Doc to dataset (#18037)

* Link to the Datasets doc

* Remove unwanted file

docs/source/en/perf_train_gpu_one.mdx

@@ -33,7 +33,7 @@ pip install transformers datasets accelerate nvidia-ml-py3
 
 The `nvidia-ml-py3` library allows us to monitor the memory usage of the models from within Python. You might be familiar with the `nvidia-smi` command in the terminal - this library allows to access the same information in Python directly.
 
-Then we create some dummy data. We create random token IDs between 100 and 30000 and binary labels for a classifier. In total we get 512 sequences each with length 512 and store them in a [`Dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=dataset#datasets.Dataset) with PyTorch format.
+Then we create some dummy data. We create random token IDs between 100 and 30000 and binary labels for a classifier. In total we get 512 sequences each with length 512 and store them in a [`~datasets.Dataset`] with PyTorch format.
 
 
 ```py

docs/source/en/preprocessing.mdx

@@ -244,7 +244,7 @@ For example, the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) data
  'sampling_rate': 8000}
 ```
 
-1. Use 🤗 Datasets' [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.cast_column) method to upsample the sampling rate to 16kHz:
+1. Use 🤗 Datasets' [`~datasets.Dataset.cast_column`] method to upsample the sampling rate to 16kHz:
 
 ```py
 >>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))

docs/source/en/tasks/asr.mdx

@@ -117,7 +117,7 @@ The preprocessing function needs to:
 ...     return batch
 ```
 
-Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to apply the preprocessing function over the entire dataset. You can speed up the map function by increasing the number of processes with `num_proc`. Remove the columns you don't need:
+Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing function over the entire dataset. You can speed up the map function by increasing the number of processes with `num_proc`. Remove the columns you don't need:
 
 ```py
 >>> encoded_minds = minds.map(prepare_dataset, remove_columns=minds.column_names["train"], num_proc=4)

docs/source/en/tasks/audio_classification.mdx

@@ -129,7 +129,7 @@ The preprocessing function needs to:
 ...     return inputs
 ```
 
-Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once. Remove the columns you don't need, and rename `intent_class` to `label` because that is what the model expects:
+Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once. Remove the columns you don't need, and rename `intent_class` to `label` because that is what the model expects:
 
 ```py
 >>> encoded_minds = minds.map(preprocess_function, remove_columns="audio", batched=True)

docs/source/en/tasks/image_classification.mdx

@@ -95,7 +95,7 @@ Create a preprocessing function that will apply the transforms and return the `p
 ...     return examples
 ```
 
-Use 🤗 Dataset's [`with_transform`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?#datasets.Dataset.with_transform) method to apply the transforms over the entire dataset. The transforms are applied on-the-fly when you load an element of the dataset:
+Use 🤗 Dataset's [`~datasets.Dataset.with_transform`] method to apply the transforms over the entire dataset. The transforms are applied on-the-fly when you load an element of the dataset:
 
 ```py
 >>> food = food.with_transform(transforms)

docs/source/en/tasks/language_modeling.mdx

@@ -118,7 +118,7 @@ Here is how you can create a preprocessing function to convert the list to a str
 ...     return tokenizer([" ".join(x) for x in examples["answers.text"]], truncation=True)
 ```
 
-Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once and increasing the number of processes with `num_proc`. Remove the columns you don't need:
+Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once and increasing the number of processes with `num_proc`. Remove the columns you don't need:
 
 ```py
 >>> tokenized_eli5 = eli5.map(
@@ -245,7 +245,7 @@ At this point, only three steps remain:
 ```
 </pt>
 <tf>
-To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
+To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~datasets.Dataset.to_tf_dataset`]. Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
 
 ```py
 >>> tf_train_set = lm_dataset["train"].to_tf_dataset(
@@ -352,7 +352,7 @@ At this point, only three steps remain:
 ```
 </pt>
 <tf>
-To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
+To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~datasets.Dataset.to_tf_dataset`]. Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
 
 ```py
 >>> tf_train_set = lm_dataset["train"].to_tf_dataset(

docs/source/en/tasks/multiple_choice.mdx

@@ -79,7 +79,7 @@ The preprocessing function needs to do:
 ...     return {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
 ```
 
-Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
+Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
 
 ```py
 tokenized_swag = swag.map(preprocess_function, batched=True)
@@ -224,7 +224,7 @@ At this point, only three steps remain:
 ```
 </pt>
 <tf>
-To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs in `columns`, targets in `label_cols`, whether to shuffle the dataset order, batch size, and the data collator:
+To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~datasets.Dataset.to_tf_dataset`]. Specify inputs in `columns`, targets in `label_cols`, whether to shuffle the dataset order, batch size, and the data collator:
 
 ```py
 >>> data_collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)

docs/source/en/tasks/question_answering.mdx

@@ -126,7 +126,7 @@ Here is how you can create a function to truncate and map the start and end toke
 ...     return inputs
 ```
 
-Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once. Remove the columns you don't need:
+Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once. Remove the columns you don't need:
 
 ```py
 >>> tokenized_squad = squad.map(preprocess_function, batched=True, remove_columns=squad["train"].column_names)
@@ -199,7 +199,7 @@ At this point, only three steps remain:
 ```
 </pt>
 <tf>
-To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and the start and end positions of an answer in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
+To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~datasets.Dataset.to_tf_dataset`]. Specify inputs and the start and end positions of an answer in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
 
 ```py
 >>> tf_train_set = tokenized_squad["train"].to_tf_dataset(

docs/source/en/tasks/sequence_classification.mdx

@@ -66,7 +66,7 @@ Create a preprocessing function to tokenize `text` and truncate sequences to be
 ...     return tokenizer(examples["text"], truncation=True)
 ```
 
-Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
+Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
 
 ```py
 tokenized_imdb = imdb.map(preprocess_function, batched=True)
@@ -144,7 +144,7 @@ At this point, only three steps remain:
 </Tip>
 </pt>
 <tf>
-To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
+To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~datasets.Dataset.to_tf_dataset`]. Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
 
 ```py
 >>> tf_train_set = tokenized_imdb["train"].to_tf_dataset(

docs/source/en/tasks/summarization.mdx

@@ -85,7 +85,7 @@ The preprocessing function needs to:
 ...     return model_inputs
 ```
 
-Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
+Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
 
 ```py
 >>> tokenized_billsum = billsum.map(preprocess_function, batched=True)
@@ -160,7 +160,7 @@ At this point, only three steps remain:
 ```
 </pt>
 <tf>
-To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
+To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~datasets.Dataset.to_tf_dataset`]. Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
 
 ```py
 >>> tf_train_set = tokenized_billsum["train"].to_tf_dataset(

docs/source/en/tasks/token_classification.mdx

@@ -126,7 +126,7 @@ Here is how you can create a function to realign the tokens and labels, and trun
 ...     return tokenized_inputs
 ```
 
-Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to tokenize and align the labels over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
+Use 🤗 Datasets [`~datasets.Dataset.map`] function to tokenize and align the labels over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
 
 ```py
 >>> tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)
@@ -199,7 +199,7 @@ At this point, only three steps remain:
 ```
 </pt>
 <tf>
-To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
+To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~datasets.Dataset.to_tf_dataset`]. Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
 
 ```py
 >>> tf_train_set = tokenized_wnut["train"].to_tf_dataset(

docs/source/en/tasks/translation.mdx

@@ -87,7 +87,7 @@ The preprocessing function needs to:
 ...     return model_inputs
 ```
 
-Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
+Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
 
 ```py
 >>> tokenized_books = books.map(preprocess_function, batched=True)
@@ -162,7 +162,7 @@ At this point, only three steps remain:
 ```
 </pt>
 <tf>
-To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
+To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~datasets.Dataset.to_tf_dataset`]. Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
 
 ```py
 >>> tf_train_set = tokenized_books["train"].to_tf_dataset(

docs/source/en/training.mdx

@@ -169,7 +169,7 @@ The [`DefaultDataCollator`] assembles tensors into a batch for the model to trai
 
 </Tip>
 
-Next, convert the tokenized datasets to TensorFlow datasets with the [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset) method. Specify your inputs in `columns`, and your label in `label_cols`:
+Next, convert the tokenized datasets to TensorFlow datasets with the [`~datasets.Dataset.to_tf_dataset`] method. Specify your inputs in `columns`, and your label in `label_cols`:
 
 ```py
 >>> tf_train_dataset = small_train_dataset.to_tf_dataset(