Instructions to use K-Monitor/kmdb_classification_hubert_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use K-Monitor/kmdb_classification_hubert_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="K-Monitor/kmdb_classification_hubert_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("K-Monitor/kmdb_classification_hubert_v2") model = AutoModelForSequenceClassification.from_pretrained("K-Monitor/kmdb_classification_hubert_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from K-Monitor/kmdb_classification_hubert_v2: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/K-Monitor/kmdb_classification_hubert_v2/resolve/main/model.safetensors
- Command line
-
hf download hf://K-Monitor/kmdb_classification_hubert_v2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/K-Monitor/kmdb_classification_hubert_v2/resolve/main/model.safetensors
443 MB
- Xet hash:
- ae322278e22b6a606dfb985fd51f0748359e3f3e63be10110940f2a756573d7f
- Size of remote file:
- 443 MB
- SHA256:
- 772aeb23e34f5f41787af95b7ed8414e3785277b975173c52dc971a477eb914f
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