Update README.md

README.md

@@ -43,19 +43,60 @@ Sahrawat, Dhruva, Debanjan Mahata, Haimin Zhang, Mayank Kulkarni, Agniv Sharma,
 * 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 (link incoming).
+* Sometimes the output can make no sense.
 
 ### ❓ How to use
 ```python
-
+# Model parameters
+from transformers import (
+    Text2TextGenerationPipeline,
+    BartForConditionalGeneration,
+    AutoTokenizer,
+)
+import numpy as np
+
+
+class KeyphraseGenerationPipeline(Text2TextGenerationPipeline):
+    def __init__(self, model, keyphrase_sep_token=";", *args, **kwargs):
+        super().__init__(
+            model=BartForConditionalGeneration.from_pretrained(model),
+            tokenizer=AutoTokenizer.from_pretrained(model),
+            *args,
+            **kwargs
+        )
+        self.keyphrase_sep_token = keyphrase_sep_token
+
+    def postprocess(self, model_outputs):
+        results = super().postprocess(
+            model_outputs=model_outputs
+        )
+        return np.unique([result.strip() for result in results[0].get("generated_text").split(self.keyphrase_sep_token)])
 ```
 
 ```python
+model_name = "DeDeckerThomas/keyphrase-generation-keybart-inspec"
+generator = KeyphraseGenerationPipeline(model=model_name)
 
-```
+```python
+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.
+""".replace(
+    "\n", ""
+)
+
+keyphrases = generator(text)
+
+print(keyphrases)
 
 ```
 # Output
-
+['artificial intelligence' 'classical machine learning methods'
+ 'keyphrase extraction' 'lingu' 'statistics' 'text analysis']
 ```
 
 ## 📚 Training Dataset