Sentence Similarity
Transformers
PyTorch
bert
feature-extraction
custom_code
text-embeddings-inference
Instructions to use efederici/multilingual-e5-small-4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efederici/multilingual-e5-small-4096 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("efederici/multilingual-e5-small-4096", trust_remote_code=True) model = AutoModel.from_pretrained("efederici/multilingual-e5-small-4096", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5641bdd1688eeded01ffd5c25cc935a632ddcca99a43bee26d13ac58231a5d3f
- Size of remote file:
- 478 MB
- SHA256:
- 54a9213044d4d1b74b0feae5ce8dd4d33d55b8b4cd61f34a1016ff1cf991ef8e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.