Instructions to use Integer-Ctrl/cross-encoder-bert-tiny-1gb-bs32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Integer-Ctrl/cross-encoder-bert-tiny-1gb-bs32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Integer-Ctrl/cross-encoder-bert-tiny-1gb-bs32")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Integer-Ctrl/cross-encoder-bert-tiny-1gb-bs32") model = AutoModelForSequenceClassification.from_pretrained("Integer-Ctrl/cross-encoder-bert-tiny-1gb-bs32") - 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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Trained on a subset of ~1GB from the msmarco dataset "Train Triples Small".
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Batch size: 32
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library_name: sentence-transformers
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pipeline_tag: text-ranking
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---
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Trained on a subset of ~1GB from the msmarco dataset "Train Triples Small".
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Batch size: 32
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