Instructions to use claritylab/MARS-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use claritylab/MARS-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="claritylab/MARS-Encoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("claritylab/MARS-Encoder") model = AutoModelForSequenceClassification.from_pretrained("claritylab/MARS-Encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1069a98cf17d7ff80e0292b2596074066b69d1d4a41672d87ae7072628a3f706
- Size of remote file:
- 499 MB
- SHA256:
- 58ecfa2f5c54faf5aebd1bf74501bca3defe562f6b8a93ea5d683420860d8a81
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