Instructions to use hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM 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-Data2VecTextForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecTextForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8111556d448d4fbf765d92b4277f2ed67e6ecb06cb795e7b6a49fe5de1df0a5
- Size of remote file:
- 358 kB
- SHA256:
- 695959e616b8582b3244af3c5d8ff855af3211de621896c311fb6528c1e8debd
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