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Fix typos in translated quicktour docs (#35302)

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* fix: quicktour typos

* fix: one more
This commit is contained in:
Jacky Lee
2024-12-17 09:32:00 -08:00
committed by GitHub
parent deac971c46
commit 4302b27719
9 changed files with 47 additions and 47 deletions
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8
docs/source/ar/quicktour.md
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@@ -347,8 +347,8 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py ```py
>>> from transformers import AutoModel >>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True) >>> pt_model = AutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
``` ```
</pt> </pt>
<tf> <tf>
@@ -356,8 +356,8 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py ```py
>>> from transformers import TFAutoModel >>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True) >>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
``` ```
</tf> </tf>
</frameworkcontent> </frameworkcontent>

8
docs/source/de/quicktour.md
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@@ -383,8 +383,8 @@ Ein besonders cooles 🤗 Transformers-Feature ist die Möglichkeit, ein Modell
```py ```py
>>> from transformers import AutoModel >>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True) >>> pt_model = AutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
``` ```
</pt> </pt>
<tf> <tf>
@@ -392,8 +392,8 @@ Ein besonders cooles 🤗 Transformers-Feature ist die Möglichkeit, ein Modell
```py ```py
>>> from transformers import TFAutoModel >>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True) >>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
``` ```
</tf> </tf>
</frameworkcontent> </frameworkcontent>

8
docs/source/es/quicktour.md
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@@ -385,8 +385,8 @@ Una característica particularmente interesante de 🤗 Transformers es la habil
```py ```py
>>> from transformers import AutoModel >>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True) >>> pt_model = AutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
``` ```
</pt> </pt>
<tf> <tf>
@@ -394,8 +394,8 @@ Una característica particularmente interesante de 🤗 Transformers es la habil
```py ```py
>>> from transformers import TFAutoModel >>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True) >>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
``` ```
</tf> </tf>
</frameworkcontent> </frameworkcontent>

8
docs/source/fr/quicktour.md
View File

@@ -354,8 +354,8 @@ Une fonctionnalité particulièrement cool 🤗 Transformers est la possibilité
```py ```py
>>> from transformers import AutoModel >>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True) >>> pt_model = AutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
``` ```
</pt> </pt>
<tf> <tf>
@@ -363,8 +363,8 @@ Une fonctionnalité particulièrement cool 🤗 Transformers est la possibilité
```py ```py
>>> from transformers import TFAutoModel >>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True) >>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
``` ```
</tf> </tf>
</frameworkcontent> </frameworkcontent>

8
docs/source/it/quicktour.md
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@@ -385,8 +385,8 @@ Una caratteristica particolarmente interessante di 🤗 Transformers è la sua a
```py ```py
>>> from transformers import AutoModel >>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True) >>> pt_model = AutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
``` ```
</pt> </pt>
<tf> <tf>
@@ -394,8 +394,8 @@ Una caratteristica particolarmente interessante di 🤗 Transformers è la sua a
```py ```py
>>> from transformers import TFAutoModel >>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True) >>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
``` ```
</tf> </tf>
</frameworkcontent> </frameworkcontent>

8
docs/source/ja/quicktour.md
View File

@@ -386,8 +386,8 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py ```py
>>> from transformers import AutoModel >>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True) >>> pt_model = AutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
``` ```
</pt> </pt>
@@ -396,8 +396,8 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py ```py
>>> from transformers import TFAutoModel >>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True) >>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
``` ```
</tf> </tf>
</frameworkcontent> </frameworkcontent>

8
docs/source/ko/quicktour.md
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@@ -361,8 +361,8 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py ```py
>>> from transformers import AutoModel >>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True) >>> pt_model = AutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
``` ```
</pt> </pt>
<tf> <tf>
@@ -370,8 +370,8 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py ```py
>>> from transformers import TFAutoModel >>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True) >>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
``` ```
</tf> </tf>
</frameworkcontent> </frameworkcontent>

8
docs/source/pt/quicktour.md
View File

@@ -383,8 +383,8 @@ Um recurso particularmente interessante dos 🤗 Transformers é a capacidade de
```py ```py
>>> from transformers import AutoModel >>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True) >>> pt_model = AutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
``` ```
</pt> </pt>
<tf> <tf>
@@ -392,8 +392,8 @@ Um recurso particularmente interessante dos 🤗 Transformers é a capacidade de
```py ```py
>>> from transformers import TFAutoModel >>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True) >>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
``` ```
</tf> </tf>
</frameworkcontent> </frameworkcontent>

8
docs/source/te/quicktour.md
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@@ -366,8 +366,8 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py ```py
>>> from transformers import AutoModel >>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True) >>> pt_model = AutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
``` ```
</pt> </pt>
<tf> <tf>
@@ -375,8 +375,8 @@ tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
```py ```py
>>> from transformers import TFAutoModel >>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True) >>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
``` ```
</tf> </tf>
</frameworkcontent> </frameworkcontent>