Update README.md

README.md

@@ -38,14 +38,14 @@ Ultimately, these pipelines were used to identiry the relevant comic universe fr
 
 ## Results (Val/Test)
 - Test:
-  - Zero-shot: acc 0.0, f1 0.0, cm [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], ~216.700s/ex
-  - One-shot: acc 0.84, f1 0.89605, cm [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 54, 0, 0, 1, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 29, 7, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 1, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], ~129.000s/ex
-  - 5-shot:   acc 0.85, f1 0.90739, cm [[0, 0, 0, 0, 0, 0], [0, 56, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0], [0, 0, 0, 27, 13, 0], [0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 1]], ~156.470s/ex
+  - Zero-shot: acc 0.0, f1 0.0, ~216.700s/ex
+  - One-shot: acc 0.84, f1 0.89605, ~129.000s/ex
+  - 5-shot:   acc 0.85, f1 0.90739, ~156.470s/ex
 
 ## Tradeoffs
-- Zero-shot: lowest cost/latency (no context tokens from examples), lowest expected quality in many tasks.
-- One-shot (adaptive): small added context cost but potentially large quality boost if the nearest example is highly relevant. Additional latency is minimal (one extra example tokens).
-- Few-shot (K=5): higher context token cost and latency; can substantially improve quality for tasks where the model benefits from examples. Cost scales roughly linearly with K for token-based APIs; latency also increases with prompt size and with model call throughput limits.
+- Zero-shot: No context tokens from examples, lowest expected quality. Since this is a multi-class dataset (not binary), the zero-shot is unsuitable for this model. 
+- One-shot (adaptive): small added context cost but potentially large quality boost if the nearest example is highly relevant. Less latency than one-shot as examples help minimize potential classes (one extra example tokens).
+- Few-shot (K=5): higher context token cost and latency; substantially improvse quality for this task as the model benefits from examples to define classes. Cost scales roughly linearly with K for token-based APIs; latency also increases with prompt size and with model call throughput limits.
 - Selection cost: computing TF-IDF on the training set is cheap and local while retaining reproducability (consistent example selection).
 
 ## Limits & Risks