Update README.md

README.md

@@ -188,18 +188,21 @@ All of our models attained good accuracy values, in the range of 0.65, as can be
 </figure>
 
 We are currently in the process of applying our language models to downstream tasks.
-
-* test 
-* test 2
-* test 3
+For simplicity, we will abbreviate the different models as follows:
+* **BERT-m**: bert-base-multilingual-cased 
+* **BERT-wwm**: dccuchile/bert-base-spanish-wwm-cased
+* **BSC-BNE**: BSC-TeMU/roberta-base-bne
+* **Beta**: bertin-project/bertin-roberta-base-spanish
+* **Random**: bertin-project/bertin-base-random
+* **Stepwise**: bertin-project/bertin-base-stepwise
+* **Gaussian**: bertin-project/bertin-base-gaussian
+* **Random-512**: bertin-project/bertin-base-random-exp-512seqlen
+* **Gaussian-512**: bertin-project/bertin-base-gaussian-exp-512seqlen
 
 <figure>
  
 <caption>
-Table x. Dataset for POS nad NER is CoNLL 2002. 
-* test 
-* test 2
-* test 3
+Table 3. Metrics for different downstream tasks, comparing our different models as well as other relevant BERT variations from the literature. Dataset for POS nad NER is CoNLL 2002. 
 </caption>
  
 |     Model    | POS (F1/Acc)            | NER (F1/Acc)         |  PAWS-X (Acc) | XNLI-256 (Acc) |  XNLI-512 (Acc) | 
@@ -217,7 +220,7 @@ Table x. Dataset for POS nad NER is CoNLL 2002.
 </figure>
 
 
-## SQUAD-es
+### SQUAD-es
 Using sequence length 128 we have achieved exact match 50.96 and F1 68.74.
 
 
@@ -249,7 +252,7 @@ All models trained with max length 512 and batch size 8. These numbers are surpr
 </figure>
 
 
-## XNLI
+### XNLI
 
 <figure>