What is the process of utilizing fine-tuned BERT for training a fresh sentence-transformer?

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The process of utilizing fine-tuned BERT for training a fresh sentence-transformer can be summarized as follows:

1. Fine-tune BERT: First, fine-tune the pre-trained BERT model on a specific supervised task, such as text classification or natural language inference, using a labeled dataset.

2. Extract sentence representations: After fine-tuning, extract the final hidden state of the [CLS] token from each sentence, which serves as the sentence representation.

3. Build a sentence-transformer: Use the extracted sentence representations to train a new sentence-transformer, which is a neural network that maps a sentence into a vector space, such that semantically similar sentences are closer in distance.

4. Train the transformer: Train the sentence-transformer using a large dataset of sentence pairs, where the objective is to maximize the cosine similarity between similar pairs of sentences and minimize it for dissimilar pairs.

5. Evaluate and fine-tune: Validate the performance of the trained sentence-transformer on a downstream task, such as semantic textual similarity or paraphrase detection. Fine-tune the model if necessary.

6. Deploy the transformer: Once the model is fine-tuned and validated, deploy it to use for various tasks, such as data cleaning, search, or recommendation.