Text Classification
Transformers
Safetensors
Polish
roberta
safety
triage
polish
mental-health
crisis-detection
psychiatric-chatbot
text-embeddings-inference
Instructions to use damian-shivihs/yllia-safety-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use damian-shivihs/yllia-safety-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="damian-shivihs/yllia-safety-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("damian-shivihs/yllia-safety-classifier") model = AutoModelForSequenceClassification.from_pretrained("damian-shivihs/yllia-safety-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 612 Bytes
4238228 44d0bf4 | 1 2 3 4 5 6 7 8 9 10 | ,precision,recall,f1-score,support
ATTACK,0.7647058823529411,0.6842105263157895,0.7222222222222222,19.0
CRISIS,0.7666666666666667,0.7931034482758621,0.7796610169491526,29.0
MEDICAL_SENSITIVE,0.7377049180327869,0.8035714285714286,0.7692307692307693,56.0
OFFTOPIC,0.76,0.6333333333333333,0.6909090909090909,30.0
OK,0.8055555555555556,0.8169014084507042,0.8111888111888111,71.0
accuracy,0.7707317073170732,0.7707317073170732,0.7707317073170732,0.7707317073170732
macro avg,0.7669266045215901,0.7462240289894236,0.7546423821000092,205.0
weighted avg,0.7710666583039987,0.7707317073170732,0.7694194785968246,205.0
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