Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ForMaskedLM 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-Wav2Vec2ForMaskedLM 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-Wav2Vec2ForMaskedLM")# Load model directly from transformers import AutoProcessor, AutoModelForMaskedLM processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 056df4ade8d5816b5684c5de12bccf30300945a00c68ce1b909704bde4376048
- Size of remote file:
- 135 kB
- SHA256:
- ec53d748e5bb278d7096baff56a4b9f831ea921c28387a528f0a1eb4b0cc9415
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.