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")# pip install -U transformers accelerate # 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") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Rami/multi-label-class-classification-on-github-issues: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Rami/multi-label-class-classification-on-github-issues/resolve/d13bf63a05f9dde930bbcfeec7b028bd53670c16/pytorch_model.bin
- Command line
-
hf download hf://Rami/multi-label-class-classification-on-github-issues@d13bf63a05f9dde930bbcfeec7b028bd53670c16/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Rami/multi-label-class-classification-on-github-issues/resolve/d13bf63a05f9dde930bbcfeec7b028bd53670c16/pytorch_model.bin
268 MB
- Xet hash:
- cdab4d6f8302c582b015080a9ef3f8c18373d5997c7a810bd8b5aeb86a021edb
- Size of remote file:
- 268 MB
- SHA256:
- e33dbef32211f40bb34569ece7eb17fbe7af21dc6f15b861d54fdc635e8290cc
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