Instructions to use Patil/flan-t5-relevant_df with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patil/flan-t5-relevant_df with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Patil/flan-t5-relevant_df") model = AutoModelForSeq2SeqLM.from_pretrained("Patil/flan-t5-relevant_df") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 963e488fbb07874bd5097d59f5a6b99e4462d35bae796c3b8d7b2cbf7596c725
- Size of remote file:
- 4.66 kB
- SHA256:
- 0702e9e26a01689fee0e2d8a98eb3945ef089d07f9c15e8091d58ab0dc08161c
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