Instructions to use hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 153 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification/resolve/2a75c99db5dc8615732bcdf23d8e3d0b9ccc1eb7/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification@2a75c99db5dc8615732bcdf23d8e3d0b9ccc1eb7/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-Wav2Vec2ForSequenceClassification/resolve/2a75c99db5dc8615732bcdf23d8e3d0b9ccc1eb7/pytorch_model.bin
153 kB
- Xet hash:
- 33d041e6db08455f7412ac890a088558c9513373e0c841fc99de4e9a5843d327
- Size of remote file:
- 153 kB
- SHA256:
- accb03dae344ac162525672bbf3d8aa4fcba5fb7d69694c344e4dc57c1c84584
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