# Negative sentiment example
score = metric.score(output="This is terrible, I hate it.")
print(score.value)  # Negative compound score (e.g., -0.7650)
```

The metric returns:

* `value`: The compound sentiment score (-1.0 to 1.0)
* `metadata`: Dictionary containing all sentiment scores:
  * `pos`: Positive sentiment (0.0-1.0)
  * `neu`: Neutral sentiment (0.0-1.0)
  * `neg`: Negative sentiment (0.0-1.0)
  * `compound`: Normalized compound score (-1.0 to 1.0)

The compound score is a normalized score between -1.0 (extremely negative) and 1.0 (extremely positive), with scores:

* ≥ 0.05: Positive sentiment
* > -0.05 and \< 0.05: Neutral sentiment
* ≤ -0.05: Negative sentiment

### ROUGE

The [ROUGE (Recall-Oriented Understudy for Gisting Evaluation)](https://en.wikipedia.org/wiki/ROUGE_\(metric\)) metrics estimate how close the LLM outputs are to one or more reference summaries, commonly used for evaluating summarization and text generation tasks. It measures the overlap between an output string and a reference string, with support for multiple ROUGE types. This metrics is a wrapper around the Google Research reimplementation of ROUGE, which is based on the `rouge-score` library. You will need rouge-score library:

```bash
pip install rouge-score
```

It can be used in a following way:

```python
from opik.evaluation.metrics import ROUGE

metric = ROUGE()