Text Classification
PEFT
Safetensors
Transformers
Bengali
English
qwen3
text-generation
lora
safety-classification
bangla
banglish
safeage
text-embeddings-inference
Instructions to use mehedi4/SafeAge-Guard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mehedi4/SafeAge-Guard with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "mehedi4/SafeAge-Guard") - Transformers
How to use mehedi4/SafeAge-Guard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mehedi4/SafeAge-Guard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mehedi4/SafeAge-Guard") model = AutoModelForCausalLM.from_pretrained("mehedi4/SafeAge-Guard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download evaluation/test_by_language_variant.csv from mehedi4/SafeAge-Guard: direct link, hf CLI and curl.
- Browser
- Download file 1.45 kB
-
https://huggingface.co/mehedi4/SafeAge-Guard/resolve/main/evaluation/test_by_language_variant.csv
- Command line
-
hf download hf://mehedi4/SafeAge-Guard/evaluation/test_by_language_variant.csv
-
curl -L -o test_by_language_variant.csv https://huggingface.co/mehedi4/SafeAge-Guard/resolve/main/evaluation/test_by_language_variant.csv
1.45 kB
| language_variant,independent_groups,rows,benign_rows,attack_rows,threshold,accuracy,balanced_accuracy,macro_f1,attack_precision,attack_recall,attack_f1,benign_specificity,benign_false_positive_rate,attack_false_negative_rate,roc_auc,average_precision,brier_score,log_loss,tn,fp,fn,tp | |
| banglish,199,301,189,112,0.24,0.9335548172757475,0.9270833333333333,0.9286391654812707,0.9181818181818182,0.9017857142857143,0.9099099099099099,0.9523809523809523,0.047619047619047616,0.09821428571428571,0.9686318972033257,0.9654177662467996,0.05690496586124814,0.368174016286317,180,9,11,101 | |
| bn,199,301,189,112,0.24,0.9401993355481728,0.9287367724867724,0.9350172711571675,0.9519230769230769,0.8839285714285714,0.9166666666666666,0.9735449735449735,0.026455026455026454,0.11607142857142858,0.9785525321239606,0.9751419365097657,0.04513325913445104,0.25536597920877635,184,5,13,99 | |
| bn_en,199,301,189,112,0.24,0.9601328903654485,0.9518849206349206,0.9568520642201834,0.9716981132075472,0.9196428571428571,0.944954128440367,0.9841269841269841,0.015873015873015872,0.08035714285714286,0.9870559334845049,0.9842834513398957,0.038019239191252364,0.2395105318194226,186,3,9,103 | |
| en,199,301,189,112,0.24,0.9833887043189369,0.9813161375661376,0.9821929315995599,0.9819819819819819,0.9732142857142857,0.9775784753363229,0.9894179894179894,0.010582010582010581,0.026785714285714284,0.9977324263038548,0.9961032053482793,0.015338429390340642,0.10818121107392902,187,2,3,109 | |