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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/pt/training.md
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@@ -58,7 +58,7 @@ todo o dataset.
```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):
@@ -93,7 +93,7 @@ sabemos ter 5 labels usamos o seguinte código:
```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>
@@ -232,7 +232,7 @@ Carregue um modelo do TensorFlow com o número esperado de rótulos:
>>> import tensorflow as tf
>>> from transformers import TFAutoModelForSequenceClassification
>>> model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
>>> model = TFAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
```
A seguir, compile e ajuste o fine-tuning a seu modelo com [`fit`](https://keras.io/api/models/model_training_apis/) como
@@ -311,7 +311,7 @@ Carregue seu modelo com o número de labels esperados:
```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)
```
### Otimização e configuração do Learning Rate