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Make documentation links usable from repository previews

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Andras Schmelczer 2026-10-27 21:59:39 +00:00
commit e446d95855
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4
docs/examples/scibert/data.ipynb
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@ -6,7 +6,7 @@
"source": [
"# Explore data and feasibility of approach\n",
"\n",
"![screenshot fo the annotator tool](/media/annotator.png)\n",
"![screenshot fo the annotator tool](https://great-ai.scoutinscience.com/media/annotator.png)\n",
"\n",
"We had asked our clients and in-house experts to annotate sentences using a rigorous guideline. The aim is to decide on which sentences they would like to see in a summary for a paper.\n",
"\n",
@ -276,7 +276,7 @@
"source": [
"We get an F1-score of 0.78 without any hyperparameter-optimisation; this task may be feasible to solve with AI.\n",
"\n",
"Next: [Part 2](/examples/scibert/train)"
"Next: [Part 2](https://great-ai.scoutinscience.com/examples/scibert/train)"
]
}
],

2
docs/examples/scibert/deploy.ipynb
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@ -290,7 +290,7 @@
"metadata": {},
"source": [
"In this case, the service is built as a docker image, pushed to our image registry and subsequent rolling update is performed in the production cluster.\n",
"To check out the Dockerimage, go to [the additional files page](/examples/scibert/additional-files)."
"To check out the Dockerimage, go to [the additional files page](https://great-ai.scoutinscience.com/examples/scibert/additional-files)."
]
}
],

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docs/examples/scibert/index.md
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@ -7,8 +7,8 @@ This is a simplified example illustrating how `great-ai` is used in practice at
## Objectives
1. You will see how the [great_ai.utilities](/reference/utilities) can integrate into your Data Science workflow.
2. You will see how [great_ai.large_file](/reference/large-file) can be used to version and store your trained model.
1. You will see how the [great_ai.utilities](https://great-ai.scoutinscience.com/reference/utilities) can integrate into your Data Science workflow.
2. You will see how [great_ai.large_file](https://great-ai.scoutinscience.com/reference/large-file) can be used to version and store your trained model.
3. You will see how [GreatAI][great_ai.GreatAI] should be used to prepare your model for a robust and responsible deployment.
4. You will see multiple ways of customising your deployment.
@ -37,13 +37,13 @@ We load and analyse the data by calculating inter-rater reliability and checking
We simply fine-tune SciBERT.
After training and evaluating a model, it is exported using [great_ai.save_model][]. For more info, check out [the configuration how-to page](/how-to-guides/configure-service).
After training and evaluating a model, it is exported using [great_ai.save_model][]. For more info, check out [the configuration how-to page](https://great-ai.scoutinscience.com/how-to-guides/configure-service).
### [Deployment notebook](deploy.ipynb)
We customise the GreatAI configuration, create custom caching for the model and implement an inference function that can be hardened by wrapping it in a [GreatAI][great_ai.GreatAI] instance. We also extract the attention weights as a quasi-explanation.
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.](https://great-ai.scoutinscience.com/how-to-guides/use-service)
#### [Additional files](additional-files.md)

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docs/examples/scibert/train.ipynb
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@ -341,7 +341,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Next: [Part 3](/examples/scibert/deploy)"
"Next: [Part 3](https://great-ai.scoutinscience.com/examples/scibert/deploy)"
]
}
],