I chose DistilBERT base uncased distilled SQuAD as my Huggingface model. DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained with three objectives:
i. Distillation loss: the model was trained to return the same probabilities as the BERT base model.
ii. Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
iii. Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base model.
This way, the model learns the same inner representation of the English language than its teacher model, while being faster for inference or downstream tasks.
i. Install Sagemaker SDK, Transformers in Python3
ii. Install necessary Libraries
iii. Create SageMaker endpoint with the chosen model
iv. Create Hugging Face Model Class
v. Deploy model to SageMaker Inference
vi. Test with Sample Input
vii. Run Predictor
i. Create Dockerfile
ii. Pull image from Dockerhub
iii. Set nginx- server components to support multiple parallel incoming requests
iv. Install necessary dependencies for SageMaker Inference Toolkit
v. Install pip requirements
vi. Install Hugging Face libraries and its dependencies
vii. Copy entrypoint script to the image
viii. Copy the default custom service file to handle incoming data and inference requests
ix. Build Docker Image
x. Define an entrypoint script for the docker image
xi. Define command to be passed to the entrypoint
xii. Run Inference and Check Docker Image in 'Docker Desktop'
Review : [Bad : Good]
Baby training set: [[0.4980342 0.5019658]]
Amazon Instant Video training set: [[0.77906497 0.22093503]]
Video Games training set: [[0.32653528 0.67346472]]
Categories: ['rec.sport.baseball' 'rec.sport.hockey']
Corresponding labels: [0 1]
Naive Bayes Model Score: 0.9723618090452262
In this project, we will use an SVM trained using a baseball dataset to find the decision boundary of the strike zone. The strike zone can be thought of as a decision boundary that determines whether or not a pitch is a strike or a ball. There is a strict definition of the strike zone — in practice, however, it will vary depending on the umpire or the player at bat.
Baseball strike zones for Aaron Judge.
Optimize Hyperparameters "gamma" and "C".
Best SVM Score: 0.982 @ gamma=160, C=140.