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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18b588dd6980e56334d6955d5c77a9ab8a4fd8cafa0deac41d401219ed07537c
- Size of remote file:
- 3.5 kB
- SHA256:
- ff87d7d6b775107e843a86c6b66711c23701145a8c332f97b37864f8b2f16c96
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