Update README.md

README.md

@@ -50,8 +50,8 @@ Keyphrase extraction is a technique in text analysis where you extract the impor
 Here is where Artificial Intelligence 🤖 comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text.
 
 ## 📓 Model Description
-This model uses [KeyBART](https://huggingface.co/bloomberg/KeyBART) as its base model and fine-tunes it on the [Inspec dataset](https://huggingface.co/datasets/midas/inspec). KeyBART focuses on learning a better representation of keyphrases in a generative setting. It produces the keyphrases associated with the input. This is accomplished by predicting the original input based on a changed input. The input is changed by token masking, keyphrase masking and keyphrase replacement. This model can already be used without any fine-tuning, but can be fine-tuned if needed.
-You can find more information about the architecture in this paper: https://arxiv.org/abs/2112.08547.
+This model uses [KeyBART](https://huggingface.co/bloomberg/KeyBART) as its base model and fine-tunes it on the [Inspec dataset](https://huggingface.co/datasets/midas/inspec). KeyBART focuses on learning a better representation of keyphrases in a generative setting. It produces the keyphrases associated with the input document from a corrupted input. The input is changed by token masking, keyphrase masking and keyphrase replacement. This model can already be used without any fine-tuning, but can be fine-tuned if needed.
+You can find more information about the architecture in this [paper](https://arxiv.org/abs/2112.08547).
 
 Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learning Rich Representation of Keyphrases from Text." arXiv preprint arXiv:2112.08547 (2021).
 
@@ -60,7 +60,6 @@ Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learnin
 * This keyphrase generation model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out.
 * Only works for English documents.
 * For a custom model, please consult the [training notebook]() for more information.
-* Sometimes the output doesn't make any sense.
 
 ### ❓ How To Use
 ```python
@@ -227,7 +226,7 @@ def extract_keyphrases(examples):
     return [example.split(keyphrase_sep_token) for example in examples]
 ```
 ## 📝 Evaluation results
-Traditional evaluation methods are the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases.
+Traditional evaluation methods are the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases. In keyphrase generation you also look at F1@O where O stands for the number of ground truth keyphrases.
 The model achieves the following results on the Inspec test set: