formating How-To

README.md

@@ -17,7 +17,7 @@ The following keys are not strictly needed by the participants:
 - `"label_studio_id"`, `"section"`, `"bibcode"`: references for internal NASA/ADS use.
 
 ## Instructions for Workshop participants:
-Predictions must be given in the same JSON Lines format, must include the same "unique_id" and "tokens" keys from the dataset, as well as the list of predicted NER tags under the "pred_ner_tags" key.
+Predictions must be given in the same JSON Lines format, must include the same `"unique_id"` and `"tokens"` keys from the dataset, as well as the list of predicted NER tags under the `"pred_ner_tags"` key.
 
 ### How-To
 How to compute your scores on the training data:
@@ -25,15 +25,13 @@ How to compute your scores on the training data:
 2. pass the references and predictions datasets to the `compute_MCC()` and `compute_seqeval()` function (from the `.py` files with the same names).
 
 How to load the data (assuming `./WIESP2022-NER-DEV.jsonl` is in the current directory, change as needed).
-
-How to load into python (as list of dictionaries):
+- python (as list of dictionaries):
 ```
 import json
 with open("./WIESP2022-NER-DEV.jsonl", 'r') as f:
     wiesp_dev_json = [json.loads(l) for l in list(f)]
 ```
-
-How to load into Huggingface (as a Hugginface Dataset):
+ - into Huggingface (as a Hugginface Dataset):
 ```
 from datasets import Dataset
 wiesp_dev_from_json = Dataset.from_json(path_or_paths="./WIESP2022-NER-DEV.json")