Instructions to use EmbeddingStudio/fred_t5_query_parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EmbeddingStudio/fred_t5_query_parser with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("EmbeddingStudio/fred_t5_query_parser") model = AutoModelForSeq2SeqLM.from_pretrained("EmbeddingStudio/fred_t5_query_parser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2647739b848ddb5f7443adaf9eb4acde34200439bb952300a8cd584a7c60c739
- Size of remote file:
- 4.99 GB
- SHA256:
- 9983514f17393b01d0517b7897f8aa7c51c8f862a35b5c83ff45118cf8f2e1fb
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