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--- |
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license: apache-2.0 |
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datasets: |
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- cnmoro/QuestionClassification |
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tags: |
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- classification |
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- questioning |
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- directed |
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- generic |
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language: |
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- en |
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- pt |
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library_name: transformers |
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pipeline_tag: text-classification |
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widget: |
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- text: "What is the summary of the text?" |
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--- |
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A finetuned version of prajjwal1/bert-tiny. |
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The goal is to classify questions into "Directed" or "Generic". |
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If a question is not directed, we would change the actions we perform on a RAG pipeline (if it is generic, semantic search wouldn't be useful directly; e.g. asking for a summary). |
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(Class 0 is Generic; Class 1 is Directed) |
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The accuracy on the training dataset is around 87.5% |
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```python |
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from transformers import BertForSequenceClassification, BertTokenizerFast |
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import torch |
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# Load the model and tokenizer |
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model = BertForSequenceClassification.from_pretrained("cnmoro/bert-tiny-question-classifier") |
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tokenizer = BertTokenizerFast.from_pretrained("cnmoro/bert-tiny-question-classifier") |
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def is_question_generic(question): |
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# Tokenize the sentence and convert to PyTorch tensors |
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inputs = tokenizer( |
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question.lower(), |
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truncation=True, |
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padding=True, |
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return_tensors="pt", |
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max_length=512 |
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) |
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# Get the model's predictions |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# Extract the prediction |
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predictions = outputs.logits |
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predicted_class = torch.argmax(predictions).item() |
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return int(predicted_class) == 0 |
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``` |
