fixed readme

README.md

@@ -3,15 +3,9 @@ annotations_creators:
 - expert-generated
 language_creators:
 - found
-<<<<<<< HEAD
-languages:
-- en
-licenses:
-=======
 language:
 - en
 license:
->>>>>>> 297571f844c69c59b0a7d6325ad12c86b64aa523
 - cc-by-4.0
 multilinguality:
 - monolingual
@@ -25,10 +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>.
-<<<<<<< HEAD
-=======
 **(NOTE: loading from the Huggingface Dataset Hub directly does not work. You need to clone the repository locally.)**
->>>>>>> 297571f844c69c59b0a7d6325ad12c86b64aa523
 
 ## 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).  
@@ -58,11 +49,7 @@ 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")
 ```
-<<<<<<< HEAD
-(NOTE: currently loading from the Huggingface Dataset Hub directly does not work. You need to clone the repository locally)
-=======
 (NOTE: loading from the Huggingface Dataset Hub directly does not work. You need to clone the repository locally.)
->>>>>>> 297571f844c69c59b0a7d6325ad12c86b64aa523
 
 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).
@@ -82,10 +69,7 @@ To get scores on the validation data, zip your predictions file (a single `.json
 ├── 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.
-<<<<<<< HEAD
-=======
 ├── tag_definitions.txt: short descriptions and examples of the tags used in the task.
->>>>>>> 297571f844c69c59b0a7d6325ad12c86b64aa523
 └── 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.