Transformers
TensorBoard
Safetensors
English
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use jrc-ai/PreDA-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jrc-ai/PreDA-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jrc-ai/PreDA-large") model = AutoModelForSeq2SeqLM.from_pretrained("jrc-ai/PreDA-large") - Notebooks
- Google Colab
- Kaggle
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# [PreDA-large (Prefix-Based Dream Reports Annotation)](https://link.springer.com/chapter/10.1007/978-3-032-21477-5_13)
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## Intended uses & limitations
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# [PreDA-large (Prefix-Based Dream Reports Annotation)](https://link.springer.com/chapter/10.1007/978-3-032-21477-5_13)
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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-large](https://huggingface.co/google-t5/t5-large) 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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