Instructions to use bthomas279/text-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bthomas279/text-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bthomas279/text-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bthomas279/text-detector") model = AutoModelForSequenceClassification.from_pretrained("bthomas279/text-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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pipeline_tag: text-classification
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library_name: Transformers
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base_model: microsoft/deberta-v3-small
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datasets:
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---
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pipeline_tag: text-classification
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tags:
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- pytorch
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library_name: Transformers
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base_model: microsoft/deberta-v3-small
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datasets:
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