BERT
KEYS
DistilBertForTokenClassification
DistilBERT: is a smart tool that understands language well. It's like a super-smart assistant for understanding text.
ForTokenClassification: Now, imagine you're reading a paragraph, and you want to pick out specific words that are important, like names of people, places, or things. "ForTokenClassification" means DistilBERT is trained to do exactly that—spotting important words in the text.
"DistilBertForTokenClassification" is a specialized tool for identifying and labelling important words in the text. It helps users pinpoint key information quickly and efficiently.
AutoTokenizer
AutoTokenizer is a component used in natural language processing (NLP), It's like a wizard that automatically figures out how to chop up (or "tokenize") text into smaller parts.
Tokenization: This is the process of breaking down a piece of text into smaller chunks, like words or parts of words. For example, "Hello, how are you?" would be tokenized into ["Hello", ",", "how", "are", "you", "?"]
So, "AutoTokenizer" is like a helpful assistant that automatically picks the best way to break down text into smaller parts, making it easier to understand and work with.
Transformers: "Transformers" in the context of machine learning are smart algorithms designed to understand and work with text
AdamW: used in training machine learning models
Adam: Adam is an optimization algorithm used during the training of machine learning models. It's like a smart way to adjust the model's parameters (like weights and biases) so that it learns better from the data.
W: The "W" in "AdamW" likely indicates a modification or extension of the original Adam algorithm. These modifications are often made to improve the algorithm's performance or stability during training.
Torch: is a library
PyTorc: toolbox(teaching computers to learn from data)
With PyTorch, you can do things like:
Teach a computer to recognize pictures of cats and dogs.
Help a computer understand and generate human-like text.
Train a computer to play games or make decisions.
nn -the "nn" module in PyTorch is like a kit for building and training artificial intelligence models, making it easier for developers to create smart computer programs.
SGD
SGD stands for (Stochastic Gradient Descent), which is an optimization algorithm commonly used in machine learning for training models.
DataLoader-(A DataLoader is a component used to load and prepare data for training or inference)
Loading Data: A DataLoader is responsible for loading the dataset you want to train your model on. This dataset could consist of images, text, numerical data, or any other type of information your model needs to learn from.
Batching Data: Instead of loading the entire dataset at once, which may not fit into memory, a DataLoader loads the data in smaller chunks called "batches." Batching the data makes it easier to process and train models efficiently.
Shuffling Data: To prevent the model from memorizing the order of the data, which can lead to overfitting, a DataLoader often shuffles the data before loading it into batches. This randomizes the order of the data, ensuring that the model sees different samples during training.
Data Transformation: DataLoaders can also apply transformations to the data before loading it into batches. For example, they can resize images, normalize pixel values, or perform text preprocessing tasks.
Parallelism: In some cases, DataLoaders can load and preprocess data in parallel, taking advantage of multiple CPU cores or even distributed computing resources to speed up the data loading process.
accuracy_score,f1_score, precision_score, recall_score:
Accuracy Score: Measures the overall correctness of predictions.
Precision Score: Measures the accuracy of positive predictions.
Recall Score: Measures the model's ability to capture all positive instances.
F1 Score: The F1 score is a combination of precision and recall into a single metric.
Last updated