Instructions to use intfloat/multilingual-e5-large-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/multilingual-e5-large-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/multilingual-e5-large-instruct") 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] - Transformers
How to use intfloat/multilingual-e5-large-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="intfloat/multilingual-e5-large-instruct")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("intfloat/multilingual-e5-large-instruct") model = AutoModel.from_pretrained("intfloat/multilingual-e5-large-instruct", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Fix: Add missing sentence-transformers installation in example code
Browse files## Description
The example code fails with `ModuleNotFoundError: No module named 'sentence_transformers'` when run in a clean environment. This is due to the `sentence_transformers` package not being pre-installed.
## Changes
Added the following line at the beginning of the script to ensure all required dependencies are available:
```python
!{sys.executable} -m pip install sentence-transformers
```
## Testing
The code has been successfully tested and runs without error.
## Note
This contribution is part of an ongoing research initiative to systematically identify and correct faulty example code in Hugging Face Model Cards.
We would appreciate a timely review and integration of this patch to support code reliability and enhance reproducibility for downstream users.
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### Sentence Transformers
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```python
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from sentence_transformers import SentenceTransformer
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def get_detailed_instruct(task_description: str, query: str) -> str:
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### Sentence Transformers
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```python
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+
!{sys.executable} -m pip install sentence-transformers
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from sentence_transformers import SentenceTransformer
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def get_detailed_instruct(task_description: str, query: str) -> str:
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