---
title: DmxMetric
emoji: 🌖
colorFrom: purple
colorTo: pink
sdk: gradio
sdk_version: 4.41.0
app_file: app.py
pinned: false
license: apache-2.0
tags:
- evaluate
- metric
description: >-
  Evaluation function using lm-eval with d-Matrix integration.
  This function allows for the evaluation of language models across various tasks, 
  with the option to use d-Matrix compressed models. For more information, see
  https://github.com/EleutherAI/lm-evaluation-harness and https://github.com/d-matrix-ai/dmx-compressor
---
# Metric Card for dmxMetric

## How to Use
```python
>>>import evaluate
>>>metric = evaluate.load("d-matrix/dmxMetric", module_type="metric")
>>>results = metric._compute(model="d-matrix/gpt2",revision="distilgpt2",tasks="wikitext",dmx_config="BASIC" )
>>>print(results)
```

### Inputs
- **model** (`str`): The name or path of the model to evaluate.
- **tasks** (`Union[str, List[str]]`): The task or list of tasks to evaluate on.
- **dmx_config** (`Optional[str]`): Configuration string for d-Matrix transformations, defaults to None.
- **num_fewshot** (`Optional[int]`): Number of examples in few-shot context, defaults to None.
- **batch_size** (`Optional[Union[int, str]]`): Batch size for evaluation, defaults to None.
- **max_batch_size** (`Optional[int]`): Maximum batch size to try with automatic batch size detection, defaults to None.
- **limit** (`Optional[Union[int, float]]`): Limit the number of examples per task, defaults to None.
- **device** (`Optional[str]`): Device to run on. If None, defaults to 'cuda' if available, otherwise 'cpu'.
- **revision** (`str`): Model revision to use, defaults to 'main'.
- **trust_remote_code** (`bool`): Whether to trust remote code, defaults to False.
- **log_samples** (`bool`): If True, logs all model outputs and documents, defaults to True.
- **verbosity** (`str`): Logging verbosity level, defaults to 'INFO'.
- **kwargs**: Additional keyword arguments to pass to `lm_eval.evaluate`.

### Output Values
- **results** (`dict`): A dictionary containing the evaluation results for each task.

Output Example:
```python
{
    'wikitext': {
        'alias': 'wikitext',
        'word_perplexity,none': 56.66175009356436,
        'word_perplexity_stderr,none': 'N/A',
        'byte_perplexity,none': 2.127521665015424,
        'byte_perplexity_stderr,none': 'N/A',
        'bits_per_byte,none': 1.0891738232631387,
        'bits_per_byte_stderr,none': 'N/A'
    }
}
```

This metric outputs a dictionary containing the evaluation results for each task. In this example, the results are shown for the 'wikitext' task. The output includes various perplexity and bits-per-byte metrics, along with their standard errors (where available). The specific metrics may include:

- `alias`: The name or alias of the task.
- `word_perplexity,none`: The perplexity calculated on a word level.
- `word_perplexity_stderr,none`: The standard error of the word perplexity (if available).
- `byte_perplexity,none`: The perplexity calculated on a byte level.
- `byte_perplexity_stderr,none`: The standard error of the byte perplexity (if available).
- `bits_per_byte,none`: The average number of bits required to encode each byte of the text.
- `bits_per_byte_stderr,none`: The standard error of the bits per byte metric (if available).

Note that 'N/A' values indicate that the standard error was not calculated or not available for that metric.

## Citation(s)
https://github.com/EleutherAI/lm-evaluation-harness