Instructions to use jrc-ai/PreDA-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jrc-ai/PreDA-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jrc-ai/PreDA-small") model = AutoModelForSeq2SeqLM.from_pretrained("jrc-ai/PreDA-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# PreDA-small (Prefix-Based Dream Reports Annotation)
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## Intended uses & limitations
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# PreDA-small (Prefix-Based Dream Reports Annotation)
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Weights of the model described in the LOD 2025 paper [PreDA: Prefix-Based Dream Reports Annotation with Generative Language Models](https://link.springer.com/chapter/10.1007/978-3-032-21477-5_13). It is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the annotated [Dreambank.net](https://dreambank.net/) dataset. It achieves the following results on the evaluation set:
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## Intended uses & limitations
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