my-bert-sentiment-cpu

DIA-MVP/my-bert-sentiment-cpu

my-bert-sentiment-cpu is a text classification model from DIA-MVP. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

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).

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: distilbert
Access: Public
Created: Jun 29, 2026
Updated: Jun 29, 2026
SHA: 51dcd6f4

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Model card

From the Hugging Face model README

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