Text Classification
Transformers
Safetensors
English
bert
AI text detection
human vs AI classification
BERT fine-tuning
Human vs AI text classification
text-detection
text-embeddings-inference
Instructions to use pritamdeb68/BERTAIDetector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pritamdeb68/BERTAIDetector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pritamdeb68/BERTAIDetector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pritamdeb68/BERTAIDetector") model = AutoModelForSequenceClassification.from_pretrained("pritamdeb68/BERTAIDetector") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -69,8 +69,8 @@ Use the following code snippet to load the model:
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForSequenceClassification.from_pretrained("
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text = "Your text here"
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inputs = tokenizer(text, return_tensors="pt")
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("pritamdeb68/BERTAIDetector")
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model = AutoModelForSequenceClassification.from_pretrained("pritamdeb68/BERTAIDetector")
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text = "Your text here"
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inputs = tokenizer(text, return_tensors="pt")
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