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Jon Saad-FalconandClaude Opus 4.6
bd49383201
Evaluation framework (evals/): benchmarking system for measuring accuracy
across four categories — Chat (WildChat), Reasoning (SuperGPQA), RAG (FRAMES),
and Agentic (GAIA). Two backends: jarvis-direct (engine-level) and jarvis-agent
(agent-level with tool calling), both supporting local and cloud models.
Datasets adapted from IPW, scorers include exact match, LLM letter extraction,
and LLM-as-judge. Parallel execution via ThreadPoolExecutor with incremental
JSONL output. CLI: python -m evals {run,run-all,summarize,list}. 57 tests pass.
SVG fix: center logo content within viewBox by wrapping icon+text in a
translate(90,0) group, eliminating the left-shift visible in the README.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
37 lines
843 B
Python
37 lines
843 B
Python
"""Abstract base class for dataset providers."""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import Iterable, Optional
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from evals.core.types import EvalRecord
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class DatasetProvider(ABC):
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"""Base class for all evaluation dataset providers."""
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dataset_id: str
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dataset_name: str
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@abstractmethod
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def load(
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self,
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*,
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max_samples: Optional[int] = None,
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split: Optional[str] = None,
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seed: Optional[int] = None,
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) -> None:
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"""Load the dataset (possibly downloading from HuggingFace)."""
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@abstractmethod
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def iter_records(self) -> Iterable[EvalRecord]:
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"""Iterate over loaded records."""
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@abstractmethod
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def size(self) -> int:
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"""Return the number of loaded records."""
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__all__ = ["DatasetProvider"]
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