Update README.md

README.md

@@ -35,11 +35,25 @@ Now with the recent innovations in deep learning methods (such as recurrent neur
 
 
 ## 📓 Model Description
-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
+This model is a fine-tuned KBIR model on the Inspec dataset. KBIR or Keyphrase Boundary Infilling with Replacement is a pre-trained model 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).
+You can find more information about the architecture in this paper: https://arxiv.org/abs/2112.08547.
+
+The model is fine-tuned as a token classification problem where the text is labeled using the BIO scheme.
+| Label | Description                     |
+| ----- | ------------------------------- |
+| B     | At the beginning of a keyphrase |
+| I     | Inside a keyphrase              |
+| O     | Outside a keyphrase             |
+
+Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learning Rich Representation of Keyphrases from Text." arXiv preprint arXiv:2112.08547 (2021).
+Sahrawat, Dhruva, Debanjan Mahata, Haimin Zhang, Mayank Kulkarni, Agniv Sharma, Rakesh Gosangi, Amanda Stent, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann. "Keyphrase extraction as sequence labeling using contextualized embeddings." In European Conference on Information Retrieval, pp. 328-335. Springer, Cham, 2020.
 
 ## ✋ Intended uses & limitations
+### 🛑 Limitations
+* This keyphrase extraction model is very domain-specific and will perform very well on abstracts of scientific papers.
+* Only works for English documents.
+* For a custom model, please consult the training notebook (link incoming).
+
 ### ❓ How to use
 ```python
 # Define post_process functions
@@ -131,18 +145,10 @@ and context of a document, which is quite an improvement.
  'semantics' 'statistics' 'text analysis' 'transformers']
 ```
 
-### 🛑 Limitations
-* The model performs very well on abstracts of scientific papers. Please be aware that this model very domain-specific.
-* Only works in English.
-
 ## [📚 Training Dataset](https://huggingface.co/datasets/midas/inspec)
 ## 👷‍♂️ Training procedure
-The model is fine-tuned as a token classification problem where the text is labeled using the BIO scheme.
-- B => Begin of a keyphrase
-- I => Inside of a keyphrase
-- O => Ouside of a keyphrase
 
-For more information, you can take a look at the training notebook.
+For more information, you can take a look at the training notebook (link incoming).
 
 ### Preprocessing
 ```python
@@ -189,11 +195,4 @@ 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
-
-Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learning Rich Representation of Keyphrases from Text." arXiv preprint arXiv:2112.08547 (2021).
-
-Sahrawat, Dhruva, Debanjan Mahata, Haimin Zhang, Mayank Kulkarni, Agniv Sharma, Rakesh Gosangi, Amanda Stent, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann. "Keyphrase extraction as sequence labeling using contextualized embeddings." In European Conference on Information Retrieval, pp. 328-335. Springer, Cham, 2020.
+For more information on the evaluation process, you can take a look at the keyphrase extraction evaluation notebook.