Text Classification
Transformers
Safetensors
Laya
PyTorch
English
afm-d
afm-de
decision
system-1
modernbert
rlcd
Instructions to use ariacompute/afm-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ariacompute/afm-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ariacompute/afm-de")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ariacompute/afm-de", device_map="auto") - Laya
How to use ariacompute/afm-de 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/tokenizer.json from ariacompute/afm-de: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/ariacompute/afm-de/resolve/main/tokenizer/tokenizer.json
- Command line
-
hf download hf://ariacompute/afm-de/tokenizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ariacompute/afm-de/resolve/main/tokenizer/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.