instructions for participants + typos

README.md

@@ -3,7 +3,7 @@ license: cc-by-4.0
 ---
 # Dataset for the first Workshop on Information Extraction from Scientific Publications (WIESP/2022)
 
-## Dataset Descriptions
+## Dataset Description
 Datasets are in JSON Lines format (each line is a json dictionary).
 The datasets are formatted similarly to the CONLL2003 format in that they associate each token with an NER tag. The tags follow the "B-" and "I-" convention from the IOB2 syntax
 
@@ -16,17 +16,25 @@ The following keys are not strictly needed by the participants:
 - `"ner_ids"`: the pre-computed list of ids corresponding ner_tags, as given by the dictionary in ner_tags.json
 - `"label_studio_id"`, `"section"`, `"bibcode"`: references for internal NASA/ADS use.
 
-## How-To
+## 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.
+
+### How-To
+How to compute your scores on the training data:
+1. If not already done, convert your predictions to a Huggingface dataset with the format described above.
+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):
 ```
 import json
-with open('./WIESP2022-NER-DEV.jsonl', 'r') as f:
+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):
 ```
 from datasets import Dataset
-wiesp_dev_from_json = Dataset.from_json(path_or_paths='./WIESP2022-NER-DEV.json')
+wiesp_dev_from_json = Dataset.from_json(path_or_paths="./WIESP2022-NER-DEV.json")
 ```