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-500/scaler.pt from JavicR22/SpamVision: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/JavicR22/SpamVision/resolve/main/checkpoint-500/scaler.pt
- Command line
-
hf download hf://JavicR22/SpamVision/checkpoint-500/scaler.pt
-
curl -L -o scaler.pt https://huggingface.co/JavicR22/SpamVision/resolve/main/checkpoint-500/scaler.pt
1.38 kB
- Xet hash:
- cb87a4cc8072e38760e514f069cea44967f3981156ce0ed18814825695479561
- Size of remote file:
- 1.38 kB
- SHA256:
- e3defd38fd8f62a7a3f6971d154c9135f2e1818e83bbd2a48dbbd5417f2bd284
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.