added latest files, python syntax color

README.md

@@ -19,7 +19,7 @@ task_ids:
 - named-entity-recognition
 ---
 # 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>.
-**(NOTE: loading from the Huggingface Dataset Hub directly does not work. You need to clone the repository locally.)**
+
 
 ## Dataset Description
 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).  
@@ -36,20 +36,26 @@ 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:
-How to load the data:  
-(assuming `./WIESP2022-NER-DEV.jsonl` is in the current directory, change as needed)
+How to load the data using the Huggingface library:
+ ```python
+from datasets import load_dataset
+dataset = load_dataset("adsabs/WIESP2022-NER")
+ ```
+
+How to load the data if you cloned the repository locally:  
+(assuming you `./WIESP2022-NER-DEV.jsonl` is in the current directory, change as needed)
 - python (as list of dictionaries):
-```
+```python
 import json
 with open("./WIESP2022-NER-DEV.jsonl", 'r') as f:
     wiesp_dev_json = [json.loads(l) for l in list(f)]
 ```
  - into Huggingface (as a Huggingface Dataset):
-```
+```python
 from datasets import Dataset
 wiesp_dev_from_json = Dataset.from_json(path_or_paths="./WIESP2022-NER-DEV.jsonl")
 ```
-(NOTE: 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).
@@ -68,8 +74,11 @@ To get scores on the validation data, zip your predictions file (a single `.json
 ├── 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.
-├── tag_definitions.txt: short descriptions and examples of the tags used in the task.
+├── WIESP2022-NER-VALIDATION.jsonl : 1366 samples for validation
+├── WIESP2022-NER-TESTING-NO-LABELS.jsonl : 2505 samples for testing without the NER labels. Used for the WIESP2022 workshop.
+├── WIESP2022-NER-TESTING.jsonl : 2505 samples for testing
+├── README.MD : this file.
+├── tag_definitions.md : short descriptions and examples of the tags used in the task.
 └── 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.