How do I implement contextual embeddings in my NLP project?

How do I implement contextual embeddings in my NLP project?

To implement contextual embeddings, you can use models like BERT or ELMo. Have you chosen a specific framework?

I'm considering using TensorFlow. What are the steps to integrate BERT?

First, install the Transformers library. Then, load the BERT model and tokenizer. Would you like a code snippet?

Yes, please provide a code snippet for loading BERT.

Here's a simple example:

from transformers import BertTokenizer, BertModel

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

This will get you started!

How do I fine-tune BERT for my specific task?

You can fine-tune BERT by adding a classification layer and training it on your labeled dataset. Would you like more details?