Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Rami/multi-label-class-classification-on-github-issues with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rami/multi-label-class-classification-on-github-issues with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rami/multi-label-class-classification-on-github-issues")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rami/multi-label-class-classification-on-github-issues") model = AutoModelForSequenceClassification.from_pretrained("Rami/multi-label-class-classification-on-github-issues") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da1acb957be07dfd6fedc6b9f42efea95e1745c2d6fcf4ecb53412830c4093d8
- Size of remote file:
- 438 MB
- SHA256:
- 69c5b21d540683f0f1a2c9ba1436afcb5f788f477f927432acda6e0853dfd2d9
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