Instructions to use ramkrish120595/debug_seq2seq_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ramkrish120595/debug_seq2seq_squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ramkrish120595/debug_seq2seq_squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ramkrish120595/debug_seq2seq_squad") model = AutoModelForQuestionAnswering.from_pretrained("ramkrish120595/debug_seq2seq_squad") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2435bbdaa83ad8b57e3d09bb0bdd792a03785a384c539926b67194b3d51d7457
- Size of remote file:
- 4.03 kB
- SHA256:
- 9788cb03ed32f36712b22846f00bf3fe11185b019321753432fbb54c15e5c6e6
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.