# Cover

**Objective**: The test is intended to check what proportion of the words provided was covered by the model in the response

**Required Parameters**:

* **Response**: The sentence generated by the model.
* **Concept\_set**: A list of words in their root form, each tagged with its corresponding part of speech (e.g., "\_V" for verb, "\_N" for noun, "\_A" for adjective).

**Interpretation**: A higher score indicates that the model's response contains all concepts as specified in the concept set.

* **Example with Higher Score**:
  * Concept set: \["cat\_N", "sleep\_V", "windowsill\_N"]
  * Response: "A cat is sleeping on the windowsill." (All concepts are used in model response.)
* **Example with Lower Score**:
  * Concept set: \["sky\_N", "sun\_N", "windowsill\_N"]
  * Response: "The sky is blue and the sun is shining." (windowsill is not covered in response.)

**Note**: *Always use words in their root form in the concept set to focus on parts of speech application rather than word conjugation or inflection.*

```python
# Example with Higher Score

evaluator.add_test(
    test_names=["cover_test"],
    data={
        "concept_set" : ["cat_N", "sleep_V", "windowsill_N"],
        "response" : "A cat is sleeping on the windowsill."
    },
).run()

evaluator.print_results()
```

```python
# Example with Lower Score

evaluator.add_test(
    test_names=["cover_test"],
    data={
        "concept_set" : ["sky_N", "sun_N", "windowsill_N"],
        "response" : "The sky is blue and the sun is shining."
    },
).run()

evaluator.print_results()
```


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