Yes!!
It now shows! 
EPOCH: 0 loss 2.245065689086914
tensor([[-3.8246e-04,  2.1555e-04,  1.2193e-04,  2.0082e-04],
        [-2.8758e-04,  1.1619e-04,  1.9071e-04,  7.6792e-05],
        [-3.1537e-05, -1.1242e-05, -1.6944e-05,  5.3930e-05],
        [ 1.9776e-04, -1.0734e-04,  1.3045e-04,  4.6559e-05],
        [-4.7442e-04,  1.5603e-04,  2.1428e-05,  3.2986e-05],
        [-2.8206e-04,  9.5163e-05,  1.2406e-04,  4.7455e-05],
        [ 5.1199e-05,  1.6658e-04,  7.0462e-05,  2.4822e-05],
        [-1.6758e-05,  2.2051e-05,  4.3919e-07,  2.0111e-05],
        [ 2.0732e-04, -6.2676e-04,  2.2783e-04, -1.6364e-04],
        [-7.4985e-04,  6.7677e-04, -2.5997e-04,  1.8289e-04],
        [ 6.8787e-05,  4.0990e-04,  8.4813e-05,  1.0382e-04],
        [-2.0091e-04,  3.8331e-05,  1.9880e-04, -2.5443e-05],
        [-3.0217e-04,  1.7034e-04,  3.4256e-05,  5.5788e-05],
        [-4.9970e-04,  9.8152e-05,  2.3805e-04, -5.7322e-06],
        [-1.7798e-04, -2.1839e-05, -1.3361e-04, -5.3941e-05],
        [ 6.4214e-05, -1.7047e-04,  1.0869e-04, -2.1387e-05]])
EPOCH: 1 loss 2.224614143371582
tensor([[-0.0003,  0.0002,  0.0001,  0.0001],
        [-0.0002,  0.0001,  0.0003,  0.0000],
        [-0.0000, -0.0001, -0.0000,  0.0000],
        [ 0.0001, -0.0001,  0.0001,  0.0001],
        [-0.0004,  0.0002,  0.0001,  0.0000],
        [-0.0003,  0.0001,  0.0001, -0.0000],
        [ 0.0002,  0.0001,  0.0000,  0.0000],
        [-0.0000, -0.0000, -0.0000, -0.0000],
        [ 0.0002, -0.0005,  0.0004, -0.0001],
        [-0.0010,  0.0005, -0.0000,  0.0001],
        [ 0.0004,  0.0002, -0.0001,  0.0001],
        [-0.0004, -0.0000,  0.0007, -0.0002],
        [-0.0002,  0.0001,  0.0000,  0.0000],
        [-0.0005,  0.0001,  0.0003, -0.0001],
        [-0.0002, -0.0000, -0.0000, -0.0001],
        [ 0.0000, -0.0001,  0.0001, -0.0000]])
EPOCH: 2 loss 2.190521478652954
tensor([[-0.0002,  0.0002,  0.0001,  0.0002],
        [-0.0002,  0.0001,  0.0002,  0.0001],
        [ 0.0000, -0.0000, -0.0000,  0.0001],
        [ 0.0001, -0.0001,  0.0001,  0.0000],
        [-0.0005,  0.0002, -0.0000, -0.0001],
        [-0.0003,  0.0001,  0.0001,  0.0000],
        [ 0.0001,  0.0001,  0.0000,  0.0001],
        [-0.0000, -0.0000, -0.0000, -0.0000],
        [ 0.0002, -0.0004,  0.0000, -0.0002],
        [-0.0008,  0.0006, -0.0002,  0.0001],
        [ 0.0004,  0.0003, -0.0001,  0.0003],
        [-0.0005, -0.0001,  0.0006, -0.0002],
        [-0.0002,  0.0001, -0.0000,  0.0000],
        [-0.0005,  0.0001,  0.0002, -0.0000],
        [-0.0001, -0.0000, -0.0001, -0.0001],
        [ 0.0000, -0.0001,  0.0001, -0.0000]])
EPOCH: 3 loss 2.196777105331421
tensor([[-3.5749e-04,  1.3554e-04,  1.8028e-04,  1.6417e-04],
        [-2.2018e-04,  7.7530e-05,  1.7841e-04,  4.2579e-05],
        [-4.6930e-05,  4.5388e-06,  2.5507e-05,  1.2351e-04],
        [ 1.6042e-04, -3.2516e-05,  4.8225e-05,  3.4275e-05],
        [-5.4236e-04,  1.9008e-04,  1.2001e-04,  3.9673e-05],
        [-2.9503e-04,  9.7923e-05,  7.1795e-05,  2.3484e-06],
        [-9.2064e-07,  1.2369e-04,  1.2139e-04,  1.1705e-04],
        [-9.8637e-06, -1.8803e-05, -4.1882e-06, -2.8696e-06],
        [ 3.6163e-04, -4.7896e-04,  1.4343e-04, -2.2040e-04],
        [-8.3896e-04,  5.1561e-04, -2.8873e-05,  2.0739e-04],
        [-1.6798e-04,  3.1492e-04,  2.7706e-04,  2.6071e-04],
        [-4.1065e-04,  5.1932e-05,  2.5442e-04, -5.4233e-05],
        [-2.8720e-04,  1.4935e-04,  5.5944e-05,  5.0228e-05],
        [-4.5585e-04,  8.2353e-05,  2.0688e-04, -3.6975e-05],
        [-1.9315e-04, -1.3623e-05, -1.4761e-04, -1.0804e-05],
        [ 6.2935e-05, -1.1300e-04,  5.0673e-05, -5.8553e-05]])
EPOCH: 4 loss 2.179868459701538
tensor([[-3.5148e-04,  9.1752e-05,  1.4228e-04,  1.7029e-04],
        [-1.0502e-04,  7.8332e-05,  9.0784e-05,  6.4270e-05],
        [-4.9014e-05, -4.5404e-05, -3.8506e-05,  4.5116e-05],
        [ 1.3610e-04, -3.4125e-05, -3.3795e-06, -3.2209e-05],
        [-5.8784e-04,  1.5989e-04,  1.9802e-04,  6.7654e-05],
        [-2.3988e-04,  1.2917e-04,  5.5225e-05,  8.0370e-06],
        [ 1.9398e-04,  1.3447e-04,  1.3801e-04,  5.0111e-05],
        [-3.6221e-06, -1.7676e-05,  3.2276e-05,  1.1954e-05],
        [ 2.1130e-04, -7.2388e-04,  3.2327e-04, -2.5662e-04],
        [-9.7373e-04,  4.7264e-04,  1.7635e-04,  1.9308e-04],
        [ 3.4610e-04,  3.8229e-04, -5.5605e-05,  1.2019e-04],
        [-5.0158e-04,  1.9040e-04,  3.5456e-05,  4.1480e-05],
        [-2.8807e-04,  1.2262e-04,  4.9847e-05,  4.9944e-05],
        [-3.4162e-04,  1.0266e-04,  9.3150e-05, -9.3202e-06],
        [-1.5973e-04,  3.4818e-05, -1.3316e-04, -6.2089e-05],
        [ 6.6439e-05, -1.0870e-04,  5.7728e-05, -7.0024e-05]])

Thanks so much man! 
Since we only have 2 sentences in our training, there’s no real way to measure the performance improvement right? Maybe I’ll right a post about it and share it with the community! 
Thanks again!