Instructions to use paritc/multilabelclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paritc/multilabelclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="paritc/multilabelclassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("paritc/multilabelclassification") model = AutoModelForSequenceClassification.from_pretrained("paritc/multilabelclassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Verified signaturetraining credit from :https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/BERT/Fine_tuning_BERT_(and_friends)_for_multi_label_text_classification.ipynb {'epoch': 5.0, 'eval_accuracy': 0.2799097065462754, 'eval_f1': 0.7096149188665537, 'eval_loss': 0.31915703415870667, 'eval_roc_auc': 0.805805895058838, 'eval_runtime': 4.7187, 'eval_samples_per_second': 187.766, 'eval_steps_per_second': 23.524}3a5b8df
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