Instructions to use devangb4/scikit-issues-multilabel-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devangb4/scikit-issues-multilabel-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="devangb4/scikit-issues-multilabel-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("devangb4/scikit-issues-multilabel-classification") model = AutoModelForSequenceClassification.from_pretrained("devangb4/scikit-issues-multilabel-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from devangb4/scikit-issues-multilabel-classification: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/devangb4/scikit-issues-multilabel-classification/resolve/main/model.safetensors
- Command line
-
hf download hf://devangb4/scikit-issues-multilabel-classification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/devangb4/scikit-issues-multilabel-classification/resolve/main/model.safetensors
499 MB
- Xet hash:
- fb5c56b126b2aeef51ff105f2a963e9bfb063fed26111978b211c8a88b18cc3e
- Size of remote file:
- 499 MB
- SHA256:
- afb88d040c8c2418826a270d310bf31cfb615d5146137ea785798b0529b13edd
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