Instructions to use davebulaval/MeaningBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davebulaval/MeaningBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="davebulaval/MeaningBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("davebulaval/MeaningBERT") model = AutoModelForSequenceClassification.from_pretrained("davebulaval/MeaningBERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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# Here is MeaningBERT
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MeaningBERT is an automatic and trainable metric for assessing meaning preservation between sentences. MeaningBERT was
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proposed in our
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article [MeaningBERT: assessing meaning preservation between sentences](https://www.frontiersin.org/articles/10.3389/frai.2023.1223924/full).
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Its goal is to assess meaning preservation between two sentences that correlate highly with human judgments and sanity checks. For more details, refer to our publicly available article.
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# Here is MeaningBERT
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MeaningBERT is an automatic and trainable model and [metric]() for assessing meaning preservation between sentences. MeaningBERT was
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proposed in our
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article [MeaningBERT: assessing meaning preservation between sentences](https://www.frontiersin.org/articles/10.3389/frai.2023.1223924/full).
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Its goal is to assess meaning preservation between two sentences that correlate highly with human judgments and sanity checks. For more details, refer to our publicly available article.
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