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Andras Schmelczer 2026-10-28 12:57:37 +00:00
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docs/examples/scibert/deploy.ipynb
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@ -272,7 +272,7 @@
"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](https://great-ai.scoutinscience.com/examples/scibert/additional-files)."
"To check out the Dockerimage, go to [the additional files page](https://great-ai.schmelczer.dev/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](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.
1. You will see how the [great_ai.utilities](https://great-ai.schmelczer.dev/reference/utilities) can integrate into your Data Science workflow.
2. You will see how [great_ai.large_file](https://great-ai.schmelczer.dev/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](https://great-ai.scoutinscience.com/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.schmelczer.dev/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.](https://great-ai.scoutinscience.com/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.schmelczer.dev/how-to-guides/use-service)
#### [Additional files](additional-files.md)

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docs/examples/scibert/train.ipynb
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@ -348,7 +348,7 @@
"id": "cell-8",
"metadata": {},
"source": [
"The [saved historical run](https://great-ai.scoutinscience.com/examples/scibert/validation-results.json) selected a checkpoint with **validation binary F1 of 0.8966** (recorded value: 0.896551724137931). This validation split was also used for early stopping and checkpoint selection, so the number is not an independent test result or macro F1. The historical split seed was not recorded; the seeded code above defines future runs and does not reproduce that split. Its results are saved to `models/validation_results.json` and `models/validation-run.json`.\n",
"The [saved historical run](https://great-ai.schmelczer.dev/examples/scibert/validation-results.json) selected a checkpoint with **validation binary F1 of 0.8966** (recorded value: 0.896551724137931). This validation split was also used for early stopping and checkpoint selection, so the number is not an independent test result or macro F1. The historical split seed was not recorded; the seeded code above defines future runs and does not reproduce that split. Its results are saved to `models/validation_results.json` and `models/validation-run.json`.\n",
"\n",
"To estimate performance after model selection, evaluate once on a separate untouched test split. We make no test-set performance claim here. Next, store the selected model for deployment."
]
@ -409,7 +409,7 @@
"id": "cell-10",
"metadata": {},
"source": [
"Next: [Part 3](https://great-ai.scoutinscience.com/examples/scibert/deploy)"
"Next: [Part 3](https://great-ai.schmelczer.dev/examples/scibert/deploy)"
]
}
],