Instructions to use Akash82228/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akash82228/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Akash82228/tmp_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Akash82228/tmp_trainer") model = AutoModelForQuestionAnswering.from_pretrained("Akash82228/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7b027b37559cf49fed89c8b1a9a83177cd59dd3555b201df4d3a30419e1bdfe
- Size of remote file:
- 4.6 kB
- SHA256:
- 2e5c244ef86c5aca4c0938c1f979f01f65eb5d8759d3ac03316fdc40ebd3e1f6
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