/usr/local/lib/python3.6/site-packages/transformers/models/mt5
Edit: /usr/local/lib/python3.6/site-packages/transformers/models/mt5/modeling_mt5.py (4136B)
# 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"
class MT5Model(T5Model):
r"""
This class overrides [`T5Model`]. Please check the superclass for the appropriate documentation alongside usage
examples.
Examples:
```python
>>> 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",
]
class MT5ForConditionalGeneration(T5ForConditionalGeneration):
r"""
This class overrides [`T5ForConditionalGeneration`]. Please check the superclass for the appropriate documentation
alongside usage examples.
Examples:
```python
>>> 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",
]
class MT5EncoderModel(T5EncoderModel):
r"""
This class overrides [`T5EncoderModel`]. Please check the superclass for the appropriate documentation alongside
usage examples.
Examples:
```python
>>> 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",
]