chore: fix typos (#26756)

docs/source/en/main_classes/quantization.md

@@ -20,7 +20,7 @@ rendered properly in your Markdown viewer.
 
 🤗 Transformers has integrated `optimum` API to perform GPTQ quantization on language models. You can load and quantize your model in 8, 4, 3 or even 2 bits without a big drop of performance and faster inference speed! This is supported by most GPU hardwares.
 
-To learn more about the the quantization model, check out: 
+To learn more about the quantization model, check out: 
 - the [GPTQ](https://arxiv.org/pdf/2210.17323.pdf) paper
 - the `optimum` [guide](https://huggingface.co/docs/optimum/llm_quantization/usage_guides/quantization) on GPTQ quantization
 - the [`AutoGPTQ`](https://github.com/PanQiWei/AutoGPTQ) library used as the backend

docs/source/en/preprocessing.md

@@ -306,7 +306,7 @@ Create a function to preprocess the dataset so the audio samples are the same le
 ...     return inputs
 ```
 
-Apply the `preprocess_function` to the the first few examples in the dataset:
+Apply the `preprocess_function` to the first few examples in the dataset:
 
 ```py
 >>> processed_dataset = preprocess_function(dataset[:5])