Instructions to use namnthust/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namnthust/content with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="namnthust/content")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("namnthust/content") model = AutoModelForQuestionAnswering.from_pretrained("namnthust/content", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c3587f03c90386f5c109436735436a62788da4696285b74c1bcab09d64d103f
- Size of remote file:
- 18.3 MB
- SHA256:
- 51c292478d94ec3a01461bdfa82eb0885d262eb09e615679b2d69dedb6ad09e7
路
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