Instructions to use Israhassan/EncoderDecoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Israhassan/EncoderDecoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Israhassan/EncoderDecoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Israhassan/EncoderDecoder") model = AutoModelForSequenceClassification.from_pretrained("Israhassan/EncoderDecoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Israhassan/EncoderDecoder: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/Israhassan/EncoderDecoder/resolve/a9d4fc324c7e1c8418b160c117c531d5bc42eb3a/pytorch_model.bin
- Command line
-
hf download hf://Israhassan/EncoderDecoder@a9d4fc324c7e1c8418b160c117c531d5bc42eb3a/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Israhassan/EncoderDecoder/resolve/a9d4fc324c7e1c8418b160c117c531d5bc42eb3a/pytorch_model.bin
1.63 GB
- Xet hash:
- 308cdd62e4346a95dcbfe36f0112989eee579f55d977ac062bf6ad60e9411494
- Size of remote file:
- 1.63 GB
- SHA256:
- 6f37742d94f3ed860d3d7fd61ca301a3d6ebf87c43086ed5560ad0e43a380f2a
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