Repository: huggingface/transformers
Pull request: Fix typos in English/Chinese documentation

# What does this PR do?

Fix typos and fix other formatting errors.

## Before submitting
- [X] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface, @SunMarc and @qgallouedec
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Changed files:
filename: docs/source/en/run_scripts.md
patch: @@ -104,7 +104,7 @@ torchrun \
     ...
 ```
 
-PyTorch supports TPUs, hardware designed to accelerate performance, through the [PyTorch/XLA](https://github.com/pytorch/xla/blob/master/README.md) package. Launch the `xla_spawn.py` script and use `num _cores` to set the number of TPU cores to train with.
+PyTorch supports TPUs, hardware designed to accelerate performance, through the [PyTorch/XLA](https://github.com/pytorch/xla/blob/master/README.md) package. Launch the `xla_spawn.py` script and use `num_cores` to set the number of TPU cores to train with.
 
 ```bash
 python xla_spawn.py --num_cores 8 pytorch/summarization/run_summarization.py \

filename: docs/source/en/video_processors.md
patch: @@ -14,17 +14,16 @@ rendered properly in your Markdown viewer.
 
 -->
 
-
 # Video Processor
 
-A **Video Processor** is a utility responsible for preparing input features for video models, as well as handling the post-processing of their outputs. It provides transformations such as resizing, normalization, and conversion into PyTorch. 
+A **Video Processor** is a utility responsible for preparing input features for video models, as well as handling the post-processing of their outputs. It provides transformations such as resizing, normalization, and conversion into PyTorch.
 
 The video processor extends the functionality of image processors by allowing the models to handle videos with a distinct set of arguments compared to images. It serves as the bridge between raw video data and the model, ensuring that input features are optimized for the VLM.
 
 Use [`~BaseVideoProcessor.from_pretrained`] to load a video processors configuration (image size, whether to normalize and rescale, etc.) from a video model on the Hugging Face [Hub](https://hf.co) or local directory. The configuration for each pretrained model should be saved in a [video_preprocessor_config.json] file but older models might have the config saved in [preprocessor_config.json](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf/blob/main/preprocessor_config.json) file. Note that the latter is less preferred and will be removed in the future.
 
+## Usage Example
 
-### Usage Example
 Here's an example of how to load a video processor with [`llava-hf/llava-onevision-qwen2-0.5b-ov-hf`](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf) model:
 
 ```python

filename: docs/source/zh/main_classes/deepspeed.md
patch: @@ -236,7 +236,7 @@ deepspeed --num_gpus=1 examples/pytorch/translation/run_translation.py \
 }
 ```
 
-这会启用`optimizer offload `和一些其他重要功能。您可以尝试不同的buffer大小，有关详细信息，请参见下面的讨论。
+这会启用`optimizer offload`和一些其他重要功能。您可以尝试不同的buffer大小，有关详细信息，请参见下面的讨论。
 
 关于这种启用类型的实际使用示例，请参阅 [此帖](https://github.com/huggingface/transformers/issues/8771#issuecomment-759176685)。
 

filename: docs/source/zh/pipeline_tutorial.md
patch: @@ -306,5 +306,5 @@ pipe = pipeline(model="facebook/opt-1.3b", device_map="auto", model_kwargs={"loa
 output = pipe("This is a cool example!", do_sample=True, top_p=0.95)
 ```
 
-请注意，您可以将`checkpoint `替换为任何支持大模型加载的Hugging Face模型，比如BLOOM！
+请注意，您可以将`checkpoint`替换为任何支持大模型加载的Hugging Face模型，比如BLOOM！
 

filename: docs/source/zh/tasks/asr.md
patch: @@ -83,7 +83,7 @@ DatasetDict({
 })
 ```
 
-虽然数据集包含 `lang_id `和 `english_transcription` 等许多有用的信息，但在本指南中，
+虽然数据集包含 `lang_id` 和 `english_transcription` 等许多有用的信息，但在本指南中，
 您将专注于 `audio` 和 `transcription`。使用 [`~datasets.Dataset.remove_columns`] 方法删除其他列：
 
 ```py
@@ -167,7 +167,7 @@ Wav2Vec2 分词器仅训练了大写字符，因此您需要确保文本与分
 它还会动态地将您的文本和标签填充到其批次中最长元素的长度（而不是整个数据集），以使它们具有统一的长度。
 虽然可以通过在 `tokenizer` 函数中设置 `padding=True` 来填充文本，但动态填充更有效。
 
-与其他数据整理器不同，这个特定的数据整理器需要对 `input_values` 和 `labels `应用不同的填充方法：
+与其他数据整理器不同，这个特定的数据整理器需要对 `input_values` 和 `labels` 应用不同的填充方法：
 
 ```py
 >>> import torch