Feature Extraction
Transformers
Safetensors
xlm-roberta
retrieval
dense-retrieval
information-retrieval
embedding
agentic-search
deep-research
text-embeddings-inference
Instructions to use Yuqi-Zhou/LRAT-multilingual-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yuqi-Zhou/LRAT-multilingual-e5-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Yuqi-Zhou/LRAT-multilingual-e5-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Yuqi-Zhou/LRAT-multilingual-e5-large") model = AutoModel.from_pretrained("Yuqi-Zhou/LRAT-multilingual-e5-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47a73718d409a1bc16bce5f69b6b88d95b95e961b2527c391b783e4e8db1798e
- Size of remote file:
- 7.95 kB
- SHA256:
- aa361bcb9646b0b30619ebe97d58619257c26d744a211b9e6e442af499954edf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.