Merge pull request #15 from christopher-delphai/master

Fixed urls in readme

README.md

@@ -57,8 +57,8 @@ You can edit the code in the recipe script to customize how Prodigy behaves.
 | [`ner.teach`](ner/ner_teach.py)                      | Collect the best possible training data for a named entity recognition model with the model in the loop. Based on your annotations, Prodigy will decide which questions to ask next.                                                                      |
 | [`ner.match`](ner/ner_match.py)                      | Suggest phrases that match a given patterns file, and mark whether they are examples of the entity you're interested in. The patterns file can include exact strings or token patterns for use with spaCy's `Matcher`.                                    |
 | [`ner.manual`](ner/ner_manual.py)                    | Mark spans manually by token. Requires only a tokenizer and no entity recognizer, and doesn't do any active learning.                                                                                                                                     |
-| [`ner.manual.bert`](other/transformers_tokenizerspy) | Use BERT word piece tokenizer for efficient manual NER annotation for transformer models.                                                                                                                                                                 |
-| [`ner.make-gold`](ner/ner_make-gold.py)              | Create gold-standard data by correcting a model's predictions manually.                                                                                                                                                                                   |
+| [`ner.manual.bert`](other/transformers_tokenizers.py) | Use BERT word piece tokenizer for efficient manual NER annotation for transformer models.                                                                                                                                                                 |
+| [`ner.make-gold`](ner/ner_make_gold.py)              | Create gold-standard data by correcting a model's predictions manually.                                                                                                                                                                                   |
 | [`ner.silver-to-gold`](ner/ner_silver_to_gold.py)    | Take an existing "silver" dataset with binary accept/reject annotations, merge the annotations to find the best possible analysis given the constraints defined in the annotations, and manually edit it to create a perfect and complete "gold" dataset. |
 
 ### Text Classification