Update README.md

README.md

@@ -63,7 +63,6 @@ Keyphrase extraction models are transformer models fine-tuned as a token classif
 * This keyphrase extraction model is very domain-specific and will perform very well on abstracts of scientific articles. It's not recommended to use this model for other domains, but you are free to test it out.
 * Limited amount of predicted keyphrases.
 * Only works for English documents.
-* For a custom model, please consult the [training notebook]() for more information.
 
 ### ❓ How To Use
 ```python
@@ -136,8 +135,6 @@ print(keyphrases)
 You can find more information in the [paper](https://arxiv.org/abs/1704.02853).
 
 ## 👷‍♂️ Training procedure
-For more in detail information, you can take a look at the [training notebook]().
-
 ### Training parameters
 
 | Parameter | Value |
@@ -253,7 +250,5 @@ The model achieves the following results on the Semeval2017 test set:
 |:---------------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:|
 | Semeval2017 Test Set  | 0.41 | 0.20 | 0.25 | 0.37 | 0.34 | 0.34  | 0.36 | 0.50 | 0.40 |
 
-For more information on the evaluation process, you can take a look at the keyphrase extraction [evaluation notebook]().
-
 ## 🚨 Issues
 Please feel free to start discussions in the Community Tab.