my-bert-sentiment-a40

DIA-MVP/my-bert-sentiment-a40

my-bert-sentiment-a40 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 NVIDIA A40.

Downloads · 30 days

24% of all-time downloads

All-time downloads

Public

Parameters

67M

268 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors268 MB · 100%

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: d63a2f6c

Try a prompt

Base models

Model card

From the Hugging Face model README

DistilBERT — SST-2 sentiment (NVIDIA A40)

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 NVIDIA A40.

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× NVIDIA A40
Compute	0.0233 GPU-hours
Energy	0.0061 (measured) kWh
Carbon	0.0004 (measured) kgCO₂eq
Water	0.011–0.025 (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-a40 python scripts/train_bert_demo.py