Instructions to use amberoad/bert-multilingual-passage-reranking-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amberoad/bert-multilingual-passage-reranking-msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amberoad/bert-multilingual-passage-reranking-msmarco")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amberoad/bert-multilingual-passage-reranking-msmarco") model = AutoModelForSequenceClassification.from_pretrained("amberoad/bert-multilingual-passage-reranking-msmarco") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7094804587b083321c384b02ed8c1d2664008c96e22fe92c3753804995b65d90
- Size of remote file:
- 669 MB
- SHA256:
- 1d4cd912eb99c7d8d5a9e3a58a8cdecd47fda4fcf59bd0fec8a0b06b1584b099
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