ihk
/

ojobert

@@ -16,9 +16,12 @@ _Nesta, the UK's innovation agency, has been scraping online job adverts since 2
 _Although we are unable to share the raw data openly, we aim to open source **our models, algorithms and tools** so that anyone can use them for their own research and analysis._
 This model is pre-trained from a `distilbert-base-uncased` checkpoint on 100k sentences from scraped online job postings as part of the Open Jobs Observatory.
-🖨️ Use
 To use the model:
 ```
@@ -33,7 +36,7 @@ An example use is as follows:
 text = "Would you like to join a major [MASK] company?"
 model(text, top_k=3)
->> [{'score': 0.1886572688817978,
   'token': 13859,
   'token_str': 'pharmaceutical',
   'sequence': 'would you like to join a major pharmaceutical company?'},
@@ -46,7 +49,8 @@ model(text, top_k=3)
   'token_str': 'construction',
   'sequence': 'would you like to join a major construction company?'}]
-⚖️ Training results
 The fine-tuning metrics are as follows:
 - eval_loss: 2.5871026515960693

 _Although we are unable to share the raw data openly, we aim to open source **our models, algorithms and tools** so that anyone can use them for their own research and analysis._
+## 📟 About
 This model is pre-trained from a `distilbert-base-uncased` checkpoint on 100k sentences from scraped online job postings as part of the Open Jobs Observatory.
+## 🖨️ Use
 To use the model:
 ```
 text = "Would you like to join a major [MASK] company?"
 model(text, top_k=3)
+[{'score': 0.1886572688817978,
   'token': 13859,
   'token_str': 'pharmaceutical',
   'sequence': 'would you like to join a major pharmaceutical company?'},
   'token_str': 'construction',
   'sequence': 'would you like to join a major construction company?'}]
+## ⚖️ Training results
 The fine-tuning metrics are as follows:
 - eval_loss: 2.5871026515960693