Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use chingoduc/spam-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chingoduc/spam-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chingoduc/spam-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chingoduc/spam-classifier") model = AutoModelForSequenceClassification.from_pretrained("chingoduc/spam-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from chingoduc/spam-classifier: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/chingoduc/spam-classifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://chingoduc/spam-classifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/chingoduc/spam-classifier/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- f5c9a3caebd1857af35dca797157fce3e0ff3c865a9e1468854ae94bf40643f2
- Size of remote file:
- 268 MB
- SHA256:
- ac1887bee3f8e64ac6d464aef273569c50bcc5566681d1f6ea254e400372f1e8
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