chore: docs for eval #203
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chore: docs for eval #203
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mhordynski
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Nov 25, 2024
| metrics = [SomeMetric(OmegaConf.create({...})) for SomeMetric in your_metrics] | ||
| metric_set = MetricSet(*metrics) | ||
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| dataloader = YourDataLoaderClass(OmegaConf.create({...})) |
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The code in how-to's should be self-sufficient and ready to be copy-pasted and executed by yourself
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| To create a custom DataLoader for your specific needs, you need to implement the `load` method in a class that inherits from the `DataLoader` interface. | ||
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| Please find the [working example](optimize.md#define-the-data-loader) here. |
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I don't think linking to the optimizer is enough here, we should have a standalone example going in-detail about data loaders.
| To create a custom evaluation pipeline for your specific use case, you need to implement the `__call__` method as part of the `ragbits.evaluate.pipelines.base.EvaluationPipeline` interface. | ||
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| Please find the [working example](optimize.md#define-the-optimized-pipeline-structure) here |
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I don't think linking to the optimizer is enough here, we should have a standalone example going in-detail about pipelines.
| on your data, however you are not limited to this. In order to implement custom ones for your specific use case you would need to inherit from `ragbits.evaluate.metrics.base.Metric` | ||
| abstract class and implement `compute` method. | ||
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| Please find the [working example](optimize.md#define-the-metrics-and-run-the-experiment) here. |
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I don't think linking to the optimizer is enough here, we should have a standalone example going in-detail about metrics.
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| # How to create custom Metric for Ragbits evaluation | |||
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| `ragbits.evaluate` package provides the implementation of metrics that measure the quality of document search pipeline within `ragbits.evaluate.metrics.document_search` | |||
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I think we should have an index of all metrics predefined in ragbits, with each of them described.
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