Instructions to use bergum/product_description_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bergum/product_description_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bergum/product_description_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bergum/product_description_encoder") model = AutoModel.from_pretrained("bergum/product_description_encoder") - Notebooks
- Google Colab
- Kaggle
Commit ·
e4b4472
1
Parent(s): 1297b3d
Adding `safetensors` variant of this model (#1)
Browse files- Adding `safetensors` variant of this model (1987cacb6da348c084d4b8e00d3e3317fb839f3a)
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 sha256:4ed979006ab43fb9e17e2d2ffbceaae66862a6574810f38f6bf2a7b4034438a9
|
| 3 |
+
size 90868370
|