Instructions to use ChillingDream/dap-mbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChillingDream/dap-mbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ChillingDream/dap-mbert-base")# Load model directly from transformers import AutoTokenizer, BertForRLM tokenizer = AutoTokenizer.from_pretrained("ChillingDream/dap-mbert-base") model = BertForRLM.from_pretrained("ChillingDream/dap-mbert-base") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model (#2)
Browse files- Adding `safetensors` variant of this model (cf5256e1cfaa06f58dcfab219af0c5b8cf0e9198)
Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:209ae1c24f6d973f9ae0f4ae925cde96a071041cea91bb9820a38c0308ba2dd6
|
| 3 |
+
size 711441304
|