Update README.md

README.md

@@ -11,7 +11,7 @@ metrics:
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 - text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text. Since this is a time-consuming process, Artificial Intelligence is used to automate it. Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries.  Now with the recent innovations in deep learning methods (such as recurrent neural networks and transformers, GANS, …), keyphrase extraction can be improved. These new methods also focus on the semantics and context of a document, which is quite an improvement."
   example_title: "Example 1"
-- text: "In this work, we explore how to learn taskspecific language models aimed towards learning rich representation of keyphrases fromtext documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In thediscriminative setting, we introduce a newpre-training objective - Keyphrase BoundaryInfilling with Replacement (KBIR), showinglarge gains in performance (upto 9.26 pointsin F1) over SOTA, when LM pre-trained usingKBIR is fine-tuned for the task of keyphraseextraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases relatedto the input text in the CatSeq format, insteadof the denoised original input. This also ledto gains in performance (upto 4.33 points inF1@M) over SOTA for keyphrase generation.Additionally, we also fine-tune the pre-trainedlanguage models on named entity recognition(NER), question answering (QA), relation extraction (RE), abstractive summarization andachieve comparable performance with that ofthe SOTA, showing that learning rich representation of keyphrases is indeed beneficial formany other fundamental NLP tasks."
+- text: "In this work, we explore how to learn taskspecific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In thediscriminative setting, we introduce a newpre-training objective - Keyphrase BoundaryInfilling with Replacement (KBIR), showinglarge gains in performance (upto 9.26 pointsin F1) over SOTA, when LM pre-trained usingKBIR is fine-tuned for the task of keyphraseextraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases relatedto the input text in the CatSeq format, insteadof the denoised original input. This also ledto gains in performance (upto 4.33 points inF1@M) over SOTA for keyphrase generation.Additionally, we also fine-tune the pre-trainedlanguage models on named entity recognition(NER), question answering (QA), relation extraction (RE), abstractive summarization andachieve comparable performance with that ofthe SOTA, showing that learning rich representation of keyphrases is indeed beneficial formany other fundamental NLP tasks."
   example_title: "Example 2"
 model-index:
 - name: DeDeckerThomas/keyphrase-extraction-kbir-inspec
@@ -147,12 +147,16 @@ and context of a document, which is quite an improvement.
  'semantics' 'statistics' 'text analysis' 'transformers']
 ```
 
-## [📚 Training Dataset](https://huggingface.co/datasets/midas/inspec)
-## 👷‍♂️ Training procedure
+## 📚 Training Dataset
+Inspec is a keyphrase extraction/generation dataset consisting of 2000 English scientific papers from the scientific domains of Computers and Control and Information Technology published between 1998 to 2002. The keyphrases are annotated by professional indexers or editors.
+
+You can find more information here: https://huggingface.co/datasets/midas/inspec
 
-For more information, you can take a look at the training notebook (link incoming).
+## 👷‍♂️ Training procedure
+For more in detail information, you can take a look at the training notebook (link incoming).
 
 ### Preprocessing
+The documents in the dataset are already preprocessed into list of words with the corresponding labels. The only thing that must be done is the realignment of the labels and the tokenization.
 ```python
 def preprocess_fuction(all_samples_per_split):
     tokenized_samples = tokenizer.batch_encode_plus(