Fix some typos in docs (#36502)

Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

awesome-transformers.md

@@ -29,7 +29,7 @@ Keywords: inpainting, SD, Stable Diffusion
 
 ## [flair](https://github.com/flairNLP/flair)
 
-FLAIR is a powerful PyTorch NLP framework, convering several important tasks: NER, sentiment-analysis, part-of-speech tagging, text and document embeddings, among other things.
+FLAIR is a powerful PyTorch NLP framework, covering several important tasks: NER, sentiment-analysis, part-of-speech tagging, text and document embeddings, among other things.
 
 Keywords: NLP, text embedding, document embedding, biomedical, NER, PoS, sentiment-analysis
 

benchmark/benchmarks_entrypoint.py

@@ -136,7 +136,7 @@ if __name__ == "__main__":
                 continue
             logger.debug(f"loading: {entry.name}")
             module = import_from_path(entry.name.split(".")[0], entry.path)
-            logger.info(f"runnning benchmarks in: {entry.name}")
+            logger.info(f"running benchmarks in: {entry.name}")
             module.run_benchmark(logger, branch, commit_id, commit_msg)
         except ImportModuleException as e:
             logger.error(e)

docs/source/ar/agents.md

@@ -195,7 +195,7 @@ You have access to the following tools:
 To solve the task, you must plan forward to proceed in a series of steps, in a cycle of 'Thought:', 'Code:', and 'Observation:' sequences.
 
 At each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task, then the tools that you want to use.
-Then in the 'Code:' sequence, you shold write the code in simple Python. The code sequence must end with '/End code' sequence.
+Then in the 'Code:' sequence, you should write the code in simple Python. The code sequence must end with '/End code' sequence.
 During each intermediate step, you can use 'print()' to save whatever important information you will then need.
 These print outputs will then be available in the 'Observation:' field, for using this information as input for the next step.
 
@@ -205,7 +205,7 @@ Here are a few examples using notional tools:
 ---
 {examples}
 
-Above example were using notional tools that might not exist for you. You only have acces to those tools:
+Above example were using notional tools that might not exist for you. You only have access to those tools:
 <<tool_names>>
 You also can perform computations in the python code you generate.
 

docs/source/ar/serialization.md

@@ -116,11 +116,11 @@ optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_s
 
 <Tip warning={true}>
 
-لم يعد يتم دعم `tranformers.onnx`  يُرجى تصدير النماذج باستخدام 🤗 Optimum كما هو موضح أعلاه. سيتم إزالة هذا القسم في الإصدارات القادمة.
+لم يعد يتم دعم `transformers.onnx`  يُرجى تصدير النماذج باستخدام 🤗 Optimum كما هو موضح أعلاه. سيتم إزالة هذا القسم في الإصدارات القادمة.
 
 </Tip>
 
-لتصدير نموذج 🤗 Transformers إلى ONNX باستخدام `tranformers.onnx`، ثبّت التبعيات الإضافية:
+لتصدير نموذج 🤗 Transformers إلى ONNX باستخدام `transformers.onnx`، ثبّت التبعيات الإضافية:
 
 ```bash
 pip install transformers[onnx]

docs/source/zh/serialization.md

@@ -128,11 +128,11 @@ optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_s
 
 <Tip warning={true}>
 
-`tranformers.onnx` 不再进行维护，请如上所述，使用 🤗 Optimum 导出模型。这部分内容将在未来版本中删除。
+`transformers.onnx` 不再进行维护，请如上所述，使用 🤗 Optimum 导出模型。这部分内容将在未来版本中删除。
 
 </Tip>
 
-要使用 `tranformers.onnx` 将 🤗 Transformers 模型导出为 ONNX，请安装额外的依赖项：
+要使用 `transformers.onnx` 将 🤗 Transformers 模型导出为 ONNX，请安装额外的依赖项：
 
 ```bash
 pip install transformers[onnx]