Instructions to use optimum/bge-base-en-v1.5-neuronx-bs-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum/bge-base-en-v1.5-neuronx-bs-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="optimum/bge-base-en-v1.5-neuronx-bs-1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("optimum/bge-base-en-v1.5-neuronx-bs-1") model = AutoModel.from_pretrained("optimum/bge-base-en-v1.5-neuronx-bs-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -26,7 +26,5 @@ emb_model = NeuronModelForSenetenceTransformers.from_pretrained("optimum/bge-bas
|
|
| 26 |
inputs = tokenizer("Hamilton is considered to be the best musical of human history.", return_tensors="pt")
|
| 27 |
emb = emb_model(**inputs)
|
| 28 |
|
| 29 |
-
print(emb.keys())
|
| 30 |
-
|
| 31 |
# ["token_embeddings", "sentence_embedding"]
|
| 32 |
```
|
|
|
|
| 26 |
inputs = tokenizer("Hamilton is considered to be the best musical of human history.", return_tensors="pt")
|
| 27 |
emb = emb_model(**inputs)
|
| 28 |
|
|
|
|
|
|
|
| 29 |
# ["token_embeddings", "sentence_embedding"]
|
| 30 |
```
|