# Model's Output[[lighteval.models.model_output.ModelResponse]]

All models will generate an ouput per Doc supplied to the `generation` or `loglikelihood` fuctions.

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<docstring><name>class lighteval.models.model_output.ModelResponse</name><anchor>lighteval.models.model_output.ModelResponse</anchor><source>https://github.com/huggingface/lighteval/blob/vr_1037/src/lighteval/models/model_output.py#L29</source><parameters>[{"name": "input", "val": ": str | list | None = None"}, {"name": "input_tokens", "val": ": list = <factory>"}, {"name": "text", "val": ": list = <factory>"}, {"name": "output_tokens", "val": ": list = <factory>"}, {"name": "text_post_processed", "val": ": list[str] | None = None"}, {"name": "reasonings", "val": ": list = <factory>"}, {"name": "logprobs", "val": ": list = <factory>"}, {"name": "argmax_logits_eq_gold", "val": ": list = <factory>"}, {"name": "logits", "val": ": list[list[float]] | None = None"}, {"name": "unconditioned_logprobs", "val": ": list[float] | None = None"}, {"name": "truncated_tokens_count", "val": ": int = 0"}, {"name": "padded_tokens_count", "val": ": int = 0"}]</parameters><paramsdesc>- **input** (str | list | None) --
  The original input prompt or context that was fed to the model.
  Used for debugging and analysis purposes.

- **input_tokens** (list[int]) --
  The tokenized representation of the input prompt.
  Useful for understanding how the model processes the input.

- **text** (list[str]) --
  The generated text responses from the model. Each element represents
  one generation (useful when num_samples > 1).
  **Required for**: Generative metrics, exact match, llm as a judge, etc.

- **text_post_processed** (Optional[list[str]]) --
  The generated text responses from the model, but post processed.
  Atm, post processing removes thinking/reasoning steps.

  Careful! This is not computed by default, but in a separate step by calling
  `post_process` on the ModelResponse object.
  **Required for**: Generative metrics that require direct answers.

- **logprobs** (list[float]) --
  Log probabilities of the generated tokens or sequences.
  **Required for**: loglikelihood and perplexity metrics.

- **argmax_logits_eq_gold** (list[bool]) --
  Whether the argmax logits match the gold/expected text.
  Used for accuracy calculations in multiple choice and classification tasks.
  **Required for**: certain loglikelihood metrics.


- **unconditioned_logprobs** (Optional[list[float]]) --
  Log probabilities from an unconditioned model (e.g., without context).
  Used for PMI (Pointwise Mutual Information) normalization.
  **Required for**: PMI metrics.</paramsdesc><paramgroups>0</paramgroups></docstring>
A class to represent the response from a model during evaluation.

This dataclass contains all the information returned by a model during inference,
including generated text, log probabilities, token information, and metadata.
Different attributes are required for different types of evaluation metrics.



Usage Examples:

**For generative tasks (text completion, summarization):**
<ExampleCodeBlock anchor="lighteval.models.model_output.ModelResponse.example">

```python
response = ModelResponse(
    text=["The capital of France is Paris."],
    input_tokens=[1, 2, 3, 4],
    output_tokens=[[5, 6, 7, 8]]
)
```

</ExampleCodeBlock>

**For multiple choice tasks:**
<ExampleCodeBlock anchor="lighteval.models.model_output.ModelResponse.example-2">

```python
response = ModelResponse(
    logprobs=[-0.5, -1.2, -2.1, -1.8],  # Logprobs for each choice
    argmax_logits_eq_gold=[False, False, False, False],  # Whether correct choice was selected
    input_tokens=[1, 2, 3, 4],
    output_tokens=[[5], [6], [7], [8]]
)
```

</ExampleCodeBlock>

**For perplexity calculation:**
<ExampleCodeBlock anchor="lighteval.models.model_output.ModelResponse.example-3">

```python
response = ModelResponse(
    text=["The model generated this text."],
    logprobs=[-1.2, -0.8, -1.5, -0.9, -1.1],  # Logprobs for each token
    input_tokens=[1, 2, 3, 4, 5],
    output_tokens=[[6], [7], [8], [9], [10]]
)
```

</ExampleCodeBlock>

**For PMI analysis:**
<ExampleCodeBlock anchor="lighteval.models.model_output.ModelResponse.example-4">

```python
response = ModelResponse(
    text=["The answer is 42."],
    logprobs=[-1.1, -0.9, -1.3, -0.7],  # Conditioned logprobs
    unconditioned_logprobs=[-2.1, -1.8, -2.3, -1.5],  # Unconditioned logprobs
    input_tokens=[1, 2, 3, 4],
    output_tokens=[[5], [6], [7], [8]]
)
```

</ExampleCodeBlock>

Notes:
- For most evaluation tasks, only a subset of attributes is required
- The `text` attribute is the most commonly used for generative tasks
- `logprobs` are essential for probability-based metrics like perplexity


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