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feat(benchmarks): benchmark-backed model-card skeleton (#194) (#216)
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feat(benchmarks): benchmark-backed model-card skeleton (#194)
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feat(benchmarks): populate the registry from the Group E harness run
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feat: batched inference for generative judges (#227) (#238)
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feat: batched inference for generative judges (#227)
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feat: add AnyGuardrail.evaluate() dispatcher for uniform judge invoca…
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feat: add AnyGuardrail.evaluate() dispatcher for uniform judge invocation (#230)
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feat: add AnyGuardrail.evaluate() dispatcher for uniform judge invocation (#230)
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fix(glider): honor the provider argument instead of building a privat…
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fix: Glider honors its provider argument instead of building a private pipeline (#226)
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fix: Glider honors its provider argument instead of building a private pipeline (#226)
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fix: correct output_shapes SCORE accuracy drift (#225) (#232)
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fix: correct output_shapes SCORE accuracy drift (#225)
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fix: packaging & discovery robustness (py.typed, broken pangolin vari…
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fix: packaging & discovery robustness (py.typed, broken pangolin variant, get_all_supported_models)
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fix: correct output_shapes SCORE accuracy drift (#225)
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fix: packaging & discovery robustness (py.typed, broken pangolin variant, get_all_supported_models)
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fix: gli_guard and wild_guard both work regardless of transformers ma…
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fix: gli_guard and wild_guard both work regardless of transformers major version
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