Text Classification
Transformers
Safetensors
English
bert
phishing-detection
bert-mini
text-embeddings-inference
Instructions to use lleratodev/720-bert-mini-phishing-fine-tune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lleratodev/720-bert-mini-phishing-fine-tune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lleratodev/720-bert-mini-phishing-fine-tune")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lleratodev/720-bert-mini-phishing-fine-tune") model = AutoModelForSequenceClassification.from_pretrained("lleratodev/720-bert-mini-phishing-fine-tune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4eaac2a9e9976f9c82ef1371e658afeecae6b5b4717496999d3a01337d852a47
- Size of remote file:
- 5.3 kB
- SHA256:
- e4751ac4988a9b1a4390e58db6a7264e46b47d7a5ed9eed75b135304adad7c33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.