Instructions to use amber-tech/bert-multilingual-passage-reranking-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amber-tech/bert-multilingual-passage-reranking-msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amber-tech/bert-multilingual-passage-reranking-msmarco")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amber-tech/bert-multilingual-passage-reranking-msmarco") model = AutoModelForSequenceClassification.from_pretrained("amber-tech/bert-multilingual-passage-reranking-msmarco", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from amber-tech/bert-multilingual-passage-reranking-msmarco: direct link, hf CLI and curl.
- Browser
- Download file 669 MB
-
https://huggingface.co/amber-tech/bert-multilingual-passage-reranking-msmarco/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://amber-tech/bert-multilingual-passage-reranking-msmarco/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/amber-tech/bert-multilingual-passage-reranking-msmarco/resolve/main/pytorch_model.bin
669 MB
- Xet hash:
- b56eff9a6e295b420c4b0db99329bb160b74b9db6ffb76f430786a7d5a51dceb
- Size of remote file:
- 669 MB
- SHA256:
- 188287a61bb87387f5a2783ccfffb4f649ceed50d8fc7bbff4a7cb964105cbc1
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