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:
- 49f3db3a83919c4378f3dabb4ab942785966535fd5d7f3cb9d6153a3d29df4da
- Size of remote file:
- 268 MB
- SHA256:
- ea9d8a5cd6ef59044f46c98dd4409e8dc5edaa1f7cf8339c9e185ab13f6dd37d
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