KEYWORD QUERY VS STATEMENT/QUESTION CLASSIFIER FOR NEURAL SEARCH
| Train Loss | Validation Acc. | Test Acc. |
|---|---|---|
| 0.000806 | 0.99 | 0.997 |
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("shahrukhx01/bert-mini-finetune-question-detection")
model = AutoModelForSequenceClassification.from_pretrained("shahrukhx01/bert-mini-finetune-question-detection")
Trained to add feature for classifying queries between Keyword Query or Question + Statement Query using classification in Haystack
Problem Statement: One common challenge that we saw in deployments: We need to distinguish between real questions and keyword queries that come in. We only want to route questions to the Reader branch in order to maximize the accuracy of results and minimize computation efforts/costs.
Baseline: https://www.kaggle.com/shahrukhkhan/question-v-statement-detection
Dataset: https://www.kaggle.com/stefanondisponibile/quora-question-keyword-pairs
Kaggle Notebook: https://www.kaggle.com/shahrukhkhan/question-vs-statement-classification-mini-bert/