encode_plus wrongly tokenizing a symbol
When I used a the bio dataset (linnaeus-IOB) to train bert on, the dataset includes the symbol (>=)  [greater than or equal], the tokenizer is separating them into two tokens 

**Expected output is either**:
`>`
`##=`
**or** 
`>= `


What tokenizer are you using? Is it one already trained? If so, which? If not, on what did you train your tokenizer? What is your code? What is your output?

If possible, please fill the template. It's much easier to help you if you provide all the information we need.<|||||>1. Trained using a pretrained biobert for NER task:
`tokenizer = BertTokenizer.from_pretrained("monologg/biobert_v1.0_pubmed_pmc")`

2. encode_plus method
`encoded_dict = tokenizer.encode_plus(
                        sent_str,                  # Sentence to encode.
                        add_special_tokens = True, 
                        max_length = 75,          
                        pad_to_max_length = True,
                        return_attention_mask = True,   
                        return_tensors = 'pt',     
                   )`
3. Dataset of 
> linnaeus-IOB: 
includes in one line `>= label O`

3. Current output
the line of `>=` is split into two symbols without hashes
I get 
`> label O`
`= label O`<|||||>Same for the bio dataset BC4CHEMD-IOBES, it has (R), and the tokenizer split them into three tokens without hashes<|||||>Right, I fail to understand why you think this is wrongly tokenized? This tokenizer does not have the token `>=` in its vocabulary.

You can check with:

```py
">" in tokenizer.get_vocab()  # Returns True
">=" in tokenizer.get_vocab()  # returns False
```<|||||>@LysandreJik yes sir, thank you

But I'm trying to understand why `encode_plus` did not add hashes `##` before `=`  while tokenization

I was expecting to see 
`>`
`##=`
so they would relate to the same token
but that was not the case

Is my question clear?
<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
