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
Training in progress, epoch 4
Browse files
pytorch_model.bin
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runs/May16_15-27-07_7ab11e6c2e67/events.out.tfevents.1684250840.7ab11e6c2e67.965.19
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