reqs, better instructions, more link

README.md

@@ -9,9 +9,7 @@ licenses:
 - cc-by-4.0
 multilinguality:
 - monolingual
-pretty_name: 'WIESP2022-NER
-
-  '
+pretty_name: 'WIESP2022-NER'
 size_categories:
 - 1K<n<10K
 source_datasets: []
@@ -20,12 +18,12 @@ task_categories:
 task_ids:
 - named-entity-recognition
 ---
-# Dataset for the first Workshop on Information Extraction from Scientific Publications (WIESP/2022)
-(<a href="https://ui.adsabs.harvard.edu/WIESP/" target="_blank">Website</a>)
+# Dataset for the first <a href="https://ui.adsabs.harvard.edu/WIESP/" style="color:blue">Workshop on Information Extraction from Scientific Publications (WIESP/2022)</a>.
 
 ## 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. 
+Datasets with text fragments from astrophysics papers, provided by the [NASA Astrophysical Data System](https://ui.adsabs.harvard.edu/) with manually tagged astronomical facilities and other entities of interest (e.g., celestial objects).  
+Datasets are in JSON Lines format (each line is a json dictionary).  
+The datasets are formatted similarly to the CONLL2003 format. Each token is associated with an NER tag. The tags follow the "B-" and "I-" convention from the [IOB2 syntax]("https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)")
 
 Each entry consists of a dictionary with the following keys:
 - `"unique_id"`: a unique identifier for this data sample. Must be included in the predictions.
@@ -37,13 +35,6 @@ 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.
-
-### 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)
 - python (as list of dictionaries):
@@ -57,16 +48,27 @@ with open("./WIESP2022-NER-DEV.jsonl", 'r') as f:
 from datasets import Dataset
 wiesp_dev_from_json = Dataset.from_json(path_or_paths="./WIESP2022-NER-DEV.jsonl")
 ```
-
 (NOTE: currently loading from the Huggingface Dataset Hub directly does not work. You need to clone the repository locally)
 
+How to compute your scores on the training data:
+1. format your predictions as a list of dictionaries, each with 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 (see `WIESP2022-NER-DEV-sample-predictions.jsonl` for an example).
+2. pass the references and predictions datasets to the `compute_MCC()` and `compute_seqeval()` functions (from the `.py` files with the same names).
+
+Requirement to run the scoring scripts:  
+[NumPy](https://numpy.org/install/)  
+[scikit-learn](https://scikit-learn.org/stable/install.html)  
+[seqeval](https://github.com/chakki-works/seqeval#installation)
+
+To get scores on the validation data, zip your predictions file (a single `.jsonl' file formatted following the same instructions as above) and upload the `.zip` file to the [Codalabs](https://codalab.lisn.upsaclay.fr/competitions/5062) competition.
+
 ## File list
 ```
 ├── WIESP2022-NER-TRAINING.jsonl : 1753 samples for training.
 ├── WIESP2022-NER-DEV.jsonl : 20 samples for development.
+├── WIESP2022-NER-DEV-sample-predictions.jsonl : an example file with properly formatted predictions on the development data.
 ├── WIESP2022-NER-VALIDATION-NO-LABELS.jsonl : 1366 samples for validation without the NER labels. Used for the WIESP2022 workshop.
 ├── README.MD: this file.
 └── scoring-scripts/ : scripts used to evaluate submissions.
     ├── compute_MCC.py : computes the Matthews correlation coefficient between two datasets.
-    └── compute_seqeval.py : computes the seqeval scores (precision, recall, f1 overall and for each class) between two datasets.
+    └── compute_seqeval.py : computes the seqeval scores (precision, recall, f1, overall and for each class) between two datasets.
 ```