Update README

README.md

@@ -13,11 +13,11 @@ language:
 Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. 
 It was introduced in this [paper](https://www.blank.org/). Three BERT models were released: **RoBERT-small**, RoBERT-base and RoBERT-large, all versions uncased.
 
-| Model        | Weights   |   L    |   H    |    A   | MLM accuracy | NSP accuracy |
-|--------------|:---------:|:------:|:------:|:------:|:------------:|:------------:|
-| *RoBERT-small* | *19M*       | *12*     | *256*    | *8*      | *0.5363*       | *0.9687*       |
-| RoBERT-base  | 114M      | 12     | 768    | 12     | 0.6511       | 0.9802       |
-| RoBERT-large | 341M      | 24     | 1024   | 24     | 0.6929       | 0.9843       |
+| Model          | Weights   |   L    |   H    |    A   | MLM accuracy   | NSP accuracy   |
+|----------------|:---------:|:------:|:------:|:------:|:--------------:|:--------------:|
+| *RoBERT-small* | *19M*     | *12*   | *256*  | *8*    | *0.5363*       | *0.9687*       |
+| RoBERT-base    | 114M      | 12     | 768    | 12     | 0.6511         | 0.9802         |
+| RoBERT-large   | 341M      | 24     | 1024   | 24     | 0.6929         | 0.9843         |
 
 
 
@@ -53,12 +53,12 @@ outputs = model(**inputs)
 
 The model is trained on the following compilation of corpora. Note that we present the statistics after the cleaning process.
 
-Corpus   | Words | Sentences | Size (GB)
--------|---------|----------|----------|
-Oscar  | 1.78B | 87M | 10.8
-RoTex  | 240M | 14M | 1.5
-RoWiki | 50M | 2M | 0.3
-Total | 2.07B | 103M | 12.6
+| Corpus    | Words     | Sentences | Size (GB)|
+|-----------|-----------|-----------|----------|
+| Oscar     | 1.78B     | 87M       | 10.8     |
+| RoTex     | 240M      | 14M       | 1.5      |
+| RoWiki    | 50M       | 2M        | 0.3      |
+| **Total** | **2.07B** | **103M**  | **12.6** |
 
 
 ## Eval results
@@ -67,45 +67,45 @@ Total | 2.07B | 103M | 12.6
 
 We report Macro-averaged F1 score (in %)
 
-Model   | Dev | Test
--------|---------|----------
-multilingual-BERT  | 68.96 | 69.57
-XLM-R-base | 71.26 | 71.71
-BERT-base-ro | 70.49 | 71.02
-RoBERT-small | 66.32 | 66.37
-RoBERT-base | 70.89 | 71.61
-RoBERT-large | 72.48 | 72.11
+| Model            | Dev      | Test     |
+| -----------------|----------|----------|
+| multilingual-BERT| 68.96    | 69.57    |
+| XLM-R-base       | 71.26    | 71.71    |
+| BERT-base-ro     | 70.49    | 71.02    |
+| *RoBERT-small*   | *66.32*  | *66.37*  |
+| RoBERT-base      | 70.89    | 71.61    |
+| RoBERT-large     | **72.48**| **72.11**|
 
 ### Moldavian vs. Romanian Dialect and Cross-dialect Topic identification
 
 We report results on [VarDial 2019](https://sites.google.com/view/vardial2019/campaign) Moldavian vs. Romanian Cross-dialect Topic identification Challenge, as Macro-averaged F1 score (in %).
 
-Model   | Dialect Classification | MD to RO | RO to MD
--------|---------|----------|----------|
-2-CNN + SVM | 93.40 | 65.09 | 75.21
-Char+Word SVM | 96.20 | 69.08 | 81.93
-BiGRU | 93.30 | 70.10 | 80.30
-multilingual-BERT  | 95.34 | 68.76 | 78.24
-XLM-R-base | 96.28 | 69.93 | 8228
-BERT-base-ro | 96.20 | 69.93 | 78.79
-RoBERT-small | 95.67 | 69.01 | 80.40
-RoBERT-base | 97.39 | 68.30 | 81.09
-RoBERT-large | 97.78 | 69.91 | 83.65
+| Model             | Dialect Classification | MD to RO | RO to MD|
+|-------------------|------------------------|----------|----------|
+| 2-CNN + SVM       | 93.40                  | 65.09    | 75.21    |
+| Char+Word SVM     | 96.20                  | 69.08    | 81.93    |
+| BiGRU             | 93.30                  | **70.10**| 80.30    |
+| multilingual-BERT | 95.34                  | 68.76    | 78.24    |
+| XLM-R-base        | 96.28                  | 69.93    | 82.28    |
+| BERT-base-ro      | 96.20                  | 69.93    | 78.79    |
+| *RoBERT-small*    | *95.67*                | *69.01*  | *80.40*  |
+| RoBERT-base       | 97.39                  | 68.30    | 81.09    |
+| RoBERT-large      | **97.78**              | 69.91    | **83.65**|
 
 ### Diacritics Restoration
 
 Challenge can be found [here](https://diacritics-challenge.speed.pub.ro/). We report results on the official test set, as accuracies in %.
 
-Model   | word level | char level
--------|---------|----------
-BiLSTM  | 99.42 | -
-CharCNN | 98.40 | 99.65
-CharCNN + multilingual-BERT | 99.72 | 99.94
-CharCNN + XLM-R-base | 99.76 | 99.95
-CharCNN + BERT-base-ro | 99.79 | 99.95
-CharCNN + RoBERT-small | 99.73 | 99.94
-CharCNN + RoBERT-base | 99.78 | 99.95
-CharCNN + RoBERT-large | 99.76 | 99.95
+| Model                       | word level | char level |
+|-----------------------------|------------|------------|
+| BiLSTM                      | 99.42      | -          |
+| CharCNN                     | 98.40      | 99.65      |
+| CharCNN + multilingual-BERT | 99.72      | 99.94      |
+| CharCNN + XLM-R-base        | 99.76      | **99.95**  |
+| CharCNN + BERT-base-ro      | **99.79**  | **99.95**  |
+| *CharCNN + RoBERT-small*    | *99.73*    | *99.94*    |
+| CharCNN + RoBERT-base       | 99.78      | **99.95**  |
+| CharCNN + RoBERT-large      | 99.76      | **99.95**  |
 
 
 ### BibTeX entry and citation info