update

loss_metric.py

@@ -11,28 +11,25 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""TODO: Add a description here."""
+"""Calculation of the cross-entropy loss function using the huggingface evaluate module."""
 
 import evaluate
 import datasets
 
+from torch import nn
 
-# TODO: Add BibTeX citation
 _CITATION = """\
 @InProceedings{huggingface:module,
-title = {A great new module},
-authors={huggingface, Inc.},
-year={2020}
+title = {Loss Metric},
+authors={YU YE},
+year={2024}
 }
 """
 
-# TODO: Add description of the module here
 _DESCRIPTION = """\
-This new module is designed to solve this great ML task and is crafted with a lot of care.
+Calculation of the cross-entropy loss function using the huggingface evaluate module.
 """
 
-
-# TODO: Add description of the arguments of the module here
 _KWARGS_DESCRIPTION = """
 Calculates how good are predictions given some references, using certain scores
 Args:
@@ -41,25 +38,21 @@ Args:
     references: list of reference for each prediction. Each
         reference should be a string with tokens separated by spaces.
 Returns:
-    accuracy: description of the first score,
-    another_score: description of the second score,
+    loss: description of the first score,
 Examples:
     Examples should be written in doctest format, and should illustrate how
     to use the function.
 
-    >>> my_new_module = evaluate.load("my_new_module")
+    >>> my_new_module = evaluate.load("Aye10032/loss_metric")
     >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
     >>> print(results)
-    {'accuracy': 1.0}
+    {'loss': 1.0}
 """
 
-# TODO: Define external resources urls if needed
-BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
-
 
 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
 class loss_metric(evaluate.Metric):
-    """TODO: Short description of my evaluation module."""
+    """Calculation of the cross-entropy loss function using the huggingface evaluate module."""
 
     def _info(self):
         # TODO: Specifies the evaluate.EvaluationModuleInfo object
@@ -83,13 +76,12 @@ class loss_metric(evaluate.Metric):
 
     def _download_and_prepare(self, dl_manager):
         """Optional: download external resources useful to compute the scores"""
-        # TODO: Download external resources if needed
         pass
 
     def _compute(self, predictions, references):
         """Returns the scores"""
-        # TODO: Compute the different scores of the module
-        accuracy = sum(i == j for i, j in zip(predictions, references)) / len(predictions)
+        loss_func = nn.CrossEntropyLoss()
+        loss = loss_func(predictions, references)
         return {
-            "accuracy": accuracy,
-        }
+            "loss": loss,
+        }

requirements.txt

@@ -1 +1,2 @@
-git+https://github.com/huggingface/evaluate@main
+git+https://github.com/huggingface/evaluate@main
+torch