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:
- 48ce1595dd94994ad1128c9ff13e8f7d6ec7cbae6d029e93fd01a0cfd3c9647a
- Size of remote file:
- 118 kB
- SHA256:
- 50e7446785f68104cc24cbab325bf315fbb822f2380adadf2bf0e05eea3511f0
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