Feature Extraction
Transformers
Safetensors
PyTorch
cmekg_gnn
graph-learning
gatv2
knowledge-graph
medical-kg
chinese-medical
contrastive-learning
pyg
Instructions to use MaxinT23/CMeKG-JK-GATv2-Text2KG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaxinT23/CMeKG-JK-GATv2-Text2KG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MaxinT23/CMeKG-JK-GATv2-Text2KG")# pip install -U transformers accelerate # Load model directly from transformers import CMeKGText2KG model = CMeKGText2KG.from_pretrained("MaxinT23/CMeKG-JK-GATv2-Text2KG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,4 +1,3 @@
|
|
| 1 |
-
```Plain Text
|
| 2 |
---
|
| 3 |
license: mit
|
| 4 |
pipeline_tag: feature-extraction
|
|
@@ -15,7 +14,6 @@ tags:
|
|
| 15 |
library_name: transformers
|
| 16 |
base_model: BAAI/bge-m3
|
| 17 |
---
|
| 18 |
-
```
|
| 19 |
|
| 20 |
# CMeKG\-JK\-GATv2\-Text2KG
|
| 21 |
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
license: mit
|
| 3 |
pipeline_tag: feature-extraction
|
|
|
|
| 14 |
library_name: transformers
|
| 15 |
base_model: BAAI/bge-m3
|
| 16 |
---
|
|
|
|
| 17 |
|
| 18 |
# CMeKG\-JK\-GATv2\-Text2KG
|
| 19 |
|