Update README.md

README.md

@@ -7,7 +7,7 @@ datasets:
 tags:
 - keyphrase-extraction
 metric:
-- f1
+- seqeval
 
 ---
 ** Work in progress **
@@ -18,7 +18,7 @@ Now with the recent innovations in deep learning methods (such as recurrent neur
 
 
 ## 📓 Model Description
-KBIR pre-trained model fine-tuned on the Inspec dataset. KBIR
+This model is a KBIR pre-trained model fine-tuned on the Inspec dataset. KBIR
 Keyphrase Boundary Infilling with Replacement (KBIR) which utilizes a multi-task learning setup for optimizing a combined loss of Masked Language Modeling (MLM), Keyphrase Boundary Infilling (KBI) and Keyphrase Replacement Classification (KRC).
 Paper: https://arxiv.org/abs/2112.08547
 
@@ -165,6 +165,7 @@ def preprocess_fuction(all_samples_per_split):
 ```
 ## 📝Evaluation results
 
+One of the 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.
 The model achieves the following results on the Inspec test set:
 
 | Dataset           | P@5  | R@5  | F1@5 | P@10 | R@10 | F1@10 | P@M  | R@M  | F1@M |
@@ -172,6 +173,7 @@ The model achieves the following results on the Inspec test set:
 | Inspec Test Set   | 0.53 | 0.47 | 0.46 | 0.36 | 0.58 | 0.41  | 0.58 | 0.60 | 0.56 |
 
 For more information on the evaluation process, you can take a look at the keyphrase extraction evaluation notebook.
+
 ### Bibliography
 Debanjan Mahata, Navneet Agarwal, Dibya Gautam, Amardeep Kumar, Sagar Dhiman, Anish Acharya, & Rajiv Ratn Shah. (2021). LDkp Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5501744