Instructions to use LinaSaba/distilbert-base-task-multi-label-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinaSaba/distilbert-base-task-multi-label-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LinaSaba/distilbert-base-task-multi-label-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LinaSaba/distilbert-base-task-multi-label-classification") model = AutoModelForSequenceClassification.from_pretrained("LinaSaba/distilbert-base-task-multi-label-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from LinaSaba/distilbert-base-task-multi-label-classification: direct link, hf CLI and curl.
- Browser
- Download file 3.71 kB
-
https://huggingface.co/LinaSaba/distilbert-base-task-multi-label-classification/resolve/refs%2Fpr%2F3/training_args.bin
- Command line
-
hf download hf://LinaSaba/distilbert-base-task-multi-label-classification@refs/pr/3/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/LinaSaba/distilbert-base-task-multi-label-classification/resolve/refs%2Fpr%2F3/training_args.bin
3.71 kB
- Xet hash:
- 4591734b6c03ab57eb45f698b33c9a0fd65d7bd1a7bc5b2118a278fa5a217dfd
- Size of remote file:
- 3.71 kB
- SHA256:
- fe35d553200337b3a8d53846af18cdee57ce24a40a11637bf619b5438d2a6b63
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