Instructions to use model-attribution-challenge/bert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use model-attribution-challenge/bert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="model-attribution-challenge/bert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("model-attribution-challenge/bert-base-cased") model = AutoModelForMaskedLM.from_pretrained("model-attribution-challenge/bert-base-cased") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1d8bdcee6021e2c25f0325e84889b61c2eb26b843eef5659c247af138d64f050
|
| 3 |
+
size 435755784
|