Text Classification
Transformers
PyTorch
ONNX
Safetensors
English
distilbert
neural-search
neural-search-query-classification
text-embeddings-inference
Instructions to use ilert/SoQbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ilert/SoQbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ilert/SoQbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ilert/SoQbert") model = AutoModelForSequenceClassification.from_pretrained("ilert/SoQbert") - Notebooks
- Google Colab
- Kaggle
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README.md
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### Model Description
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The model processes input text to determine whether it is a statement or a question.
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### Training Data
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The model was trained on a diverse dataset containing examples of both statements and questions. The training process involved fine-tuning the pre-trained DistilBERT model on this specific classification task. The dataset included various types of questions and statements from different contexts to ensure robustness.
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### Performance
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### Model Description
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The model processes input text to determine whether it is a statement or a question.
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### Training Data
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The model was trained on a diverse dataset containing examples of both statements and questions. The training process involved fine-tuning the pre-trained DistilBERT model on this specific classification task. The dataset included various types of questions and statements from different contexts to ensure robustness.
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* Quora Question Keyword Pairs
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* Questions vs Statements Classification
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* ilert related Questions
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### Performance
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