Text Classification
Transformers
TensorBoard
Safetensors
English
bert
Generated from Trainer
small_BERT
phishing_classifier
classification
text-embeddings-inference
Instructions to use David-Egea/bert-small-phishing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use David-Egea/bert-small-phishing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="David-Egea/bert-small-phishing")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("David-Egea/bert-small-phishing") model = AutoModelForSequenceClassification.from_pretrained("David-Egea/bert-small-phishing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from David-Egea/bert-small-phishing: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/David-Egea/bert-small-phishing/resolve/main/training_args.bin
- Command line
-
hf download hf://David-Egea/bert-small-phishing/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/David-Egea/bert-small-phishing/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- e37ca72d073bac6483846fec8209be644c83e6afd29150879cefe9fb1a70d059
- Size of remote file:
- 4.92 kB
- SHA256:
- dd800429d4a637bc788a7277fa302dfdcbdea9e3e1328055c8f465f6da40beee
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