Update README.md

Author: sgrvinod

README.md

@@ -472,19 +472,19 @@ __Can I use pretrained word embeddings (GloVe, CBOW, skipgram, etc.) instead of
 
 Yes, you could, with the `load_pretrained_embeddings()` method in the `Decoder` class. You could also choose to fine-tune (or not) with the `fine_tune_embeddings()` method.
 
-After creating the Decoder in [`train.py`](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning/blob/master/train.py), you should provide the pretrained vectors to `load_pretrained_embeddings()` stacked in the same order as in the `word_map`. For words that you don't have pretrained vectors for, like <start>, you can initialize embeddings randomly like we did in `init_weights()`. I recommend fine-tuning to learn more meaningful vectors for these randomly initialized vectors.
+After creating the Decoder in [`train.py`](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning/blob/master/train.py), you should provide the pretrained vectors to `load_pretrained_embeddings()` stacked in the same order as in the `word_map`. For words that you don't have pretrained vectors for, like `<start>`, you can initialize embeddings randomly like we did in `init_weights()`. I recommend fine-tuning to learn more meaningful vectors for these randomly initialized vectors.
 
 ```python
 decoder = DecoderWithAttention(attention_dim=attention_dim,
                                embed_dim=emb_dim,
                                decoder_dim=decoder_dim,
                                vocab_size=len(word_map),
                                dropout=dropout)
-decoder.load_pretrained_embeddings(pretrained_embeddings)
+decoder.load_pretrained_embeddings(pretrained_embeddings)  # pretrained_embeddings should be of dimensions (len(word_map), emb_dim)
 decoder.fine_tune_embeddings(True)  # or False
 ```
 
-Also make sure to change the `embed_dim` parameter to the size of your pre-trained embeddings. This should automatically adjust the input size of the decoder LSTM to accomodate them.
+Also make sure to change the `emb_dim` parameter to the size of your pre-trained embeddings. This should automatically adjust the input size of the decoder LSTM to accomodate them.
 
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