is there an efficient way to predict? about tf_ner

[cedar33]

Thanks a lot, this is exactly what I need, I have learned so much from your code, thank you again

[guillaumegenthial]

Hi @cedar33 ,
Yes, there is an efficient way to predict. Have a look at the serve.py script. It provides an example of how to use a predictor on a batch of size 1. You can create bigger batches as long as you pad the inputs. It will be efficient.

[ahmadshabbir2468]

After batch size =50  we read 50 lines from file so server.py code will be like this  ????

def file_read_from_head(fname, nlines):
    from itertools import islice
    with open(fname) as f:
        for line in islice(f, nlines):
            print(line)
    return nlines


fifty_LINE= file_read_from_head('example1.txt', 50) #we read 50 line from file

if __name__ == '__main__':
    export_dir = 'saved_model'
    subdirs = [x for x in Path(export_dir).iterdir()
               if x.is_dir() and 'temp' not in str(x)]
    latest = str(sorted(subdirs)[-1])
    predict_fn = predictor.from_saved_model(latest)
    for LINE in fifty_LINE: 

	    words = [w.encode() for w in LINE.split()]
	    nwords = len(words)
	    predictions = predict_fn({'words': [words], 'nwords': [nwords]})
	    print(predictions)
#Loop over each line ??? or just 
#words = [w.encode() for w in fifty_LINE.split()] is enough

[ahmadshabbir2468]

Thanks a lot, this is exactly what I need, I have learned so much from your code, thank you again

Much appreciated Brother
Kind regards Ahmad