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:
- 2b74c76283145568974e4b367937705a08fcdd043c24c98261d30512142a3525
- Size of remote file:
- 990 MB
- SHA256:
- f74d08adf8e6d8ee148ae01ed9ee08455269cb52a37d1228fc90fcea9dcd452c
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