Instructions to use kisti/korscideberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kisti/korscideberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kisti/korscideberta")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kisti/korscideberta") model = AutoModelForMaskedLM.from_pretrained("kisti/korscideberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -183,7 +183,7 @@ Masked Sentence - 'Deep Learning is so < mask >! I love < mask > networks.'
|
|
| 183 |
|
| 184 |
### Compute Infrastructure
|
| 185 |
|
| 186 |
-
KISTI 국가슈퍼컴퓨팅센터 NEURON 시스템. HPE ClusterStor E1000, Lustre, Slurm
|
| 187 |
|
| 188 |
#### Hardware
|
| 189 |
|
|
|
|
| 183 |
|
| 184 |
### Compute Infrastructure
|
| 185 |
|
| 186 |
+
KISTI 국가슈퍼컴퓨팅센터 NEURON 시스템. HPE ClusterStor E1000, HP Apollo 6500 Gen10 Plus, Lustre, Slurm, CentOS 7.9
|
| 187 |
|
| 188 |
#### Hardware
|
| 189 |
|