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
File size: 590 Bytes
fb08c8a b29e333 fb08c8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"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"
} |