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README.md
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- exact_match
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library_name: transformers
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pipeline_tag: question-answering
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co2_eq_emissions:
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---
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# QAmembert
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*Carbon emissions were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.*
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- **Hardware Type:** A100 PCIe 40/80GB
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- **Hours used:**
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- **Cloud Provider:** Private Infrastructure
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- **Carbon Efficiency (kg/kWh):** 0.076kg (estimated from [electricitymaps](https://app.electricitymaps.com/zone/FR) ; we take the average carbon intensity in France for the month of March 2023, as we are unable to use the data for the day of training, which are not available.)
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- **Carbon Emitted** *(Power consumption x Time x Carbon produced based on location of power grid)*: 0.
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## Citations
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- exact_match
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library_name: transformers
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pipeline_tag: question-answering
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co2_eq_emissions: 100
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---
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# QAmembert
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*Carbon emissions were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.*
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- **Hardware Type:** A100 PCIe 40/80GB
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- **Hours used:** 5h and 36 min
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- **Cloud Provider:** Private Infrastructure
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- **Carbon Efficiency (kg/kWh):** 0.076kg (estimated from [electricitymaps](https://app.electricitymaps.com/zone/FR) ; we take the average carbon intensity in France for the month of March 2023, as we are unable to use the data for the day of training, which are not available.)
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- **Carbon Emitted** *(Power consumption x Time x Carbon produced based on location of power grid)*: 0.1 kg eq. CO2
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## Citations
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