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:
- 128e4bea076b1238bd45d07d4676afd1e6788fe105da8bcc2d0d8cb931b1700d
- Size of remote file:
- 17.1 MB
- SHA256:
- 8373f9cd3d27591e1924426bcc1c8799bc5a9affc4fc857982c5d66668dd1f41
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.