Contents
Can you use Bert to measure semantic similarity?
An important note here is that BERT is not trained for semantic sentence similarity directly like the Universal Sentence Encoder or InferSent models. Therefore, BERT embeddings cannot be used directly to apply cosine distance to measure similarity.
Can a Bert model be used for FAQ retrieval?
On the other hand, the relevance between the query and answer can be learned by using QA pairs in a FAQ database. The recently-proposed BERT model is used for the relevance calculation. Since the number of QA pairs in FAQ page is not enough to train a model, we cope with this issue by leveraging FAQ sets that are similar to the one in question.
Can a sentence have more than one semantic similarity?
Semantic similarity can have several dimensions, and sentences may be similar in one but opposite in the other. Like all models, BERT is not the perfect solution that fits all problem areas and multiple models may need to be evaluated for performance depending on the task.
Is it possible to use Bert in a document?
Moreover, BERT requires quadratic memory with respect to the input length which would not be feasible with documents. It is quite common practice to average word embeddings to get a sentence representation. You can try the same thing with BERT and average the [CLS] vectors from BERT over sentences in a document.
Is there a Bert model for two sentences?
We will fine-tune a BERT model that takes two sentences as inputs and that outputs a similarity score for these two sentences. Note: install HuggingFace transformers via pip install transformers (version >= 2.11.0).
Can you use Bert to measure cosine distance?
Therefore, BERT embeddings cannot be used directly to apply cosine distance to measure similarity. However, there are easy wrapper services and implementations like the popular bert-as-a-service that can be used to that effect.
How are args similar to Bert-keras sentence pairs?
Args: sentence_pairs: Array of premise and hypothesis input sentences. labels: Array of labels. batch_size: Integer batch size. shuffle: boolean, whether to shuffle the data. include_targets: boolean, whether to incude the labels.