Instructions to use skimai/spanberta-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skimai/spanberta-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="skimai/spanberta-base-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("skimai/spanberta-base-cased") model = AutoModel.from_pretrained("skimai/spanberta-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update pytorch_model.bin
Browse files- pytorch_model.bin +3 -0
pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9164ac51785e9784b598335745b43d92c66281ff2608d5998f5ffe528d8cc3ca
|
| 3 |
+
size 498625760
|