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:

Label Meaning Risk signal in Gomi
satisfaction Positive / constructive tone No
neutral Neutral tone No
caution Hedging, workaround, “revisit later” Yes
frustration Negative / stressed tone Yes

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

Training

Item Detail
Base model distilbert-base-uncased
Training data GitRatBCSAD/gomi-datasetsopenreview/openreview_labeled_2k.csv
Script scripts/train_sentiment.py in the Gomi ML repo

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

Files in this repo

File Description
config.json Model config (4 labels)
model.safetensors Fine-tuned weights
tokenizer.json Tokenizer
tokenizer_config.json Tokenizer config

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

Repo Role
gomi-datasets CSV / JIT training data
gomi-risk Logistic regression + SHAP background

License

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

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