Updated the Readme minor modifications in description section in semf1.py

README.md

@@ -16,15 +16,15 @@ description: >-
   for more details. 
 ---
 
-# Metric Card for SemF1
+# Metric Card for Sem-F1
 
 ## Metric Description
-SEM-F1 metric leverages the pre-trained contextual embeddings and evaluates the model generated semantic overlap 
+Sem-F1 metric leverages the pre-trained contextual embeddings and evaluates the model generated semantic overlap 
 summary with the reference overlap summary. It evaluates the semantic overlap summary at the sentence level and 
 computes precision, recall and F1 scores.
 
 ## How to Use
-SEM-F1 takes 2 mandatory arguments: 
+Sem-F1 takes 2 mandatory arguments:
     `predictions`: (a list of system generated documents in the form of sentences i.e. List[List[str]]),
     `references`: (a list of ground-truth documents in the form of sentences i.e. List[List[str]])
 
@@ -42,32 +42,41 @@ metric = load("semf1")
 results = metric.compute(predictions=predictions, references=references)
 ```
 
-It also accepts multiple optional arguments:
-TODO: List optional arguments
+It also accepts another optional arguments:
 
-### Inputs
-TODO:
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
+`model_type: Optional[str]`: 
+The model to use for encoding the sentences. 
+Options are: 
+[`pv1`](https://huggingface.co/sentence-transformers/paraphrase-distilroberta-base-v1), 
+[`stsb`](https://huggingface.co/sentence-transformers/stsb-roberta-large), 
+[`use`](https://huggingface.co/sentence-transformers/use-cmlm-multilingual). 
+The default value is `use`.  
+
+[//]: # (### 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
 
-BERTScore outputs a dictionary with the following values:
+`precision`: The [precision](https://huggingface.co/metrics/precision) for each sentence from the `predictions` + `references` lists, which ranges from 0.0 to 1.0. 
+
+`recall`: The [recall](https://huggingface.co/metrics/recall) for each sentence from the `predictions` + `references` lists, which ranges from 0.0 to 1.0.
+
+`f1`: The [F1 score](https://huggingface.co/metrics/f1) for each sentence from the `predictions` + `references` lists, which ranges from 0.0 to 1.0.
 
-`precision`: The [precision](https://huggingface.co/metrics/precision) for each system summary, which ranges from 0.0 to 1.0. 
+[//]: # (#### Values from Popular Papers)
 
-`recall`: The [recall](https://huggingface.co/metrics/recall) for each system summary, which ranges from 0.0 to 1.0.
+[//]: # (*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.*)
 
-`f1`: The [F1 score](https://huggingface.co/metrics/f1) for each system summary, which ranges from 0.0 to 1.0.
+[//]: # (### Examples)
 
-#### 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.*
+[//]: # (*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.*)
 
-### 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)
 
-## Limitations and Bias
-*Note any known limitations or biases that the metric has, with links and references if possible.*
+[//]: # (*Note any known limitations or biases that the metric has, with links and references if possible.*)
 
 ## Citation
 ```bibtex
@@ -92,6 +101,6 @@ BERTScore outputs a dictionary with the following values:
 ```
 
 ## Further References
-TODO: Add links to the slides and video
  - [Paper](https://aclanthology.org/2022.emnlp-main.49/)
- - [Presentation Slides]()
+ - [Presentation Slides](https://auburn.box.com/s/rs5p7sttaonbvljnq0i5tk7xxw0vonn3)
+ - [Video](https://auburn.box.com/s/c1bmb8c0a2emc9xhnjfalvqo2100yxvf)

semf1.py

@@ -66,8 +66,10 @@ Args:
             stsb - stsb-roberta-large 
             use - Universal Sentence Encoder
 Returns:
-    accuracy: description of the first score,
-    another_score: description of the second score,
+    precision: Precision.
+    recall: Recall.
+    f1: F1 score.
+    
 Examples:
 
     >>> import evaluate
@@ -85,7 +87,6 @@ Examples:
     [0.77, 0.56]
 """
 
-[["I go to School.", "You are stupid."]]
 
 class Encoder(metaclass=abc.ABCMeta):
     @abc.abstractmethod