Broken links fixed related to datasets docs (#27569)

fixed the broken links belogs to dataset library of transformers

docs/source/de/preprocessing.md

@@ -209,7 +209,7 @@ Audioeingaben werden anders vorverarbeitet als Texteingaben, aber das Endziel bl
 pip install datasets
 ```
 
-Laden Sie den [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) Datensatz (weitere Informationen zum Laden eines Datensatzes finden Sie im 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html)):
+Laden Sie den [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) Datensatz (weitere Informationen zum Laden eines Datensatzes finden Sie im 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub)):
 
 ```py
 >>> from datasets import load_dataset, Audio
@@ -344,7 +344,7 @@ Laden wir den [food101](https://huggingface.co/datasets/food101) Datensatz für
 >>> dataset = load_dataset("food101", split="train[:100]")
 ```
 
-Als Nächstes sehen Sie sich das Bild mit dem Merkmal 🤗 Datensätze [Bild] (https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=image#datasets.Image) an:
+Als Nächstes sehen Sie sich das Bild mit dem Merkmal 🤗 Datensätze [Bild] (https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=image#datasets.Image) an:
 
 ```py
 >>> dataset[0]["image"]
@@ -385,7 +385,7 @@ Bei Bildverarbeitungsaufgaben ist es üblich, den Bildern als Teil der Vorverarb
 ...     return examples
 ```
 
-3. Dann verwenden Sie 🤗 Datasets [`set_transform`](https://huggingface.co/docs/datasets/process.html#format-transform), um die Transformationen im laufenden Betrieb anzuwenden:
+3. Dann verwenden Sie 🤗 Datasets [`set_transform`](https://huggingface.co/docs/datasets/process#format-transform), um die Transformationen im laufenden Betrieb anzuwenden:
 
 ```py
 >>> dataset.set_transform(transforms)

docs/source/de/quicktour.md

@@ -121,7 +121,7 @@ Erstellen wir eine [`pipeline`] mit der Aufgabe die wir lösen und dem Modell we
 >>> speech_recognizer = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
 ```
 
-Als nächstes laden wir den Datensatz (siehe 🤗 Datasets [Quick Start](https://huggingface.co/docs/datasets/quickstart.html) für mehr Details) welches wir nutzen möchten. Zum Beispiel laden wir den [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) Datensatz:
+Als nächstes laden wir den Datensatz (siehe 🤗 Datasets [Quick Start](https://huggingface.co/docs/datasets/quickstart) für mehr Details) welches wir nutzen möchten. Zum Beispiel laden wir den [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) Datensatz:
 
 ```py
 >>> from datasets import load_dataset, Audio

docs/source/de/training.md

@@ -43,7 +43,7 @@ Laden Sie zunächst den Datensatz [Yelp Reviews](https://huggingface.co/datasets
  'text': 'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularly...that takes something special!\\nThe cashier took my friends\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \\"serving off their orders\\" when they didn\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\\nThe manager was rude when giving me my order. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\\nI\'ve eaten at various McDonalds restaurants for over 30 years. I\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'}
 ```
 
-Wie Sie nun wissen, benötigen Sie einen Tokenizer, um den Text zu verarbeiten und eine Auffüll- und Abschneidungsstrategie einzubauen, um mit variablen Sequenzlängen umzugehen. Um Ihren Datensatz in einem Schritt zu verarbeiten, verwenden Sie die 🤗 Methode Datasets [`map`](https://huggingface.co/docs/datasets/process.html#map), um eine Vorverarbeitungsfunktion auf den gesamten Datensatz anzuwenden:
+Wie Sie nun wissen, benötigen Sie einen Tokenizer, um den Text zu verarbeiten und eine Auffüll- und Abschneidungsstrategie einzubauen, um mit variablen Sequenzlängen umzugehen. Um Ihren Datensatz in einem Schritt zu verarbeiten, verwenden Sie die 🤗 Methode Datasets [`map`](https://huggingface.co/docs/datasets/process#map), um eine Vorverarbeitungsfunktion auf den gesamten Datensatz anzuwenden:
 
 ```py
 >>> from transformers import AutoTokenizer

docs/source/en/preprocessing.md

@@ -220,7 +220,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 
 For audio tasks, you'll need a [feature extractor](main_classes/feature_extractor) to prepare your dataset for the model. The feature extractor is designed to extract features from raw audio data, and convert them into tensors.
 
-Load the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) for more details on how to load a dataset) to see how you can use a feature extractor with audio datasets:
+Load the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub) for more details on how to load a dataset) to see how you can use a feature extractor with audio datasets:
 
 ```py
 >>> from datasets import load_dataset, Audio
@@ -340,7 +340,7 @@ You can use any library you like for image augmentation. For image preprocessing
 
 </Tip>
 
-Load the [food101](https://huggingface.co/datasets/food101) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) for more details on how to load a dataset) to see how you can use an image processor with computer vision datasets:
+Load the [food101](https://huggingface.co/datasets/food101) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub) for more details on how to load a dataset) to see how you can use an image processor with computer vision datasets:
 
 <Tip>
 
@@ -354,7 +354,7 @@ Use 🤗 Datasets `split` parameter to only load a small sample from the trainin
 >>> dataset = load_dataset("food101", split="train[:100]")
 ```
 
-Next, take a look at the image with 🤗 Datasets [`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=image#datasets.Image) feature:
+Next, take a look at the image with 🤗 Datasets [`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=image#datasets.Image) feature:
 
 ```py
 >>> dataset[0]["image"]
@@ -467,7 +467,7 @@ from [`DetrImageProcessor`] and define a custom `collate_fn` to batch images tog
 
 For tasks involving multimodal inputs, you'll need a [processor](main_classes/processors) to prepare your dataset for the model. A processor couples together two processing objects such as as tokenizer and feature extractor.
 
-Load the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) for more details on how to load a dataset) to see how you can use a processor for automatic speech recognition (ASR):
+Load the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub) for more details on how to load a dataset) to see how you can use a processor for automatic speech recognition (ASR):
 
 ```py
 >>> from datasets import load_dataset

docs/source/en/training.md

@@ -43,7 +43,7 @@ Begin by loading the [Yelp Reviews](https://huggingface.co/datasets/yelp_review_
  'text': 'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularly...that takes something special!\\nThe cashier took my friends\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \\"serving off their orders\\" when they didn\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\\nThe manager was rude when giving me my order. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\\nI\'ve eaten at various McDonalds restaurants for over 30 years. I\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'}
 ```
 
-As you now know, you need a tokenizer to process the text and include a padding and truncation strategy to handle any variable sequence lengths. To process your dataset in one step, use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/process.html#map) method to apply a preprocessing function over the entire dataset:
+As you now know, you need a tokenizer to process the text and include a padding and truncation strategy to handle any variable sequence lengths. To process your dataset in one step, use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/process#map) method to apply a preprocessing function over the entire dataset:
 
 ```py
 >>> from transformers import AutoTokenizer

docs/source/es/preprocessing.md

@@ -195,7 +195,7 @@ Las entradas de audio se preprocesan de forma diferente a las entradas textuales
 pip install datasets
 ```
 
-Carga la tarea de detección de palabras clave del benchmark [SUPERB](https://huggingface.co/datasets/superb) (consulta el [tutorial 🤗 Dataset](https://huggingface.co/docs/datasets/load_hub.html) para que obtengas más detalles sobre cómo cargar un dataset):
+Carga la tarea de detección de palabras clave del benchmark [SUPERB](https://huggingface.co/datasets/superb) (consulta el [tutorial 🤗 Dataset](https://huggingface.co/docs/datasets/load_hub) para que obtengas más detalles sobre cómo cargar un dataset):
 
 ```py
 >>> from datasets import load_dataset, Audio
@@ -234,7 +234,7 @@ Por ejemplo, carga el dataset [LJ Speech](https://huggingface.co/datasets/lj_spe
  'sampling_rate': 22050}
 ```
 
-1. Usa el método 🤗 Datasets' [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.cast_column) para reducir la tasa de muestreo a 16kHz:
+1. Usa el método 🤗 Datasets' [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.cast_column) para reducir la tasa de muestreo a 16kHz:
 
 ```py
 >>> lj_speech = lj_speech.cast_column("audio", Audio(sampling_rate=16_000))
@@ -329,7 +329,7 @@ Vamos a cargar el dataset [food101](https://huggingface.co/datasets/food101) par
 >>> dataset = load_dataset("food101", split="train[:100]")
 ```
 
-A continuación, observa la imagen con la función 🤗 Datasets [`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=image#datasets.Image):
+A continuación, observa la imagen con la función 🤗 Datasets [`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=image#datasets.Image):
 
 ```py
 >>> dataset[0]["image"]
@@ -370,7 +370,7 @@ Para las tareas de visión por computadora es común añadir algún tipo de aume
 ...     return examples
 ```
 
-3. A continuación, utiliza 🤗 Datasets [`set_transform`](https://huggingface.co/docs/datasets/process.html#format-transform) para aplicar las transformaciones sobre la marcha:
+3. A continuación, utiliza 🤗 Datasets [`set_transform`](https://huggingface.co/docs/datasets/process#format-transform) para aplicar las transformaciones sobre la marcha:
 
 ```py
 >>> dataset.set_transform(transforms)

docs/source/es/tasks/image_classification.md

@@ -99,7 +99,7 @@ Crea una función de preprocesamiento que aplique las transformaciones y devuelv
 ...     return examples
 ```
 
-Utiliza el método [`with_transform`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?#datasets.Dataset.with_transform) de 🤗 Dataset para aplicar las transformaciones sobre todo el dataset. Las transformaciones se aplican sobre la marcha cuando se carga un elemento del dataset:
+Utiliza el método [`with_transform`](https://huggingface.co/docs/datasets/package_reference/main_classes?#datasets.Dataset.with_transform) de 🤗 Dataset para aplicar las transformaciones sobre todo el dataset. Las transformaciones se aplican sobre la marcha cuando se carga un elemento del dataset:
 
 ```py
 >>> food = food.with_transform(transforms)

docs/source/es/tasks/language_modeling.md

@@ -249,7 +249,7 @@ A este punto, solo faltan tres pasos:
 ```
 </pt>
 <tf>
-Para realizar el fine-tuning de un modelo en TensorFlow, comienza por convertir tus datasets al formato `tf.data.Dataset` con [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Especifica los inputs y etiquetas en `columns`, ya sea para mezclar el dataset, tamaño de lote, y el data collator:
+Para realizar el fine-tuning de un modelo en TensorFlow, comienza por convertir tus datasets al formato `tf.data.Dataset` con [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_tf_dataset). Especifica los inputs y etiquetas en `columns`, ya sea para mezclar el dataset, tamaño de lote, y el data collator:
 
 ```py
 >>> tf_train_set = lm_dataset["train"].to_tf_dataset(
@@ -356,7 +356,7 @@ A este punto, solo faltan tres pasos:
 ```
 </pt>
 <tf>
-Para realizar el fine-tuning de un modelo en TensorFlow, comienza por convertir tus datasets al formato `tf.data.Dataset` con [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Especifica los inputs y etiquetas en `columns`, ya sea para mezclar el dataset, tamaño de lote, y el data collator:
+Para realizar el fine-tuning de un modelo en TensorFlow, comienza por convertir tus datasets al formato `tf.data.Dataset` con [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_tf_dataset). Especifica los inputs y etiquetas en `columns`, ya sea para mezclar el dataset, tamaño de lote, y el data collator:
 
 ```py
 >>> tf_train_set = lm_dataset["train"].to_tf_dataset(

docs/source/es/training.md

@@ -102,7 +102,7 @@ Especifica dónde vas a guardar los checkpoints de tu entrenamiento:
 
 ### Métricas
 
-El [`Trainer`] no evalúa automáticamente el rendimiento del modelo durante el entrenamiento. Tendrás que pasarle a [`Trainer`] una función para calcular y hacer un reporte de las métricas. La biblioteca de 🤗 Datasets proporciona una función de [`accuracy`](https://huggingface.co/metrics/accuracy) simple que puedes cargar con la función `load_metric` (ver este [tutorial](https://huggingface.co/docs/datasets/metrics.html) para más información):
+El [`Trainer`] no evalúa automáticamente el rendimiento del modelo durante el entrenamiento. Tendrás que pasarle a [`Trainer`] una función para calcular y hacer un reporte de las métricas. La biblioteca de 🤗 Datasets proporciona una función de [`accuracy`](https://huggingface.co/metrics/accuracy) simple que puedes cargar con la función `load_metric` (ver este [tutorial](https://huggingface.co/docs/datasets/metrics) para más información):
 
 ```py
 >>> import numpy as np
@@ -172,7 +172,7 @@ El [`DefaultDataCollator`] junta los tensores en un batch para que el modelo se
 
 </Tip>
 
-A continuación, convierte los datasets tokenizados en datasets de TensorFlow con el método [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Especifica tus entradas en `columns` y tu etiqueta en `label_cols`:
+A continuación, convierte los datasets tokenizados en datasets de TensorFlow con el método [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_tf_dataset). Especifica tus entradas en `columns` y tu etiqueta en `label_cols`:
 
 ```py
 >>> tf_train_dataset = small_train_dataset.to_tf_dataset(
@@ -342,7 +342,7 @@ Para hacer un seguimiento al progreso del entrenamiento, utiliza la biblioteca [
 
 ### Métricas
 
-De la misma manera que necesitas añadir una función de evaluación al [`Trainer`], necesitas hacer lo mismo cuando escribas tu propio ciclo de entrenamiento. Pero en lugar de calcular y reportar la métrica al final de cada época, esta vez acumularás todos los batches con [`add_batch`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=add_batch#datasets.Metric.add_batch) y calcularás la métrica al final.
+De la misma manera que necesitas añadir una función de evaluación al [`Trainer`], necesitas hacer lo mismo cuando escribas tu propio ciclo de entrenamiento. Pero en lugar de calcular y reportar la métrica al final de cada época, esta vez acumularás todos los batches con [`add_batch`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=add_batch#datasets.Metric.add_batch) y calcularás la métrica al final.
 
 ```py
 >>> metric = load_metric("accuracy")

docs/source/it/preprocessing.md

@@ -194,7 +194,7 @@ Gli input audio sono processati in modo differente rispetto al testo, ma l'obiet
 pip install datasets
 ```
 
-Carica il dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) (vedi il 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) per avere maggiori dettagli su come caricare un dataset):
+Carica il dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) (vedi il 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub) per avere maggiori dettagli su come caricare un dataset):
 
 ```py
 >>> from datasets import load_dataset, Audio
@@ -233,7 +233,7 @@ Per esempio, il dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds1
  'sampling_rate': 8000}
 ```
 
-1. Usa il metodo di 🤗 Datasets' [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.cast_column) per alzare la frequenza di campionamento a 16kHz:
+1. Usa il metodo di 🤗 Datasets' [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.cast_column) per alzare la frequenza di campionamento a 16kHz:
 
 ```py
 >>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
@@ -370,7 +370,7 @@ Per le attività di visione, è usuale aggiungere alcuni tipi di data augmentati
 ...     return examples
 ```
 
-3. Poi utilizza 🤗 Datasets [`set_transform`](https://huggingface.co/docs/datasets/process.html#format-transform)per applicare al volo la trasformazione:
+3. Poi utilizza 🤗 Datasets [`set_transform`](https://huggingface.co/docs/datasets/process#format-transform)per applicare al volo la trasformazione:
 
 ```py
 >>> dataset.set_transform(transforms)

docs/source/it/quicktour.md

@@ -125,7 +125,7 @@ Crea una [`pipeline`] con il compito che vuoi risolvere e con il modello che vuo
 ... )
 ```
 
-Poi, carica un dataset (vedi 🤗 Datasets [Quick Start](https://huggingface.co/docs/datasets/quickstart.html) per maggiori dettagli) sul quale vuoi iterare. Per esempio, carichiamo il dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14):
+Poi, carica un dataset (vedi 🤗 Datasets [Quick Start](https://huggingface.co/docs/datasets/quickstart) per maggiori dettagli) sul quale vuoi iterare. Per esempio, carichiamo il dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14):
 
 ```py
 >>> from datasets import load_dataset, Audio

docs/source/it/training.md

@@ -43,7 +43,7 @@ Inizia caricando il dataset [Yelp Reviews](https://huggingface.co/datasets/yelp_
  'text': 'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularly...that takes something special!\\nThe cashier took my friends\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \\"serving off their orders\\" when they didn\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\\nThe manager was rude when giving me my order. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\\nI\'ve eaten at various McDonalds restaurants for over 30 years. I\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'}
 ```
 
-Come già sai, hai bisogno di un tokenizer per processare il testo e includere una strategia di padding e truncation per gestire sequenze di lunghezza variabile. Per processare il dataset in un unico passo, usa il metodo [`map`](https://huggingface.co/docs/datasets/process.html#map) di 🤗 Datasets che applica la funzione di preprocessing all'intero dataset:
+Come già sai, hai bisogno di un tokenizer per processare il testo e includere una strategia di padding e truncation per gestire sequenze di lunghezza variabile. Per processare il dataset in un unico passo, usa il metodo [`map`](https://huggingface.co/docs/datasets/process#map) di 🤗 Datasets che applica la funzione di preprocessing all'intero dataset:
 
 ```py
 >>> from transformers import AutoTokenizer
@@ -103,7 +103,7 @@ Specifica dove salvare i checkpoints del tuo addestramento:
 
 ### Metriche
 
-[`Trainer`] non valuta automaticamente le performance del modello durante l'addestramento. Dovrai passare a [`Trainer`] una funzione che calcola e restituisce le metriche. La libreria 🤗 Datasets mette a disposizione una semplice funzione [`accuracy`](https://huggingface.co/metrics/accuracy) che puoi caricare con la funzione `load_metric` (guarda questa [esercitazione](https://huggingface.co/docs/datasets/metrics.html) per maggiori informazioni):
+[`Trainer`] non valuta automaticamente le performance del modello durante l'addestramento. Dovrai passare a [`Trainer`] una funzione che calcola e restituisce le metriche. La libreria 🤗 Datasets mette a disposizione una semplice funzione [`accuracy`](https://huggingface.co/metrics/accuracy) che puoi caricare con la funzione `load_metric` (guarda questa [esercitazione](https://huggingface.co/docs/datasets/metrics) per maggiori informazioni):
 
 ```py
 >>> import numpy as np
@@ -346,7 +346,7 @@ Per tenere traccia dei tuoi progressi durante l'addestramento, usa la libreria [
 
 ### Metriche
 
-Proprio come è necessario aggiungere una funzione di valutazione del [`Trainer`], è necessario fare lo stesso quando si scrive il proprio ciclo di addestramento. Ma invece di calcolare e riportare la metrica alla fine di ogni epoca, questa volta accumulerai tutti i batch con [`add_batch`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=add_batch#datasets.Metric.add_batch) e calcolerai la metrica alla fine.
+Proprio come è necessario aggiungere una funzione di valutazione del [`Trainer`], è necessario fare lo stesso quando si scrive il proprio ciclo di addestramento. Ma invece di calcolare e riportare la metrica alla fine di ogni epoca, questa volta accumulerai tutti i batch con [`add_batch`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=add_batch#datasets.Metric.add_batch) e calcolerai la metrica alla fine.
 
 ```py
 >>> metric = load_metric("accuracy")

docs/source/ja/preprocessing.md

@@ -227,7 +227,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 オーディオタスクの場合、データセットをモデル用に準備するために[特徴抽出器](main_classes/feature_extractor)が必要です。
 特徴抽出器は生のオーディオデータから特徴を抽出し、それらをテンソルに変換するために設計されています。
 
-[PolyAI/minds14](https://huggingface.co/datasets/PolyAI/minds14)データセットをロードして（データセットのロード方法の詳細については🤗 [Datasetsチュートリアル](https://huggingface.co/docs/datasets/load_hub.html)を参照）、
+[PolyAI/minds14](https://huggingface.co/datasets/PolyAI/minds14)データセットをロードして（データセットのロード方法の詳細については🤗 [Datasetsチュートリアル](https://huggingface.co/docs/datasets/load_hub)を参照）、
 オーディオデータセットで特徴抽出器をどのように使用できるかを確認してみましょう：
 
 ```python
@@ -349,7 +349,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 
 </Tip>
 
-コンピュータビジョンのデータセットで画像プロセッサを使用する方法を示すために、[food101](https://huggingface.co/datasets/food101)データセットをロードします（データセットのロード方法の詳細については🤗[Datasetsチュートリアル](https://huggingface.co/docs/datasets/load_hub.html)を参照）：
+コンピュータビジョンのデータセットで画像プロセッサを使用する方法を示すために、[food101](https://huggingface.co/datasets/food101)データセットをロードします（データセットのロード方法の詳細については🤗[Datasetsチュートリアル](https://huggingface.co/docs/datasets/load_hub)を参照）：
 
 <Tip>
 
@@ -363,7 +363,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 >>> dataset = load_dataset("food101", split="train[:100]")
 ```
 
-次に、🤗 Datasetsの [`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=image#datasets.Image) 機能で画像を見てみましょう：
+次に、🤗 Datasetsの [`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=image#datasets.Image) 機能で画像を見てみましょう：
 
 ```python
 >>> dataset[0]["image"]
@@ -419,7 +419,7 @@ AutoImageProcessorを[`AutoImageProcessor.from_pretrained`]を使用してロー
 画像を増強変換の一部として正規化したい場合は、`image_processor.image_mean` と `image_processor.image_std` の値を使用してください。
 </Tip>
 
-3. 次に、🤗 Datasetsの[`set_transform`](https://huggingface.co/docs/datasets/process.html#format-transform)を使用して、変換をリアルタイムで適用します：
+3. 次に、🤗 Datasetsの[`set_transform`](https://huggingface.co/docs/datasets/process#format-transform)を使用して、変換をリアルタイムで適用します：
 
 ```python
 >>> dataset.set_transform(transforms)
@@ -474,7 +474,7 @@ AutoImageProcessorを[`AutoImageProcessor.from_pretrained`]を使用してロー
 
 マルチモーダル入力を使用するタスクの場合、モデル用にデータセットを準備するための[プロセッサ](main_classes/processors)が必要です。プロセッサは、トークナイザや特徴量抽出器などの2つの処理オブジェクトを結合します。
 
-自動音声認識（ASR）のためのプロセッサの使用方法を示すために、[LJ Speech](https://huggingface.co/datasets/lj_speech)データセットをロードします（データセットのロード方法の詳細については🤗 [Datasets チュートリアル](https://huggingface.co/docs/datasets/load_hub.html)を参照）：
+自動音声認識（ASR）のためのプロセッサの使用方法を示すために、[LJ Speech](https://huggingface.co/datasets/lj_speech)データセットをロードします（データセットのロード方法の詳細については🤗 [Datasets チュートリアル](https://huggingface.co/docs/datasets/load_hub)を参照）：
 
 ```python
 >>> from datasets import load_dataset

docs/source/ja/training.md

@@ -49,7 +49,7 @@ rendered properly in your Markdown viewer.
 ```
 
 トークナイザがテキストを処理し、可変のシーケンス長を処理するためのパディングと切り捨て戦略を含める必要があることをご存知の通り、
-データセットを1つのステップで処理するには、🤗 Datasets の [`map`](https://huggingface.co/docs/datasets/process.html#map) メソッドを使用して、
+データセットを1つのステップで処理するには、🤗 Datasets の [`map`](https://huggingface.co/docs/datasets/process#map) メソッドを使用して、
 データセット全体に前処理関数を適用します：
 
 ```py

docs/source/ko/preprocessing.md

@@ -220,7 +220,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 
 오디오 작업은 모델에 맞는 데이터 세트를 준비하기 위해 [특성 추출기](main_classes/feature_extractor)가 필요합니다. 특성 추출기는 원시 오디오 데이터에서 특성를 추출하고 이를 텐서로 변환하는 것이 목적입니다.
 
-오디오 데이터 세트에 특성 추출기를 사용하는 방법을 보기 위해 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) 데이터 세트를 가져오세요. (데이터 세트를 가져오는 방법은 🤗 [데이터 세트 튜토리얼](https://huggingface.co/docs/datasets/load_hub.html)에서 자세히 설명하고 있습니다.)
+오디오 데이터 세트에 특성 추출기를 사용하는 방법을 보기 위해 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) 데이터 세트를 가져오세요. (데이터 세트를 가져오는 방법은 🤗 [데이터 세트 튜토리얼](https://huggingface.co/docs/datasets/load_hub)에서 자세히 설명하고 있습니다.)
 
 ```py
 >>> from datasets import load_dataset, Audio
@@ -346,7 +346,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 </Tip>
 
 [food101](https://huggingface.co/datasets/food101) 데이터 세트를 가져와서 컴퓨터 비전 데이터 세트에서 이미지 프로세서를 어떻게 사용하는지 알아보세요.
-데이터 세트를 불러오는 방법은 🤗 [데이터 세트 튜토리얼](https://huggingface.co/docs/datasets/load_hub.html)을 참고하세요.
+데이터 세트를 불러오는 방법은 🤗 [데이터 세트 튜토리얼](https://huggingface.co/docs/datasets/load_hub)을 참고하세요.
 
 <Tip>
 
@@ -360,7 +360,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 >>> dataset = load_dataset("food101", split="train[:100]")
 ```
 
-다음으로, 🤗 Datasets의 [`image`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=image#datasets.Image)로 이미지를 확인해보세요:
+다음으로, 🤗 Datasets의 [`image`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=image#datasets.Image)로 이미지를 확인해보세요:
 
 ```py
 >>> dataset[0]["image"]
@@ -418,7 +418,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 
 </Tip>
 
-3. 🤗 Datasets의 [`set_transform`](https://huggingface.co/docs/datasets/process.html#format-transform)를 사용하여 실시간으로 변환을 적용합니다:
+3. 🤗 Datasets의 [`set_transform`](https://huggingface.co/docs/datasets/process#format-transform)를 사용하여 실시간으로 변환을 적용합니다:
 
 ```py
 >>> dataset.set_transform(transforms)
@@ -476,7 +476,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 프로세서는 토크나이저와 특성 추출기와 같은 두 가지 처리 객체를 결합합니다.
 
 [LJ Speech](https://huggingface.co/datasets/lj_speech) 데이터 세트를 가져와서 자동 음성 인식(ASR)을 위한 프로세서를 사용하는 방법을 확인하세요.
-(데이터 세트를 가져오는 방법에 대한 자세한 내용은 🤗 [데이터 세트 튜토리얼](https://huggingface.co/docs/datasets/load_hub.html)에서 볼 수 있습니다.)
+(데이터 세트를 가져오는 방법에 대한 자세한 내용은 🤗 [데이터 세트 튜토리얼](https://huggingface.co/docs/datasets/load_hub)에서 볼 수 있습니다.)
 
 ```py
 >>> from datasets import load_dataset

docs/source/ko/training.md

@@ -43,7 +43,7 @@ rendered properly in your Markdown viewer.
  'text': 'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularly...that takes something special!\\nThe cashier took my friends\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \\"serving off their orders\\" when they didn\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\\nThe manager was rude when giving me my order. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\\nI\'ve eaten at various McDonalds restaurants for over 30 years. I\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'}
 ```
 
-텍스트를 처리하고 서로 다른 길이의 시퀀스 패딩 및 잘라내기 전략을 포함하려면 토크나이저가 필요합니다. 데이터셋을 한 번에 처리하려면 🤗 Dataset [`map`](https://huggingface.co/docs/datasets/process.html#map) 메서드를 사용하여 전체 데이터셋에 전처리 함수를 적용하세요:
+텍스트를 처리하고 서로 다른 길이의 시퀀스 패딩 및 잘라내기 전략을 포함하려면 토크나이저가 필요합니다. 데이터셋을 한 번에 처리하려면 🤗 Dataset [`map`](https://huggingface.co/docs/datasets/process#map) 메서드를 사용하여 전체 데이터셋에 전처리 함수를 적용하세요:
 
 ```py
 >>> from transformers import AutoTokenizer

docs/source/pt/quicktour.md

@@ -119,7 +119,7 @@ Crie uma [`pipeline`] com a tarefa que deseja resolver e o modelo que deseja usa
 >>> speech_recognizer = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
 ```
 
-A seguir, carregue uma base de dados (confira a 🤗 [Iniciação em Datasets](https://huggingface.co/docs/datasets/quickstart.html) para mais detalhes) que você gostaria de iterar sobre. Por exemplo, vamos carregar o dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14):
+A seguir, carregue uma base de dados (confira a 🤗 [Iniciação em Datasets](https://huggingface.co/docs/datasets/quickstart) para mais detalhes) que você gostaria de iterar sobre. Por exemplo, vamos carregar o dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14):
 
 ```py
 >>> from datasets import load_dataset, Audio

docs/source/pt/tasks/sequence_classification.md

@@ -148,7 +148,7 @@ O [`Trainer`] aplicará o preenchimento dinâmico por padrão quando você defin
 </Tip>
 </pt>
 <tf>
-Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato `tf.data.Dataset` com [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Nessa execução você deverá especificar as entradas e rótulos (no parâmetro `columns`), se deseja embaralhar o conjunto de dados, o tamanho do batch e o data collator:
+Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato `tf.data.Dataset` com [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_tf_dataset). Nessa execução você deverá especificar as entradas e rótulos (no parâmetro `columns`), se deseja embaralhar o conjunto de dados, o tamanho do batch e o data collator:
 
 ```py
 >>> tf_train_set = tokenized_imdb["train"].to_tf_dataset(

docs/source/pt/tasks/token_classification.md

@@ -201,7 +201,7 @@ Nesse ponto, restam apenas três passos:
 ```
 </pt>
 <tf>
-Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato `tf.data.Dataset` com [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Nessa execução você deverá especificar as entradas e rótulos (no parâmetro `columns`), se deseja embaralhar o conjunto de dados, o tamanho do batch e o data collator:
+Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato `tf.data.Dataset` com [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_tf_dataset). Nessa execução você deverá especificar as entradas e rótulos (no parâmetro `columns`), se deseja embaralhar o conjunto de dados, o tamanho do batch e o data collator:
 
 ```py
 >>> tf_train_set = tokenized_wnut["train"].to_tf_dataset(

docs/source/pt/training.md

@@ -52,7 +52,7 @@ Comece carregando o dataset [Yelp Reviews](https://huggingface.co/datasets/yelp_
 
 Como já sabe, é necessário ter um tokenizador para processar o texto e incluir uma estratégia de padding e truncamento,
 para manejar qualquer tamanho varíavel de sequência. Para processar o seu dataset em apenas um passo, utilize o método de
-🤗 Datasets [`map`](https://huggingface.co/docs/datasets/process.html#map) para aplicar uma função de preprocessamento sobre
+🤗 Datasets [`map`](https://huggingface.co/docs/datasets/process#map) para aplicar uma função de preprocessamento sobre
 todo o dataset.
 
 ```py
@@ -126,7 +126,7 @@ Especifique onde salvar os checkpoints do treinamento:
 O [`Trainer`] não avalia automaticamente o rendimento do modelo durante o treinamento. Será necessário passar ao
 [`Trainer`] uma função para calcular e fazer um diagnóstico sobre as métricas. A biblioteca 🤗 Datasets proporciona
 uma função de [`accuracy`](https://huggingface.co/metrics/accuracy) simples que pode ser carregada com a função
-`load_metric` (ver este [tutorial](https://huggingface.co/docs/datasets/metrics.html) para mais informações):
+`load_metric` (ver este [tutorial](https://huggingface.co/docs/datasets/metrics) para mais informações):
 
 ```py
 >>> import numpy as np
@@ -203,7 +203,7 @@ Assegure-se de especificar os `return_tensors` para retornar os tensores do Tens
 </Tip>
 
 Em seguida, converta os datasets tokenizados em datasets do TensorFlow com o método
-[`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset).
+[`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_tf_dataset).
 Especifique suas entradas em `columns` e seu rótulo em `label_cols`:
 
 ```py
@@ -385,7 +385,7 @@ uma barra de progresso sobre o número de passos percorridos no treinamento atua
 
 Da mesma forma que é necessário adicionar uma função de avaliação ao [`Trainer`], é necessário fazer o mesmo quando
 escrevendo o próprio ciclo de treinamento. Contudo, em vez de calcular e retornar a métrica final de cada época,
-você deverá adicionar todos os batches com [`add_batch`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=add_batch#datasets.Metric.add_batch)
+você deverá adicionar todos os batches com [`add_batch`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=add_batch#datasets.Metric.add_batch)
 e calcular a métrica apenas no final.
 
 ```py