Instructions to use brutusxu/distilbert-base-cross-encoder-first-p with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brutusxu/distilbert-base-cross-encoder-first-p with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="brutusxu/distilbert-base-cross-encoder-first-p")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("brutusxu/distilbert-base-cross-encoder-first-p") model = AutoModelForSequenceClassification.from_pretrained("brutusxu/distilbert-base-cross-encoder-first-p", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`
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by tomaarsen HF Staff - opened
README.md
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distilbert-base-uncased trained on MSMARCO Document Reranking task,
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#### usage
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library_name: sentence-transformers
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pipeline_tag: text-ranking
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---
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distilbert-base-uncased trained on MSMARCO Document Reranking task,
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#### usage
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