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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("claritylab/MARS-Encoder") model = AutoModelForSequenceClassification.from_pretrained("claritylab/MARS-Encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (8fbbffcee27a62735abe4121d6b69be060bc54d0) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>1a234c9
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Update README.md
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updated readme
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Added initial version of MARS Encoder
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