From acb8586dd9536f5cb78765d0a1cd6015da5af0c5 Mon Sep 17 00:00:00 2001 From: co63oc Date: Tue, 4 Mar 2025 01:53:53 +0800 Subject: [PATCH] Fix some typos in docs (#36502) Co-authored-by: Matt --- awesome-transformers.md | 2 +- benchmark/benchmarks_entrypoint.py | 2 +- docs/source/ar/agents.md | 4 ++-- docs/source/ar/serialization.md | 4 ++-- docs/source/zh/serialization.md | 4 ++-- 5 files changed, 8 insertions(+), 8 deletions(-) diff --git a/awesome-transformers.md b/awesome-transformers.md index d706498a08..f9676f29b2 100644 --- a/awesome-transformers.md +++ b/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 diff --git a/benchmark/benchmarks_entrypoint.py b/benchmark/benchmarks_entrypoint.py index 7925e29028..df348b0852 100644 --- a/benchmark/benchmarks_entrypoint.py +++ b/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) diff --git a/docs/source/ar/agents.md b/docs/source/ar/agents.md index 1213b35008..c7efd8f02f 100644 --- a/docs/source/ar/agents.md +++ b/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: <> You also can perform computations in the python code you generate. diff --git a/docs/source/ar/serialization.md b/docs/source/ar/serialization.md index 2df620d862..6f437dea06 100644 --- a/docs/source/ar/serialization.md +++ b/docs/source/ar/serialization.md @@ -116,11 +116,11 @@ optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_s -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. -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] diff --git a/docs/source/zh/serialization.md b/docs/source/zh/serialization.md index b9cc74e584..6885ae30c4 100644 --- a/docs/source/zh/serialization.md +++ b/docs/source/zh/serialization.md @@ -128,11 +128,11 @@ optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_s -`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。 -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]