Instructions to use cmarkea/bloomz-3b-reranking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cmarkea/bloomz-3b-reranking with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cmarkea/bloomz-3b-reranking") model = AutoModelForSequenceClassification.from_pretrained("cmarkea/bloomz-3b-reranking") - Notebooks
- Google Colab
- Kaggle
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## Evaluation
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To assess the performance of the reranker, we will utilize the "validation" split of the [SQuAD](
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the first question from each paragraph, along with the paragraph constituting the excerpt that should be ranked Top-1 for an Oracle modeling. What's intriguing is that
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the number of themes is limited, and each excerpt from a corresponding theme that does not match the question forms a hard negative (other excerpts outside the theme are
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simple negatives). Thus, we can construct the following table, with each theme showing the number of excerpts and associated questions:
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## Evaluation
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To assess the performance of the reranker, we will utilize the "validation" split of the [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) dataset. We will select
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the first question from each paragraph, along with the paragraph constituting the excerpt that should be ranked Top-1 for an Oracle modeling. What's intriguing is that
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the number of themes is limited, and each excerpt from a corresponding theme that does not match the question forms a hard negative (other excerpts outside the theme are
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simple negatives). Thus, we can construct the following table, with each theme showing the number of excerpts and associated questions:
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