Instructions to use Jellevdl/checkpoint-20000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jellevdl/checkpoint-20000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Jellevdl/checkpoint-20000")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Jellevdl/checkpoint-20000") model = AutoModelForQuestionAnswering.from_pretrained("Jellevdl/checkpoint-20000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3da89e013d53bebbea478cc9a1def9a141cb2a135f02f690234d99015c52bacd
- Size of remote file:
- 436 MB
- SHA256:
- 911508f137ff23718ef0e0b9fb5872c845a0315cdf2957fb2bae4be48a058598
路
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