Text Classification
Transformers
Safetensors
Laya
system-one
calibrated-decisions
rlcd
classification
routing
scoring
guardrails
moderation
reinforcement-learning
commercial-use
Instructions to use convaiinnovations/laya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use convaiinnovations/laya with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="convaiinnovations/laya")# pip install -U transformers accelerate # Load model directly from transformers import LayaTypedDecisions model = LayaTypedDecisions.from_pretrained("convaiinnovations/laya", device_map="auto") - Laya
How to use convaiinnovations/laya 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
- Inference
- Notebooks
- Google Colab
- Kaggle
Decision model benchmarks
#10
by jglowa - opened
I have found two decision model benchmarks featuring Laya: JevBench and Jev Decision Index.
In both of them Laya performs poorly. In JevBench it scores 54.4 points (Jev is 74.4) and in Decision Index it scores 16.4 points (Jev is 59.5).
Could you make a new snapshot of Laya which would score better out of the box, without fine-tuning?
Thank you!