Question: I want to load a pre-trained word2vec embedding with gensim into a PyTorch embedding layer.

How do I get the embedding weights loaded by gensim into the PyTorch embedding layer?

Answer: 
```
from gensim.models import Word2Vec

model = Word2Vec(reviews,size=100, window=5, min_count=5, workers=4)
#gensim model created

import torch

weights = torch.FloatTensor(model.wv.vectors)
embedding = nn.Embedding.from_pretrained(weights)

```
Had similar problem: "after training and saving embeddings in **binary** format using gensim, how I load them to torchtext?"

I just saved the file to txt format and then follow the superb [tutorial](https://github.com/bentrevett/pytorch-sentiment-analysis/blob/master/C%20-%20Loading%2C%20Saving%20and%20Freezing%20Embeddings.ipynb) of loading custom word embeddings.

```
def convert_bin_emb_txt(out_path,emb_file):
    txt_name = basename(emb_file).split(".")[0] +".txt"
    emb_txt_file = os.path.join(out_path,txt_name)
    emb_model = KeyedVectors.load_word2vec_format(emb_file,binary=True)
    emb_model.save_word2vec_format(emb_txt_file,binary=False)
    return emb_txt_file

emb_txt_file = convert_bin_emb_txt(out_path,emb_bin_file)
custom_embeddings = vocab.Vectors(name=emb_txt_file,
                                  cache='custom_embeddings',
                                  unk_init=torch.Tensor.normal_)

TEXT.build_vocab(train_data,
                 max_size=MAX_VOCAB_SIZE,
                 vectors=custom_embeddings,
                 unk_init=torch.Tensor.normal_)

```

tested for: PyTorch: 1.2.0 and TorchText: 0.4.0.

I added this answer because with the accepted answer I was not sure how to follow the linked [tutorial](https://github.com/bentrevett/pytorch-sentiment-analysis/blob/master/C%20-%20Loading%2C%20Saving%20and%20Freezing%20Embeddings.ipynb) and initialize all words not in the embeddings using the normal distribution and how to make the vectors and equal to zero.