Hi, I am new with skipgram. I went through the documents, it is taking a lot of time to train both skipgram and / or skipgram with negative sampling. The issue happens when the notebook connection is disconnected.

Example training code:

```
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Get our noise distribution
# Using word frequencies calculated earlier in the notebook
word_freqs = np.array(sorted(freqs.values(), reverse=True))
unigram_dist = word_freqs/word_freqs.sum()
noise_dist = torch.from_numpy(unigram_dist**(0.75)/np.sum(unigram_dist**(0.75)))
# instantiating the model
embedding_dim = 300
model = SkipGramNeg(len(vocab_to_int), embedding_dim, noise_dist=noise_dist).to(device)
# using the loss that we defined
criterion = NegativeSamplingLoss()
optimizer = optim.Adam(model.parameters(), lr=0.003)
print_every = 1500
steps = 0
epochs = 5
# train for some number of epochs
for e in range(epochs):
# get our input, target batches
for input_words, target_words in get_batches(train_words, 512):
steps += 1
inputs, targets = torch.LongTensor(input_words), torch.LongTensor(target_words)
inputs, targets = inputs.to(device), targets.to(device)
# input, outpt, and noise vectors
input_vectors = model.forward_input(inputs)
output_vectors = model.forward_output(targets)
noise_vectors = model.forward_noise(inputs.shape[0], 5)
# negative sampling loss
loss = criterion(input_vectors, output_vectors, noise_vectors)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# loss stats
if steps % print_every == 0:
print("Epoch: {}/{}".format(e+1, epochs))
print("Loss: ", loss.item()) # avg batch loss at this point in training
valid_examples, valid_similarities = cosine_similarity(model.in_embed, device=device)
_, closest_idxs = valid_similarities.topk(6)
valid_examples, closest_idxs = valid_examples.to('cpu'), closest_idxs.to('cpu')
for ii, valid_idx in enumerate(valid_examples):
closest_words = [int_to_vocab[idx.item()] for idx in closest_idxs[ii]][1:]
print(int_to_vocab[valid_idx.item()] + " | " + ', '.join(closest_words))
print("...\n")
```

I am looking to the way to save and continue training efficiently.

Thanks.