Update README.md

README.md

@@ -34,8 +34,6 @@ Subword segmentation with unigram language model supports probabilistic subword
 |Pre-segmentation required?|[No](#whitespace-is-treated-as-a-basic-symbol)|Yes|Yes|
 |Customizable normalization (e.g., NFKC)|[Yes](doc/normalization.md)|No|N/A|
 |Direct id generation|[Yes](#end-to-end-example)|No|N/A|
-|Training speed|N/A|N/A|N/A|
-|Segmentation speed|N/A|N/A|N/A|
 
 Note that BPE algorithm used in WordPiece is slightly different from the original BPE.
 
@@ -50,6 +48,10 @@ vocabulary. Unlike most unsupervised word segmentation algorithms, which
 assume an infinite vocabulary, SentencePiece trains the segmentation model such
 that the final vocabulary size is fixed, e.g., 8k, 16k, or 32k.
 
+Note that SentencePices specifies the final vocabulary size for training, which is different from the 
+[subword-nmt](https://github.com/rsennrich/subword-nmt) that uses the number of merge operations.
+The number of merge operations is a BPE-specific parameter and not applicable to other segmentation algorithms, including unigram, word and character.
+
 #### Whitespace is treated as a basic symbol
 The first step of Natural Language processing is text tokenization. For
 example, a standard English tokenizer would segment the text "Hello world." into the