Instructions to use aehrm/gepabert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrm/gepabert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aehrm/gepabert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("aehrm/gepabert") model = AutoModelForMaskedLM.from_pretrained("aehrm/gepabert") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#1
by librarian-bot - opened
README.md
CHANGED
|
@@ -3,6 +3,7 @@ language: de
|
|
| 3 |
license: mit
|
| 4 |
metrics:
|
| 5 |
- accuracy
|
|
|
|
| 6 |
model-index:
|
| 7 |
- name: GePaBERT
|
| 8 |
results: []
|
|
|
|
| 3 |
license: mit
|
| 4 |
metrics:
|
| 5 |
- accuracy
|
| 6 |
+
base_model: deepset/gbert-large
|
| 7 |
model-index:
|
| 8 |
- name: GePaBERT
|
| 9 |
results: []
|