Update README.md

README.md

@@ -15,7 +15,7 @@ This is an adaptation of pre-trained Portuguese fastText Word Embeddings to a [s
 
 The original pre-trained word embeddings can be found at: [http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc](http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc).  
 
-This model maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This model maps sentences & paragraphs to a 600 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
 ## Usage (Sentence-Transformers)
 
@@ -46,7 +46,7 @@ SentenceTransformer(
   (0): WordEmbeddings(
     (emb_layer): Embedding(929606, 600)
   )
-  (1): Pooling({'word_embedding_dimension': 300, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (1): Pooling({'word_embedding_dimension': 600, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
 )
 ```
 
@@ -54,7 +54,7 @@ SentenceTransformer(
 
 ```bibtex
 @inproceedings{hartmann2017portuguese,
-    title  = Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks},
+    title  = {Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks},
     author = {Hartmann, Nathan S and
               Fonseca, Erick R and
               Shulby, Christopher D and