Feature Extraction
Transformers
Safetensors
wav2vec2
pretraining
bioacoustics
audio
self-supervised-learning
dolphin
bottlenose-dolphin
whistle
openwhistle
Instructions to use dolphinteam/OpenWhistle-BioClust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dolphinteam/OpenWhistle-BioClust with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dolphinteam/OpenWhistle-BioClust")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("dolphinteam/OpenWhistle-BioClust") model = AutoModelForPreTraining.from_pretrained("dolphinteam/OpenWhistle-BioClust", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from dolphinteam/OpenWhistle-BioClust: direct link, hf CLI and curl.
- Browser
- Download file 56 MB
-
https://huggingface.co/dolphinteam/OpenWhistle-BioClust/resolve/main/trainer_state.json
- Command line
-
hf download hf://dolphinteam/OpenWhistle-BioClust/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/dolphinteam/OpenWhistle-BioClust/resolve/main/trainer_state.json
56 MB
- Xet hash:
- ae78a31e4f1c08a7cb27dfc36d087b2f364dc8bd0cdaf8b0edc9dcbd66d4b122
- Size of remote file:
- 56 MB
- SHA256:
- 488239343f2ca657565ec28fadbed4dea6d9efee8247441a997257661936869a
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