Text Classification
Transformers
Safetensors
Laya
system-one
calibrated-decisions
rlcd
classification
routing
scoring
guardrails
moderation
reinforcement-learning
commercial-use
Instructions to use kp-forks/laya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kp-forks/laya with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kp-forks/laya")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kp-forks/laya", device_map="auto") - Laya
How to use kp-forks/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
- Notebooks
- Google Colab
- Kaggle
Commit ·
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Add the Laya logo and use the lockup in the model card
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tags: [laya, system-one, calibrated-decisions, rlcd, classification, routing, scoring, guardrails, moderation, reinforcement-learning, commercial-use]
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**Multilingual, non-autoregressive System 1 decision model.** Give it a **state** (text, email,
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ticket, or JSON) and **typed questions**; it returns typed answers with probabilities in a
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tags: [laya, system-one, calibrated-decisions, rlcd, classification, routing, scoring, guardrails, moderation, reinforcement-learning, commercial-use]
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**Multilingual, non-autoregressive System 1 decision model.** Give it a **state** (text, email,
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