ready

tasks/text.py

@@ -3,9 +3,9 @@ from datetime import datetime
 from datasets import load_dataset
 from sklearn.metrics import accuracy_score
 
-from skops import hub_utils
-import skops.io as sio
 import skops
+from skops.hub_utils import download
+from skops.io import load
 
 import pandas as pd
 
@@ -89,14 +89,15 @@ async def evaluate_text(request: TextEvaluationRequest):
     # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
     #--------------------------------------------------------------------------------------------   
 
-    #get unknown types
-    unknown = skops.io.get_untrusted_types(file = hf_hub_download(repo_id=REPO_ID, filename=FILENAME))
+    test_df = pd.DataFrame(dataset['test'])
+    
+    #download model
+    download(repo_id = "kantundpeterpan/frugal-ai-toy", dst = "skops_test")
+    #get unknwown types
+    unknown = skops.io.get_untrusted_types(file = "skops_test/tfidf_rf.skops")
     
     #load model
-    model = sio.load(
-        hf_hub_download(repo_id=REPO_ID, filename=FILENAME),
-        trusted=unknown
-    )
+    model = model = load("skops_test/tfidf_rf.skops", trusted = unknown)
     
     # Make predictions
     true_labels = test_dataset["label"]