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Unverified Commit 73a59a2f authored by Francisco Kurucz's avatar Francisco Kurucz Committed by GitHub
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Fix references to model google mt5 small (#32497)

parent cba7bcf8
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Showing with 9 additions and 9 deletions
src/transformers/models/mt5/modeling_mt5.py
View file @ 73a59a2f
......@@ -1435,7 +1435,7 @@ class MT5Model(MT5PreTrainedModel):
@add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
# Copied from transformers.models.t5.modeling_t5.T5Model.forward with T5->MT5, t5->mt5
# Copied from transformers.models.t5.modeling_t5.T5Model.forward with google-t5/->google/, T5->MT5, t5->mt5
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
......@@ -1462,8 +1462,8 @@ class MT5Model(MT5PreTrainedModel):
```python
>>> from transformers import AutoTokenizer, MT5Model
>>> tokenizer = AutoTokenizer.from_pretrained("google-mt5/mt5-small")
>>> model = MT5Model.from_pretrained("google-mt5/mt5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
>>> model = MT5Model.from_pretrained("google/mt5-small")
>>> input_ids = tokenizer(
... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
......@@ -1665,7 +1665,7 @@ class MT5ForConditionalGeneration(MT5PreTrainedModel):
@add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
# Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.forward with T5->MT5, t5->mt5
# Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.forward with google-t5/->google/, T5->MT5, t5->mt5
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
......@@ -1698,8 +1698,8 @@ class MT5ForConditionalGeneration(MT5PreTrainedModel):
```python
>>> from transformers import AutoTokenizer, MT5ForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("google-mt5/mt5-small")
>>> model = MT5ForConditionalGeneration.from_pretrained("google-mt5/mt5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
>>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
>>> # training
>>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
......@@ -1990,7 +1990,7 @@ class MT5EncoderModel(MT5PreTrainedModel):
@add_start_docstrings_to_model_forward(MT5_ENCODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
# Copied from transformers.models.t5.modeling_t5.T5EncoderModel.forward with T5->MT5, t5->mt5
# Copied from transformers.models.t5.modeling_t5.T5EncoderModel.forward with google-t5/->google/, T5->MT5, t5->mt5
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
......@@ -2009,8 +2009,8 @@ class MT5EncoderModel(MT5PreTrainedModel):
```python
>>> from transformers import AutoTokenizer, MT5EncoderModel
>>> tokenizer = AutoTokenizer.from_pretrained("google-mt5/mt5-small")
>>> model = MT5EncoderModel.from_pretrained("google-mt5/mt5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
>>> model = MT5EncoderModel.from_pretrained("google/mt5-small")
>>> input_ids = tokenizer(
... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids # Batch size 1
......
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