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") - Inference
- Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#2
by librarian-bot - opened
README.md
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---
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tags:
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- generated_from_trainer
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model-index:
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- name: multi-label-class-classification-on-github-issues
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results: []
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---
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tags:
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- generated_from_trainer
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base_model: neuralmagic/oBERT-12-upstream-pruned-unstructured-97
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model-index:
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- name: multi-label-class-classification-on-github-issues
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results: []
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