Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use pippinnie/scam_text_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pippinnie/scam_text_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pippinnie/scam_text_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pippinnie/scam_text_classifier") model = AutoModelForSequenceClassification.from_pretrained("pippinnie/scam_text_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from pippinnie/scam_text_classifier: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/pippinnie/scam_text_classifier/resolve/main/tf_model.h5
- Command line
-
hf download hf://pippinnie/scam_text_classifier/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/pippinnie/scam_text_classifier/resolve/main/tf_model.h5
268 MB
- Xet hash:
- 7b8e1d41b740cb102c380cbc8a3b0e3568908cbb7c652dc57cccb2cddd2c0a83
- Size of remote file:
- 268 MB
- SHA256:
- f6edc1ab694fdc71faa704b6af7ac5af889c2320ba6ed7d049c8c33258dbd315
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