Instructions to use Srini99/TQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Srini99/TQA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Srini99/TQA")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Srini99/TQA") model = AutoModelForQuestionAnswering.from_pretrained("Srini99/TQA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a1c5dc216be4f65f88fcb0f78b5e2175f57874e17f7d51196e2b65f18d5b9806
- Size of remote file:
- 2.24 GB
- SHA256:
- d2ddd451ac9374fa1ad5369775b02a69063e95d3e91e417017392d2f0cde50bf
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