Update README.md

README.md

@@ -59,9 +59,9 @@ The texts in the dataset are in Catalan and Portuguese.
 
 Two separated txt files are provided with the sentences sorted in the same order:
 
-- ca-pt_2023_09_01_full.ca: contains the Catalan sentences
+- ca-pt_2023_09_01_full.ca: contains 9 892 953 Catalan sentences
 
-- ca-pt_2023_09_01_full.pt: contains the Portuguese sentences
+- ca-pt_2023_09_01_full.pt: contains 9 892 953 Portuguese sentences
 
 ### Data Splits
 
@@ -73,25 +73,29 @@ The dataset contains a single split: `train`.
 
 The dataset is a combination of the following datasets:
 
-| Dataset       	| Sentences  	| Sentences after Cleaning|
-|-------------------|----------------|-------------------|
-| CCMatrix  v1  	| 12.674.684  	| 	3.765.459|
-| WikiMatrix  	| 358.873 	| 317.649 	|
-| GNOME	| 5.211	|	1.752|
-| KDE4    	| 166.208   	|  117.828 	|
-| QED           	| 53.635 	|   43.736 	|
-| TED2020 v1    	| 48.942 	| 41.461 |
-| OpenSubtitles	| 384.142	| 235.604	|
-| GlobalVoices| 4.035 	|	3.430|
-| Tatoeba | 754 | 723 |
-| Europarl | 1.692.106 | 3.765.459 |
-| **Total**     	| **15.391.745** | **6.159.631** |
-
-All corpora except Europarl were collected from [Opus](https://opus.nlpl.eu/). The Europarl corpus is a synthetic parallel corpus created from the original Spanish-Catalan corpus by [SoftCatalà](https://github.com/Softcatala/Europarl-catalan).
+| Dataset       	| Sentences |
+|-------------------|-----------|
+| CCMatrix  v1  	| 3.765.459 |
+| WikiMatrix     	| 317.649 	|
+| GNOME	            | 1.752     |
+| KDE4    	        | 117.828 	|
+| QED               | 43.736 	|
+| TED2020 v1    	| 41.461    |
+| OpenSubtitles	    | 235.604	|
+| GlobalVoices      | 3.430     |
+| Tatoeba           | 723       |
+| Europarl          | 3.765.459 |
+| **Total**     	| **6.159.631** |
+
+All corpora except Europarl were collected from [Opus](https://opus.nlpl.eu/).
+The Europarl corpus is a synthetic parallel corpus created from the original Spanish-Catalan corpus by [SoftCatalà](https://github.com/Softcatala/Europarl-catalan).
+
 
 ### Data preparation
 
- All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75. This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE). The filtered datasets are then concatenated to form a final corpus of 6.159.631 and before training the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py)
+ All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75.
+ This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
+ The filtered datasets are then concatenated to form a final corpus of 6.159.631 and before training the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py)
 
 ### Personal and Sensitive Information
 
@@ -105,7 +109,8 @@ The purpose of this dataset is to help develop Machines Translation tasks for lo
 
 ### Discussion of Biases
 
-We are aware that since part of the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset. Nonetheless, we have not applied any steps to reduce their impact.
+We are aware that since part of the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset.
+Nonetheless, we have not applied any steps to reduce their impact.
 
 ### Other Known Limitations