Text Classification
Transformers
Safetensors
modernbert
enterprise-ai
action-ranking
listwise-ranking
system-1
dynamic-actions
text-embeddings-inference
Instructions to use yasserrmd/enterprise-reflex-v1-l with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yasserrmd/enterprise-reflex-v1-l with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yasserrmd/enterprise-reflex-v1-l")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yasserrmd/enterprise-reflex-v1-l") model = AutoModelForSequenceClassification.from_pretrained("yasserrmd/enterprise-reflex-v1-l", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Enterprise Reflex V1-L
Status: Research Experiment — Listwise Arm A
V1-L isolates one change: independent binary compatibility training is replaced by grouped listwise ranking while retaining ModernBERT-base and the V1 pair serialization.
Frozen 200 extreme benchmark
| Metric | V1 baseline | V1-L |
|---|---|---|
| Top-1 | 48.00% | 47.50% |
| Top-2 | 79.00% | 77.50% |
| Top-3 | 90.50% | 88.00% |
| MRR | - | 0.685845 |
| System-1 coverage | - | 33.00% |
| Unsafe System-1 failures | - | 27 |
Temperature: 2.992507
Validation-selected System-2 threshold: 0.845
This is a research ranking model, not an authorization or policy engine.
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Model tree for yasserrmd/enterprise-reflex-v1-l
Base model
answerdotai/ModernBERT-base