Instructions to use tceron/info-seek-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tceron/info-seek-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tceron/info-seek-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tceron/info-seek-classifier") model = AutoModelForSequenceClassification.from_pretrained("tceron/info-seek-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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tags: []
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---
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# Model Card for Model ID
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tags: []
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---
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# Run the classifier:
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python classi-infoseek/run_hf_info_seek_classifier.py --input_csv /path/to/new_dataset.csv --output_csv /path/to/preds_new_dataset.csv
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Required column: "content" which is the query.
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Optional column: prompt_id (if missing, it auto-creates one from row index)
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Output columns: prompt_id, prediction_id, prediction
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# Model Card for Model ID
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This classifier has been trained with ModernBERT large. It classifies user interactions with LLMs into 5 categories:
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0: "information seeking",
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1: "content creation",
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2: "coding",
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3: "not english",
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4: "no request",
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