diff --git a/chapters/en/_toctree.yml b/chapters/en/_toctree.yml
index a230a1e58..6c305105d 100644
--- a/chapters/en/_toctree.yml
+++ b/chapters/en/_toctree.yml
@@ -62,9 +62,9 @@
- local: chapter3/4
title: A full training loop
- local: chapter3/5
- title: Fine-tuning, Check!
- - local: chapter3/6
title: Understanding Learning Curves
+ - local: chapter3/6
+ title: Fine-tuning, Check!
- local: chapter3/7
title: End-of-chapter quiz
quiz: 3
diff --git a/chapters/en/chapter3/5.mdx b/chapters/en/chapter3/5.mdx
index e24553dfd..ee107eaf8 100644
--- a/chapters/en/chapter3/5.mdx
+++ b/chapters/en/chapter3/5.mdx
@@ -1,46 +1,419 @@
-
+# Understanding Learning Curves[[understanding-learning-curves]]
-# Fine-tuning, Check![[fine-tuning-check]]
+
-
+Now that you've learned how to implement fine-tuning using both the `Trainer` API and custom training loops, it's crucial to understand how to interpret the results. Learning curves are invaluable tools that help you evaluate your model's performance during training and identify potential issues before they reduce performance.
+
+In this section, we'll explore how to read and interpret accuracy and loss curves, understand what different curve shapes tell us about our model's behavior, and learn how to address common training issues.
+
+## What are Learning Curves?[[what-are-learning-curves]]
+
+Learning curves are visual representations of your model's performance metrics over time during training. The two most important curves to monitor are:
+
+- **Loss curves**: Show how the model's error (loss) changes over training steps or epochs
+- **Accuracy curves**: Show the percentage of correct predictions over training steps or epochs
+
+These curves help us understand whether our model is learning effectively and can guide us in making adjustments to improve performance. In Transformers, these metrics are individually computed for each batch and then logged to the disk. We can then use libraries like [Weights & Biases](https://wandb.ai/) to visualize these curves and track our model's performance over time.
+
+### Loss Curves[[loss-curves]]
+
+The loss curve shows how the model's error decreases over time. In a typical successful training run, you'll see a curve similar to the one below:
+
+
+
+- **High initial loss**: The model starts without optimization, so predictions are initially poor
+- **Decreasing loss**: As training progresses, the loss should generally decrease
+- **Convergence**: Eventually, the loss stabilizes at a low value, indicating that the model has learned the patterns in the data
+
+As in previous chapters, we can use the `Trainer` API to track these metrics and visualize them in a dashboard. Below is an example of how to do this with Weights & Biases.
+
+```python
+# Example of tracking loss during training with the Trainer
+from transformers import Trainer, TrainingArguments
+import wandb
+
+# Initialize Weights & Biases for experiment tracking
+wandb.init(project="transformer-fine-tuning", name="bert-mrpc-analysis")
-That was comprehensive! In the first two chapters you learned about models and tokenizers, and now you know how to fine-tune them for your own data using modern best practices. To recap, in this chapter you:
+training_args = TrainingArguments(
+ output_dir="./results",
+ eval_strategy="steps",
+ eval_steps=50,
+ save_steps=100,
+ logging_steps=10, # Log metrics every 10 steps
+ num_train_epochs=3,
+ per_device_train_batch_size=16,
+ per_device_eval_batch_size=16,
+ report_to="wandb", # Send logs to Weights & Biases
+)
-* Learned about datasets on the [Hub](https://huggingface.co/datasets) and modern data processing techniques
-* Learned how to load and preprocess datasets efficiently, including using dynamic padding and data collators
-* Implemented fine-tuning and evaluation using the high-level `Trainer` API with the latest features
-* Implemented a complete custom training loop from scratch with PyTorch
-* Used 🤗 Accelerate to make your training code work seamlessly on multiple GPUs or TPUs
-* Applied modern optimization techniques like mixed precision training and gradient accumulation
+trainer = Trainer(
+ model=model,
+ args=training_args,
+ train_dataset=tokenized_datasets["train"],
+ eval_dataset=tokenized_datasets["validation"],
+ data_collator=data_collator,
+ processing_class=tokenizer,
+ compute_metrics=compute_metrics,
+)
+
+# Train and automatically log metrics
+trainer.train()
+```
+
+### Accuracy Curves[[accuracy-curves]]
+
+The accuracy curve shows the percentage of correct predictions over time. Unlike loss curves, accuracy curves should generally increase as the model learns and can typically include more steps than the loss curve.
+
+
+
+- **Start low**: Initial accuracy should be low, as the model has not yet learned the patterns in the data
+- **Increase with training**: Accuracy should generally improve as the model learns if it is able to learn the patterns in the data
+- **May show plateaus**: Accuracy often increases in discrete jumps rather than smoothly, as the model makes predictions that are close to the true labels
-🎉 **Congratulations!** You've mastered the fundamentals of fine-tuning transformer models. You're now ready to tackle real-world ML projects!
+💡 **Why Accuracy Curves Are "Steppy"**: Unlike loss, which is continuous, accuracy is calculated by comparing discrete predictions to true labels. Small improvements in model confidence might not change the final prediction, causing accuracy to remain flat until a threshold is crossed.
+
+
+
+### Convergence[[convergence]]
+
+Convergence occurs when the model's performance stabilizes and the loss and accuracy curves level off. This is a sign that the model has learned the patterns in the data and is ready to be used. In simple terms, we are aiming for the model to converge to a stable performance every time we train it.
+
+
+
+Once models have converged, we can use them to make predictions on new data and refer to evaluation metrics to understand how well the model is performing.
+
+## Interpreting Learning Curve Patterns[[interpreting-learning-curve-patterns]]
-📖 **Continue Learning**: Explore these resources to deepen your knowledge:
-- [🤗 Transformers task guides](https://huggingface.co/docs/transformers/main/en/tasks/sequence_classification) for specific NLP tasks
-- [🤗 Transformers examples](https://huggingface.co/docs/transformers/main/en/notebooks) for comprehensive notebooks
+Different curve shapes reveal different aspects of your model's training. Let's examine the most common patterns and what they mean.
-🚀 **Next Steps**:
-- Try fine-tuning on your own dataset using the techniques you've learned
-- Experiment with different model architectures available on the [Hugging Face Hub](https://huggingface.co/models)
-- Join the [Hugging Face community](https://discuss.huggingface.co/) to share your projects and get help
+### Healthy Learning Curves[[healthy-learning-curves]]
+
+A well-behaved training run typically shows curve shapes similar to the one below:
+
+
+
+Let's look at the illustration above. It displays both the loss curve (on the left) and the corresponding accuracy curve (on the right). These curves have distinct characteristics.
+
+The loss curve shows the value of the model's loss over time. Initially, the loss is high and then it gradually decreases, indicating that the model is improving. A decrease in the loss value suggests that the model is making better predictions, as the loss represents the error between the predicted output and the true output.
+
+Now let's shift our focus to the accuracy curve. It represents the model's accuracy over time. The accuracy curve begins at a low value and increases as training progresses. Accuracy measures the proportion of correctly classified instances. So, as the accuracy curve rises, it signifies that the model is making more correct predictions.
+
+One notable difference between the curves is the smoothness and the presence of "plateaus" on the accuracy curve. While the loss decreases smoothly, the plateaus on the accuracy curve indicate discrete jumps in accuracy instead of a continuous increase. This behavior is attributed to how accuracy is measured. The loss can improve if the model's output gets closer to the target, even if the final prediction is still incorrect. Accuracy, however, only improves when the prediction crosses the threshold to be correct.
+
+For example, in a binary classifier distinguishing cats (0) from dogs (1), if the model predicts 0.3 for an image of a dog (true value 1), this is rounded to 0 and is an incorrect classification. If in the next step it predicts 0.4, it's still incorrect. The loss will have decreased because 0.4 is closer to 1 than 0.3, but the accuracy remains unchanged, creating a plateau. The accuracy will only jump up when the model predicts a value greater than 0.5 that gets rounded to 1.
+
+
+
+**Characteristics of healthy curves:**
+- **Smooth decline in loss**: Both training and validation loss decrease steadily
+- **Close training/validation performance**: Small gap between training and validation metrics
+- **Convergence**: Curves level off, indicating the model has learned the patterns
-This is just the beginning of your journey with 🤗 Transformers. In the next chapter, we'll explore how to share your models and tokenizers with the community and contribute to the ever-growing ecosystem of pretrained models.
+### Practical Examples[[practical-examples]]
+
+Let's work through some practical examples of learning curves. First, we will highlight some approaches to monitor the learning curves during training. Below, we will break down the different patterns that can be observed in the learning curves.
+
+#### During Training[[during-training]]
-The skills you've developed here - data preprocessing, training configuration, evaluation, and optimization - are fundamental to any machine learning project. Whether you're working on text classification, named entity recognition, question answering, or any other NLP task, these techniques will serve you well.
+During the training process (after you've hit `trainer.train()`), you can monitor these key indicators:
+
+1. **Loss convergence**: Is the loss still decreasing or has it plateaued?
+2. **Overfitting signs**: Is validation loss starting to increase while training loss decreases?
+3. **Learning rate**: Are the curves too erratic (LR too high) or too flat (LR too low)?
+4. **Stability**: Are there sudden spikes or drops that indicate problems?
+
+#### After Training[[after-training]]
+
+After the training process is complete, you can analyze the complete curves to understand the model's performance.
+
+1. **Final performance**: Did the model reach acceptable performance levels?
+2. **Efficiency**: Could the same performance be achieved with fewer epochs?
+3. **Generalization**: How close are training and validation performance?
+4. **Trends**: Would additional training likely improve performance?
-💡 **Pro Tips for Success**:
-- Always start with a strong baseline using the `Trainer` API before implementing custom training loops
-- Use the 🤗 Hub to find pretrained models that are close to your task for better starting points
-- Monitor your training with proper evaluation metrics and don't forget to save checkpoints
-- Leverage the community - share your models and datasets to help others and get feedback on your work
+🔍 **W&B Dashboard Features**: Weights & Biases automatically creates beautiful, interactive plots of your learning curves. You can:
+- Compare multiple runs side by side
+- Add custom metrics and visualizations
+- Set up alerts for anomalous behavior
+- Share results with your team
+Learn more in the [Weights & Biases documentation](https://docs.wandb.ai/).
+
+#### Overfitting[[overfitting]]
+
+Overfitting occurs when the model learns too much from the training data and is unable to generalize to different data (represented by the validation set).
+
+
+
+**Symptoms:**
+
+- Training loss continues to decrease while validation loss increases or plateaus
+- Large gap between training and validation accuracy
+- Training accuracy much higher than validation accuracy
+
+**Solutions for overfitting:**
+- **Regularization**: Add dropout, weight decay, or other regularization techniques
+- **Early stopping**: Stop training when validation performance stops improving
+- **Data augmentation**: Increase training data diversity
+- **Reduce model complexity**: Use a smaller model or fewer parameters
+
+In the sample below, we use early stopping to prevent overfitting. We set the `early_stopping_patience` to 3, which means that if the validation loss does not improve for 3 consecutive epochs, the training will be stopped.
+
+```python
+# Example of detecting overfitting with early stopping
+from transformers import EarlyStoppingCallback
+
+training_args = TrainingArguments(
+ output_dir="./results",
+ eval_strategy="steps",
+ eval_steps=100,
+ save_strategy="steps",
+ save_steps=100,
+ load_best_model_at_end=True,
+ metric_for_best_model="eval_loss",
+ greater_is_better=False,
+ num_train_epochs=10, # Set high, but we'll stop early
+)
+
+# Add early stopping to prevent overfitting
+trainer = Trainer(
+ model=model,
+ args=training_args,
+ train_dataset=tokenized_datasets["train"],
+ eval_dataset=tokenized_datasets["validation"],
+ data_collator=data_collator,
+ processing_class=tokenizer,
+ compute_metrics=compute_metrics,
+ callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+)
+```
+
+#### 2. Underfitting[[underfitting]]
+
+Underfitting occurs when the model is too simple to capture the underlying patterns in the data. This can happen for several reasons:
+
+- The model is too small or lacks capacity to learn the patterns
+- The learning rate is too low, causing slow learning
+- The dataset is too small or not representative of the problem
+- The model is not properly regularized
+
+
+
+**Symptoms:**
+- Both training and validation loss remain high
+- Model performance plateaus early in training
+- Training accuracy is lower than expected
+
+**Solutions for underfitting:**
+- **Increase model capacity**: Use a larger model or more parameters
+- **Train longer**: Increase the number of epochs
+- **Adjust learning rate**: Try different learning rates
+- **Check data quality**: Ensure your data is properly preprocessed
+
+In the sample below, we train for more epochs to see if the model can learn the patterns in the data.
+
+```python
+from transformers import TrainingArguments
+
+training_args = TrainingArguments(
+ output_dir="./results",
+ -num_train_epochs=5,
+ +num_train_epochs=10,
+)
+```
+
+#### 3. Erratic Learning Curves[[erratic-learning-curves]]
+
+Erratic learning curves occur when the model is not learning effectively. This can happen for several reasons:
+
+- The learning rate is too high, causing the model to overshoot the optimal parameters
+- The batch size is too small, causing the model to learn slowly
+- The model is not properly regularized, causing it to overfit to the training data
+- The dataset is not properly preprocessed, causing the model to learn from noise
+
+
+
+**Symptoms:**
+- Frequent fluctuations in loss or accuracy
+- Curves show high variance or instability
+- Performance oscillates without clear trend
+
+Both training and validation curves show erratic behavior.
+
+
+
+**Solutions for erratic curves:**
+- **Lower learning rate**: Reduce step size for more stable training
+- **Increase batch size**: Larger batches provide more stable gradients
+- **Gradient clipping**: Prevent exploding gradients
+- **Better data preprocessing**: Ensure consistent data quality
+
+In the sample below, we lower the learning rate and increase the batch size.
+
+```python
+from transformers import TrainingArguments
+
+training_args = TrainingArguments(
+ output_dir="./results",
+ -learning_rate=1e-5,
+ +learning_rate=1e-4,
+ -per_device_train_batch_size=16,
+ +per_device_train_batch_size=32,
+)
+```
+
+## Key Takeaways[[key-takeaways]]
+
+Understanding learning curves is crucial for becoming an effective machine learning practitioner. These visual tools provide immediate feedback about your model's training progress and help you make informed decisions about when to stop training, adjust hyperparameters, or try different approaches. With practice, you'll develop an intuitive understanding of what healthy learning curves look like and how to address issues when they arise.
+
+
+
+💡 **Key Takeaways:**
+- Learning curves are essential tools for understanding model training progress
+- Monitor both loss and accuracy curves, but remember they have different characteristics
+- Overfitting shows as diverging training/validation performance
+- Underfitting shows as poor performance on both training and validation data
+- Tools like Weights & Biases make it easy to track and analyze learning curves
+- Early stopping and proper regularization can address most common training issues
+
+🔬 **Next Steps**: Practice analyzing learning curves on your own fine-tuning experiments. Try different hyperparameters and observe how they affect the curve shapes. This hands-on experience is the best way to develop intuition for reading training progress.
+
+
+
+## Section Quiz[[section-quiz]]
+
+Test your understanding of learning curves and training analysis:
+
+### 1. What does it typically mean when training loss decreases but validation loss starts increasing?
+
+
+
+### 2. Why do accuracy curves often show a "steppy" or plateau-like pattern rather than smooth increases?
+
+
+
+### 3. What is the best approach when you observe erratic, highly fluctuating learning curves?
+
+
+
+### 4. When should you consider using early stopping?
+
+
+
+### 5. What indicates that your model might be underfitting?
+
+
+
diff --git a/chapters/en/chapter3/6.mdx b/chapters/en/chapter3/6.mdx
index ee107eaf8..e24553dfd 100644
--- a/chapters/en/chapter3/6.mdx
+++ b/chapters/en/chapter3/6.mdx
@@ -1,419 +1,46 @@
-# Understanding Learning Curves[[understanding-learning-curves]]
+
-
+# Fine-tuning, Check![[fine-tuning-check]]
-Now that you've learned how to implement fine-tuning using both the `Trainer` API and custom training loops, it's crucial to understand how to interpret the results. Learning curves are invaluable tools that help you evaluate your model's performance during training and identify potential issues before they reduce performance.
-
-In this section, we'll explore how to read and interpret accuracy and loss curves, understand what different curve shapes tell us about our model's behavior, and learn how to address common training issues.
-
-## What are Learning Curves?[[what-are-learning-curves]]
-
-Learning curves are visual representations of your model's performance metrics over time during training. The two most important curves to monitor are:
-
-- **Loss curves**: Show how the model's error (loss) changes over training steps or epochs
-- **Accuracy curves**: Show the percentage of correct predictions over training steps or epochs
-
-These curves help us understand whether our model is learning effectively and can guide us in making adjustments to improve performance. In Transformers, these metrics are individually computed for each batch and then logged to the disk. We can then use libraries like [Weights & Biases](https://wandb.ai/) to visualize these curves and track our model's performance over time.
-
-### Loss Curves[[loss-curves]]
-
-The loss curve shows how the model's error decreases over time. In a typical successful training run, you'll see a curve similar to the one below:
-
-
-
-- **High initial loss**: The model starts without optimization, so predictions are initially poor
-- **Decreasing loss**: As training progresses, the loss should generally decrease
-- **Convergence**: Eventually, the loss stabilizes at a low value, indicating that the model has learned the patterns in the data
-
-As in previous chapters, we can use the `Trainer` API to track these metrics and visualize them in a dashboard. Below is an example of how to do this with Weights & Biases.
-
-```python
-# Example of tracking loss during training with the Trainer
-from transformers import Trainer, TrainingArguments
-import wandb
-
-# Initialize Weights & Biases for experiment tracking
-wandb.init(project="transformer-fine-tuning", name="bert-mrpc-analysis")
-
-training_args = TrainingArguments(
- output_dir="./results",
- eval_strategy="steps",
- eval_steps=50,
- save_steps=100,
- logging_steps=10, # Log metrics every 10 steps
- num_train_epochs=3,
- per_device_train_batch_size=16,
- per_device_eval_batch_size=16,
- report_to="wandb", # Send logs to Weights & Biases
-)
-
-trainer = Trainer(
- model=model,
- args=training_args,
- train_dataset=tokenized_datasets["train"],
- eval_dataset=tokenized_datasets["validation"],
- data_collator=data_collator,
- processing_class=tokenizer,
- compute_metrics=compute_metrics,
-)
-
-# Train and automatically log metrics
-trainer.train()
-```
-
-### Accuracy Curves[[accuracy-curves]]
-
-The accuracy curve shows the percentage of correct predictions over time. Unlike loss curves, accuracy curves should generally increase as the model learns and can typically include more steps than the loss curve.
+
-
+That was comprehensive! In the first two chapters you learned about models and tokenizers, and now you know how to fine-tune them for your own data using modern best practices. To recap, in this chapter you:
-- **Start low**: Initial accuracy should be low, as the model has not yet learned the patterns in the data
-- **Increase with training**: Accuracy should generally improve as the model learns if it is able to learn the patterns in the data
-- **May show plateaus**: Accuracy often increases in discrete jumps rather than smoothly, as the model makes predictions that are close to the true labels
+* Learned about datasets on the [Hub](https://huggingface.co/datasets) and modern data processing techniques
+* Learned how to load and preprocess datasets efficiently, including using dynamic padding and data collators
+* Implemented fine-tuning and evaluation using the high-level `Trainer` API with the latest features
+* Implemented a complete custom training loop from scratch with PyTorch
+* Used 🤗 Accelerate to make your training code work seamlessly on multiple GPUs or TPUs
+* Applied modern optimization techniques like mixed precision training and gradient accumulation
-💡 **Why Accuracy Curves Are "Steppy"**: Unlike loss, which is continuous, accuracy is calculated by comparing discrete predictions to true labels. Small improvements in model confidence might not change the final prediction, causing accuracy to remain flat until a threshold is crossed.
-
-
-
-### Convergence[[convergence]]
-
-Convergence occurs when the model's performance stabilizes and the loss and accuracy curves level off. This is a sign that the model has learned the patterns in the data and is ready to be used. In simple terms, we are aiming for the model to converge to a stable performance every time we train it.
-
-
-
-Once models have converged, we can use them to make predictions on new data and refer to evaluation metrics to understand how well the model is performing.
-
-## Interpreting Learning Curve Patterns[[interpreting-learning-curve-patterns]]
+🎉 **Congratulations!** You've mastered the fundamentals of fine-tuning transformer models. You're now ready to tackle real-world ML projects!
-Different curve shapes reveal different aspects of your model's training. Let's examine the most common patterns and what they mean.
+📖 **Continue Learning**: Explore these resources to deepen your knowledge:
+- [🤗 Transformers task guides](https://huggingface.co/docs/transformers/main/en/tasks/sequence_classification) for specific NLP tasks
+- [🤗 Transformers examples](https://huggingface.co/docs/transformers/main/en/notebooks) for comprehensive notebooks
-### Healthy Learning Curves[[healthy-learning-curves]]
-
-A well-behaved training run typically shows curve shapes similar to the one below:
-
-
-
-Let's look at the illustration above. It displays both the loss curve (on the left) and the corresponding accuracy curve (on the right). These curves have distinct characteristics.
-
-The loss curve shows the value of the model's loss over time. Initially, the loss is high and then it gradually decreases, indicating that the model is improving. A decrease in the loss value suggests that the model is making better predictions, as the loss represents the error between the predicted output and the true output.
-
-Now let's shift our focus to the accuracy curve. It represents the model's accuracy over time. The accuracy curve begins at a low value and increases as training progresses. Accuracy measures the proportion of correctly classified instances. So, as the accuracy curve rises, it signifies that the model is making more correct predictions.
-
-One notable difference between the curves is the smoothness and the presence of "plateaus" on the accuracy curve. While the loss decreases smoothly, the plateaus on the accuracy curve indicate discrete jumps in accuracy instead of a continuous increase. This behavior is attributed to how accuracy is measured. The loss can improve if the model's output gets closer to the target, even if the final prediction is still incorrect. Accuracy, however, only improves when the prediction crosses the threshold to be correct.
-
-For example, in a binary classifier distinguishing cats (0) from dogs (1), if the model predicts 0.3 for an image of a dog (true value 1), this is rounded to 0 and is an incorrect classification. If in the next step it predicts 0.4, it's still incorrect. The loss will have decreased because 0.4 is closer to 1 than 0.3, but the accuracy remains unchanged, creating a plateau. The accuracy will only jump up when the model predicts a value greater than 0.5 that gets rounded to 1.
-
-
-
-**Characteristics of healthy curves:**
-- **Smooth decline in loss**: Both training and validation loss decrease steadily
-- **Close training/validation performance**: Small gap between training and validation metrics
-- **Convergence**: Curves level off, indicating the model has learned the patterns
+🚀 **Next Steps**:
+- Try fine-tuning on your own dataset using the techniques you've learned
+- Experiment with different model architectures available on the [Hugging Face Hub](https://huggingface.co/models)
+- Join the [Hugging Face community](https://discuss.huggingface.co/) to share your projects and get help
-### Practical Examples[[practical-examples]]
-
-Let's work through some practical examples of learning curves. First, we will highlight some approaches to monitor the learning curves during training. Below, we will break down the different patterns that can be observed in the learning curves.
-
-#### During Training[[during-training]]
+This is just the beginning of your journey with 🤗 Transformers. In the next chapter, we'll explore how to share your models and tokenizers with the community and contribute to the ever-growing ecosystem of pretrained models.
-During the training process (after you've hit `trainer.train()`), you can monitor these key indicators:
-
-1. **Loss convergence**: Is the loss still decreasing or has it plateaued?
-2. **Overfitting signs**: Is validation loss starting to increase while training loss decreases?
-3. **Learning rate**: Are the curves too erratic (LR too high) or too flat (LR too low)?
-4. **Stability**: Are there sudden spikes or drops that indicate problems?
-
-#### After Training[[after-training]]
-
-After the training process is complete, you can analyze the complete curves to understand the model's performance.
-
-1. **Final performance**: Did the model reach acceptable performance levels?
-2. **Efficiency**: Could the same performance be achieved with fewer epochs?
-3. **Generalization**: How close are training and validation performance?
-4. **Trends**: Would additional training likely improve performance?
+The skills you've developed here - data preprocessing, training configuration, evaluation, and optimization - are fundamental to any machine learning project. Whether you're working on text classification, named entity recognition, question answering, or any other NLP task, these techniques will serve you well.
-🔍 **W&B Dashboard Features**: Weights & Biases automatically creates beautiful, interactive plots of your learning curves. You can:
-- Compare multiple runs side by side
-- Add custom metrics and visualizations
-- Set up alerts for anomalous behavior
-- Share results with your team
+💡 **Pro Tips for Success**:
+- Always start with a strong baseline using the `Trainer` API before implementing custom training loops
+- Use the 🤗 Hub to find pretrained models that are close to your task for better starting points
+- Monitor your training with proper evaluation metrics and don't forget to save checkpoints
+- Leverage the community - share your models and datasets to help others and get feedback on your work
-Learn more in the [Weights & Biases documentation](https://docs.wandb.ai/).
-
-#### Overfitting[[overfitting]]
-
-Overfitting occurs when the model learns too much from the training data and is unable to generalize to different data (represented by the validation set).
-
-
-
-**Symptoms:**
-
-- Training loss continues to decrease while validation loss increases or plateaus
-- Large gap between training and validation accuracy
-- Training accuracy much higher than validation accuracy
-
-**Solutions for overfitting:**
-- **Regularization**: Add dropout, weight decay, or other regularization techniques
-- **Early stopping**: Stop training when validation performance stops improving
-- **Data augmentation**: Increase training data diversity
-- **Reduce model complexity**: Use a smaller model or fewer parameters
-
-In the sample below, we use early stopping to prevent overfitting. We set the `early_stopping_patience` to 3, which means that if the validation loss does not improve for 3 consecutive epochs, the training will be stopped.
-
-```python
-# Example of detecting overfitting with early stopping
-from transformers import EarlyStoppingCallback
-
-training_args = TrainingArguments(
- output_dir="./results",
- eval_strategy="steps",
- eval_steps=100,
- save_strategy="steps",
- save_steps=100,
- load_best_model_at_end=True,
- metric_for_best_model="eval_loss",
- greater_is_better=False,
- num_train_epochs=10, # Set high, but we'll stop early
-)
-
-# Add early stopping to prevent overfitting
-trainer = Trainer(
- model=model,
- args=training_args,
- train_dataset=tokenized_datasets["train"],
- eval_dataset=tokenized_datasets["validation"],
- data_collator=data_collator,
- processing_class=tokenizer,
- compute_metrics=compute_metrics,
- callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
-)
-```
-
-#### 2. Underfitting[[underfitting]]
-
-Underfitting occurs when the model is too simple to capture the underlying patterns in the data. This can happen for several reasons:
-
-- The model is too small or lacks capacity to learn the patterns
-- The learning rate is too low, causing slow learning
-- The dataset is too small or not representative of the problem
-- The model is not properly regularized
-
-
-
-**Symptoms:**
-- Both training and validation loss remain high
-- Model performance plateaus early in training
-- Training accuracy is lower than expected
-
-**Solutions for underfitting:**
-- **Increase model capacity**: Use a larger model or more parameters
-- **Train longer**: Increase the number of epochs
-- **Adjust learning rate**: Try different learning rates
-- **Check data quality**: Ensure your data is properly preprocessed
-
-In the sample below, we train for more epochs to see if the model can learn the patterns in the data.
-
-```python
-from transformers import TrainingArguments
-
-training_args = TrainingArguments(
- output_dir="./results",
- -num_train_epochs=5,
- +num_train_epochs=10,
-)
-```
-
-#### 3. Erratic Learning Curves[[erratic-learning-curves]]
-
-Erratic learning curves occur when the model is not learning effectively. This can happen for several reasons:
-
-- The learning rate is too high, causing the model to overshoot the optimal parameters
-- The batch size is too small, causing the model to learn slowly
-- The model is not properly regularized, causing it to overfit to the training data
-- The dataset is not properly preprocessed, causing the model to learn from noise
-
-
-
-**Symptoms:**
-- Frequent fluctuations in loss or accuracy
-- Curves show high variance or instability
-- Performance oscillates without clear trend
-
-Both training and validation curves show erratic behavior.
-
-
-
-**Solutions for erratic curves:**
-- **Lower learning rate**: Reduce step size for more stable training
-- **Increase batch size**: Larger batches provide more stable gradients
-- **Gradient clipping**: Prevent exploding gradients
-- **Better data preprocessing**: Ensure consistent data quality
-
-In the sample below, we lower the learning rate and increase the batch size.
-
-```python
-from transformers import TrainingArguments
-
-training_args = TrainingArguments(
- output_dir="./results",
- -learning_rate=1e-5,
- +learning_rate=1e-4,
- -per_device_train_batch_size=16,
- +per_device_train_batch_size=32,
-)
-```
-
-## Key Takeaways[[key-takeaways]]
-
-Understanding learning curves is crucial for becoming an effective machine learning practitioner. These visual tools provide immediate feedback about your model's training progress and help you make informed decisions about when to stop training, adjust hyperparameters, or try different approaches. With practice, you'll develop an intuitive understanding of what healthy learning curves look like and how to address issues when they arise.
-
-
-
-💡 **Key Takeaways:**
-- Learning curves are essential tools for understanding model training progress
-- Monitor both loss and accuracy curves, but remember they have different characteristics
-- Overfitting shows as diverging training/validation performance
-- Underfitting shows as poor performance on both training and validation data
-- Tools like Weights & Biases make it easy to track and analyze learning curves
-- Early stopping and proper regularization can address most common training issues
-
-🔬 **Next Steps**: Practice analyzing learning curves on your own fine-tuning experiments. Try different hyperparameters and observe how they affect the curve shapes. This hands-on experience is the best way to develop intuition for reading training progress.
-
-
-
-## Section Quiz[[section-quiz]]
-
-Test your understanding of learning curves and training analysis:
-
-### 1. What does it typically mean when training loss decreases but validation loss starts increasing?
-
-
-
-### 2. Why do accuracy curves often show a "steppy" or plateau-like pattern rather than smooth increases?
-
-
-
-### 3. What is the best approach when you observe erratic, highly fluctuating learning curves?
-
-
-
-### 4. When should you consider using early stopping?
-
-
-
-### 5. What indicates that your model might be underfitting?
-
-
-
diff --git a/chapters/en/chapter3/7.mdx b/chapters/en/chapter3/7.mdx
index f6bbaaba1..ffb970e40 100644
--- a/chapters/en/chapter3/7.mdx
+++ b/chapters/en/chapter3/7.mdx
@@ -1,268 +1,27 @@
-# End-of-chapter quiz[[end-of-chapter-quiz]]
+# End-of-chapter Certificate
-
-Test what you learned in this chapter!
+Congratulations on completing the course! You've learned how to fine-tune pretrained models, understand learning curves, and share your models with the community. Now it's time to take the quiz to test your knowledge and get your certificate.
-### 1. The emotion dataset contains Twitter messages labeled with emotions. Search for it in the [Hub](https://huggingface.co/datasets), and read the dataset card. Which of these is not one of its basic emotions?
+To take the quiz, you will need to follow these steps:
-
-
-### 2. Search for the ar_sarcasm dataset in the [Hub](https://huggingface.co/datasets). Which task does it support?
-
-dataset card!"
- },
- {
- text: "Named entity recognition",
- explain: "That's not it — take another look at the dataset card!"
- },
- {
- text: "Question answering",
- explain: "Alas, this question was not answered correctly. Try again!"
- }
- ]}
-/>
-
-### 3. How does the BERT model expect a pair of sentences to be processed?
-
-[SEP] special token is needed to separate the two sentences, but that's not the only thing!"
- },
- {
- text: "[CLS] Tokens_of_sentence_1 Tokens_of_sentence_2",
- explain: "A [CLS] special token is required at the beginning, but that's not the only thing!"
- },
- {
- text: "[CLS] Tokens_of_sentence_1 [SEP] Tokens_of_sentence_2 [SEP]",
- explain: "That's correct!",
- correct: true
- },
- {
- text: "[CLS] Tokens_of_sentence_1 [SEP] Tokens_of_sentence_2",
- explain: "A [CLS] special token is needed at the beginning as well as a [SEP] special token to separate the two sentences, but that's not all!"
- }
- ]}
-/>
+1. Sign in to your Hugging Face account.
+2. Answer the questions in the quiz.
+3. Submit your answers.
-### 4. What are the benefits of the Dataset.map() method?
-
-
-### 5. What does dynamic padding mean?
-
-
-
-### 6. What is the purpose of a collate function?
-
-DataCollatorWithPadding specifically."
- },
- {
- text: "It puts together all the samples in a batch.",
- explain: "You can pass the collate function as an argument of a DataLoader. We used the DataCollatorWithPadding function, which pads all items in a batch so they have the same length.",
- correct: true
- },
- {
- text: "It preprocesses the whole dataset.",
- explain: "That would be a preprocessing function, not a collate function."
- },
- {
- text: "It truncates the sequences in the dataset.",
- explain: "A collate function is involved in handling individual batches, not the whole dataset. If you're interested in truncating, you can use the truncate argument of tokenizer."
- }
- ]}
-/>
+## Multiple Choice Quiz
-### 7. What happens when you instantiate one of the AutoModelForXxx classes with a pretrained language model (such as bert-base-uncased) that corresponds to a different task than the one for which it was trained?
+In this quiz, you will be asked to select the correct answer from a list of options. We'll test you on the fundamentals of supervised finetuning.
-AutoModelForSequenceClassification with bert-base-uncased, we got warnings when instantiating the model. The pretrained head is not used for the sequence classification task, so it's discarded and a new head is instantiated with random weights.",
- correct: true
- },
- {
- text: "The head of the pretrained model is discarded.",
- explain: "Something else needs to happen. Try again!"
- },
- {
- text: "Nothing, since the model can still be fine-tuned for the different task.",
- explain: "The head of the pretrained model was not trained to solve this task, so we should discard the head!"
- }
- ]}
-/>
-
-### 8. What's the purpose of TrainingArguments?
-
-Trainer.",
- explain: "Nice one!",
- correct: true
- },
- {
- text: "It specifies the size of the model.",
- explain: "The model size is defined by the model configuration, not the class TrainingArguments."
- },
- {
- text: "It just contains the hyperparameters used for evaluation.",
- explain: "In the example, we specified where the model and its checkpoints will be saved. Try again!"
- },
- {
- text: "It just contains the hyperparameters used for training.",
- explain: "In the example, we used an evaluation_strategy as well, so this impacts evaluation. Try again!"
- }
- ]}
-/>
-
-### 9. Why should you use the 🤗 Accelerate library?
-
-Trainer, not the 🤗 Accelerate library. Try again!"
- },
- {
- text: "It makes our training loops work on distributed strategies.",
- explain: "With 🤗 Accelerate, your training loops will work for multiple GPUs and TPUs.",
- correct: true
- },
- {
- text: "It provides more optimization functions.",
- explain: "No, the 🤗 Accelerate library does not provide any optimization functions."
- }
- ]}
-/>
-
-### 10. What is the purpose of the processing_class parameter in the Trainer?
-
-
-
-### 11. Which modern optimization technique can help with memory efficiency during training?
-
-
+