Results after training in 80% of 15189 instances 

20it [00:11,  1.85it/s]Train: wpb=2121, num_updates=20, accuracy=44.1, loss=0.00\
50it [00:28,  1.76it/s]Train: wpb=2121, num_updates=50, accuracy=55.4, loss=0.00\
100it [00:55,  1.88it/s]Train: wpb=2117, num_updates=100, accuracy=64.5, loss=0.00\
200it [01:48,  1.85it/s]Train: wpb=2132, num_updates=200, accuracy=71.6, loss=0.00\
300it [02:42,  1.88it/s]Train: wpb=2147, num_updates=300, accuracy=75.1, loss=0.00\
380it [03:24,  1.86it/s]\
Train: wpb=2142, num_updates=380, accuracy=76.9, loss=0.00\
| epoch 000 | train accuracy=76.9%, train loss=0.00\
| epoch 000 | valid accuracy=85.7%, valid loss=0.00\


20it [00:10,  1.85it/s]Train: wpb=2121, num_updates=20, accuracy=84.6, loss=0.00\
50it [00:27,  1.77it/s]Train: wpb=2121, num_updates=50, accuracy=84.6, loss=0.00\
100it [00:54,  1.87it/s]Train: wpb=2117, num_updates=100, accuracy=85.1, loss=0.00\
200it [01:47,  1.86it/s]Train: wpb=2132, num_updates=200, accuracy=85.4, loss=0.00\
300it [02:41,  1.88it/s]Train: wpb=2147, num_updates=300, accuracy=85.6, loss=0.00\
380it [03:24,  1.86it/s]\
Train: wpb=2142, num_updates=380, accuracy=85.8, loss=0.00\
| epoch 001 | train accuracy=85.8%, train loss=0.00\
| epoch 001 | valid accuracy=88.3%, valid loss=0.00

We have to change the loss function... It seems to be a problem...\

**You can evaluate the performance of our model by writing the following example:**
*"google chrome before 18. 0. 1025. 142 does not properly validate the renderer's navigation requests, which has unspecified impact and remote attack vectors."*

The result should be similar to:
['B-vendor', 'B-application', 'B-version', 'I-version', 'I-version', 'I-version', 'I-version', 'I-version', 'I-version', 'I-version', 'I-version', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-relevant_term', 'O', 'O', 'O', 'O', 'O', 'O', 'B-relevant_term', 'B-relevant_term', 'O', 'O']