# HellaSwag

[HellaSwag](https://arxiv.org/pdf/1905.07830) is a dataset for commonsense inference. The model is prompted with a sentence and the model is tasked to pick the sentence choice that is the best suited continuation.

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Contributed by [@jjallaire](https://github.com/jjallaire)
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## Usage

First, install the `inspect_ai` and `inspect_evals` Python packages with:

```bash
pip install inspect_ai
pip install git+https://github.com/UKGovernmentBEIS/inspect_evals
```

Or, if developing on a clone of the `inspect_evals` repo, you can install the package in editable mode with:

```bash
pip install -e ".[dev]"
```

Then, evaluate against one or more models with:

```bash
inspect eval inspect_evals/hellaswag --model openai/gpt-4o
```

After running evaluations, you can view their logs using the `inspect view` command:

```bash
inspect view
```

If you don't want to specify the `--model` each time you run an evaluation, create a `.env` configuration file in your working directory that defines the `INSPECT_EVAL_MODEL` environment variable along with your API key. For example:

```bash
INSPECT_EVAL_MODEL=anthropic/claude-3-5-sonnet-20240620
ANTHROPIC_API_KEY=<anthropic-api-key>
```
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## Options

You can control a variety of options from the command line. For example:

```bash
inspect eval inspect_evals/hellaswag --limit 10
inspect eval inspect_evals/hellaswag --max-connections 10
inspect eval inspect_evals/hellaswag --temperature 0.5
```

See `inspect eval --help` for all available options.
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## Dataset
Here is an example prompt from the dataset (after it has been further processed by Inspect):

>Choose the most plausible continuation for the story.
>
>Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of A,B,C,D.
>
>A man is sitting on a roof. he
>
>A) is using wrap to wrap a pair of skis.  
>B) is ripping level tiles off.  
>C) is holding a rubik's cube.  
>D) starts pulling up roofing on a roof.  

The model is then expected to generate reasoning steps and provide a final answer.

## Scoring
An accuracy is calculated over the datapoints.
