Update README.md

README.md

@@ -120,7 +120,7 @@ for split in ("random", "stepwise", "gaussian"):
 
 We then used the same setup as Liu et al. (2019) but trained only for half the steps (250k) on a sequence length of 128. In particular, `Gaussian` trained for the 250k steps, while `Random` was stopped at 230k and `Stepwise` at 180k (this was a decision based on an analysis of training performance and the computational resources available at the time).
 
-Then, we continued training the most promising model for a few steps (~25k) more on sequence length 512. We tried two strategies for this, since it is not easy to find clear details about this change in the literature.
+Then, we continued training the most promising model for a few steps (~25k) more on sequence length 512. We tried two strategies for this, since it is not easy to find clear details about this change in the literature. It turns out this decision had a big impact in the final performance.
 
 For `Random` sampling we trained with seq len 512 during the last 20 steps of the 250 training steps, keeping the optimizer state intact. Results for this are underwhelming, as seen in Figure 7:
 
@@ -131,7 +131,7 @@ For `Random` sampling we trained with seq len 512 during the last 20 steps of th
 <caption>Figure 7. Training profile for Random sampling. Note the drop in performance after the change from 128 to 512 sequence lenght.</caption>
 </figure>
 
-For `Gaussian` sampling we started a new optimizer after 230 steps with 128 seq len, using a short warmup interval. Results are much better (we do not have a graph since training needed to be restarted several times).
+For `Gaussian` sampling we started a new optimizer after 230 steps with 128 seq len, using a short warmup interval. Results are much better using this procedure. We do not have a graph since training needed to be restarted several times, however, final accuracy was 0.6873 compared to 0.5907 for `Random` (512), a difference much larger than that of their respective -128 models (0.6520 for `Random`, 0.6608 for `Gaussian`).
 
 ## Results
 
@@ -153,7 +153,25 @@ Our final models were trained on a different number of steps and sequence length
 | XNLI        | Accuracy |    0.8016 |       WiP | 0.8130 | 0.7876 |    WiP |
 
 
-<caption>Table 1. Evaluation made by the Barcelona Supercomputing Center of their models and BERTIN (beta).</caption>
+<caption>Table 1. Evaluation made by the Barcelona Supercomputing Center of their models and BERTIN (beta, seq len 128).</caption>
+</figure>
+
+All of our models attained good accuracy values, in the range of 0.65, as can be seen in Table 2:
+
+<figure>
+ 
+| Model                                              || Accuracy |  
+|----------------------------------------------------|----------|
+| flax-community/bertin-roberta-large-spanish        | 0.6537   |  
+| bertin-project/bertin-roberta-base-spanish         | 0.6547   |  
+| bertin-project/bertin-base-random                  | 0.6520   | 
+| bertin-project/bertin-base-stepwise                | 0.6487   |   
+| bertin-project/bertin-base-gaussian                | 0.6608   |  
+| bertin-project/bertin-base-random-exp-512seqlen    | 0.5907   |  
+| bertin-project/bertin-base-gaussian-exp-512seqlen  | **0.6873**   | 
+
+
+<caption>Table 2. Accuracy for the different language models.</caption>
 </figure>
 
 We are currently in the process of applying our language models to downstream tasks.
@@ -180,7 +198,7 @@ All models trained with max length 512 and batch size 8, using the CoNLL 2002 da
 | bertin-project/bertin-base-gaussian-exp-512seqlen  | **0.9662**   | **0.9714**   | 
 
 
-<caption>Table 2. Results for POS.</caption>
+<caption>Table 3. Results for POS.</caption>
 </figure>
 
 
@@ -203,7 +221,7 @@ All models trained with max length 512 and batch size 8, using the CoNLL 2002 da
 | bertin-project/bertin-base-gaussian-exp-512seqlen  | **0.8764**   |  **0.9819**   |
 
 
-<caption>Table 3. Results for NER.</caption>
+<caption>Table 4. Results for NER.</caption>
 </figure>
 
 
@@ -226,7 +244,7 @@ All models trained with max length 512 and batch size 8. The accuracy values in
 | bertin-project/bertin-base-gaussian-exp-512seqlen  |  **0.875**   |
 
 
-<caption>Table 4. Results for PAWS-X.</caption>
+<caption>Table 5. Results for PAWS-X.</caption>
 </figure>
 
 **CNLI** 
@@ -248,7 +266,7 @@ All models trained with max length 256 and batch size 16.
 | bertin-project/bertin-base-gaussian-exp-512seqlen  | 0.7878   |
 
 
-<caption>Table 5. Results for CNLI.</caption>
+<caption>Table 6. Results for CNLI.</caption>
 </figure>
 
 # Conclusions