Text Classification
Transformers
Safetensors
multilingual
distilbert
phishing
email-security
text-embeddings-inference
Instructions to use eugenioderodev/fishstop-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eugenioderodev/fishstop-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eugenioderodev/fishstop-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eugenioderodev/fishstop-bert") model = AutoModelForSequenceClassification.from_pretrained("eugenioderodev/fishstop-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "version": 1, | |
| "method": "temperature_scaling", | |
| "temperature": 1.7604503631591797, | |
| "threshold": 0.5985404849052429, | |
| "band": 0.0, | |
| "positive_label_id": 1, | |
| "validation_f1_at_threshold": 0.9872773536895675, | |
| "selective_accuracy": 0.9895068205666316, | |
| "selective_coverage": 1.0, | |
| "target_selective_accuracy": 0.95, | |
| "minimum_selective_coverage": 0.8, | |
| "model_type": "distilbert", | |
| "aggregation": "maximum_positive_logit_margin", | |
| "max_length": 512, | |
| "stride": 128, | |
| "max_chunks": 8, | |
| "dataset_sha256": "c89e1f5d2bb92b5a5dd5beb8a235a798932ed19eb9d75c2f740a94b5fd450816" | |
| } |