Instructions to use Samavia/prompts_summarization_model_trained_on_reduced_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Samavia/prompts_summarization_model_trained_on_reduced_data with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Samavia/prompts_summarization_model_trained_on_reduced_data") model = AutoModelForSeq2SeqLM.from_pretrained("Samavia/prompts_summarization_model_trained_on_reduced_data") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92d91ebb8b0bc534e499988ff6d19f13c4deb4facf01b29af8b52e7fdae3979d
- Size of remote file:
- 5.18 kB
- SHA256:
- 7b3ed695238a577d8d510ead73fb6a09bfa3227f606c81c8b87205fe281ebf32
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