Instructions to use microsoft/deberta-v3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-v3-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="microsoft/deberta-v3-base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/deberta-v3-base", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Listed on OpenModelMap
#18 opened 1 day ago
by
duola15
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture
#17 opened 14 days ago
by
vigneshwar234
fine-tuning the deberta on token length greater than 512
#16 opened 5 months ago
by
iamhdave
Reflective Understanding in Language Models
#15 opened 8 months ago
by
elly99
Adding `safetensors` variant of this model
#14 opened 10 months ago
by
SFconvertbot
deberta base generator model rquest
#13 opened almost 2 years ago
by
hd2323
Deberta max length
2
#12 opened almost 2 years ago
by
dataminer1
added license file based on tag
#11 opened about 2 years ago
by
iamankit
Fix typo in README.md
#10 opened about 2 years ago
by
nelsonauner
Inference time
#9 opened about 2 years ago
by
avifaiza
Bizzare responses
👍 2
1
#8 opened about 2 years ago
by
umarbutler
[AUTOMATED] Model Memory Requirements
#6 opened over 2 years ago
by
model-sizer-bot
Examples yield bad results
1
#5 opened over 2 years ago
by
xorima
Adding `safetensors` variant of this model
#4 opened almost 3 years ago
by
SFconvertbot
Generator Model
👍 4
2
#3 opened over 3 years ago
by
prajwal967
Adding ONNX file of this model
#2 opened over 3 years ago
by
couturierc