Update README.md

Author: sgrvinod

README.md

@@ -472,7 +472,7 @@ __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 [`eval.py`](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning/blob/master/eval.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,