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@ -5,14 +5,14 @@ Let's see `great-ai` in action by going over the life-cycle of a simple service.
## Objectives
1. You will see how the [great_ai.utilities](/reference/utilities) can integrate into your Data Science workflow.
2. You will use [great_ai.large_file](/reference/large_file) to version and store your trained model.
2. You will use [great_ai.large_file](/reference/large-file) to version and store your trained model.
3. You will use [GreatAI][great_ai.GreatAI] to prepare your model for a robust and responsible deployment.
## Overview
You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the [SciBERT paper](https://arxiv.org/abs/1903.10676) in which SciBERT [achieved an F1-score of 0.6571](https://paperswithcode.com/sota/sentence-classification-on-paper-field). We are going to outperform it using a trivial text classification model: a [Linear SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html).
You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the [SciBERT paper](https://arxiv.org/abs/1903.10676){ target=_blank } in which SciBERT [achieved an F1-score of 0.6571](https://paperswithcode.com/sota/sentence-classification-on-paper-field){ target=_blank }. We are going to outperform it using a trivial text classification model: a [Linear SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html){ target=_blank }.
We use the same synthetic dataset derived from the [Microsoft Academic Graph](https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/). The dataset is [available here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag).
We use the same synthetic dataset derived from the [Microsoft Academic Graph](https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/){ target=_blank }. The dataset is [available here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag){ target=_blank }.
!!! success
You are ready to start the tutorial. Feel free to come back to the [summary](#summary) section once you're finished.
@ -70,7 +70,7 @@ def predict_domain(sentence, model):
1. [@use_model][great_ai.use_model] loads and injects your model into the `predict_domain` function's `model` argument.
You can freely reference it knowing that the function is always provided with it.
Finally, we test the model's inference function through the GreatAI dashboard. [The only thing left is to deploy the hardened-service properly.](/how-to-guides/use-service)
Finally, we test the model's inference function through the GreatAI dashboard. [The only thing left is to deploy the hardened service properly.](/how-to-guides/use-service)
<div style="display: flex; justify-content: space-evenly;" markdown>
[:material-book: Learn about all the features](/how-to-guides/create-service){ .md-button .md-button--primary }