Instructions to use liamcripwell/sle-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liamcripwell/sle-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="liamcripwell/sle-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("liamcripwell/sle-base") model = AutoModelForSequenceClassification.from_pretrained("liamcripwell/sle-base") - Notebooks
- Google Colab
- Kaggle
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# sle-base
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This is model for the SLE metric described in the original paper. It is based on [`roberta-base`](https://huggingface.co/roberta-base) with an added regression head.
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Install the [python library](https://github.com/liamcripwell/sle).
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# sle-base
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This is a model for the SLE metric described in the original paper. It is based on [`roberta-base`](https://huggingface.co/roberta-base) with an added regression head.
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Install the [python library](https://github.com/liamcripwell/sle).
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