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")# 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") - Inference
- Notebooks
- Google Colab
- Kaggle
Missing `sentence_bert_config.json`
When I tried to use the model with HuggingFaceInstructEmbeddings(model_name="intfloat/multilingual-e5-large-instruct") I got an error with loading Transformer, because of the missing of sentence_..._config json files. Adding, for example sentence_bert_config.json from intfloat/multilingual-e5-large, makes it work.
Could you check and add the config file, please.
Otherwise, greate embedding model. Thanks for your work!
Please refer to the pull request at https://huggingface.co/intfloat/multilingual-e5-large-instruct/discussions/2
This model works with newer versions of the sentence-transformers library, please upgrade it and try again.