Minor

README.md

@@ -27,6 +27,7 @@ computes precision, recall and F1 scores.
 ## How to Use
 
 Sem-F1 takes 2 mandatory arguments:  
+
 - `predictions` - List of predictions. Format varies based on `tokenize_sentences` and `multi_references` flags.
 - `references`: List of references. Format varies based on `tokenize_sentences` and `multi_references` flags.
 
@@ -49,19 +50,20 @@ for score in results:
 
 
 Sem-F1 also accepts multiple optional arguments:
-   - `model_type (str)`: Model to use for encoding sentences. Options: ['pv1', 'stsb', 'use']
-     - `pv1` - [paraphrase-distilroberta-base-v1](https://huggingface.co/sentence-transformers/paraphrase-distilroberta-base-v1)
-     - `stsb` - [stsb-roberta-large](https://huggingface.co/sentence-transformers/stsb-roberta-large)
-     - `use` - [Universal Sentence Encoder](https://huggingface.co/sentence-transformers/use-cmlm-multilingual) (Default)
 
-          Furthermore, you can use any model on Huggingface/SentenceTransformer that is supported by SentenceTransformer 
-          such as `all-mpnet-base-v2` or `roberta-base`
+- `model_type (str)`: Model to use for encoding sentences. Options: ['pv1', 'stsb', 'use']
+  - `pv1` - [paraphrase-distilroberta-base-v1](https://huggingface.co/sentence-transformers/paraphrase-distilroberta-base-v1)
+  - `stsb` - [stsb-roberta-large](https://huggingface.co/sentence-transformers/stsb-roberta-large)
+  - `use` - [Universal Sentence Encoder](https://huggingface.co/sentence-transformers/use-cmlm-multilingual) (Default)
+
+       Furthermore, you can use any model on Huggingface/SentenceTransformer that is supported by SentenceTransformer 
+       such as `all-mpnet-base-v2` or `roberta-base`
 
-   - `tokenize_sentences (bool)`: Flag to indicate whether to tokenize the sentences in the input documents. Default: True.
-   - `multi_references (bool)`: Flag to indicate whether multiple references are provided. Default: False.
-   - `gpu (Union[bool, str, int, List[Union[str, int]]])`: Whether to use GPU, CPU or multiple-processes for computation.
-   - `batch_size (int)`: Batch size for encoding. Default: 32.
-   - `verbose (bool)`: Flag to indicate verbose output. Default: False.
+- `tokenize_sentences (bool)`: Flag to indicate whether to tokenize the sentences in the input documents. Default: True.
+- `multi_references (bool)`: Flag to indicate whether multiple references are provided. Default: False.
+- `gpu (Union[bool, str, int, List[Union[str, int]]])`: Whether to use GPU, CPU or multiple-processes for computation.
+- `batch_size (int)`: Batch size for encoding. Default: 32.
+- `verbose (bool)`: Flag to indicate verbose output. Default: False.
 
 Refer to the inputs descriptions for more detailed usage as follows:
 
@@ -78,6 +80,7 @@ print(metric.inputs_description)
 
 ### Output Values
 List of `Scores` dataclass corresponding to each sample - 
+
  - `precision: float`: Precision score, which ranges from 0.0 to 1.0.
  - `recall: List[float]`: Recall score corresponding to each reference
  - `f1: float`: F1 score (between precision and average recall).