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Fix typos and grammar issues in documentation and code (#39598)

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- Fix Cyrillic 'Р' to Latin 'P' in Portuguese language link (README.md)
- Fix 'meanginful' to 'meaningful' in training documentation
- Fix duplicate 'Cohere' reference in modular transformers documentation
- Fix duplicate 'the the' in trainer and chat command comments

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Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Maxime Grenu
2025-07-23 14:43:11 +02:00
committed by GitHub
parent 82603b6cc2
commit 0fe03afeb8
5 changed files with 5 additions and 5 deletions
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2
docs/source/en/modular_transformers.md
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@@ -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 |
|---|---|

2
docs/source/en/training.md
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@@ -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.