Instructions to use darapota/repa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darapota/repa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="darapota/repa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("darapota/repa") model = AutoModelForSequenceClassification.from_pretrained("darapota/repa") - Notebooks
- Google Colab
- Kaggle
Delete label_encoder.joblib
Browse files- label_encoder.joblib +0 -3
label_encoder.joblib
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:d27c95bec371218940a143acd7bb374065280c3290f685c9427170df213816a6
|
| 3 |
-
size 729
|
|
|
|
|
|
|
|
|
|
|
|