Zero-Shot Classification
Laya
Safetensors
GGUF
Hebrew
hebrew
decision-model
calibrated
scam-detection
routing
Instructions to use BrainboxAI/nitzotz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Laya
How to use BrainboxAI/nitzotz with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 2,893 Bytes
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"encoder": "HalleluBERT/HalleluBERT_large",
"head_layers": 2,
"max_len": 512,
"head_max_len": 256,
"act_costs": {
"escalate": 0.5
},
"amp_dtype": "bf16",
"model_name": "nitzotz",
"temperature": [
1.0970542430877686,
1.1389989852905273,
1.1619657278060913
],
"training": {
"encoder_init": "HalleluBERT-large after extractive QA on HeQ v1.1 train (test-overlapping passages removed)",
"data": "116,854 items: synthetic Hebrew messages, HeQ v1.1 train, MASSIVE he-IL train, reading and topic items on FineWeb-2 Hebrew passages, warnings about scams, short scams and look-alike messages, 'none of the options' items, reworded copies (see README)",
"items_total": 116854,
"train_items": 112029,
"calib_items": 4825,
"epochs_planned": 2,
"epochs_run": 2,
"kept_epoch": 2,
"heldout_by_epoch": [
{
"acc": 0.9389,
"soft_nll": 0.4909,
"acc_base_families": 0.9359,
"by_family": {
"aug_heq": 0.9399,
"aug_massive": 0.9464,
"aug_scam": 0.9624,
"aug_type": 0.9299,
"aug_urgency": 0.8488,
"claim": 0.9498,
"heq_read": 0.964,
"heq_unans": 0.892,
"heq_verify": 0.9733,
"massive_intent": 0.9286,
"rel6_scam": 0.9574,
"rel6_type": 0.931,
"rel_scam": 0.9767,
"rel_type": 0.9216,
"routing": 0.9224,
"spam_type": 0.9329,
"urgency": 0.92
},
"epoch": 1,
"step": 3501,
"train_seconds": 1746.6
},
{
"acc": 0.9505,
"soft_nll": 0.4741,
"acc_base_families": 0.9452,
"by_family": {
"aug_heq": 0.9563,
"aug_massive": 0.9375,
"aug_scam": 0.9731,
"aug_type": 0.9482,
"aug_urgency": 0.8721,
"claim": 0.9658,
"heq_read": 0.976,
"heq_unans": 0.896,
"heq_verify": 0.98,
"massive_intent": 0.9314,
"rel6_scam": 0.9787,
"rel6_type": 0.9425,
"rel_scam": 0.9833,
"rel_type": 0.9515,
"routing": 0.9361,
"spam_type": 0.9486,
"urgency": 0.8971
},
"epoch": 2,
"step": 7002,
"train_seconds": 1757.1
}
],
"steps": 7002,
"batch": 32,
"micro_batch": 16,
"lr_encoder": 1e-05,
"lr_head": 0.0001,
"warmup": 0.1,
"schedule": "cosine",
"seed": 1,
"split_seed": 1234,
"loss": "soft cross-entropy",
"amp": "bf16",
"device": "cuda",
"train_seconds": 3520.6,
"head_init": "fresh (no laya checkpoint weights)",
"class_weights": "none",
"option_shuffle_families": [
"read2_multi",
"read2_neg",
"read2_para",
"read2_pos"
],
"holdout_by_parent": true,
"parent_copies_dropped": 0
}
} |