numerical entity extraction from unstructured texts using python
So far my research shows that you can treat numbers as words. 
This raises an issue : learning 5 will be ok, but 19684 will be to rare to be learned.
One proposal is to convert into words. "nineteen thousands six hundred eighty four" and embedding each word. The inconvenient is that you are now learning a (minimum) 6 dimensional vector (one dimension per word)
Based on your usage, you can also embed 0 to 3000 with distinct ids, and say 3001 to 10000 will map id 3001 in your dictionary, and then add one id in your dictionary for each 10x.
I want to extract numerical entities like temperature and duration mentioned in unstructured formats of texts using neural models like CRF using python. I would like to know how to proceed for numerical extraction as most of the examples available on the internet are for specific words or strings extraction. 
Input: 'For 5 minutes there, I felt like baking in an oven at 350 degrees F'
Output: temperature: 350
        duration: 5 minutes