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Update all references to canonical models (#29001)

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* Script & Manual edition

* Update
This commit is contained in:
Lysandre Debut
2024-02-16 08:16:58 +01:00
committed by GitHub
parent 1e402b957d
commit f497f564bb
561 changed files with 2682 additions and 2687 deletions
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docs/source/ja/create_a_model.md
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@@ -89,7 +89,7 @@ DistilBertConfig {
事前学習済みモデルの属性は、[`~PretrainedConfig.from_pretrained`] 関数で変更できます：
```py
>>> my_config = DistilBertConfig.from_pretrained("distilbert-base-uncased", activation="relu", attention_dropout=0.4)
>>> my_config = DistilBertConfig.from_pretrained("distilbert/distilbert-base-uncased", activation="relu", attention_dropout=0.4)
```
Once you are satisfied with your model configuration, you can save it with [`PretrainedConfig.save_pretrained`]. Your configuration file is stored as a JSON file in the specified save directory.
@@ -136,13 +136,13 @@ Once you are satisfied with your model configuration, you can save it with [`Pre
```py
>>> model = DistilBertModel.from_pretrained("distilbert-base-uncased")
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
```
事前学習済みの重みをロードする際、モデルが🤗 Transformersによって提供されている場合、デフォルトのモデル設定が自動的にロードされます。ただし、必要に応じてデフォルトのモデル設定属性の一部またはすべてを独自のもので置き換えることができます。
```py
>>> model = DistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
```
</pt>
<tf>
@@ -163,13 +163,13 @@ Once you are satisfied with your model configuration, you can save it with [`Pre
```py
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
```
事前学習済みの重みをロードする際、モデルが🤗 Transformersによって提供されている場合、デフォルトのモデル構成が自動的にロードされます。ただし、必要であればデフォルトのモデル構成属性の一部またはすべてを独自のもので置き換えることもできます：
```py
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
```
</tf>
</frameworkcontent>
@@ -186,7 +186,7 @@ Once you are satisfied with your model configuration, you can save it with [`Pre
```py
>>> from transformers import DistilBertForSequenceClassification
>>> model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
>>> model = DistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
新しいタスクにこのチェックポイントを簡単に再利用するには、異なるモデルヘッドに切り替えます。
@@ -196,7 +196,7 @@ Once you are satisfied with your model configuration, you can save it with [`Pre
```py
>>> from transformers import DistilBertForQuestionAnswering
>>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
>>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
</pt>
@@ -206,7 +206,7 @@ Once you are satisfied with your model configuration, you can save it with [`Pre
```py
>>> from transformers import TFDistilBertForSequenceClassification
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
別のタスクにこのチェックポイントを簡単に再利用することができ、異なるモデルヘッドに切り替えるだけです。
@@ -217,7 +217,7 @@ Once you are satisfied with your model configuration, you can save it with [`Pre
```py
>>> from transformers import TFDistilBertForQuestionAnswering
>>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
>>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
</tf>
</frameworkcontent>
@@ -257,7 +257,7 @@ Once you are satisfied with your model configuration, you can save it with [`Pre
```py
>>> from transformers import DistilBertTokenizer
>>> slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
>>> slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
[`DistilBertTokenizerFast`]クラスを使用して高速なトークナイザを作成します：
@@ -265,7 +265,7 @@ Once you are satisfied with your model configuration, you can save it with [`Pre
```py
>>> from transformers import DistilBertTokenizerFast
>>> fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
>>> fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert/distilbert-base-uncased")
```
<Tip>