@@ -193,8 +193,8 @@ Pass your text to the tokenizer:
...
@@ -193,8 +193,8 @@ Pass your text to the tokenizer:
The tokenizer returns a dictionary containing:
The tokenizer returns a dictionary containing:
* [input_ids](./glossary#input-ids): numerical representions of your tokens.
* [input_ids](./glossary#input-ids): numerical representations of your tokens.
* [atttention_mask](.glossary#attention-mask): indicates which tokens should be attended to.
* [attention_mask](.glossary#attention-mask): indicates which tokens should be attended to.
A tokenizer can also accept a list of inputs, and pad and truncate the text to return a batch with uniform length:
A tokenizer can also accept a list of inputs, and pad and truncate the text to return a batch with uniform length:
...
@@ -525,4 +525,4 @@ All models are a standard [`tf.keras.Model`](https://www.tensorflow.org/api_docs
...
@@ -525,4 +525,4 @@ All models are a standard [`tf.keras.Model`](https://www.tensorflow.org/api_docs
## What's next?
## What's next?
Now that you've completed the 🤗 Transformers quick tour, check out our guides and learn how to do more specific things like writing a custom model, fine-tuning a model for a task, and how to train a model with a script. If you're interested in learning more about 🤗 Transformers core concepts, grab a cup of coffee and take a look at our Conceptual Guides!
Now that you've completed the 🤗 Transformers quick tour, check out our guides and learn how to do more specific things like writing a custom model, fine-tuning a model for a task, and how to train a model with a script. If you're interested in learning more about 🤗 Transformers core concepts, grab a cup of coffee and take a look at our Conceptual Guides!
