Add model description (DIA lab card)

51dcd6f verified about 2 months ago

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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
  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; water is estimated from a default water-usage-effectiveness range. Carbon uses the local grid's intensity (Ontario, ~0.03 kgCO₂eq/kWh).

Reproduce

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

DIA-MVP
/

my-bert-sentiment-cpu

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

Training footprint

Reproduce

Links