Feature Extraction
Transformers
Safetensors
bert
tevatron
tevatron-elastic
information-retrieval
retriever
elastic
text-embeddings-inference
Instructions to use utahnlp/tevatron-elastic-bert-retriever-mrl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use utahnlp/tevatron-elastic-bert-retriever-mrl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="utahnlp/tevatron-elastic-bert-retriever-mrl")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("utahnlp/tevatron-elastic-bert-retriever-mrl") model = AutoModel.from_pretrained("utahnlp/tevatron-elastic-bert-retriever-mrl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b23326dec8572b48f9577062ae3edeeb3cdc5eecd36ddcd663f9147bdec07a6d
- Size of remote file:
- 219 MB
- SHA256:
- b28144fd641ac52406be1cbe2f4c92134b9b60c6de83e47186120a7735077091
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