fix

README.md

@@ -1,13 +1,50 @@
 ---
-title: Relation Extraction
-emoji: 🐢
-colorFrom: red
-colorTo: red
+title: relation_extraction
+datasets:
+- none
+tags:
+- evaluate
+- metric
+description: "TODO: add a description here"
 sdk: gradio
-sdk_version: 3.45.1
+sdk_version: 3.19.1
 app_file: app.py
 pinned: false
-license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Metric Card for relation_extraction
+
+***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.*

app.py

@@ -2,9 +2,11 @@ import evaluate
 from evaluate.utils import launch_gradio_widget
 
 # Define the path to your custom metric directory
-metric_path = "./custom_metric"
+metric_path = "Ikala-allen/relation_extraction"
 
 
 module = evaluate.load(metric_path)
 launch_gradio_widget(module)
 
+
+

custom_metric/custom_metric.py → relation_extraction.py

@@ -2,6 +2,7 @@ import evaluate
 import datasets
 import numpy as np
 
+# TODO: Add BibTeX citation
 _CITATION = """\
 @InProceedings{huggingface:module,
 title = {A great new module},
@@ -119,10 +120,10 @@ class relation_extraction(evaluate.Metric):
     def _compute(self, predictions, references, mode="strict", relation_types=[]):
         """Returns the scores"""
         # TODO: Compute the different scores of the module
-        print(predictions)
+        
         predictions = convert_format(predictions)
         references = convert_format(references)
-        print(predictions)
+        
         assert mode in ["strict", "boundaries"]
 
         # construct relation_types from ground truth if not given