Update README.md

README.md

@@ -1,9 +1,58 @@
 ---
-library_name: transformers.js
-base_model:
-- cnmoro/bert-tiny-question-classifier
+license: apache-2.0
+datasets:
+- cnmoro/QuestionClassification
+tags:
+- classification
+- questioning
+- directed
+- generic
+language:
+- en
+- pt
+library_name: transformers
+pipeline_tag: text-classification
+widget:
+- text: "What is the summary of the text?"
 ---
 
-# bert-tiny-question-classifier (ONNX)
+(This model has a v2, use it instead: https://huggingface.co/cnmoro/granite-question-classifier)
 
-This is an ONNX version of [cnmoro/bert-tiny-question-classifier](https://huggingface.co/cnmoro/bert-tiny-question-classifier). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
+A finetuned version of prajjwal1/bert-tiny.
+
+The goal is to classify questions into "Directed" or "Generic".
+
+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).
+
+(Class 0 is Generic; Class 1 is Directed)
+
+The accuracy on the training dataset is around 87.5%
+
+```python
+from transformers import BertForSequenceClassification, BertTokenizerFast
+import torch
+
+# Load the model and tokenizer
+model = BertForSequenceClassification.from_pretrained("cnmoro/bert-tiny-question-classifier")
+tokenizer = BertTokenizerFast.from_pretrained("cnmoro/bert-tiny-question-classifier")
+
+def is_question_generic(question):
+    # Tokenize the sentence and convert to PyTorch tensors
+    inputs = tokenizer(
+        question.lower(),
+        truncation=True,
+        padding=True,
+        return_tensors="pt",
+        max_length=512
+    )
+
+    # Get the model's predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Extract the prediction
+    predictions = outputs.logits
+    predicted_class = torch.argmax(predictions).item()
+
+    return int(predicted_class) == 0
+```