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
multi-label-class-classification-on-github-issues / runs /Dec01_02-33-04_9e50f2f8301e /events.out.tfevents.1669862086.9e50f2f8301e.116.0
- Xet hash:
- 68957c6cf23808a17577968136070a20f113d270d92409b2a785ed50957ab04d
- Size of remote file:
- 11.8 kB
- SHA256:
- c1f0b4c4588db895b9f41751ebb9aec90eee00154f5f2fa9756202d01d85aa11
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.