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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/de/training.md
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@@ -48,7 +48,7 @@ Wie Sie nun wissen, benötigen Sie einen Tokenizer, um den Text zu verarbeiten u
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
>>> def tokenize_function(examples):
@@ -86,7 +86,7 @@ Beginnen Sie mit dem Laden Ihres Modells und geben Sie die Anzahl der erwarteten
```py
>>> from transformers import AutoModelForSequenceClassification
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
```
<Tip>
@@ -187,7 +187,7 @@ Wir können sie also ohne Tokenisierung direkt in ein NumPy-Array konvertieren!
```py
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
tokenized_data = tokenizer(dataset["text"], return_tensors="np", padding=True)
# Tokenizer returns a BatchEncoding, but we convert that to a dict for Keras
tokenized_data = dict(tokenized_data)
@@ -202,7 +202,7 @@ from transformers import TFAutoModelForSequenceClassification
from tensorflow.keras.optimizers import Adam
# Load and compile our model
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased")
model = TFAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased")
# Lower learning rates are often better for fine-tuning transformers
model.compile(optimizer=Adam(3e-5))
@@ -333,7 +333,7 @@ Laden Sie Ihr Modell mit der Anzahl der erwarteten Kennzeichnungen:
```py
>>> from transformers import AutoModelForSequenceClassification
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
```
### Optimierer und Lernratensteuerung