1- How many sequences does a Transformer model expect by default?

Ans- Transformer models expect multiple sequences by default.

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2- How do you handle multiple sequences in a batch for a transformer model?

Ans- You handle multiple sequences by batching them together using the tokenizer and then converting them to tensors.

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3- What challenge arises when handling multiple sequences of different lengths?

Ans- Sequences of different lengths cannot be converted to a tensor directly, requiring padding to make them of uniform length.

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4- What is padding in the context of handling multiple sequences?

And- Padding is adding a special token to shorter sequences so that all sequences in a batch have the same length.

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5- How does padding affect the model's attention mechanism?

Ans- Padding tokens can be incorrectly attended to by the model, potentially skewing results unless an attention mask is used.

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6- What is an attention mask in transformers, and why is it important?

And- An attention mask is a tensor of 1s and 0s indicating which tokens should be attended to, ensuring the model ignores padding tokens.

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7- Why did the model fail when a single sequence was passed without batching?

Ans- Transformer models expect a batch of sequences; a single sequence needs to be batched by adding an extra dimension.

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8- How do you convert a list of token IDs into a tensor for a model?

Ans- You use torch.tensor() to convert the list of token IDs into a tensor, ensuring it has the correct batch dimension.

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9- What happens if you don't use an attention mask with padded sequences?

Ans- The model may attend to padding tokens, leading to incorrect or inconsistent predictions.

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10- What is the maximum sequence length most transformer models can handle?

Ans- Most transformer models handle sequences up to 512 or 1024 tokens in length.

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11- How do you deal with sequences longer than the model's maximum supported length?

Ans- You can either truncate the sequences to the maximum length or use models designed for longer sequences, like Longformer.

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12- What function in the Hugging Face library automatically handles batching and padding?

Ans- The `tokenizer` function handles batching and padding automatically when using the `return_tensors="pt"` argument.

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13- What is the role of the `max_sequence_length` parameter in handling long sequences?

Ans- The `max_sequence_length` parameter is used to truncate sequences that exceed the model's supported length.

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14- Can you pass individual sequences through a model without batching them?

Ans- No, sequences need to be batched, even if there’s only one sequence, by adding an extra dimension to the input tensor.

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15- What would the logits output like if you padded and used an attention mask correctly?

Ans- The logits output for each sequence in the batch would match the output as if each sequence were processed individually.

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16- What happens if you pass an unbatched tensor to a transformer model?

Ans- The model will raise an `IndexError` because it expects a batch dimension in the input tensor.

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17- What is the purpose of the `AutoTokenizer` class from the Transformer library?

Ans- `AutoTokenizer` is used to handle tokenization, padding, truncation, and conversion of text to input IDs for models.

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18- How do you initialize a tokenizer using a pre-trained model with 🤗 Transformers?

Ans- Use `AutoTokenizer.from_pretrained(checkpoint)` where `checkpoint` is the name of the pre-trained model.

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19- What does the `tokenizer(sequence)` function return?

Ans- It returns a dictionary containing the input IDs and attention masks necessary for model input.

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20- How can you handle padding for sequences using the tokenizer

Ans- You can pad sequences to the longest sequence, the model's max length, or a specified max length using `padding` and `max_length` parameters.

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21- What is the purpose of truncation in tokenization?

Ans- Truncation shortens sequences that exceed a maximum length to fit within the model’s constraints.

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22- How can you convert tokenized sequences into tensors for different frameworks?

Ans- Use `return_tensors` parameter with values like `"pt"` for PyTorch, `"tf"` for TensorFlow, and `"np"` for NumPy arrays.

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23- What special tokens are added by the tokenizer for sequence classification models like DistilBERT?

Ans- Special tokens like `[CLS]` at the beginning and `[SEP]` at the end are added to the sequences.

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24- How does the tokenizer ensure compatibility with pretrained models during inference?

Ans- The tokenizer adds necessary special tokens and processes the input as expected by the pretrained model for accurate results.

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25- How do you handle multiple sequences with the tokenizer?

Ans- Pass a list of sequences to the tokenizer, which will handle padding and truncation as required.

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26- How can you perform both tokenization and model inference in a single script using 🤗 Transformers?

Ans- Tokenize the sequences with `tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")` and pass the result to the model with `model(**tokens)`.

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27- How do you load a dataset from the Hugging Face datasets library?

Ans- Use `load_dataset()` to load datasets, e.g., `raw_datasets = load_dataset("glue", "mrpc")`.

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28- Which tokenizer is used for tokenizing the MRPC dataset in this example?

Ans- `AutoTokenizer.from_pretrained(checkpoint)` is used, where `checkpoint` is typically set to a model like `bert-base-uncased`.

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29- What function is used to tokenize sentences in this example?

Ans- A custom `tokenize_function` is defined to tokenize pairs of sentences using the tokenizer.

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30-  How do you apply the tokenization function to the dataset?

Ans- Use `map()` method, e.g., `tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)`.

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31- What is the role of DataCollatorWithPadding in the preprocessing pipeline?

Ans- It automatically pads input sequences to the same length in a batch.

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32- How do you remove unnecessary columns from the dataset?

Ans- Use `remove_columns()` method, e.g., `tokenized_datasets = tokenized_datasets.remove_columns(["sentence1", "sentence2", "idx"])`.


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33- Why is the set_format("torch") method used on the dataset?

Ans- To ensure the dataset returns PyTorch tensors instead of lists.

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34- How do you rename the label column to match the expected input of the model?

Ans- Use `rename_column()`, e.g., `tokenized_datasets = tokenized_datasets.rename_column("label", "labels")`.

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35- What library is used to create dataloaders for training and evaluation?

Ans- PyTorch’s DataLoader class from torch.utils.data.

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36- Which model is used for sequence classification in this example?

Ans- `AutoModelForSequenceClassification` from the `transformers` library.

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37- How do you define an optimizer for training the model?

Ans- Use `AdamW` optimizer from the `transformers` library.

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38- How is the learning rate scheduler defined in this example?

Ans- Using `get_scheduler()` with linear decay, e.g., `lr_scheduler = get_scheduler("linear", optimizer=optimizer, num_training_steps=num_training_steps)`.

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39- What is the purpose of the tqdm library in the training loop?

Ans- To provide a progress bar for the training steps.

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40- How is the model and data moved to a GPU?

Ans- Use `model.to(device)` where `device = torch.device("cuda")`.

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41- How do you backpropagate the loss during training?

Ans- Use `loss.backward()` after calculating the loss from the model’s outputs.

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42- Which library is used for evaluation metrics?

Ans- The `evaluate` library from Hugging Face.

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43- How do you accumulate batches during the evaluation phase?

Ans- Use `metric.add_batch()` method in the evaluation loop.

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44- What metrics are computed at the end of evaluation?

Ans- Accuracy and F1 score, computed using `metric.compute()`.

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45- How do you enable distributed training with 🤗 Accelerate?

Ans- Instantiate an `Accelerator` object and wrap dataloaders, model, and optimizer using `accelerator.prepare()`.

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46- What change is made to the backward pass when using 🤗 Accelerate?

Ans- Replace `loss.backward()` with `accelerator.backward(loss)` to handle distributed backpropagation.

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47- How do you launch a distributed training script using 🤗 Accelerate?

Ans- Use `accelerate launch train.py` after configuring with `accelerate config`.

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48- What does dynamic padding mean?

Ans- It's when you pad your inputs when the batch is created, to the maximum length of the sentences inside that batch.

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49- What is the purpose of a collate function?

Ans- It puts together all the samples in a batch.

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50- What’s the purpose of TrainingArguments ?

Ans-  It contains all the hyperparameters used for training and evaluation with the Trainer.

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51- Why should you use the 🤗 Accelerate library?

Ans- It makes our training loops work on distributed strategies.

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52- What is the purpose of the 🤗 Datasets library?

Ans- To provide tools for loading, preprocessing, and manipulating datasets for machine learning tasks.

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53- Why is data preprocessing important before training models?

Ans- It ensures the data is clean, consistent, and suitable for training, leading to better model performance.

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54- How do you load a dataset using the 🤗 Datasets library?

Ans- Use the load_dataset() function, specifying the format and relevant arguments like file paths and delimiters.

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55- What command can be used to download and extract data in a Jupyter notebook?

Ans- Use the !wget command to download files and !unzip to extract them.

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56- How can you randomly shuffle and select a sample from a dataset?

Ans- Chain the Dataset.shuffle() and Dataset.select() functions.

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57- How can you check the first few rows of a dataset sample?

Ans- By slicing the dataset like sample[:3].

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58- How can you rename a column in a 🤗 Dataset?

Ans- Use the DatasetDict.rename_column() function.

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59- What function would you use to remove rows with missing values in a specific column?

Ans- Use the Dataset.filter() function with a condition.

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60- How can you convert all text in a column to lowercase?

Ans- Use the Dataset.map() function with a custom function like str.lower().

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61- How can you add a new column to a dataset in 🤗 Datasets?

Ans- Use the Dataset.map() function to apply a transformation that returns a dictionary with the new column.

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62- What is the purpose of the Dataset.add_column() function?

To add a new column to the dataset from a list or NumPy array.

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63- How can you unescape HTML character codes in text data?

Ans- Use Python’s html.unescape() function within a Dataset.map() operation.

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64- How can you filter out reviews that contain fewer than 30 words?

Ans- Use Dataset.filter() with a lambda function to check the length of each review.

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65- What does the batched argument in Dataset.map() do?

Ans- It allows processing multiple examples at once, speeding up the map operation.

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66- Why might you want to sort your dataset by a specific column?

Ans- To analyze the distribution of data and identify extreme values or patterns.

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67- What steps would you take to clean and prepare a dataset for training a model?

Ans- Load the data, explore it, clean it (e.g., handle missing values, normalize text), and transform it (e.g., add new features, filter data).

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68-  What challenges are faced when working with large datasets like transformers?

Ans- Memory limitations; large datasets can overwhelm the available system memory.

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69- How does the 🤗 Datasets library help in handling large datasets?

Ans- It provides memory-efficient techniques such as memory mapping and streaming.

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70- What is the Pile dataset?

Ans- A large-scale, diverse 825 GB dataset created by EleutherAI, comprising various English text data sources.

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71- What is the PubMed Abstracts dataset?

Ans- A subset of the Pile dataset containing abstracts from 15 million biomedical publications.

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72- Which library is essential for loading and working with large datasets in Python?

Ans- The datasets library from Hugging Face.

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73- What command is used to install the necessary libraries for working with the Pile dataset?

Ans- !pip install zstandard

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74- How do you load a dataset from a remote source using the datasets library?

Ans- By using the load_dataset function along with the dataset’s URL.

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73- Why might one use the PubMed Abstracts dataset in a project?

Ans- To leverage a large collection of biomedical literature for natural language processing tasks.

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74- What is FAISS, and what is it used for?

Ans- FAISS is a library developed by Facebook AI for efficient similarity search and clustering of dense vectors.

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75- What is semantic search, and how does it differ from traditional keyword search?

Ans- Semantic search uses embeddings to find similar documents based on meaning rather than just matching keywords.

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76- How do Transformer-based language models create embeddings?

Ans- Transformer models represent text as embedding vectors by processing tokens through multiple attention layers.

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77- Why would you use CLS pooling for embeddings?

Ans- CLS pooling extracts the embedding from the [CLS] token, which often contains a summary of the input text.

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78- What dataset format does the load_dataset() function return by default?

Ans- It returns a Dataset object when the train split is specified.

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79- Why should pull requests be filtered out from the GitHub issues dataset when building a search engine?

Ans- Pull requests are typically less useful for answering user queries and can introduce noise.

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80-How do you filter out rows with no comments in a dataset using Dataset.filter()?

Ans- By applying a lambda function that checks for non-empty comments.

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81- Why is it beneficial to drop unnecessary columns in a dataset before processing?

Ans- To reduce data complexity and focus on the most informative features for the search engine.

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82- What does the explode() function do in Pandas?

Ans- It transforms a column with list-like elements into multiple rows, each containing a single list element.

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83- How can you concatenate multiple text fields into a single string for embedding?

Ans- By using a custom function in Dataset.map() to combine fields like title, body, and comments.

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84- What is the purpose of converting embeddings to NumPy arrays when using FAISS?

Ans- FAISS requires embeddings to be in NumPy array format for indexing and querying.

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85- Why would you use a GPU to speed up the embedding process?

Ans- A GPU can handle the parallel processing required for large-scale text embeddings more efficiently than a CPU.

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86- What function is used to add a FAISS index to a dataset?

Ans- The add_faiss_index() function is used to create a FAISS index for a specified column.

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87- How do you perform a nearest neighbor search using FAISS in a dataset?

Ans- By using the get_nearest_examples() function to find embeddings similar to an input query.

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88- What is the significance of using the set_format("pandas") function with a dataset?

Ans- It allows for easy manipulation and analysis of the dataset using Pandas DataFrame methods.

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89- Suppose you load one of the GLUE tasks as follows:
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    from datasets import load_dataset

    dataset = load_dataset("glue", "mrpc", split="train")

What of the following commands will produce a random sample of 50 elements from dataset?

Ans-  dataset.shuffle().select(range(50))

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90- What is Memory Mapping?

Ans- A mapping between RAM and filesystem storage

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91- Which of the following are the main benefits of memory mapping?

Ans- - Applications can access segments of data in an extremely large file without having to read the whole file into RAM first.
     - Accessing memory-mapped files is faster than reading from or writing to disk.

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92- What is semantic search?

Ans- A way to search for matching documents by understanding the contextual meaning of a query

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93- For asymmetric semantic search, you usually have

Ans- A short query and a longer paragraph that answers the query

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94- Why would you want to train a new tokenizer instead of using an existing one?

Ans- To tailor the tokenizer to the specific language or text type, improving model performance on that data.

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95- What is the purpose of a tokenizer in natural language processing?

Ans- A tokenizer breaks text into smaller parts (words or subwords) that a model can process.

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96- How does tokenization affect the performance of language models?

Ans- Proper tokenization ensures that the model can accurately understand and process the input text, leading to better predictions.

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97- What type of dataset is ideal for training a new tokenizer?

Ans- A large, diverse dataset that closely resembles the language or text type the tokenizer will be used on.

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98- What is the role of the get_training_corpus function in the tokenizer training process?

Ans- It prepares and iterates through batches of text data for training the tokenizer.

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99- Why is it important to check the structure of the dataset before training a tokenizer?

Ans- To ensure the dataset is suitable and properly formatted for the training process.

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100- Why do you start with an existing tokenizer when training a new one?

Ans- To leverage the pre-trained tokenizer's structure and vocabulary, allowing for faster and more efficient training.

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101- What is the significance of the train_new_from_iterator function in the Hugging Face library?

Ans- It allows training a new tokenizer on custom data while inheriting the structure of an existing tokenizer.

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102- How do you determine the vocabulary size when training a new tokenizer?

Ans- The vocabulary size (e.g., 52000 tokens) is often chosen based on the complexity of the language and the desired balance between coverage and efficiency.

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103- Why is it important to save a trained tokenizer?

Ans- To reuse the tokenizer in future tasks without retraining, saving time and resources.

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104- How can you load a previously saved tokenizer?

Ans- By using 
     
     AutoTokenizer.from_pretrained("path_to_save_tokenizer") to load the tokenizer from the saved directory.

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105- What are the advantages of saving a tokenizer with the save_pretrained method?

Ans- It ensures the tokenizer is stored in a standard format that can be easily loaded and shared.

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106- How can a custom-trained tokenizer improve the performance of models on specific tasks?

Ans- By better understanding and tokenizing the language or text type, leading to more accurate model predictions.

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107- In which scenarios would training a custom tokenizer be particularly useful?

Ans- When working with niche languages, domain-specific jargon, or code.

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108- What challenges might you face when training a new tokenizer on a custom dataset?

Ans- Ensuring sufficient and representative data, managing large datasets, and balancing vocabulary size.

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109- What libraries are commonly used for training tokenizers in Python?

Ans- Hugging Face’s 🤗 Transformers and datasets libraries.

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110- What is the role of the AutoTokenizer class in Hugging Face's Transformers library?

Ans- It provides a convenient interface to load and use various pre-trained tokenizers.

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111- Why might you use the load_dataset function from the datasets library?

Ans- To easily access and load large datasets for training tokenizers or models.

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112- What common errors might occur when training a tokenizer and how would you address them?

Ans- Errors like memory overflow or incorrect data formatting; they can be addressed by optimizing data handling and ensuring data cleanliness.

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113- How can you test if a trained tokenizer works correctly on new data?

Ans- By tokenizing sample text and verifying that the tokens are consistent with the language or data type.

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114- What are the key differences between slow and fast tokenizers in the Hugging Face Transformers library?

Ans- Fast tokenizers are written in Rust and offer parallelization, making them significantly faster than slow tokenizers written in Python.

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115- What is the BatchEncoding object in Hugging Face tokenizers?

Ans- BatchEncoding is a special object that acts like a dictionary and provides additional methods for fast tokenizers, such as offset mapping.

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116- How can you determine if a tokenizer is a fast tokenizer?

Ans- You can check if a tokenizer is fast by accessing its is_fast attribute or the is_fast attribute of its encoding.

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117- What is offset mapping in fast tokenizers?

Ans- Offset mapping is a feature in fast tokenizers that tracks the original span of text each token comes from, enabling functionalities like word-to-token mapping.

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118- How does the word_ids() method in a fast tokenizer help in text processing tasks?

Ans- The word_ids() method maps each token to its corresponding word, aiding in tasks like named entity recognition (NER) and part-of-speech (POS) tagging.

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119- What method would you use to map tokens to their original characters in a text?

Ans- You can use the token_to_chars() method to map tokens back to their original characters in the text.

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120- What is the purpose of the aggregation_strategy parameter in the token-classification pipeline?

Ans- The aggregation_strategy parameter determines how to group tokens into entities, with strategies like "simple," "first," "max," and "average."

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121- Why might a fast tokenizer be slower than a slow tokenizer when tokenizing a single sentence?

Ans- A fast tokenizer might be slower for a single sentence due to the overhead of its advanced features, but it excels in batch processing.

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121- What is the main advantage of using fast tokenizers for large datasets?

Ans- Fast tokenizers provide significant speed advantages when processing large datasets in parallel due to their efficient design in Rust.

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122- How can the word_to_chars() method be used in a fast tokenizer?

Ans- The word_to_chars() method allows you to retrieve the character span of a word in the original text, using the word’s index from the tokenized output.

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123- What is the purpose of the question-answering pipeline in the Transformers library?

Ans- To extract answers from a context based on a given question.

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124- Which deep learning libraries are integrated with 🤗 Transformers?

Ans- Jax, PyTorch, and TensorFlow.

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125- What is the default model used for the question-answering pipeline in 🤗 Transformers?

Ans- distilbert-base-cased-distilled-squad.

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126- How does the question-answering model identify the start and end of an answer?

Ans- By predicting the indices of the start and end tokens of the answer.

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127- What is the significance of using start_logits and end_logits in the question-answering pipeline?

Ans- They represent the model’s confidence scores for the start and end tokens of the answer.

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128- Why do we need to mask certain logits in the question-answering pipeline?

Ans- To exclude irrelevant parts like the question and special tokens from affecting the answer extraction.

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129- How are start and end probabilities computed from logits in the question-answering pipeline?

Ans- By applying a softmax function to the logits.

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130- What is the purpose of using the torch.triu() function in the context of the question-answering pipeline?

Ans- To mask out invalid start-end token pairs where the start token index is greater than the end token index.

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131- How do you convert token indices to character indices in the question-answering pipeline?

Ans- Using the offset mapping provided by the tokenizer to map tokens to their corresponding character positions in the context.

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132- How does the question-answering pipeline handle very long contexts?

Ans- It truncates the context to fit within the model's maximum input length, typically using strategies like only_second.

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133- What happens when the answer is located beyond the truncated context in the question-answering pipeline?

Ans- The pipeline might not find the answer if it's beyond the truncation limit, requiring a strategy to handle long contexts.

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134- Why do we apply a softmax function to the logits in a question-answering model?

Ans- To convert the logits into probabilities, making it easier to identify the most likely start and end tokens.

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135- How can the question-answering pipeline return multiple possible answers?

Ans- By setting the top_k parameter, which returns the top k answer predictions with the highest probabilities.

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136- What are the challenges of using the question-answering pipeline with long contexts?

Ans- The context might be truncated, potentially excluding the answer, and special strategies are needed to manage this.

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137- What does the term start_probabilities[start_index] * end_probabilities[end_index] represent in the question-answering pipeline?

Ans- It represents the joint probability of a specific start and end token pair being the correct answer span.

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138- What is the purpose of normalization in text preprocessing?

Ans- To clean up the text by removing unnecessary spaces, converting to lowercase, and handling accents.

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139- What does Unicode normalization do in the context of text preprocessing?

Ans- It standardizes characters in different forms to a consistent format (e.g., NFC or NFKC).

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140- How does the BERT tokenizer handle normalization?

Ans- It applies lowercasing and removes accents when using the bert-base-uncased model.

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141- What is pre-tokenization in the context of tokenization?

Ans- It involves splitting the text into smaller entities, like words, before further processing.

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142- Why is pre-tokenization necessary?

Ans- To break down raw text into manageable pieces (e.g., words) that can be further split into subwords.

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143- How does pre-tokenization work in the BERT tokenizer?

Ans- It splits text based on spaces and punctuation.

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144- What is the role of the pre_tokenize_str method in a tokenizer?

Ans- It allows you to see how the tokenizer splits the text during the pre-tokenization step.

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145- How does GPT-2's pre-tokenization differ from BERT's?

Ans- GPT-2 keeps spaces, replacing them with a special symbol (Ġ), allowing recovery of original spaces during decoding.

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146- What is unique about the T5 tokenizer's pre-tokenization process?

Ans- It replaces spaces with an underscore (▁) and splits only on spaces, not punctuation.

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147- What are the three main subword tokenization algorithms used in Transformer models?

Ans- Byte-Pair Encoding (BPE), WordPiece, and Unigram.

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148- How does SentencePiece differ from other tokenization algorithms?

Ans- It treats text as a sequence of Unicode characters and replaces spaces with a special character (▁), useful for languages without spaces.

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149- Why is SentencePiece considered reversible in terms of tokenization?

Ans- Because decoding simply involves concatenating tokens and replacing special characters with spaces.

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150- What is the primary advantage of SentencePiece for certain languages?

Ans- It does not require pre-tokenization, making it suitable for languages where spaces are not used, like Chinese or Japanese.

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151- What is the difference between the bert-base-cased and bert-base-uncased tokenizers?

Ans- The bert-base-cased tokenizer retains the case of the text, while the bert-base-uncased tokenizer converts everything to lowercase.

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152- How does the BPE algorithm approach tokenization?

Ans- It starts with a small vocabulary and learns to merge the most common pairs of tokens.

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153- How does the WordPiece algorithm differ from BPE?

Ans- WordPiece merges token pairs based on the best score, prioritizing pairs where individual tokens are less frequent.

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154- What is the key characteristic of the Unigram tokenization algorithm?

Ans- It starts with a large vocabulary and removes tokens that minimize the loss computed on the whole corpus.

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155- How does the normalize_str method help in understanding a tokenizer's behavior?

Ans- It shows how text is normalized by the tokenizer, such as converting to lowercase and removing accents.

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156- What are the primary preprocessing steps before tokenization?

Ans- Normalization and pre-tokenization.

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157- What kind of preprocessing does the AutoTokenizer from 🤗 Transformers perform?

Ans- It handles normalization and pre-tokenization automatically based on the specified model.

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158- What is Byte-Pair Encoding (BPE)?

Ans- BPE is a tokenization technique that iteratively merges the most frequent pairs of characters or subwords to create a compact representation of a text.

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159- How does BPE handle unknown characters during tokenization?

Ans- BPE uses byte-level tokenization to ensure every character, even those not seen during training, is represented without converting to an unknown token.

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160-  What is the initial step in the BPE training process?

Ans- The initial step in BPE training is to compute the unique set of words in the corpus and build a base vocabulary of all the symbols used in these words.

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161- What is the primary goal of BPE during tokenization?

Ans- The primary goal of BPE is to iteratively merge frequent pairs of tokens to efficiently encode the corpus with a limited vocabulary.

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162- Why is BPE particularly useful in NLP tasks?

Ans- BPE is useful in NLP because it strikes a balance between word-based and character-based tokenization, reducing vocabulary size while maintaining the ability to handle out-of-vocabulary words.

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163- How does BPE determine which pairs of tokens to merge?

Ans- BPE determines which pairs to merge based on their frequency in the corpus, merging the most frequent pairs first.

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164- What happens when BPE reaches the desired vocabulary size?

Ans- Once the desired vocabulary size is reached, BPE stops merging tokens, and the resulting vocabulary is used for tokenization.

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165- How does byte-level BPE differ from regular BPE?

Ans- Byte-level BPE operates on byte representations rather than Unicode characters, allowing for a more compact and flexible vocabulary that handles all possible characters.

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166- Can BPE handle multi-character subwords?

Ans- Yes, as BPE progresses, it merges characters into subwords, which can consist of multiple characters.

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167- Why is BPE commonly used in Transformer models?

BPE is used in Transformer models because it efficiently tokenizes text, reducing the overall model complexity while maintaining high flexibility for different languages.

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168- What role does BPE play in reducing model vocabulary size?

Ans- BPE reduces vocabulary size by combining frequent subwords, thus requiring fewer tokens to represent the same text.

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169- How does BPE handle rare words in the training corpus?

Ans- BPE breaks down rare words into subword tokens, ensuring that even uncommon words are tokenized effectively.

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170- What is a merge rule in the context of BPE?

Ans- A merge rule in BPE specifies how two consecutive tokens should be combined into a single token during tokenization.

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171- What are the advantages of using BPE for tokenization over word-based tokenization?

Ans- BPE reduces the number of out-of-vocabulary tokens and captures subword patterns, making it more effective for handling diverse and rare words.

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172- How does BPE affect the tokenization of compound words or phrases?

Ans- BPE can efficiently tokenize compound words by merging subwords, reducing the number of tokens and capturing semantic meaning.

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173- What happens to the corpus after each merge in BPE?

Ans- After each merge, the corpus is updated by replacing the merged pair with the new token, and the process repeats.

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174- Why does BPE start by splitting words into individual characters?

Ans- BPE starts with individual characters to ensure a fine-grained level of tokenization, allowing for flexible merging based on frequency.

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175- How does BPE tokenization handle different languages?

Ans- BPE tokenization is language-agnostic and works well across languages by learning subword units that are common within each language's corpus.

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176- How is the vocabulary in BPE updated during the training process?

Ans- The vocabulary in BPE is updated by adding new tokens resulting from each merge rule learned during training.

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177- Can BPE be used for tokenizing non-text data?

Ans- BPE is primarily designed for text data but can be adapted for any sequential data where frequent patterns can be merged.

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178- What is WordPiece tokenization?

Ans- WordPiece is a subword tokenization method used in models like BERT to handle unknown words by breaking them into smaller, more manageable pieces.

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179- How does WordPiece differ from Byte Pair Encoding (BPE)?

Ans- While both methods merge tokens based on frequency, WordPiece uses a scoring formula to prioritize merges, whereas BPE typically merges the most frequent pairs directly.

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180- What is the role of the initial vocabulary in WordPiece?

Ans- The initial vocabulary includes special tokens and individual characters, serving as the starting point for tokenizing and merging subwords.

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181- Explain the merging rule scoring formula used in WordPiece.

Ans- The formula is: { score = freq_of_pair / freq_of_first_element × freq_of_second_element }, which helps prioritize less frequent but frequently co-occurring pairs.

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182- How does WordPiece handle tokenization for a new word?

Ans- It finds the longest subword in the vocabulary to tokenize the word, splitting it accordingly.

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183- What happens if a word cannot be split into known subwords in WordPiece?

Ans- The word is marked as unknown and tokenized as [UNK].

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184- Describe the process for building the initial vocabulary in WordPiece.

Ans- It involves collecting all unique characters and their prefixed forms from the training corpus and creating a list of special tokens.

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185- How are pair scores computed in WordPiece tokenization?

Ans- Pair scores are computed based on the frequency of token pairs and the frequencies of the individual tokens in the pairs.

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186- What is the purpose of the merge_pair function in WordPiece?

Ans- It merges the most promising token pairs in the splits based on computed scores to expand the vocabulary.

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187- How does WordPiece handle the training of its tokenization vocabulary?

Ans- The vocabulary is trained by iteratively merging the highest-scoring token pairs until the desired vocabulary size is reached.

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188- What does the encode_word function do in the context of WordPiece?

Ans- It tokenizes a new word by splitting it into subwords based on the learned vocabulary.

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189- Explain how WordPiece tokenization deals with handling unknown words.

Ans- It uses the [UNK] token for words that cannot be broken down into known subwords, ensuring the model can handle out-of-vocabulary words.

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190- Why is the scoring formula for token merging important in WordPiece?

Ans- The scoring formula balances the frequency of token pairs with their individual frequencies, allowing for more effective and meaningful token merges.

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191- How does WordPiece tokenization contribute to handling out-of-vocabulary words in NLP models?

Ans- By breaking words into smaller subwords, WordPiece ensures that even unseen words can be represented using known subword tokens, improving model robustness.

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192- What are the main advantages of using WordPiece tokenization over traditional word-level tokenization?

Ans- WordPiece provides better handling of rare and unknown words and reduces the vocabulary size needed compared to word-level tokenization, leading to more efficient and effective language models.

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193- What is Unigram tokenization?

Ans- Unigram tokenization is a method that treats each token as independent and selects the tokenization with the highest probability based on the token frequencies.

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194- How does Unigram tokenization differ from BPE or WordPiece?

Ans- Unlike BPE and WordPiece, which start with a minimal vocabulary and merge tokens, Unigram starts with a large vocabulary and prunes tokens to reach a desired size.

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195- What is the purpose of the Unigram model in tokenization?

Ans- The Unigram model determines the tokenization by maximizing the likelihood of a sequence based on independent token probabilities.

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196- How is the vocabulary initialized in Unigram tokenization?

Ans- The vocabulary is initialized with all basic characters and the most common substrings in the corpus.

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197- Describe the training process of Unigram tokenization.

Ans- The training process involves iteratively removing tokens from a large initial vocabulary based on their impact on the overall loss until the desired vocabulary size is achieved.

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198- What role does the loss function play in Unigram tokenization?

Ans- The loss function evaluates how well the current vocabulary tokenizes the corpus and guides the removal of less impactful tokens.

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199- How are tokens selected for removal during Unigram tokenization training?

Ans- Tokens are selected for removal based on how little their absence increases the overall loss, with the least impactful tokens removed first.

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200- Why is the Viterbi algorithm used in Unigram tokenization?

Ans- The Viterbi algorithm is used to find the most likely tokenization path by optimizing the token sequence's probability.

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201- How are token frequencies used in Unigram tokenization?

Ans- Token frequencies determine the probability of each token, which is used to compute the likelihood of different tokenizations.

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202- What happens to tokens that cannot be removed during the training process?

Ans- Base characters are never removed to ensure that any word can still be tokenized.

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203- Why is a large initial vocabulary used in Unigram tokenization?

Ans- A large initial vocabulary allows the model to explore various token combinations and gradually prune less useful tokens.

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204- How is the final vocabulary size determined in Unigram tokenization?

Ans- The final vocabulary size is reached by repeatedly pruning tokens until the desired size is met.

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205- Which models commonly use Unigram tokenization?

Ans- Models like ALBERT, T5, mBART, Big Bird, and XLNet commonly use Unigram tokenization via SentencePiece.

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206- Why is Unigram tokenization suitable for multilingual models?

Ans- Unigram tokenization is effective for multilingual models because it can handle a wide range of subword units and reduce vocabulary size without losing linguistic diversity.

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207- How does Unigram tokenization handle unknown words?

Ans- Unigram tokenization can handle unknown words by breaking them down into the smallest possible subwords based on the available vocabulary.

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208- What is normalization in tokenization?

Ans- Normalization involves cleaning up the text, such as removing spaces, accents, or performing Unicode normalization.

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209- Why is normalization important in building a tokenizer?

Ans- It ensures consistency in the text format, which helps improve the accuracy of tokenization and downstream tasks.

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210- What are some common normalization techniques?

Ans- Lowercasing, stripping accents, and handling control characters are common normalization techniques.

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211- What is pre-tokenization in the tokenization process?

Ans- Pre-tokenization is the step where the input text is split into smaller units like words or subwords before further processing.

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212- Why is pre-tokenization necessary?

Ans- Pre-tokenization helps break down the text into manageable pieces, making it easier for the tokenizer to handle and process the text.

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213- What are some methods used in pre-tokenization?

Ans- Methods include splitting by whitespace, punctuation, or using specific algorithms like WordPiece or Byte-Pair Encoding (BPE).

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214- What happens when you run input through the tokenization model?

Ans- The pre-tokenized words are converted into a sequence of tokens, which are numerical representations that the model can process.

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215- What are some common models used in tokenization?

Ans- BPE, WordPiece, and Unigram are common models used for tokenization.

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216- How is a tokenizer trained using a specific model?

Ans- A tokenizer is trained by feeding it a large corpus and adjusting its vocabulary based on the frequency of token occurrences.

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217- What is post-processing in the tokenization pipeline?

Ans- Post-processing involves adding special tokens, generating attention masks, and creating token type IDs to prepare the tokens for model input.

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218- Why is post-processing essential in tokenization?

Ans- It ensures that the tokenized output is formatted correctly for the specific model being used, including handling special cases like sentence pairs.

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219- What role do special tokens play in post-processing?

Ans- Special tokens like [CLS] and [SEP] mark the start and end of sentences, helping the model distinguish different parts of the input.

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220- What are the key steps in a tokenization pipeline?

Ans- The key steps are normalization, pre-tokenization, running input through the model, and post-processing.

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221- How can different components of the tokenization pipeline be customized?

Ans- Each component, like the normalizer or pre-tokenizer, can be customized by choosing different algorithms or combining multiple methods using sequences.

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222- What is the role of the Tokenizer class in the 🤗 Tokenizers library?

Ans- The Tokenizer class serves as the central object that ties together the different components of the tokenization process.

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223- Why is acquiring a corpus important for training a tokenizer?

Ans- A corpus provides the text data needed to train the tokenizer and build a vocabulary that represents the language effectively.

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224- How can you acquire a training corpus for a tokenizer?

Ans- A corpus can be acquired by loading datasets from libraries like datasets or by creating text files from existing datasets.

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225- What is a WordPiece tokenizer and where is it commonly used?

Ans- A WordPiece tokenizer breaks words into subwords based on frequency and is commonly used in models like BERT.

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226- How do you initialize a WordPiece tokenizer using the 🤗 Tokenizers library?

Ans- You initialize it by creating a Tokenizer object with the models.WordPiece model, specifying special tokens like [UNK].

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227- What is the purpose of the unk_token in a WordPiece tokenizer?

Ans- The unk_token is used to represent any characters or words that are not present in the tokenizer’s vocabulary.

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228- How do you set up normalization for a BERT tokenizer?

Ans- You can use normalizers.BertNormalizer or manually set up a sequence of normalizers like lowercasing and stripping accents.

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229- Why is the BertNormalizer often used for BERT tokenizers?

Ans- The BertNormalizer implements specific preprocessing steps like lowercasing and handling Chinese characters that are required for BERT models.

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230- How do you set up pre-tokenization for a BERT tokenizer?

Ans- Use the pre_tokenizers.BertPreTokenizer or define a custom pre-tokenizer like pre_tokenizers.Whitespace.

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231- What is the difference between Whitespace and WhitespaceSplit pre-tokenizers?

Ans- Whitespace splits on whitespace and punctuation, while WhitespaceSplit only splits on whitespace.

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232- How do you train a WordPiece tokenizer using a corpus?

Ans- Use a WordPieceTrainer to specify parameters like vocab_size and special_tokens, and then call train_from_iterator or train.

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233- Why is it important to include special tokens when training a tokenizer?

Ans- Special tokens are crucial for the model's operation, and if not included during training, they won't be recognized by the tokenizer.

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234- What is the role of a TemplateProcessor in post-processing?

Ans- The TemplateProcessor helps define how special tokens like [CLS] and [SEP] are added to the tokenized output, especially for sentence pairs.

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235- How do you set up a TemplateProcessor for a BERT tokenizer?

Ans- Define the template using special tokens and their corresponding token type IDs, and assign it to the tokenizer’s post-processor.

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236- What is the purpose of decoding in the tokenization process?

Ans- Decoding converts the token IDs back into human-readable text, allowing you to reconstruct the original input.

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237- How do you set up a decoder for a WordPiece tokenizer?

Ans- Use decoders.WordPiece with a prefix like "##" to merge subwords back into complete words during decoding.

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238- What is the significance of the prefix in a WordPiece decoder?

Ans- The prefix indicates that the token is part of a subword, and helps in correctly merging tokens during decoding.

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239- How can you save a trained tokenizer?

Ans- Save the tokenizer to a JSON file using the save method.

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240- How can you load a saved tokenizer for future use?

Ans- Load the tokenizer from a file using the from_file method.

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241- When should you train a new tokenizer?

Ans- When your dataset is different from the one used by an existing pretrained model, and you want to pretrain a new model

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242- What is the advantage of using a generator of lists of texts compared to a list of lists of texts when using train_new_from_iterator() ?

Ans- You will avoid loading the whole dataset into memory at once.

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243- What are the advantages of using a “fast” tokenizer?

Ans- - It can process inputs faster than a slow tokenizer when you batch lots of inputs together.
     - It has some additional features allowing you to map tokens to the span of text that created them.

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244- How does the token-classification pipeline handle entities that span over several tokens?

Ans- - There is a label for the beginning of an entity and a label for the continuation of an entity.
     - In a given word, as long as the first token has the label of the entity, the whole word is considered labeled with that entity.
     - When a token has the label of a given entity, any other following token with the same label is considered part of the same entity, unless it's labeled as the start of a new entity.

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245- How does the question-answering pipeline handle long contexts?

Ans- It splits the context into several parts (with overlap) and finds the maximum score for an answer in each part.

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246- What is normalization?

Ans- It's any cleanup the tokenizer performs on the texts in the initial stages.

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247- What is pre-tokenization for a subword tokenizer?

Ans- It's the step before the tokenizer model is applied, to split the input into words.

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248- Select the sentences that apply to the BPE model of tokenization.

Ans- - BPE is a subword tokenization algorithm that starts with a small vocabulary and learns merge rules.
     - BPE tokenizers learn merge rules by merging the pair of tokens that is the most frequent.
     - BPE tokenizes words into subwords by splitting them into characters and then applying the merge rules.

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249- Select the sentences that apply to the WordPiece model of tokenization.

Ans- - WordPiece is a subword tokenization algorithm that starts with a small vocabulary and learns merge rules.
     - A WordPiece tokenizer learns a merge rule by merging the pair of tokens that maximizes a score that privileges frequent pairs with less frequent individual parts.
     - WordPiece tokenizes words into subwords by finding the longest subword starting from the beginning that is in the vocabulary, then repeating the process for the rest of the text.

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250- Select the sentences that apply to the Unigram model of tokenization.

Ans- - Unigram is a subword tokenization algorithm that starts with a big vocabulary and progressively removes tokens from it.
     - Unigram adapts its vocabulary by minimizing a loss computed over the whole corpus.
     - Unigram tokenizes words into subwords by finding the most likely segmentation into tokens, according to the model.

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251- What is token classification?

Ans- Token classification involves attributing a label to each token in a sentence for tasks like NER, POS tagging, and chunking.

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252- What is Named Entity Recognition (NER)?

Ans- NER identifies entities like persons, locations, or organizations in a sentence by labeling each token.

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253- What is Part-of-Speech (POS) tagging?

Ans- POS tagging marks each word in a sentence with its corresponding part of speech, such as noun, verb, or adjective.

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254- What is chunking in token classification?

Ans- Chunking groups tokens that belong to the same entity, using labels like B- for the beginning and I- for inside a chunk.

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255- What dataset is commonly used for token classification tasks like NER?

Ans- The CoNLL-2003 dataset is commonly used for token classification tasks.

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256- How are texts in the CoNLL-2003 dataset structured for token classification?

Ans- Texts are pre-tokenized and presented as lists of words with corresponding labels for NER, POS, and chunking.

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257- What is the significance of the 'O' label in NER?

Ans- The 'O' label indicates that the word doesn't correspond to any entity.

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258- How are entities spanning multiple words labeled in NER?

Ans- The first word is labeled with a B- tag, and subsequent words are labeled with I- tags.

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259- What does the is_split_into_words flag do in tokenization?

Ans- It tells the tokenizer that the input is pre-tokenized into words, ensuring proper alignment of tokens with labels.

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260- How are special tokens like [CLS] and [SEP] handled in token classification?

Ans- Special tokens are assigned a label of -100, which is ignored in the loss calculation.

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261- Why might a tokenized word have more tokens than labels?

Ans- Words can be split into subwords during tokenization, resulting in more tokens than labels.

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262- What is the purpose of the align_labels_with_tokens function?

Ans- It adjusts the labels to match the tokenized input, ensuring proper alignment during training.

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263- Why might researchers assign -100 to subtokens within a word?

Ans- To prevent long words that split into many subtokens from disproportionately affecting the loss.

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264- What is fine-tuning in the context of masked language models?

Ans- Fine-tuning involves adapting a pre-trained language model on a specific dataset to improve performance on a particular task.

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265- Why is fine-tuning important for domain-specific tasks?

Ans- Fine-tuning allows the model to learn domain-specific vocabulary and patterns, leading to better performance on specialized tasks.

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266- What is masked language modeling (MLM)?

Ans- MLM is a pretraining task where certain words in a sentence are masked, and the model predicts the missing words based on context.

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267- What is the role of the Hugging Face Hub in fine-tuning?

Ans- The Hugging Face Hub provides pre-trained models that can be easily fine-tuned for various NLP tasks.

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268- Why would you choose DistilBERT for fine-tuning over BERT?

Ans- DistilBERT is smaller and faster than BERT, with similar performance, making it efficient for training on limited resources.

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269- What is the purpose of concatenating text examples during preprocessing?

Ans- Concatenating text helps in forming larger context windows, reducing the loss of information due to truncation.

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270- Why is domain adaptation useful in NLP?

Ans- Domain adaptation helps the model better understand and generate relevant content for specific domains, improving accuracy on downstream tasks.

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271- How does knowledge distillation contribute to training models like DistilBERT?

Ans- Knowledge distillation transfers knowledge from a larger teacher model to a smaller student model, retaining performance while reducing size.

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272- What is whole word masking in masked language modeling?

Ans- Whole word masking ensures that all subwords of a masked word are masked together, improving contextual predictions.

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273- How does tokenization affect the fine-tuning process?

Ans- Tokenization converts text into tokens that the model can process, affecting how well the model understands and predicts masked words.

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274- What is the importance of the attention_mask in masked language models?

Ans- The attention_mask indicates which tokens should be attended to during the model's forward pass, managing the focus on relevant parts of the input.

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275- What datasets are commonly used for fine-tuning masked language models?

Ans- Datasets like IMDb, for domain-specific tasks, or others available on Hugging Face's Datasets library are commonly used.

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276- How does fine-tuning affect the model's ability to autocomplete sentences?

Ans- Fine-tuning on relevant data improves the model’s predictions, making it better at autocompleting sentences in the target domain.

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277- What is the benefit of using a "fast" tokenizer in Hugging Face's Transformers?

Ans- A "fast" tokenizer speeds up the tokenization process and often provides additional features like word IDs for more precise fine-tuning.

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278- What is the significance of the AutoModelForMaskedLM class in Hugging Face?

Ans- AutoModelForMaskedLM is used to load pre-trained models specifically for masked language modeling tasks, facilitating easy fine-tuning.

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279- What is translation in NLP?

Ans- Translation is a sequence-to-sequence task where a model converts text from one language to another.

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280- How is translation related to summarization?

Ans- Both are sequence-to-sequence problems, where one sequence is transformed into another.

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281- What is style transfer in NLP?

Ans- Style transfer involves creating a model that translates text from one style to another, like formal to casual.

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282- What is generative question answering?

Ans- It’s a model that generates answers to questions based on a given context.

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283- How can you train a translation model from scratch?

Ans- By using a large corpus of text pairs in two languages.

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284- What is fine-tuning in the context of translation models?

Ans- Fine-tuning involves adapting a pre-trained translation model to a specific language pair or corpus.

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285- What is the KDE4 dataset?

Ans- KDE4 is a dataset of localized files for KDE applications, used for training translation models.

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286- How do you split a dataset for training and validation in NLP?

Ans- Use the train_test_split() method to divide the dataset into training and validation sets.

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287- What is the significance of train_test_split() in NLP datasets?

Ans- It splits the dataset into training and validation sets for reproducible results.

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288- What is the role of tokenization in translation models?

Ans- Tokenization converts text into tokens or IDs that the model can process.

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289- What is the purpose of the AutoTokenizer class in Hugging Face?

Ans- It’s used to load a pre-trained tokenizer that converts text into token IDs.

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290- What happens if you tokenize the target language with the wrong tokenizer?

Ans- It can result in incorrect tokenization, producing more tokens than expected.

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291- Why is it important to set text_targets in tokenization?

Ans- It ensures that the targets are tokenized using the correct language tokenizer.

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292- What is a Seq2SeqTrainer in Hugging Face?

Ans- It's a subclass of Trainer that handles sequence-to-sequence tasks like translation.

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293- What is the function of DataCollatorForSeq2Seq?

Ans- It dynamically pads inputs and labels, preparing them for sequence-to-sequence models.

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294- Why do we set padding labels to -100 in translation models?

Ans- To ensure that padded values are ignored during loss computation.

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295- What is the purpose of fine-tuning a Marian model?

Ans- To improve translation performance on a specific language pair or dataset.

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296- What is the significance of using train_test_split() with a seed?

Ans- It ensures reproducibility of the dataset splits.

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297- Why is it beneficial to use a pre-trained model like Marian for translation?

Ans- It speeds up training by leveraging knowledge from a large corpus of pre-trained data.

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298- What does max_length parameter control in tokenization?

Ans- It sets the maximum length of the tokenized sequences to prevent excessive length.

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299- What is text summarization?

Ans- Text summarization is the process of condensing long documents into concise summaries that capture the main points.

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300- Why is text summarization considered a challenging NLP task?

Ans- It requires understanding long passages and generating coherent text that reflects the document's main topics.

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301- What are the benefits of effective text summarization in business processes?

Ans- It speeds up processes by reducing the need for domain experts to read long documents in detail.

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302- What is unique about the summarization model described in this section?

Ans- It is a bilingual model designed to summarize texts in both English and Spanish.

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303- What corpus is used to train the bilingual summarizer?

Ans- The Multilingual Amazon Reviews Corpus.

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304- Why are the review titles used as target summaries in the training data?

Ans- They provide concise, user-generated summaries of the review content.

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305- What approach is suggested to manage large datasets for summarization?

Ans- Focus on a single domain, like book reviews, to make training manageable on limited hardware.

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306- What is the purpose of filtering the dataset for specific product categories?

Ans- To create a focused training dataset that is relevant to the summarization task.

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307- What function is used to combine English and Spanish datasets into a single dataset?

Ans- The concatenate_datasets() function from the 🤗 Datasets library.

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308- Why is it important to check the distribution of words in reviews and titles?

Ans- To avoid bias in the model, ensuring it generates more than just 1-2 word summaries.

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309- What is the advantage of using multilingual Transformer models for summarization?

Ans- They can handle multiple languages simultaneously, unlike monolingual models.

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310- How is text summarization similar to machine translation?

Ans- Both involve converting text into another form, either shorter or in another language, preserving meaning.

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311- What is the T5 architecture known for in NLP tasks?

Ans- Formulating all tasks in a text-to-text framework using prompt prefixes like "summarize:".

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312- What is mT5, and how does it differ from T5?

Ans- mT5 is a multilingual version of T5, trained on texts in over 50 languages without using prefixes.

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313- Why is it recommended to start with “small” models in NLP projects?

Ans- It allows faster debugging and iteration before scaling up to larger models.

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314- What type of tokenizer is used in mT5?

Ans- The SentencePiece tokenizer, which is based on the Unigram segmentation algorithm.

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315- Why is SentencePiece particularly useful for multilingual corpora?

Ans- It is agnostic about accents, punctuation, and non-whitespace languages like Japanese.

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316- What preprocessing step is critical for text summarization tasks?

Ans- Tokenizing and encoding both the input text and target summaries.

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317- What models are suggested for comparison in text summarization tasks?

Ans- mT5, mBART, and T5 for their multilingual and monolingual capabilities.

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318- What is the significance of using a “small” checkpoint like mt5-small?

Ans- It balances training time with performance, making it suitable for initial experiments.

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319- What is a causal language model?

Ans- A causal language model predicts the next token in a sequence based solely on the preceding tokens, often used for text generation.

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320- When should you consider training a language model from scratch?

Ans- When you have a large, domain-specific dataset that differs significantly from the data used to pretrain existing models.

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321- What are the key challenges in training a language model from scratch?

Ans- High computational cost, the need for large datasets, and ensuring proper preprocessing and tokenization.

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322- What is the role of tokenization in training a language model?

Ans- Tokenization converts raw text into numerical tokens, making it manageable for the model to learn patterns and context.

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323- Why would you filter a dataset before training a language model?

Ans- To focus on relevant data, reduce training time, and optimize the model for specific tasks.

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324- What is context length, and how does it impact language model training?

Ans- Context length is the number of tokens the model considers for prediction; longer contexts provide more information but require more memory.

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325- How does chunking work in language model training?

Ans- Chunking splits large texts into smaller pieces to fit within the model's context length, ensuring efficient use of data.

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326- What is the purpose of return_overflowing_tokens in tokenization?

Ans- It allows the model to handle inputs longer than the context length by splitting them into manageable chunks.

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327- How do you prepare a dataset for training a language model?

Ans- By tokenizing, chunking text into context-length segments, and filtering out incomplete chunks.

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328- What is the significance of the Dataset.map() function in Hugging Face Datasets?

Ans- It allows batch processing and transformation of dataset elements, such as tokenization or data augmentation.

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329- Why might you discard smaller chunks during tokenization?

Ans- To avoid padding and maintain consistent input lengths, improving model training efficiency.

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330- How does training a language model from scratch differ from fine-tuning a pretrained model?

Ans- Training from scratch requires more data and compute resources, whereas fine-tuning is faster and leverages existing knowledge.

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331- What are some real-world applications of causal language models?

Ans- Code autocompletion, text generation, and chatbot development.

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332- Why is it important to shuffle and split your dataset before training?

Ans- To ensure the model generalizes well and to prevent overfitting on a particular data sequence.

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333- What is the impact of using a smaller context length for training?

Ans- Faster training and reduced memory requirements but potentially less context for making predictions.

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334- What is extractive question answering?

Ans- It involves identifying answers as spans of text within a document.

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335- Which model is commonly fine-tuned for extractive question answering on the SQuAD dataset?

Ans- A BERT model is commonly fine-tuned for this task.

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336- What kind of questions are encoder-only models like BERT good at answering?

Ans- BERT is effective at answering factoid questions.

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337- What dataset is often used as a benchmark for extractive question answering?

Ans- The SQuAD dataset.

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338- How are answers stored in the SQuAD dataset?

Ans- Answers are stored in a dictionary with 'text' and 'answer_start' fields.

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339- What does the 'answer_start' field in the SQuAD dataset represent?

Ans- It represents the starting character index of the answer in the context.

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340- What is the significance of the Dataset.filter() method in processing the SQuAD dataset?

Ans- It helps in filtering data to ensure only one possible answer during training.

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341- How can you handle long contexts that exceed the maximum length during preprocessing?

Ans- By creating multiple training features using a sliding window approach.

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342- What is the purpose of setting return_overflowing_tokens=True in tokenization?

Ans- To get the overflowing tokens when contexts are too long.

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343- What are the labels for features where the answer is not included in the context?

Ans- The labels are set as start_position = end_position = 0.

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345- How do you map the start and end positions of an answer in the context to token indices?

Ans- By using offset mappings returned during tokenization.

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346- What method is used to determine the start and end of the context in input IDs?

Ans- The sequence_ids() method is used.

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347- What happens when the chunk of context in a feature starts after or ends before the answer?

Ans- The labels are set to (0, 0), indicating no answer is present in that chunk.

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350- Which of the following tasks can be framed as a token classification problem?

Ans- - Find the grammatical components in a sentence.
     - Find the persons mentioned in a sentence.

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351- What part of the preprocessing for token classification differs from the other preprocessing pipelines?

Ans- - The texts are given as words, so we only need to apply subword tokenization.
     - We need to make sure to truncate or pad the labels to the same size as the inputs, when applying truncation/padding.

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352- What problem arises when we tokenize the words in a token classification problem and want to label the tokens?

Ans- Each word can produce several tokens, so we end up with more tokens than we have labels.

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353- What does “domain adaptation” mean?

Ans- It's when we fine-tune a pretrained model on a new dataset, and it gives predictions that are more adapted to that dataset

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354- What are the labels in a masked language modeling problem?

Ans- Some of the tokens in the input sentence are randomly masked and the labels are the original input tokens.

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355- Which of these tasks can be seen as a sequence-to-sequence problem?

Ans- - Writing short reviews of long documents
     - Answering questions about a document
     - Translating a text in Chinese into English
     - Fixing the messages sent by my nephew/friend so they're in proper English

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356- What is the proper way to preprocess the data for a sequence-to-sequence problem?

Ans- The inputs have to be sent to the tokenizer, and the targets too, but under a special context manager.

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357- Why is there a specific subclass of Trainer for sequence-to-sequence problems?

Ans- Because sequence-to-sequence problems require a special evaluation loop

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358- When should you pretrain a new model?

Ans- - When there is no pretrained model available for your specific language
     - When you have concerns about the bias of the pretrained model you are using

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359- Why is it easy to pretrain a language model on lots and lots of texts?

Ans- Because the pretraining objective does not require humans to label the data

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360- What are the main challenges when preprocessing data for a question answering task?

Ans- - You need to deal with very long contexts, which give several training features that may or may not have the answer in them.
     - From the answer span in the text, you have to find the start and end token in the tokenized input.

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361- How is post-processing usually done in question answering?

Ans- The model gives you the start and end positions of the answer for each feature created by one example, and you just have to match them to the span in the context for the one that has the best score.

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362- What does the pipeline object from 🤗 Transformers do?

Ans- It simplifies the process of generating predictions by handling model loading, tokenization, and inference.

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363- What should be your first step when encountering an error during trainer.train()?

Ans- Manually inspect each step of the pipeline to identify where the error occurs.

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364- Why is it important to check your dataset before training?

Ans- Ensuring your dataset is correctly processed prevents errors during batching and training.

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365- What common mistake might occur when passing datasets to the Trainer?

Ans- Accidentally passing raw datasets instead of tokenized ones.

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366- How can you check if your input data is being processed correctly?

Ans- Decode the inputs to verify they match the expected format.

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367- What does it mean if you encounter a CUDA error that isn't an out-of-memory issue?

Ans- The error likely originates in the forward pass but manifests later due to GPU parallelism.

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368- Why should you debug CUDA errors on the CPU?

Ans- The CPU provides more helpful error messages and immediate feedback on issues.

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369- How can you prevent errors during the evaluation phase?

Ans- Run trainer.evaluate() independently before trainer.train() to catch potential issues early.

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370- What might cause an IndexError during loss computation?

Ans- Mismatch between the number of labels in your model and dataset.

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371- What should you do if you encounter a CUDA out-of-memory error?

Ans- Reduce the batch size or use a smaller model to fit within the GPU's memory.

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372- How can you manually test if the DataLoader is forming batches correctly?

Ans- Execute for batch in trainer.get_train_dataloader(): break to inspect the first batch.

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373- Why is it crucial to check the output of the DataCollatorWithPadding?

Ans- To ensure padding is correctly applied, matching the longest sequence in each batch.

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374- What should you verify if your model outputs unexpected logits during evaluation?

Ans- Ensure your compute_metrics() function is correctly processing the logits and labels.

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375- How can you identify whether the error is in the optimizer during training?

Ans- Isolate the optimizer step and check for errors by running trainer.create_optimizer() followed by trainer.optimizer.step().

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376- What role does the attention mask play in the training process?

Ans- It helps the model focus on the relevant parts of the input sequence, ensuring accurate training.

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377- How can data preprocessing mistakes impact model training?

Ans- Incorrect preprocessing can lead to feeding invalid inputs to the model, causing training errors.

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378- Why is overfitting to one batch usually a good debugging technique?

Ans- It allows us to verify that the model is able to reduce the loss to zero.

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