BERT emotion classifier (English)

Merged 6-class emotion classifier built on google-bert/bert-base-uncased.

This is the inference repo. Use this one with pipeline("text-classification").

The PEFT adapter-only artifact (learning / resume / smaller download) lives in
Shankarblr/bert-emotion-lora-adapter.

Labels

id	label
0	sadness
1	joy
2	love
3	anger
4	fear
5	surprise

Single-label classification. id2label / label2id are in config.json, so the pipeline prints the label name, not LABEL_3.

Use it

from transformers import pipeline

clf = pipeline(
    "text-classification",
    model="Shankarblr/shankar-bert-emotion-en",  # or your current repo id
)

print(clf("I like ML"))
print(clf("I started annoyed with laptops"))
print(clf("I am low today"))
print(clf("I am tensed if I am not going to get the job in ML"))
print(clf("I am worried with the current job market"))

Expected shape:

[{'label': 'joy', 'score': 0.77}]
[{'label': 'anger', 'score': 0.99}]
[{'label': 'sadness', 'score': 0.995}]
[{'label': 'fear', 'score': 0.98}]
[{'label': 'fear', 'score': 0.81}]

Scores come from the learning_rate=2e-4 run logged during training. Re-run inference after you replace Hub weights if your local checkpoint changed.

Load the model directly

from transformers import AutoTokenizer, AutoModelForSequenceClassification

repo = "Shankarblr/shankar-bert-emotion-en"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSequenceClassification.from_pretrained(repo)

inputs = tok("I am low today", return_tensors="pt")
pred = model(**inputs).logits.argmax(-1).item()
print(model.config.id2label[pred])

Training

Item	Value
Base	`bert-base-uncased`
Method	LoRA (PEFT), then merged for this repo
Task	6-class emotion (`dair-ai/emotion`)
Train / val / test	11,200 / 1,600 / 3,200
Trainable params	2,683,398 / 112,170,252 (2.39%)
Epochs / steps	4 / 1,400
Device	CUDA
Best run LR	2e-4 (2e-5 underfit on the same adapters)
Train wall time	~4.7 min

Why 2e-4, not 2e-5

LoRA plus a newly initialized classification head needs a larger step than full BERT fine-tunes. Same trainable parameter count on both runs; only the step size changed.

Run	LR	Val acc	Val F1	Test acc	Test F1	Avg train loss
Underfit	2e-5	0.725	0.671	0.717	0.661	1.139
Published	2e-4	0.941	0.942	0.932	0.933	0.344

Val loss on the published run: 0.152 (epoch 2) → 0.167 (epoch 3) → 0.142 (epoch 4). Small bump, then recovered. Test is within ~1 point of val.

Intended use

Short English utterances / social-style sentences
Emotion tagging demos, teaching PEFT vs merged inference, baseline for a product classifier

Not intended for:

Clinical or crisis detection
Long documents (tokenizer max length 512; this dataset is sentence-level)
Languages other than English
Multi-label emotion (one label per text only)

Limitations

Trained on dair-ai/emotion. That set is clean, short, and class-imbalanced toward joy / sadness. Real chat and tickets will look different.
love and surprise are the usual weak / confusable classes. Overall 93% can hide a weaker minority class — check per-class F1 before you ship.
Merged weights are fp32 (~110M params, ~438 MB). For a few-MB download use the adapter repo.

Files in this repo

File	Role
`model.safetensors`	Full `BertForSequenceClassification` (base + trained head, LoRA merged)
`config.json`	Architecture + label maps
`tokenizer.json` / `tokenizer_config.json`	Same WordPiece tokenizer as BERT uncased
`training_args.bin`	Hugging Face Trainer args from the run
`README.md`	This card

Do not upload checkpoint-350 … checkpoint-1400. Those are Trainer resume snapshots (optimizer + RNG), not inference artifacts.

Adapter-only (PEFT) repo: Shankarblr/bert-emotion-lora-adapter
Dataset: dair-ai/emotion
Base: google-bert/bert-base-uncased

License

MIT. Base BERT is Apache 2.0. Dataset license follows dair-ai/emotion.

Shankarblr/bert-emotion-en