Update README.md

README.md

@@ -105,7 +105,7 @@ The retriever is responsible for retrieving relevant documents from a large coll
 while the reader is responsible for extracting entities and relations from the retrieved documents.
 ReLiK can be used with the `from_pretrained` method to load a pre-trained pipeline.
 
-Here is an example of how to use ReLiK for Entity Linking:
+Here is an example of how to use ReLiK for **Entity Linking**:
 
 ```python
 from relik import Relik
@@ -151,11 +151,11 @@ We evaluate the performance of ReLiK on Entity Linking using [GERBIL](http://ger
 | [ReLiK<sub>Base<sub>](https://huggingface.co/sapienzanlp/relik-entity-linking-base)                      | 85.3 | 72.3  | 55.6 | 68.0 | 48.1 | 41.6 | 62.5 | 52.3 | 60.7 | 57.2 | 00:29      |
 | [ReLiK<sub>Large<sub>](https://huggingface.co/sapienzanlp/relik-entity-linking-large)                     | **86.4** | **75.0**  | **56.3** | **72.8** | 51.7 | **43.0** | **65.1** | **57.2** | **63.4** | **60.2** | 01:46      |
 
-*Comparison systems' evaluation (InKB Micro F1) on the *in-domain* AIDA test set and *out-of-domain* MSNBC (MSN), Derczynski (Der), KORE50 (K50), N3-Reuters-128 (R128), 
+Comparison systems' evaluation (InKB Micro F1) on the *in-domain* AIDA test set and *out-of-domain* MSNBC (MSN), Derczynski (Der), KORE50 (K50), N3-Reuters-128 (R128), 
 N3-RSS-500 (R500), OKE-15 (O15), and OKE-16 (O16) test sets. **Bold** indicates the best model. 
 GENRE uses mention dictionaries. 
 The AIT column shows the time in minutes and seconds (m:s) that the systems need to process the whole AIDA test set using an NVIDIA RTX 4090, 
-except for EntQA which does not fit in 24GB of RAM and for which an A100 is used.*
+except for EntQA which does not fit in 24GB of RAM and for which an A100 is used.
 
 ## 🤖 Models