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: 628 Bytes
4238228 44d0bf4 | 1 2 3 4 5 6 7 8 9 10 | ,precision,recall,f1-score,support
ATTACK,0.9523809523809523,0.8421052631578947,0.8938547486033519,95.0
CRISIS,0.8561643835616438,0.8680555555555556,0.8620689655172413,144.0
MEDICAL_SENSITIVE,0.7458563535911602,0.8035714285714286,0.7736389684813754,168.0
OFFTOPIC,0.8676470588235294,0.6704545454545454,0.7564102564102564,88.0
OK,0.7151898734177216,0.795774647887324,0.7533333333333333,142.0
accuracy,0.8037676609105181,0.8037676609105181,0.8037676609105181,0.8037676609105181
macro avg,0.8274477243550015,0.7959922881253496,0.8078612544691117,637.0
weighted avg,0.8115818403676124,0.8037676609105181,0.8046513575417711,637.0
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