Summarization
Transformers
PyTorch
Safetensors
multilingual
miscovery
transformer
translation
question-answering
english
arabic
Instructions to use miscovery/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miscovery/model with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="miscovery/model")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("miscovery/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a9f7f77a1d60ccea3b5c862d4d56de51ebb8d6c3db436be06545e7e7a950c8f
- Size of remote file:
- 610 MB
- SHA256:
- f63f166d49528902b95d1ccecc1f997466f715b043d419795f401dc211b3961f
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