Threshold Met Message
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Threshold Met Message has 4 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
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raw canonical shape-checked rule-derived certifiedInbound mentions (1)
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hasPrintStatementHas Print Statement(1)
- Implementation
ex:implementation
Other facts (4)
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| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Success Message | [1] |
| Rdf:type | Output Message | [2] |
| Has Text | Recall threshold met | [1] |
| Content | Recall threshold met | [2] |
Timeline
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References (2)
ctx:claims/beam/5e4120cd-154f-4526-806b-66e6ad6a75b5- full textbeam-chunktext/plain1 KB
doc:beam/5e4120cd-154f-4526-806b-66e6ad6a75b5Show excerpt
[Turn 1166] User: I'm working on a proof of concept for testing 2 retrieval tools on 400 documents, and I want to achieve 90% recall, but I'm having trouble with the implementation, can someone help me with this? ```python import numpy as …
ctx:claims/beam/eb7f55ff-6715-4dd8-81f8-023b5f9693f2- full textbeam-chunktext/plain1 KB
doc:beam/eb7f55ff-6715-4dd8-81f8-023b5f9693f2Show excerpt
retrieved_labels = relevant_labels[retrieved_indices] true_positives = np.sum(retrieved_labels) recall = true_positives / num_relevant return recall # Initialize the recall scores recall_scores = [] for tool in tools: …
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