Gomi Sentiment Model

Fine-tuned DistilBERT (distilbert-base-uncased) for commit-message sentiment in the Gomi technical-debt risk pipeline.

Classifies short commit text into four labels:

At runtime, Gomi aggregates per-file sentiment score as
(frustration + caution) / non–low-info commits.

Training

Prototype — Early checkpoint. Pin a revision tag (e.g. v1.0.0) for reproducible runs.

Files in this repo

Inference needs the files above. The checkpoints/ folder may be present from training uploads; it is not required at runtime.

Download

Hugging Face CLI

pip install huggingface_hub
huggingface-cli login   # if private

hf download GitRatBCSAD/gomi-sentiment \
  --local-dir ./distilbert_sentiment \
  --revision v1.0.0

Python

from transformers import AutoModelForSequenceClassification, AutoTokenizer

repo = "GitRatBCSAD/gomi-sentiment"
revision = "v1.0.0"  # or None for main

tokenizer = AutoTokenizer.from_pretrained(repo, revision=revision)
model = AutoModelForSequenceClassification.from_pretrained(repo, revision=revision)

Git + LFS

git lfs install
git clone https://huggingface.co/GitRatBCSAD/gomi-sentiment
cd gomi-sentiment && git lfs pull

Use with Gomi

In the Gomi ML project .env:

GOMI_SENTIMENT_MODEL_REPO=GitRatBCSAD/gomi-sentiment
GOMI_SENTIMENT_MODEL_REVISION=v1.0.0
HF_TOKEN=your_token_if_private

Or download into scripts/datasets/distilbert_sentiment/ and run gomi.py without the env var.

Training data lives in gomi-datasets, not in this repo.

Related repos

License

Apache 2.0. Respect licenses of distilbert-base-uncased and the OpenReview training data when redistributing.