---
library_name: transformers
tags:
- dia
- carbon-footprint
- energy-efficiency
- sustainability
dia_version: '0.1'
dia_report:
  scope: incremental
  lineage:
  - model: distilbert-base-uncased
    relation: finetune
  compute:
    hardware:
      gpu: cpu-80core
      count: 1
    duration_gpu_hours: 0.5381
  footprint:
    energy_kwh:
      value: 0.026
      quality: measured
    carbon_kgco2eq:
      value: 0.0017
      quality: measured
    water_liters:
      value:
      - 0.047
      - 0.104
      quality: estimated-from-default-wue
  context:
    region: ca-on
    carbon_intensity: 0.03
    wue_l_per_kwh:
    - 1.8
    - 4.0
  tool: codecarbon
license: apache-2.0
pipeline_tag: text-classification
base_model: distilbert-base-uncased
---

# DistilBERT — SST-2 sentiment (CPU (80-core))

A demo model from the **Data & Impact Accounting (DIA)** lab. It performs
binary sentiment classification (SST-2) via **full fine-tune**, with the base model `distilbert-base-uncased`, trained on
**CPU (80-core)**.

The point of this repo is not the model itself but its **`dia_report`** — a
standardized record of the energy, carbon, and water used to train it, embedded
in this card's metadata.

This footprint feeds the DIA dashboard, which rolls up a base model and all its derivatives to show the **cumulative** carbon, water, and energy cost of a model family.

## Training footprint

| Metric | Value |
|---|---|
| Hardware | 1× cpu-80core |
| Compute | 0.5381 GPU-hours |
| Energy | 0.026 (measured) kWh |
| Carbon | 0.0017 (measured) kgCO₂eq |
| Water | 0.047–0.104 (estimated-from-default-wue) L |
| Grid region | ca-on |

*Energy and carbon are measured with [CodeCarbon](https://github.com/mlco2/codecarbon);
water is estimated from a default water-usage-effectiveness range. Carbon uses the
local grid's intensity (Ontario, ~0.03 kgCO₂eq/kWh).*

## Reproduce

```bash
REPO=DIA-MVP/my-bert-sentiment-cpu python scripts/train_bert_demo.py
```

## Links

- **Footprint table (dataset):** [DIA-MVP/dia-state-lab-2026](https://huggingface.co/datasets/DIA-MVP/dia-state-lab-2026)
- **Project / paper:** [ai-impact-accounting](https://github.com/VectorInstitute/ai-impact-accounting)
- **Lab workflow:** see `LAB.md` in the repo