Feature Extraction
Transformers
Safetensors
sentence-transformers
Chinese
English
mteb
custom_code
Eval Results (legacy)
Instructions to use openbmb/MiniCPM-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-Embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="openbmb/MiniCPM-Embedding", trust_remote_code=True)# Load model directly from transformers import MiniCPM model = MiniCPM.from_pretrained("openbmb/MiniCPM-Embedding", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use openbmb/MiniCPM-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("openbmb/MiniCPM-Embedding", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -148,7 +148,7 @@ model-index:
|
|
| 148 |
revision: 0228b52cf27578f30900b9e5271d331663a030d7
|
| 149 |
metrics:
|
| 150 |
- type: ndcg_at_10
|
| 151 |
-
value: 86.
|
| 152 |
- task:
|
| 153 |
type: Retrieval
|
| 154 |
dataset:
|
|
@@ -259,6 +259,7 @@ model-index:
|
|
| 259 |
metrics:
|
| 260 |
- type: ndcg_at_10
|
| 261 |
value: 78.05
|
|
|
|
| 262 |
---
|
| 263 |
## MiniCPM-Embedding
|
| 264 |
|
|
|
|
| 148 |
revision: 0228b52cf27578f30900b9e5271d331663a030d7
|
| 149 |
metrics:
|
| 150 |
- type: ndcg_at_10
|
| 151 |
+
value: 86.6
|
| 152 |
- task:
|
| 153 |
type: Retrieval
|
| 154 |
dataset:
|
|
|
|
| 259 |
metrics:
|
| 260 |
- type: ndcg_at_10
|
| 261 |
value: 78.05
|
| 262 |
+
pipeline_tag: feature-extraction
|
| 263 |
---
|
| 264 |
## MiniCPM-Embedding
|
| 265 |
|