Opus Emotion 1

A multi-label emotion classifier over 28 labels, fine-tuned from answerdotai/ModernBERT-base on GoEmotions.

Trained in 13.6 minutes on a consumer AMD RX 7600 (8GB, gfx1102) under WSL2 with ROCm. No datacentre, no rented GPU.


macro F1	0.490
micro F1	0.582
classes never predicted	0 / 28
training time	13.6 min
throughput	213 samples/s
peak VRAM	4.59 GB

Macro F1 of 0.49 is in line with published GoEmotions baselines; the original paper reports 0.46 macro F1 with a BERT-base model.

The interesting part is not the average

A single macro-F1 number hides what actually happened. Per-class F1 ranges from 0.90 to 0.09, and the obvious explanation — rare labels do worse — only holds up halfway (r = 0.40 between log support and F1).

The clearer pattern is lexical distinctiveness:

label	support	F1
gratitude	358	0.90	announced by the word "thanks"
approval	397	0.35	a judgement call, no marker word
remorse	68	0.70	"sorry", "my fault"
annoyance	303	0.26	overlaps anger, disapproval, disappointment

gratitude and approval have near-identical support and a 2.5× gap in F1. More data would not close it. Emotions with a reliable surface marker are easy; emotions that are inferences about intent are hard, and human annotators disagree about them too — which puts a ceiling on what any model can score.

relief (F1 0.09, n=18) is the floor case: rare and subjective.

Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

name = "opus-research/opus-emotion-1"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForSequenceClassification.from_pretrained(name).eval()

text = "FUCK YOU BRO WHY ARE YOU SO FUCKING RETARDED"
with torch.no_grad():
    probs = torch.sigmoid(model(**tok(text, return_tensors="pt")).logits)[0]

for i, p in enumerate(probs):
    if p > 0.5:
        print(model.config.id2label[i], f"{p:.1%}")
# anger 97.2%

Multi-label, so use sigmoid and a threshold — not softmax. A comment can be both admiration and amusement.

Tune the threshold to your task. 0.5 is the reported operating point; dropping to 0.3 raises recall on the rare classes at some cost to precision, which is usually the right trade if you are filtering rather than labelling.

Training


Base	`answerdotai/ModernBERT-base` (149M)
Epochs / LR	4 / 5e-5, cosine, 6% warmup
Batch / max_len	32 / 96
Precision	bf16
Loss	BCEWithLogits (multi-label)
Hardware	AMD RX 7600 8GB, ROCm 7.13 via ROCDXG on WSL2

Three epochs gave macro F1 0.483; four gave 0.490. It was still improving slowly, so more epochs may add a little.

Note on the hardware

The RX 7600 is not on AMD's supported list for ROCm under WSL. It works via ROCDXG with a device-ID override in /opt/rocm/share/rocdxg/dids.conf, and by capping torch's VRAM so the Windows desktop compositor keeps a slice — without that cap, filling 8GB freezes the screen.

Limitations

English Reddit comments. Register-specific; expect degradation elsewhere.
Rare, subjective labels are weak — relief, grief, pride sit below 0.40 and should not be relied on individually.
Annotator disagreement is the ceiling, not model capacity, for several of the low scorers.
Trained on 128-token inputs, truncated at 96 during training.