Instructions to use hf-tiny-model-private/tiny-random-DebertaV2ForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-DebertaV2ForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-DebertaV2ForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DebertaV2ForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-DebertaV2ForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf52a7a74bdbc0667acaac9e84fa55b9f15f27b37f95bf5eebaca952380aadd6
- Size of remote file:
- 16.7 MB
- SHA256:
- 3ccc64e907e8f48f8afc9b1e67b6e786824bda8bc9b8c7aa1a2765e02ce940d2
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