Text Classification
Transformers
ONNX
Safetensors
English
Portuguese
bert
classification
questioning
directed
generic
text-embeddings-inference
Instructions to use cnmoro/bert-tiny-question-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cnmoro/bert-tiny-question-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cnmoro/bert-tiny-question-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cnmoro/bert-tiny-question-classifier") model = AutoModelForSequenceClassification.from_pretrained("cnmoro/bert-tiny-question-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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license: apache-2.0
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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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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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