cosmetics

doc/evaluation-scores.rst

@@ -7,7 +7,7 @@ Evaluation scores
 The scores (P: Precision, R: Recall, F1: F1-score) for all the models are performed either as 10-fold cross-validation or using an holdout dataset.
 The holdout dataset of Grobid-quantities is composed by the following examples: 
  - Quantities ML: 10 articles
- - Units ML: [UNISCOR dataset](https://grobid-quantities.readthedocs.io/en/latest/references.html#other) with around 1600 examples 
+ - Units ML: `UNISCOR dataset <https://grobid-quantities.readthedocs.io/en/latest/references.html#other>`_ with around 1600 examples 
  - Values ML: 950 examples
 
 For Deep learning models (BidLSTM_CRF/BidLSTM_CRF_FEATURES, BERT_CRF) models, we provide the average over 5 runs.
@@ -197,9 +197,9 @@ Units
 
 CRF was updated on the 10/02/2021
 
-+------------------+------------+--------+----------+-----------+--------+----------+
++------------------+------------+--------+----------+-----------+-------+-----------+
 |                  | CRF                            | BidLSTM_CRF_FEATURES          |
-+==================+============+========+======================+========+==========+
++==================+============+========+==========+===========+=======+===========+
 | Labels           | P          | R      | F1       | P         | R     | F1        |
 +------------------+------------+--------+----------+-----------+-------+-----------+
 | `<base>`         | 98.82      | 99.14  | 98.98    | 98.26     | 98.52 | 98.39     |