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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("convaiinnovations/laya", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd8562d713759821a0bc049e79abd09e5817d218d18fa51478ad16986d018764
- Size of remote file:
- 843 MB
- SHA256:
- 891102d372688fc2a094dac56a384bc537b87c63f21f9f3dac0be2b7cbc8d86c
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