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") - Notebooks
- Google Colab
- Kaggle
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README.md
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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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- text: "What is the summary of the text?"
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(This model has a v2, use it instead: https://huggingface.co/cnmoro/granite-question-classifier)
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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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