I am bulding a model for tweet sentiment classification.
I initially used gensim to learn word2vec representation of my dataset.
I have set embedding size to 32
eg:
**sentence**:
['Have', 'you', 'tried', 'adding', 'yourself']

**word2vec representation from gensim:**
tensor([[ 1.4620e-01, -8.5515e-01,  1.4198e+00,  3.4828e+00, -3.3035e-01,
          3.4886e-01,  3.8095e+00, -3.5703e+00,  2.7998e+00, -2.9370e+00,
          2.9146e+00, -3.6229e-01,  3.4922e+00,  1.8544e+00,  3.1512e+00,
          4.9468e+00, -2.1402e+00,  2.5461e+00, -1.9959e+00,  2.0394e-01,
          2.7256e-01,  9.4945e-01,  2.9587e+00,  5.5111e+00,  5.1617e+00,
         -2.9875e+00,  2.2928e+00, -1.6469e+00,  3.2937e-01,  2.4262e+00,
          1.7776e+00, -3.5884e+00],
        [-6.4258e+00, -1.1460e+00,  4.4689e+00,  4.4227e+00, -2.8765e-01,
          2.6317e+00,  5.6104e-01, -2.6672e+00, -2.3151e-01, -1.4247e+00,
         -7.8311e+00,  3.4394e+00, -3.8948e+00,  5.1647e+00, -5.1729e+00,
         -3.3228e-01,  1.5289e+00, -9.3406e-03, -1.1680e+00, -3.3537e+00,
          7.4931e+00,  8.0219e-01, -2.1190e+00, -4.0652e-01, -3.2117e-01,
          2.4333e+00,  1.4425e+00,  1.8844e+00,  1.6728e+00,  3.0737e+00,
          3.6921e+00,  2.0336e+00],
        [ 1.1453e+00,  1.7200e+00,  1.5659e-01,  2.0237e+00,  1.3018e+00,
         -2.8969e+00,  7.6323e-02, -7.1022e-01,  2.4552e+00,  1.4979e+00,
         -7.8366e-01, -1.0592e+00,  7.1787e-01, -1.4309e+00, -6.0945e-01,
          2.0332e+00,  5.0113e+00,  7.0676e-01,  2.0374e-01, -2.9717e+00,
          4.2711e-01, -1.7514e+00, -1.6970e+00, -4.4132e+00,  2.3952e-01,
          7.6439e-01, -2.8841e+00, -4.6913e+00, -1.1418e+00, -4.0310e+00,
          3.0588e+00, -2.1230e+00],
        [-1.3288e+00,  6.3822e-01,  3.4908e-01,  1.9915e+00,  3.9657e-01,
         -7.8983e-01, -2.8086e-01, -5.7929e-01,  2.0741e+00, -1.2884e+00,
         -2.5335e+00, -2.1533e-02,  2.0698e+00, -3.9439e-03,  2.5604e-01,
         -2.1838e+00,  1.3293e-01,  3.4999e-01, -2.3387e+00, -2.0124e+00,
          1.7638e+00,  3.6158e-01,  1.7742e+00, -4.8334e-01, -2.5748e+00,
         -2.7936e+00,  2.7674e-01, -2.0076e+00, -1.5897e+00, -1.1462e+00,
         -3.3607e-01, -4.0830e-01],
        [-4.8255e-01,  8.9328e-01,  1.1424e+00,  2.7374e+00, -1.3206e+00,
          5.2367e-01,  8.1122e-03, -2.6338e+00, -2.1248e+00, -2.3494e+00,
         -3.9087e+00, -3.9225e-01, -1.9196e+00,  2.0214e+00, -2.1552e+00,
          3.0173e-01,  2.9504e+00,  3.2806e-01,  5.9682e-01, -8.2841e-01,
          1.8790e+00,  1.7234e+00,  5.6904e-01, -1.1782e+00,  1.3931e+00,
          2.6821e+00,  5.2979e-02, -3.8034e+00, -6.0337e-01,  2.8629e+00,
          4.1186e+00, -5.8047e-01]])
**shape**:
(5, 32)

Now I am using torch.nn.utils.rnn.pack_padded_sequence to pass this word2vec representations as input to gru.
But it seems that its not learning, is there issue in the pretrained word2vec representations?

I also tried to increase the embedding size from 32 to 100 but its still not learning.