Text Classification
Transformers
Safetensors
Laya
system-one
calibrated-decisions
rlcd
classification
routing
scoring
guardrails
moderation
reinforcement-learning
commercial-use
Instructions to use aaroncool9/laya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aaroncool9/laya with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aaroncool9/laya")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("aaroncool9/laya", device_map="auto") - Laya
How to use aaroncool9/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
Download eval/benchmark_comparison.png from aaroncool9/laya: direct link, hf CLI and curl.
- Browser
- Download file 467 kB
-
https://huggingface.co/aaroncool9/laya/resolve/main/eval/benchmark_comparison.png
- Command line
-
hf download hf://aaroncool9/laya/eval/benchmark_comparison.png
-
curl -L -o benchmark_comparison.png https://huggingface.co/aaroncool9/laya/resolve/main/eval/benchmark_comparison.png
467 kB

- Xet hash:
- 7eeec5fa00f146898dd0d92bb3d628e0f0e978c5e970d9ee9a23f6b83aadf40d
- Size of remote file:
- 467 kB
- SHA256:
- 78e4b926bfca3950ff5441d1030a1453515973fbe5d7dd70a1c764a81f0d3d38
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