Chore: Add kwargs to docs (#1365)

* chore: add kwargs to objects docs
wandb · Mar 26, 2024 · c9be0a7 · c9be0a7
1 parent 50611c0
commit c9be0a7
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Showing 4 changed files with 7 additions and 7 deletions.
diff --git a/docs/docs/guides/core-types/datasets.md b/docs/docs/guides/core-types/datasets.md
@@ -22,14 +22,14 @@ from weave import Dataset
 weave.init('intro-example')
 
 # Create a dataset
-dataset = Dataset([
+dataset = Dataset(name='grammar', rows=[
     {'id': '0', 'sentence': "He no likes ice cream.", 'correction': "He doesn't like ice cream."},
     {'id': '1', 'sentence': "She goed to the store.", 'correction': "She went to the store."},
     {'id': '2', 'sentence': "They plays video games all day.", 'correction': "They play video games all day."}
 ])
 
 # Publish the dataset
-weave.publish(dataset, 'grammar')
+weave.publish(dataset)
 
 # Retrieve the dataset
 dataset_ref = weave.ref('grammar').get()

diff --git a/docs/docs/guides/core-types/evaluations.md b/docs/docs/guides/core-types/evaluations.md
@@ -11,7 +11,7 @@ Evaluation-driven development helps you reliably iterate on an application. The
 from weave import Evaluation
 
 evaluation = Evaluation(
-    dataset, scores=[score]
+    dataset=dataset, scorers=[score]
 )
 evaluation.evaluate(model)
 ```
@@ -28,4 +28,4 @@ Then, define a list of scoring functions. Each function should take an example a
 
 Finally, create a model and pass this to `evaluation.evaluate`, which will run `predict` on each example and score the output with each scoring function.
 
-To see this in action, follow the '[Build an Evaluation pipeline](/tutorial-eval)' tutorial.
+To see this in action, follow the '[Build an Evaluation pipeline](/tutorial-eval)' tutorial.
diff --git a/docs/docs/guides/core-types/models.md b/docs/docs/guides/core-types/models.md
@@ -32,7 +32,7 @@ You can call the model as usual with:
 import weave
 weave.init('project-name')
 
-model = YourModel('hello', 5)
+model = YourModel(attribute1='hello', attribute2=5)
 model.predict('world')
 ```
 
@@ -48,7 +48,7 @@ For example, here we create a new model:
 import weave
 weave.init('project-name')
 
-model = YourModel('howdy', 10)
+model = YourModel(attribute1='howdy', attribute2=10)
 model.predict('world')
 ```
 

diff --git a/docs/docs/tutorial-eval.md b/docs/docs/tutorial-eval.md
@@ -102,7 +102,7 @@ def fruit_name_score(target: dict, prediction: dict) -> dict:
 # highlight-next-line
 evaluation = evaluate.Evaluation(
     # highlight-next-line
-    dataset, scores=[MulticlassF1Score(class_names=["fruit", "color", "flavor"]), fruit_name_score],
+    dataset=dataset, scorers=[MulticlassF1Score(class_names=["fruit", "color", "flavor"]), fruit_name_score],
 # highlight-next-line
 )
 # highlight-next-line