.weight",
+    ]
+    _keys_to_ignore_on_save = [
+        r"encoder\.embed_tokens\.weight",
+    ]
+