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 /1669862086.2207165 /events.out.tfevents.1669862086.9e50f2f8301e.116.1
- Xet hash:
- faed415658d8b9f2d5d6ceec9ad8f362e2b20cba1ee20b1497673c278c08847e
- Size of remote file:
- 5.58 kB
- SHA256:
- 590e850e4e52cf06fc81d4f54ae1becbbc6b2dd11ba3f1cd130499dec8ba468b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.