diff --git a/README.md b/README.md
index 13afb112ca..7e909a66f3 100644
--- a/README.md
+++ b/README.md
@@ -44,7 +44,7 @@ limitations under the License.
日本語 |
हिन्दी |
Русский |
- Рortuguês |
+ Português |
తెలుగు |
Français |
Deutsch |
diff --git a/docs/source/en/modular_transformers.md b/docs/source/en/modular_transformers.md
index 76d77e2ffd..d97de838af 100644
--- a/docs/source/en/modular_transformers.md
+++ b/docs/source/en/modular_transformers.md
@@ -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 |
|---|---|
diff --git a/docs/source/en/training.md b/docs/source/en/training.md
index 7f2a622b48..c859229383 100644
--- a/docs/source/en/training.md
+++ b/docs/source/en/training.md
@@ -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.
diff --git a/src/transformers/commands/chat.py b/src/transformers/commands/chat.py
index 81a01932a0..378e932d94 100644
--- a/src/transformers/commands/chat.py
+++ b/src/transformers/commands/chat.py
@@ -426,7 +426,7 @@ class ChatCommand(BaseTransformersCLICommand):
# 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
diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
index 6dc2a11436..380ccbf406 100755
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -529,7 +529,7 @@ class Trainer:
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.