Update README.md

README.md

@@ -149,6 +149,7 @@ Our final models were trained on a different number of steps and sequence length
 <figure>
 
 <caption>Table 1. Evaluation made by the Barcelona Supercomputing Center of their models and BERTIN (beta, seq len 128).</caption>
+
 | Dataset     | Metric   | RoBERTa-b | RoBERTa-l | BETO   | mBERT  | BERTIN |
 |-------------|----------|-----------|-----------|--------|--------|--------|
 | UD-POS      | F1       |    **0.9907** |    0.9901 | 0.9900 | 0.9886 | **0.9904** |
@@ -167,6 +168,7 @@ All of our models attained good accuracy values, in the range of 0.65, as can be
 <figure>
  
 <caption>Table 2. Accuracy for the different language models.</caption>
+
 | Model                                              | Accuracy |  
 |----------------------------------------------------|----------|
 | bertin-project/bertin-roberta-base-spanish         | 0.6547   |  
@@ -191,6 +193,7 @@ All models trained with max length 512 and batch size 8, using the CoNLL 2002 da
 <figure>
  
 <caption>Table 3. Results for POS.</caption>
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 | Model                                              |    F1    | Accuracy |  
 |----------------------------------------------------|----------|----------|
 | bert-base-multilingual-cased                       | 0.9629   | 0.9687   |
@@ -212,6 +215,7 @@ All models trained with max length 512 and batch size 8, using the CoNLL 2002 da
 <figure>
  
 <caption>Table 4. Results for NER.</caption>
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 | Model                                              |    F1    | Accuracy |  
 |----------------------------------------------------|----------|----------|
 | bert-base-multilingual-cased                       | 0.8539   | 0.9779   |
@@ -233,6 +237,7 @@ All models trained with max length 512 and batch size 8. These numbers are surpr
 <figure>
  
 <caption>Table 5. Results for PAWS-X.</caption>
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 | Model                                              | Accuracy |  
 |----------------------------------------------------|----------|
 | bert-base-multilingual-cased                       | 0.5765   |
@@ -253,6 +258,7 @@ All models trained with max length 512 and batch size 8. These numbers are surpr
 <figure>
  
 <caption>Table 6. Results for XNLI with sequence length 256 and batch size 32.</caption>
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 | Model                                              | Accuracy |  
 |----------------------------------------------------|----------|
 | bert-base-multilingual-cased                       | 0.7852   |
@@ -270,7 +276,6 @@ All models trained with max length 512 and batch size 8. These numbers are surpr
 <figure>
  
 <caption>Table 7. Results for XNLI with sequence length 512 and batch size 16.</caption>
-</figure>
 
 | Model                                              | Accuracy |  
 |----------------------------------------------------|----------|
@@ -283,6 +288,7 @@ All models trained with max length 512 and batch size 8. These numbers are surpr
 | bertin-project/bertin-base-random-exp-512seqlen    | 0.7799   |  
 | bertin-project/bertin-base-gaussian-exp-512seqlen  | 0.7843   |
 
+</figure>
 
 # Conclusions