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https://github.com/filimoa/rd-tablebench


https://github.com/filimoa/rd-tablebench

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README

          

# RD Table Bench Invocation + Grading Code

This repo contains the code for invoking each provider and grading the results. This is a fork of Reducto's original repo https://github.com/reductoai/rd-tablebench with improved support for different LLM providers (OpenAI, Anthropic, Gemini).

The proprietary models that Reduco implemeted have not been touched and will not working with the grading cli.

## Installing Dependencies

```
pip install -r requirements.txt
```

## Downloading Data

https://huggingface.co/datasets/reducto/rd-tablebench/blob/main/README.md

## Env Vars

Create an `.env` file with the following:

```
# directory where the huggingface dataset is downloaded
INPUT_DIR=

# directory where the output will be saved
OUTPUT_DIR=

# note: only need keys for providers you want to use
OPENAI_API_KEY=
GEMINI_API_KEY=
ANTHROPIC_API_KEY=
...
```

## Parsing

`python -m providers.llm --model gemini-2.0-flash-exp --num-workers 10`

## Grading

`python -m grade_cli --model gemini-2.0-flash-exp`