Fix parity tests ci

.github/workflows/ci.yml

@@ -22,7 +22,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.9"
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
@@ -45,10 +45,10 @@ jobs:
       - uses: actions/checkout@v3
         with:
           fetch-depth: 0
-      - name: Set up Python 3.8
+      - name: Set up Python 3.9
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.9"
       - name: Upgrade pip
         run: python -m pip install --upgrade pip
       - name: Install dependencies

metrics/mse/mse.py

@@ -14,7 +14,7 @@
 """MSE - Mean Squared Error Metric"""
 
 import datasets
-from sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_squared_error, root_mean_squared_error
 
 import evaluate
 
@@ -112,8 +112,10 @@ def _get_feature_types(self):
 
     def _compute(self, predictions, references, sample_weight=None, multioutput="uniform_average", squared=True):
 
-        mse = mean_squared_error(
-            references, predictions, sample_weight=sample_weight, multioutput=multioutput, squared=squared
+        mse = (
+            mean_squared_error(references, predictions, sample_weight=sample_weight, multioutput=multioutput)
+            if squared
+            else root_mean_squared_error(references, predictions, sample_weight=sample_weight, multioutput=multioutput)
         )
 
         return {"mse": mse}

metrics/rl_reliability/rl_reliability.py

@@ -73,7 +73,7 @@
     >>> import numpy as np
     >>> rl_reliability = evaluate.load("rl_reliability", "online")
     >>> results = rl_reliability.compute(
-    ...     timesteps=[np.linspace(0, 2000000, 1000)],
+    ...     timesteps=[np.linspace(0, 2000000, 1000, dtype=np.int64())],
     ...     rewards=[np.linspace(0, 100, 1000)]
     ...     )
     >>> print(results["LowerCVaROnRaw"].round(4))

src/evaluate/utils/logging.py

@@ -218,7 +218,6 @@ def get_lock(self):
 
 def is_progress_bar_enabled() -> bool:
     """Return a boolean indicating whether tqdm progress bars are enabled."""
-    global _tqdm_active
     return bool(_tqdm_active)
 
 

tests/test_trainer_evaluator_parity.py

@@ -59,7 +59,7 @@ def test_text_classification_parity(self):
         ) as f:
             transformers_results = json.load(f)
 
-        eval_dataset = load_dataset("glue", "sst2", split="validation[:80]")
+        eval_dataset = load_dataset("nyu-mll/glue", "sst2", split="validation[:80]")
 
         pipe = pipeline(task="text-classification", model=model_name, tokenizer=model_name)
 
@@ -104,7 +104,7 @@ def test_text_classification_parity_two_columns(self):
         ) as f:
             transformers_results = json.load(f)
 
-        eval_dataset = load_dataset("glue", "mnli", split=f"validation_matched[:{max_eval_samples}]")
+        eval_dataset = load_dataset("nyu-mll/glue", "mnli", split=f"validation_matched[:{max_eval_samples}]")
 
         pipe = pipeline(task="text-classification", model=model_name, tokenizer=model_name, max_length=256)
 
@@ -124,7 +124,7 @@ def test_text_classification_parity_two_columns(self):
     def test_image_classification_parity(self):
         # we can not compare to the Pytorch transformers example, that uses custom preprocessing on the images
         model_name = "douwekiela/resnet-18-finetuned-dogfood"
-        dataset_name = "beans"
+        dataset_name = "AI-Lab-Makerere/beans"
         max_eval_samples = 120
 
         raw_dataset = load_dataset(dataset_name, split="validation")
@@ -193,7 +193,7 @@ def test_question_answering_parity(self):
         subprocess.run(
             f"python examples/pytorch/question-answering/run_qa.py"
             f" --model_name_or_path {model_name_v1}"
-            f" --dataset_name squad"
+            f" --dataset_name rajpurkar/squad"
             f" --do_eval"
             f" --output_dir {os.path.join(self.dir_path, 'questionanswering_squad_transformers')}"
             f" --max_eval_samples 100"
@@ -207,7 +207,7 @@ def test_question_answering_parity(self):
         ) as f:
             transformers_results = json.load(f)
 
-        eval_dataset = load_dataset("squad", split="validation[:100]")
+        eval_dataset = load_dataset("rajpurkar/squad", split="validation[:100]")
 
         pipe = pipeline(
             task="question-answering",
@@ -232,7 +232,7 @@ def test_question_answering_parity(self):
         subprocess.run(
             f"python examples/pytorch/question-answering/run_qa.py"
             f" --model_name_or_path {model_name_v2}"
-            f" --dataset_name squad_v2"
+            f" --dataset_name rajpurkar/squad_v2"
             f" --version_2_with_negative"
             f" --do_eval"
             f" --output_dir {os.path.join(self.dir_path, 'questionanswering_squadv2_transformers')}"
@@ -247,7 +247,7 @@ def test_question_answering_parity(self):
         ) as f:
             transformers_results = json.load(f)
 
-        eval_dataset = load_dataset("squad_v2", split="validation[:100]")
+        eval_dataset = load_dataset("rajpurkar/squad_v2", split="validation[:100]")
 
         pipe = pipeline(
             task="question-answering",
@@ -282,7 +282,7 @@ def test_token_classification_parity(self):
         subprocess.run(
             f"python examples/pytorch/token-classification/run_ner.py"
             f" --model_name_or_path {model_name}"
-            f" --dataset_name conll2003"
+            f" --dataset_name areias/conll2003-generative"
             f" --do_eval"
             f" --output_dir {os.path.join(self.dir_path, 'tokenclassification_conll2003_transformers')}"
             f" --max_eval_samples {n_samples}",
@@ -295,7 +295,7 @@ def test_token_classification_parity(self):
         ) as f:
             transformers_results = json.load(f)
 
-        eval_dataset = load_dataset("conll2003", split=f"validation[:{n_samples}]")
+        eval_dataset = load_dataset("areias/conll2003-generative", split=f"validation[:{n_samples}]")
 
         pipe = pipeline(task="token-classification", model=model_name)