Repository: huggingface/transformers
Pull request: Fix typos and grammar issues in documentation and code

# What does this PR do?
Summary

- Fixed Cyrillic 'Р' to Latin 'P' in Portuguese language link (README.md)
- Fixed 'meanginful' to 'meaningful' in training documentation
- Fixed duplicate 'Cohere' reference in modular transformers documentation
- Fixed duplicate 'the the' in trainer and chat command comments

Test plan

-  Verified all typos are corrected
-  Ensured no functional changes to code behavior
-  Maintained existing formatting and structure

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Fixes # (issue)


## 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.

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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)

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Changed files:
filename: README.md
patch: @@ -44,7 +44,7 @@ limitations under the License.
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ja.md">日本語</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_hd.md">हिन्दी</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ru.md">Русский</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_pt-br.md">Рortuguês</a> |
+        <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_pt-br.md">Português</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_te.md">తెలుగు</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_fr.md">Français</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_de.md">Deutsch</a> |

filename: docs/source/en/modular_transformers.md
patch: @@ -94,7 +94,7 @@ ValueError: You defined `RobertaEmbeddings` in the modular_roberta.py, it should
 
 ## Implementing a modular file
 
-The easiest way to start is by browsing Transformers for a model similar to yours in order to inherit from it. Some good starting points are [Mistral](./model_doc/mistral), [Qwen2](./model_doc/qwen2), [Cohere](./model_doc/cohere) and [Cohere](./model_doc/cohere2), and [Llama](./model_doc/llama). Refer to the table below for components your model might be using and where you can inherit from.
+The easiest way to start is by browsing Transformers for a model similar to yours in order to inherit from it. Some good starting points are [Mistral](./model_doc/mistral), [Qwen2](./model_doc/qwen2), [Cohere](./model_doc/cohere) and [Cohere2](./model_doc/cohere2), and [Llama](./model_doc/llama). Refer to the table below for components your model might be using and where you can inherit from.
 
 | Component | Model |
 |---|---|

filename: docs/source/en/training.md
patch: @@ -74,7 +74,7 @@ model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-bas
 ```
 
 > [!TIP]
-> The message above is a reminder that the models pretrained head is discarded and replaced with a randomly initialized classification head. The randomly initialized head needs to be fine-tuned on your specific task to output meanginful predictions.
+> The message above is a reminder that the models pretrained head is discarded and replaced with a randomly initialized classification head. The randomly initialized head needs to be fine-tuned on your specific task to output meaningful predictions.
 
 With the model loaded, set up your training hyperparameters in [`TrainingArguments`]. Hyperparameters are variables that control the training process - such as the learning rate, batch size, number of epochs - which in turn impacts model performance. Selecting the correct hyperparameters is important and you should experiment with them to find the best configuration for your task.
 

filename: src/transformers/commands/chat.py
patch: @@ -426,7 +426,7 @@ def is_number(s: str) -> bool:
         # 2. c. [no processing needed] lists are lists of ints because `generate` doesn't take lists of strings :)
         # We also mention in the help message that we only accept lists of ints for now.
 
-        # 3. Join the the result into a comma separated string
+        # 3. Join the result into a comma separated string
         generate_flags_string = ", ".join([f"{k}: {v}" for k, v in generate_flags_as_dict.items()])
 
         # 4. Add the opening/closing brackets

filename: src/transformers/trainer.py
patch: @@ -529,7 +529,7 @@ def __init__(
                 kernel_config = self.args.liger_kernel_config if self.args.liger_kernel_config is not None else {}
 
                 if isinstance(model, PreTrainedModel):
-                    # Patch the model with liger kernels. Use the the specified or default kernel configurations.
+                    # Patch the model with liger kernels. Use the specified or default kernel configurations.
                     _apply_liger_kernel_to_instance(model=model, **kernel_config)
                 elif hasattr(model, "get_base_model") and isinstance(model.get_base_model(), PreTrainedModel):
                     # Patch the base model with liger kernels where model is a PeftModel. Use the specified or default kernel configurations.