Instructions to use artefactory/BERTJudge-Formatted-QCR-OOD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use artefactory/BERTJudge-Formatted-QCR-OOD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="artefactory/BERTJudge-Formatted-QCR-OOD", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("artefactory/BERTJudge-Formatted-QCR-OOD", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("artefactory/BERTJudge-Formatted-QCR-OOD", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and library name
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by nielsr HF Staff - opened
README.md
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---
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datasets:
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- hgissbkh/BERTJudge-Dataset
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language:
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- en
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---
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# BERTJudge-Formatted-QCR-OOD
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BERT-as-a-Judge is a family of encoder-based models designed for efficient, reference-based evaluation of LLM outputs. Moving beyond rigid lexical extraction and matching, these models evaluate semantic correctness, accommodating variations in phrasing and formatting while using only a fraction of the computational resources required by LLM-as-a-Judge approaches.
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```
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@article{gisserotboukhlef2026bertasajudgerobustalternativelexical,
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title={BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation},
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author={Gisserot-Boukhlef, Hippolyte and Boizard, Nicolas and Malherbe, Emmanuel and Hudelot, C{\'e}line and Colombo, Pierre},
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year={2026},
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eprint={2604.09497},
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archivePrefix={arXiv},
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---
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base_model:
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- EuroBERT/EuroBERT-210m
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datasets:
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- hgissbkh/BERTJudge-Dataset
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language:
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- en
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library_name: transformers
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pipeline_tag: text-classification
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---
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# BERTJudge-Formatted-QCR-OOD
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BERT-as-a-Judge is a family of encoder-based models designed for efficient, reference-based evaluation of LLM outputs. Moving beyond rigid lexical extraction and matching, these models evaluate semantic correctness, accommodating variations in phrasing and formatting while using only a fraction of the computational resources required by LLM-as-a-Judge approaches.
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```
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@article{gisserotboukhlef2026bertasajudgerobustalternativelexical,
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title={BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation},
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author={Gisserot-Boukhlef, Hippolyte and Boizard, Nicolas and Malherbe, Emmanuel and Hudelot, C{\\'e}line and Colombo, Pierre},
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year={2026},
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eprint={2604.09497},
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archivePrefix={arXiv},
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