Sentence Similarity
Transformers
PyTorch
xlm-roberta
feature-extraction
custom_code
text-embeddings-inference
Instructions to use efederici/e5-base-multilingual-4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efederici/e5-base-multilingual-4096 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("efederici/e5-base-multilingual-4096", trust_remote_code=True) model = AutoModel.from_pretrained("efederici/e5-base-multilingual-4096", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from efederici/e5-base-multilingual-4096: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/efederici/e5-base-multilingual-4096/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://efederici/e5-base-multilingual-4096/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/efederici/e5-base-multilingual-4096/resolve/main/pytorch_model.bin
1.12 GB
- Xet hash:
- 6918c947e9622914905a7dc2e00a60770ceb2aacd815a2fc2574c3f63fc58626
- Size of remote file:
- 1.12 GB
- SHA256:
- 6a2ed01b013ef1a01178d52cb4e062eb7eb1de82032447affdee663b829c4318
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