Instructions to use thethinkmachine/MICE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thethinkmachine/MICE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thethinkmachine/MICE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("thethinkmachine/MICE") model = AutoModelForSequenceClassification.from_pretrained("thethinkmachine/MICE") - Notebooks
- Google Colab
- Kaggle
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### CO2 Emissions
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Experiments were conducted using Google Cloud Platform in region asia-south1, which has a carbon efficiency of 0.92 kgCO2eq/kWh. A cumulative of 13.
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Total emissions are estimated to be 0.87 kgCO2eq of which 100% was directly offset by the cloud provider.
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### CO2 Emissions
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Experiments were conducted using Google Cloud Platform in region asia-south1, which has a carbon efficiency of 0.92 kgCO2eq/kWh. A cumulative of 13.24 hours of computation was performed on hardware of type L4 (TDP of 72W).
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Total emissions are estimated to be 0.87 kgCO2eq of which 100% was directly offset by the cloud provider.
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