Instructions to use uhhlt/am-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uhhlt/am-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="uhhlt/am-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("uhhlt/am-roberta") model = AutoModelForMaskedLM.from_pretrained("uhhlt/am-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
4cbcbc7
-
Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (010d1fbffe262720028dd8a253e7cc9a5a632af3) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>147d787
-
Update README.md
71c9111
-
Update README.md
3715cec
-
Update README.md
3e873c2
-
Update README.md
2fcb7d0 -
Update README.md
006491a
-
Update README.md
fb1b97c -
Update README.md
591e32e -
Update README.md
628fa38 -
Update README.md
edfb9d9 -
Update README.md
6fb1675 -
Update README.md
b9bfc38 -
Update README.md
dc77c6c -
Create config.json
8c315b1 -
Delete config.json
7016673 -
Create README.md
9d1d693 -
Delete README.md
d415461 -
tet
70f16dd -
First model version
48b2471 -
Create README.md
0b2b4ce -
initial commit
dedc27c