Pin sklearn version

.github/workflows/ci.yml

@@ -56,5 +56,5 @@ jobs:
         with:
           stack-name: ${{ matrix.stack-name }}
           python-version: ${{ matrix.python-version }}
-          ref-zenml: ${{ inputs.ref-zenml || 'feature/followup-run-metadata' }}
+          ref-zenml: ${{ inputs.ref-zenml || 'develop' }}
           ref-template: ${{ inputs.ref-template || github.ref }}

README.md

@@ -14,14 +14,14 @@ Once you have ZenML installed, you can initialize a new project using this templ
 
 ```bash
 zenml init --template <short_name_of_template>
-# example: zenml init --template template-nlp
+# example: zenml init --template nlp
 ```
 
 Running the command above will result in input prompts being shown to you. If you would like to rely on default values for the ZenML project template - you can add --template-with-defaults to the same command, like this:
 
 ```bash
 zenml init --template <short_name_of_template> --template-with-defaults
-# example: zenml init --template template-nlp --template-with-defaults
+# example: zenml init --template nlp --template-with-defaults
 ```
 
 

requirements.txt

@@ -2,3 +2,5 @@ copier
 jinja2-time
 pyyaml-include<2.0
 datasets>=2.12.0,<3.0.0
+# Starting with version 1.6.0 this is incompatible with the metrics computation of the datasets library
+scikit-learn<1.6.0

template/requirements.txt

@@ -2,3 +2,4 @@ torchvision
 gradio
 zenml[server]>=0.56.3
 datasets>=2.12.0,<3.0.0
+scikit-learn<1.6.0

template/utils/misc.py

@@ -17,7 +17,7 @@ def compute_metrics(eval_pred: Tuple[np.ndarray, np.ndarray]) -> Dict[str, float
     """
     logits, labels = eval_pred
     predictions = np.argmax(logits, axis=-1)
-    # calculate the mertic using the predicted and true value
+    # calculate the metric using the predicted and true value
     accuracy = load_metric("accuracy", trust_remote_code=True).compute(
         predictions=predictions, references=labels
     )