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:
- 34433aed8a020d3b5402bea5c6c2ad13df3261f35d600fd6d97f49a39711ae2a
- Size of remote file:
- 3.52 kB
- SHA256:
- 09330e7985ff1977376a9e321947b9394dfc3fc0feb8f8906836109a497d7192
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