diff --git a/docs/source/en/tasks/language_modeling.md b/docs/source/en/tasks/language_modeling.md
index 0b9c24870219..2eac6ec12328 100644
--- a/docs/source/en/tasks/language_modeling.md
+++ b/docs/source/en/tasks/language_modeling.md
@@ -110,7 +110,7 @@ The next step is to load a DistilGPT2 tokenizer to process the `text` subfield:
 ```
 
 You'll notice from the example above, the `text` field is actually nested inside `answers`. This means you'll need to
-extract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co/docs/datasets/process.html#flatten) method:
+extract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co/docs/datasets/process#flatten) method:
 
 ```py
 >>> eli5 = eli5.flatten()
diff --git a/docs/source/en/tasks/masked_language_modeling.md b/docs/source/en/tasks/masked_language_modeling.md
index ba1e9e50dbe8..e716447b83bb 100644
--- a/docs/source/en/tasks/masked_language_modeling.md
+++ b/docs/source/en/tasks/masked_language_modeling.md
@@ -105,7 +105,7 @@ For masked language modeling, the next step is to load a DistilRoBERTa tokenizer
 ```
 
 You'll notice from the example above, the `text` field is actually nested inside `answers`. This means you'll need to e
-xtract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co/docs/datasets/process.html#flatten) method:
+xtract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co/docs/datasets/process#flatten) method:
 
 ```py
 >>> eli5 = eli5.flatten()
diff --git a/docs/source/es/tasks/language_modeling.md b/docs/source/es/tasks/language_modeling.md
index 66ac8fb0d4b5..34bd8a2f70e0 100644
--- a/docs/source/es/tasks/language_modeling.md
+++ b/docs/source/es/tasks/language_modeling.md
@@ -94,7 +94,7 @@ Para modelados de lenguaje por enmascaramiento carga el tokenizador DistilRoBERT
 >>> tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
 ```
 
-Extrae el subcampo `text` desde su estructura anidado con el método [`flatten`](https://huggingface.co/docs/datasets/process.html#flatten):
+Extrae el subcampo `text` desde su estructura anidado con el método [`flatten`](https://huggingface.co/docs/datasets/process#flatten):
 
 ```py
 >>> eli5 = eli5.flatten()
diff --git a/docs/source/ko/tasks/language_modeling.md b/docs/source/ko/tasks/language_modeling.md
index ba540825c295..bf10660c61c1 100644
--- a/docs/source/ko/tasks/language_modeling.md
+++ b/docs/source/ko/tasks/language_modeling.md
@@ -107,7 +107,7 @@ pip install transformers datasets evaluate
 >>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
 ```
 
-위의 예제에서 알 수 있듯이, `text` 필드는 `answers` 아래에 중첩되어 있습니다. 따라서 [`flatten`](https://huggingface.co/docs/datasets/process.html#flatten) 메소드를 사용하여 중첩 구조에서 `text` 하위 필드를 추출해야 합니다.
+위의 예제에서 알 수 있듯이, `text` 필드는 `answers` 아래에 중첩되어 있습니다. 따라서 [`flatten`](https://huggingface.co/docs/datasets/process#flatten) 메소드를 사용하여 중첩 구조에서 `text` 하위 필드를 추출해야 합니다.
 
 ```py
 >>> eli5 = eli5.flatten()
diff --git a/docs/source/ko/tasks/masked_language_modeling.md b/docs/source/ko/tasks/masked_language_modeling.md
index d22d439dbd51..ee835d13ebc0 100644
--- a/docs/source/ko/tasks/masked_language_modeling.md
+++ b/docs/source/ko/tasks/masked_language_modeling.md
@@ -107,7 +107,7 @@ Hugging Face 계정에 로그인하여 모델을 업로드하고 커뮤니티와
 ```
 
 위의 예제에서와 마찬가지로, `text` 필드는 `answers` 안에 중첩되어 있습니다. 
-따라서 중첩된 구조에서 [`flatten`](https://huggingface.co/docs/datasets/process.html#flatten) 메소드를 사용하여 `text` 하위 필드를 추출합니다:
+따라서 중첩된 구조에서 [`flatten`](https://huggingface.co/docs/datasets/process#flatten) 메소드를 사용하여 `text` 하위 필드를 추출합니다:
 
 ```py
 >>> eli5 = eli5.flatten()