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
Investi-gator's V2 AI-Text Classifier (deBERTa-v3-small)
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 scam/spam content.
Model Details
Model Description
This model is specifically meant to classify text in a general or social media setting.
- Developed by: [bthomas279]
- Model type: [More Information Needed]
- Language(s) (NLP): [English]
- License: [cc-by-nc-4.0]
Training Scope
Here's a list of the types of data used to train V2 of the AI-Text Detector
- Casual Reddit and Twitter Posts
- Answers to casual and expert questions
- AI -> Human paraphrasing
- AI & Human sounding sythetic posts (Claude, Gemini, ChatGPT, Grok, Deepseek
Uses
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [fp32]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
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Model tree for bthomas279/text-detector
Base model
microsoft/deberta-v3-small