🌐 [i18n-KO] Fixed Korean and English quicktour.md

docs/source/en/quicktour.md

@@ -64,7 +64,7 @@ For a complete list of available tasks, check out the [pipeline API reference](.
 | Audio classification         | assign a label to some audio data                                                                            | Audio           | pipeline(task=“audio-classification”)         |
 | Automatic speech recognition | transcribe speech into text                                                                                  | Audio           | pipeline(task=“automatic-speech-recognition”) |
 | Visual question answering    | answer a question about the image, given an image and a question                                             | Multimodal      | pipeline(task=“vqa”)                          |
-| Document question answering  | answer a question about a document, given an image and a question                                            | Multimodal      | pipeline(task="document-question-answering")  |
+| Document question answering  | answer a question about the document, given a document and a question                                        | Multimodal      | pipeline(task="document-question-answering")  |
 | Image captioning             | generate a caption for a given image                                                                         | Multimodal      | pipeline(task="image-to-text")                |
 
 Start by creating an instance of [`pipeline`] and specifying a task you want to use it for. In this guide, you'll use the [`pipeline`] for sentiment analysis as an example:
@@ -289,7 +289,7 @@ See the [task summary](./task_summary) for tasks supported by an [`AutoModel`] c
 
 </Tip>
 
-Now pass your preprocessed batch of inputs directly to the model by passing the dictionary keys directly to the tensors:
+Now pass your preprocessed batch of inputs directly to the model. You can pass the tensors as-is:
 
 ```py
 >>> tf_outputs = tf_model(tf_batch)
@@ -410,7 +410,7 @@ All models are a standard [`torch.nn.Module`](https://pytorch.org/docs/stable/nn
 
 Depending on your task, you'll typically pass the following parameters to [`Trainer`]:
 
-1. A [`PreTrainedModel`] or a [`torch.nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module):
+1. You'll start with a [`PreTrainedModel`] or a [`torch.nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module):
 
    ```py
    >>> from transformers import AutoModelForSequenceClassification
@@ -432,7 +432,7 @@ Depending on your task, you'll typically pass the following parameters to [`Trai
    ... )
    ```
 
-3. A preprocessing class like a tokenizer, image processor, feature extractor, or processor:
+3. Load a preprocessing class like a tokenizer, image processor, feature extractor, or processor:
 
    ```py
    >>> from transformers import AutoTokenizer
@@ -512,7 +512,7 @@ All models are a standard [`tf.keras.Model`](https://www.tensorflow.org/api_docs
    >>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
    ```
 
-2. A preprocessing class like a tokenizer, image processor, feature extractor, or processor:
+2. Load a preprocessing class like a tokenizer, image processor, feature extractor, or processor:
 
    ```py
    >>> from transformers import AutoTokenizer