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
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README.md
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<!-- Provide a quick summary of what the model is/does. -->
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On social media, it's very easy to run into posts that may not be authentic. This makes some question if the content their consuming is real or legitimate. For others it can lead them to consume misinformation or engage with users and content that don't have their best interest at heart. That's when Investi-gator steps in to help users take back control of their social media experience, using this model to classify
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## Model Details
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On social media, it's very easy to run into posts that may not be authentic. This makes some question if the content their consuming is real or legitimate. For others it can lead them to consume misinformation or engage with users and content that don't have their best interest at heart. That's when Investi-gator steps in to help users take back control of their social media experience, using this model to classify content with AI generated text.
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## Model Details
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