Instructions to use rasa/LaBSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rasa/LaBSE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rasa/LaBSE")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rasa/LaBSE") model = AutoModel.from_pretrained("rasa/LaBSE") - Inference
- Notebooks
- Google Colab
- Kaggle
Update tf_model.h5
Browse files- tf_model.h5 +3 -0
tf_model.h5
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a4c358a3541bd7758ef714f2b94f913f7162c37eba005c1744dba47b01eb0a07
|
| 3 |
+
size 1883969304
|