Feature Extraction
Transformers
ONNX
English
bert
mteb
sparse sparsity quantized onnx embeddings int8
Eval Results (legacy)
Instructions to use RedHatAI/bge-base-en-v1.5-sparse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/bge-base-en-v1.5-sparse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RedHatAI/bge-base-en-v1.5-sparse")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RedHatAI/bge-base-en-v1.5-sparse") model = AutoModel.from_pretrained("RedHatAI/bge-base-en-v1.5-sparse") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -743,7 +743,7 @@ pip install -U deepsparse-nightly[sentence_transformers]
|
|
| 743 |
|
| 744 |
```python
|
| 745 |
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
|
| 746 |
-
model =
|
| 747 |
|
| 748 |
# Our sentences we like to encode
|
| 749 |
sentences = ['This framework generates embeddings for each input sentence',
|
|
|
|
| 743 |
|
| 744 |
```python
|
| 745 |
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
|
| 746 |
+
model = DeepSparseSentenceTransformer('zeroshot/bge-base-en-v1.5-sparse', export=False)
|
| 747 |
|
| 748 |
# Our sentences we like to encode
|
| 749 |
sentences = ['This framework generates embeddings for each input sentence',
|