Update README.md

README.md

@@ -67,17 +67,19 @@ SequenceClassifierOutput(loss=None, logits=tensor([[-0.4635, -1.0112,  4.7935,
          -2.4848,  0.2630, -0.4584, -2.4327, -1.1654,  0.3897, -0.3374, -1.2418,
          -0.1045,  0.2827, -1.5667, -0.0963]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)
 >>> classes = torch.softmax(logits.logits, dim = -1)
+>>> torch.set_printoptions(precision=3, sci_mode=False)
 >>> classes
-tensor([[3.6522e-03, 2.1118e-03, 7.0082e-01, 1.3621e-02, 2.9527e-02, 1.1071e-02,
-         2.7143e-02, 4.3466e-02, 2.9051e-02, 1.0417e-02, 6.1027e-04, 1.4051e-02,
-         1.3132e-02, 1.2132e-03, 1.4089e-03, 3.8160e-03, 5.2022e-03, 8.8345e-04,
-         1.1242e-02, 4.7424e-03, 6.3974e-03, 3.1215e-03, 9.0975e-03, 6.3689e-03,
-         4.8384e-04, 7.5519e-03, 3.6707e-03, 5.0970e-04, 1.8101e-03, 8.5720e-03,
-         4.1427e-03, 1.6769e-03, 5.2292e-03, 7.7021e-03, 1.2117e-03, 5.2723e-03]],
+tensor([[    0.004,     0.002,     0.701,     0.014,     0.030,     0.011,
+             0.027,     0.043,     0.029,     0.010,     0.001,     0.014,
+             0.013,     0.001,     0.001,     0.004,     0.005,     0.001,
+             0.011,     0.005,     0.006,     0.003,     0.009,     0.006,
+             0.000,     0.008,     0.004,     0.001,     0.002,     0.009,
+             0.004,     0.002,     0.005,     0.008,     0.001,     0.005]],
        grad_fn=<SoftmaxBackward0>)
 >>> top_class = torch.argmax(logits.logits, dim = -1)
->>> top_class = top_class.detach().numpy()[0]
->>> model.config.id2label[top_class]
+>>> top_class
+tensor([2])
+>>> model.config.id2label[top_class.numpy()[0]]
 'up'
 ```