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Transformers from the Ground Up - Sebastian Raschka | PyData Jeddah
Discover the power of transformers for natural language processing, including pre-training, fine-tuning, and real-world applications from chatbots to machine translation.
- Transformers are a kind of neural network architecture that can be very powerful for certain tasks, but they are also resource-intensive and may not be feasible for all use cases.
- Pre-training is the process of training a neural network on a large corpus of text to generate a generalized language model, which can then be fine-tuned for specific tasks.
- BERT is a popular pre-trained language model that can perform a variety of tasks, including classification and generation, and has been fine-tuned for many specific use cases.
- Fine-tuning is the process of training a pre-trained model on a small dataset of labeled data to adapt it to a specific task.
- Self-attention is a key mechanism in transformers that allows the model to focus on specific parts of the input sequence when generating the output.
- Multi-head attention is an extension of self-attention that allows the model to learn multiple simultaneous representations of the input sequence.
- GPT and GPT-3 are pre-trained language models that can generate text and have performed well on many language generation tasks.
- Transformers can be used for classification, but they are more commonly used for generation tasks such as language translation and text summarization.
- The attention mask is a mechanism used in transformers to handle variable-length input sequences and ensure that the model only attends to relevant parts of the input.
- Few-shot learning is a technique that allows a model to learn a new task with a small number of examples, which is useful for tasks where there is limited labeled data.
- Transformers have many applications, including text generation, machine translation, and text classification, and can be used in a variety of domains such as customer service chatbots and natural language processing.
- There are many libraries available for working with transformers, including PyTorch and TensorFlow, and many pre-trained models are available for fine-tuning.