Text Classification
Transformers
Safetensors
PyTorch
Spanish
bert
spam-detection
sms
beto
spanish
Eval Results (legacy)
text-embeddings-inference
Instructions to use JavicR22/SpamVision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JavicR22/SpamVision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JavicR22/SpamVision")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JavicR22/SpamVision") model = AutoModelForSequenceClassification.from_pretrained("JavicR22/SpamVision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-300/optimizer.pt from JavicR22/SpamVision: direct link, hf CLI and curl.
- Browser
- Download file 879 MB
-
https://huggingface.co/JavicR22/SpamVision/resolve/main/checkpoint-300/optimizer.pt
- Command line
-
hf download hf://JavicR22/SpamVision/checkpoint-300/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/JavicR22/SpamVision/resolve/main/checkpoint-300/optimizer.pt
879 MB
- Xet hash:
- 509a0b3f426d70e7f48cd06e68ef76be73e81b5145ab79bd14ee41eb158b8e89
- Size of remote file:
- 879 MB
- SHA256:
- 6b4f5005181f82ea283a702e1b07f2fb3afc585c64351149536a93cc861344b7
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