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:
- 53b212ceb2fa33c4c9ce8b480ebcad3e5dec92c40a93a9d13fa9aa1fa90a5cf8
- Size of remote file:
- 1.97 GB
- SHA256:
- 1143bdcf0f03f134086592b76b4a8a9a60cbf46ba835fe9202c797b0a721a48d
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