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docs(weave): Move pairwise scoring workaround to FAQ (#3772)
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J2-D2-3PO authored Feb 27, 2025
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86 changes: 0 additions & 86 deletions docs/docs/guides/core-types/models.md
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Expand Up @@ -76,92 +76,6 @@ A `Model` is a combination of data (which can include configuration, trained mod
model.predict('world')
```

## Pairwise evaluation of models

When [scoring](../evaluation/scorers.md) models in a Weave [evaluation](../core-types/evaluations.md), absolute value metrics (e.g. `9/10` for Model A and `8/10` for Model B) are typically harder to assign than than relative ones (e.g. Model A performs better than Model B). _Pairwise evaluation_ allows you to compare the outputs of two models by ranking them relative to each other. This approach is particularly useful when you want to determine which model performs better for subjective tasks such as text generation, summarization, or question answering. With pairwise evaluation, you can obtain a relative preference ranking that reveals which model is best for specific inputs.

The following code sample demonstrates how to implement a pairwise evaluation in Weave by creating a [class-based scorer](../evaluation/scorers.md#class-based-scorers) called `PreferenceScorer`. The `PreferenceScorer` compares two models, `ModelA` and `ModelB`, and returns a relative score of the model outputs based on explicit hints in the input text.

```python
from weave import Model, Evaluation, Scorer, Dataset
from weave.flow.model import ApplyModelError, apply_model_async

class ModelA(Model):
@weave.op
def predict(self, input_text: str):
if "Prefer model A" in input_text:
return {"response": "This is a great answer from Model A"}
return {"response": "Meh, whatever"}

class ModelB(Model):
@weave.op
def predict(self, input_text: str):
if "Prefer model B" in input_text:
return {"response": "This is a thoughtful answer from Model B"}
return {"response": "I don't know"}

class PreferenceScorer(Scorer):
@weave.op
async def _get_other_model_output(self, example: dict) -> Any:
"""Get output from the other model for comparison.
Args:
example: The input example data to run through the other model
Returns:
The output from the other model
"""

other_model_result = await apply_model_async(
self.other_model,
example,
None,
)

if isinstance(other_model_result, ApplyModelError):
return None

return other_model_result.model_output

@weave.op
async def score(self, output: dict, input_text: str) -> dict:
"""Compare the output of the primary model with the other model.
Args:
output (dict): The output from the primary model.
other_output (dict): The output from the other model being compared.
inputs (str): The input text used to generate the outputs.
Returns:
dict: A flat dictionary containing the comparison result and reason.
"""
other_output = await self._get_other_model_output(
{"input_text": inputs}
)
if other_output is None:
return {"primary_is_better": False, "reason": "Other model failed"}

if "Prefer model A" in input_text:
primary_is_better = True
reason = "Model A gave a great answer"
else:
primary_is_better = False
reason = "Model B is preferred for this type of question"

return {"primary_is_better": primary_is_better, "reason": reason}

dataset = Dataset(
rows=[
{"input_text": "Prefer model A: Question 1"}, # Model A wins
{"input_text": "Prefer model A: Question 2"}, # Model A wins
{"input_text": "Prefer model B: Question 3"}, # Model B wins
{"input_text": "Prefer model B: Question 4"}, # Model B wins
]
)

model_a = ModelA()
model_b = ModelB()
pref_scorer = PreferenceScorer(other_model=model_b)
evaluation = Evaluation(dataset=dataset, scorers=[pref_scorer])
evaluation.evaluate(model_a)
```
</TabItem>
<TabItem value="typescript" label="TypeScript">
```plaintext
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89 changes: 89 additions & 0 deletions docs/docs/guides/tracking/faqs.md
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Expand Up @@ -51,3 +51,92 @@ When your program is exiting it may appear to pause while any remaining enqueued
## How is Weave data ingestion calculated?

We define ingested bytes as bytes that we receive, process, and store on your behalf. This includes trace metadata, LLM inputs/outputs, and any other information you explicitly log to Weave, but does not include communication overhead (e.g., HTTP headers) or any other data that is not placed in long-term storage. We count bytes as "ingested" only once at the time they are received and stored.

## What is pairwise evaluation and how do I do it?

When [scoring](../evaluation/scorers.md) models in a Weave [evaluation](../core-types/evaluations.md), absolute value metrics (e.g. `9/10` for Model A and `8/10` for Model B) are typically harder to assign than relative ones (e.g. Model A performs better than Model B). _Pairwise evaluation_ allows you to compare the outputs of two models by ranking them relative to each other. This approach is particularly useful when you want to determine which model performs better for subjective tasks such as text generation, summarization, or question answering. With pairwise evaluation, you can obtain a relative preference ranking that reveals which model is best for specific inputs.

:::important
This approach is a workaround and may change in future releases. We are actively working on a more robust API to support pairwise evaluations. Stay tuned for updates!
:::

The following code sample demonstrates how to implement a pairwise evaluation in Weave by creating a [class-based scorer](../evaluation/scorers.md#class-based-scorers) called `PreferenceScorer`. The `PreferenceScorer` compares two models, `ModelA` and `ModelB`, and returns a relative score of the model outputs based on explicit hints in the input text.

```python
from weave import Model, Evaluation, Scorer, Dataset
from weave.flow.model import ApplyModelError, apply_model_async

class ModelA(Model):
@weave.op
def predict(self, input_text: str):
if "Prefer model A" in input_text:
return {"response": "This is a great answer from Model A"}
return {"response": "Meh, whatever"}

class ModelB(Model):
@weave.op
def predict(self, input_text: str):
if "Prefer model B" in input_text:
return {"response": "This is a thoughtful answer from Model B"}
return {"response": "I don't know"}

class PreferenceScorer(Scorer):
@weave.op
async def _get_other_model_output(self, example: dict) -> Any:
"""Get output from the other model for comparison.
Args:
example: The input example data to run through the other model
Returns:
The output from the other model
"""

other_model_result = await apply_model_async(
self.other_model,
example,
None,
)

if isinstance(other_model_result, ApplyModelError):
return None

return other_model_result.model_output

@weave.op
async def score(self, output: dict, input_text: str) -> dict:
"""Compare the output of the primary model with the other model.
Args:
output (dict): The output from the primary model.
input_text (str): The input text used to generate the outputs.
Returns:
dict: A flat dictionary containing the comparison result and reason.
"""
other_output = await self._get_other_model_output(
{"input_text": input_text}
)
if other_output is None:
return {"primary_is_better": False, "reason": "Other model failed"}

if "Prefer model A" in input_text:
primary_is_better = True
reason = "Model A gave a great answer"
else:
primary_is_better = False
reason = "Model B is preferred for this type of question"

return {"primary_is_better": primary_is_better, "reason": reason}

dataset = Dataset(
rows=[
{"input_text": "Prefer model A: Question 1"}, # Model A wins
{"input_text": "Prefer model A: Question 2"}, # Model A wins
{"input_text": "Prefer model B: Question 3"}, # Model B wins
{"input_text": "Prefer model B: Question 4"}, # Model B wins
]
)

model_a = ModelA()
model_b = ModelB()
pref_scorer = PreferenceScorer(other_model=model_b)
evaluation = Evaluation(dataset=dataset, scorers=[pref_scorer])
evaluation.evaluate(model_a)
```

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