implements model

README.md

@@ -1,11 +1,10 @@
 ---
 title: cross_entropy_loss
-datasets:
 -  
 tags:
 - evaluate
 - metric
-description: "TODO: add a description here"
+description: "computes the cross entropy loss"
 sdk: gradio
 sdk_version: 3.19.1
 app_file: app.py
@@ -14,37 +13,7 @@ pinned: false
 
 # Metric Card for cross_entropy_loss
 
-***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
 
 ## Metric Description
-*Give a brief overview of this metric, including what task(s) it is usually used for, if any.*
 
-## How to Use
-*Give general statement of how to use the metric*
-
-*Provide simplest possible example for using the metric*
-
-### Inputs
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
-
-### Output Values
-
-*Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*
-
-*State the range of possible values that the metric's output can take, as well as what in that range is considered good. For example: "This metric can take on any value between 0 and 100, inclusive. Higher scores are better."*
-
-#### Values from Popular Papers
-*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.*
-
-### Examples
-*Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.*
-
-## Limitations and Bias
-*Note any known limitations or biases that the metric has, with links and references if possible.*
-
-## Citation
-*Cite the source where this metric was introduced.*
-
-## Further References
-*Add any useful further references.*
+A simple metric that calculates cross-entropy loss. Created so that I can log losses from different training tasks within a Hugging Face trainer for multi-task training.

cross_entropy_loss.py

@@ -11,50 +11,30 @@
 # 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."""
+"""Cross Entropy Loss Metric"""
 
 import evaluate
 import datasets
-
+import torch.nn.functional as F
+import torch
 
 # TODO: Add BibTeX citation
-_CITATION = """\
-@InProceedings{huggingface:module,
-title = {A great new module},
-authors={huggingface, Inc.},
-year={2020}
-}
+_CITATION = """
 """
 
 # 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.
+A simple metric that calculates cross-entropy loss. Created so that I can log losses from different training tasks within a Hugging Face trainer for multi-task training.
 """
 
 
 # TODO: Add description of the arguments of the module here
 _KWARGS_DESCRIPTION = """
-Calculates how good are predictions given some references, using certain scores
-Args:
-    predictions: list of predictions to score. Each predictions
-        should be a string with tokens separated by spaces.
-    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,
-Examples:
-    Examples should be written in doctest format, and should illustrate how
-    to use the function.
-
-    >>> my_new_module = evaluate.load("my_new_module")
-    >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
-    >>> print(results)
-    {'accuracy': 1.0}
+    prediction_scores: the logits
+    references: the labels
 """
 
 # 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)
@@ -70,26 +50,22 @@ class cross_entropy_loss(evaluate.Metric):
             citation=_CITATION,
             inputs_description=_KWARGS_DESCRIPTION,
             # This defines the format of each prediction and reference
-            features=datasets.Features({
-                'predictions': datasets.Value('int64'),
-                'references': datasets.Value('int64'),
-            }),
-            # Homepage of the module for documentation
-            homepage="http://module.homepage",
-            # Additional links to the codebase or references
-            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
-            reference_urls=["http://path.to.reference.url/new_module"]
+            features=datasets.Features(
+                {
+                    "prediction_scores": datasets.Sequence(datasets.Value("float")),
+                    "references": datasets.Value("int32")
+                }
+            )
         )
 
-    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):
+    def _compute(self, prediction_scores, 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 = F.cross_entropy(input=torch.from_numpy(prediction_scores).flatten(start_dim=0, end_dim=1),
+                            target=torch.from_numpy(references).flatten(),
+                            ignore_index=-100).item()
         return {
-            "accuracy": accuracy,
+            "cross_entropy_loss": loss
         }