Text Classification
Transformers
Safetensors
Laya
English
modernbert
fill-mask
system-one
calibrated-decisions
rlcd
typed-decisions
classification
agent-observability
invoice-processing
security-incidents
customer-service
commercial-use
text-embeddings-inference
Instructions to use michaelfeil/laya-typed-decisions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelfeil/laya-typed-decisions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="michaelfeil/laya-typed-decisions")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("michaelfeil/laya-typed-decisions") model = AutoModelForMaskedLM.from_pretrained("michaelfeil/laya-typed-decisions", device_map="auto") - Laya
How to use michaelfeil/laya-typed-decisions 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 tokenizer.json from michaelfeil/laya-typed-decisions: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/michaelfeil/laya-typed-decisions/resolve/main/tokenizer.json
- Command line
-
hf download hf://michaelfeil/laya-typed-decisions/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/michaelfeil/laya-typed-decisions/resolve/main/tokenizer.json
3.58 MB
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