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:
- cfc8a3db870ceb99498ac7700900b356108f763007e933cb34dcbd942ac01274
- Size of remote file:
- 2.24 GB
- SHA256:
- e401afcf0630b76a9d80b4464db8de51d3f717c517d2faad43eb0cf33cd9ed2c
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.