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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/zh/create_a_model.md
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@@ -87,7 +87,7 @@ DistilBertConfig {
预训练模型的属性可以在 [`~PretrainedConfig.from_pretrained`] 函数中进行修改：
```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)
```
当你对模型配置满意时，可以使用 [`~PretrainedConfig.save_pretrained`] 来保存配置。你的配置文件将以 JSON 文件的形式存储在指定的保存目录中：
@@ -128,13 +128,13 @@ DistilBertConfig {
使用 [`~PreTrainedModel.from_pretrained`] 创建预训练模型：
```py
>>> model = DistilBertModel.from_pretrained("distilbert-base-uncased")
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
```
当加载预训练权重时，如果模型是由 🤗 Transformers 提供的，将自动加载默认模型配置。然而，如果你愿意，仍然可以将默认模型配置的某些或者所有属性替换成你自己的配置：
```py
>>> model = DistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
```
</pt>
<tf>
@@ -152,13 +152,13 @@ DistilBertConfig {
使用 [`~TFPreTrainedModel.from_pretrained`] 创建预训练模型：
```py
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
```
当加载预训练权重时，如果模型是由 🤗 Transformers 提供的，将自动加载默认模型配置。然而，如果你愿意，仍然可以将默认模型配置的某些或者所有属性替换成自己的配置：
```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>
@@ -174,7 +174,7 @@ DistilBertConfig {
```py
>>> from transformers import DistilBertForSequenceClassification
>>> model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
>>> model = DistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
通过切换到不同的模型头，可以轻松地将此检查点重复用于其他任务。对于问答任务，你可以使用 [`DistilBertForQuestionAnswering`] 模型头。问答头（question answering head）与序列分类头类似，不同点在于它是隐藏状态输出之上的线性层。
@@ -182,7 +182,7 @@ DistilBertConfig {
```py
>>> from transformers import DistilBertForQuestionAnswering
>>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
>>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
</pt>
<tf>
@@ -191,7 +191,7 @@ DistilBertConfig {
```py
>>> from transformers import TFDistilBertForSequenceClassification
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
通过切换到不同的模型头,可以轻松地将此检查点重复用于其他任务。对于问答任务，你可以使用 [`TFDistilBertForQuestionAnswering`] 模型头。问答头（question answering head）与序列分类头类似，不同点在于它是隐藏状态输出之上的线性层。
@@ -199,7 +199,7 @@ DistilBertConfig {
```py
>>> from transformers import TFDistilBertForQuestionAnswering
>>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
>>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
</tf>
</frameworkcontent>
@@ -232,7 +232,7 @@ DistilBertConfig {
```py
>>> from transformers import DistilBertTokenizer
>>> slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
>>> slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
使用 [`DistilBertTokenizerFast`] 类创建快速分词器：
@@ -240,7 +240,7 @@ DistilBertConfig {
```py
>>> from transformers import DistilBertTokenizerFast
>>> fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
>>> fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert/distilbert-base-uncased")
```
<Tip>