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:
- 12adad9badddf220e7463a5145a4a01047e14007fa89ff16fbda2974b3a6b1e3
- Size of remote file:
- 610 MB
- SHA256:
- 8c4cd709b1c3d1d5e8b2a7db275c12a312498b5872393a342e3a46ac8363ba8c
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