---
license: apache-2.0
library_name: adapter-transformers
tags:
  - adapters
  - mad-x
  - emotion-detection
  - multi-label-classification
  - multilingual
  - low-resource
  - xlm-roberta
base_model:
  - FacebookAI/xlm-roberta-large
  - UBC-NLP/serengeti-E250
datasets:
  - brighter-dataset/BRIGHTER-emotion-categories
language:
  - en
  - hi
  - ru
  - ha
  - rw
  - su
  - yo
  - vmw
  - pcm
---

# All Emotion Adapters: MAD-X adapters for multilingual emotion detection

Trained MAD-X adapters from my MSc dissertation, **"Multilingual Models Have Feelings Too?"**
(MSc Applied Artificial Intelligence, University of Huddersfield, 2026).

They detect six emotions in text as a **multi-label** task (a text can have several emotions, or none):

`anger, disgust, fear, joy, sadness, surprise`

They cover 9 languages across three resource levels, from the BRIGHTER dataset (SemEval-2025 Task 11):

| Tier | Languages (code) |
|---|---|
| High resource | English (`eng`), Hindi (`hin`), Russian (`rus`) |
| Mid resource | Hausa (`hau`), Kinyarwanda (`kin`), Sundanese (`sun`) |
| Low resource | Yoruba (`yor`), Emakhuwa (`vmw`), Nigerian Pidgin (`pcm`) |

- **Dissertation code and results:** https://github.com/Momina0398/EmotionDetection
- **API that serves these adapters (FastAPI, Docker, CI):** https://github.com/Momina0398/emotion-api

## What is in this repo

```
adapters_xlmr_weighted/     47 runs on xlm-roberta-large (all 9 languages)
adapters_serengeti/         26 runs on UBC-NLP/serengeti-E250 (5 African languages)
lang_sun_lapt/              Sundanese language adapter, extra pre-training (LAPT)
lang_vmw_lapt/              Emakhuwa language adapter, LAPT, xlm-roberta-large
lang_vmw_lapt_serengeti/    Emakhuwa language adapter, LAPT, SERENGETI
```

Each run folder is named `<lang>_track_<a|c>_<C1|C2|C3>` and contains three parts:

```
lang/   language adapter (invertible MAD-X language adapter)
task/   emotion task adapter ("emotion_task")
head/   6-label multi-label classification head
```

**Track**
- `track_a`: target-language training data was available (supervised)
- `track_c`: no target-language training data (cross-lingual transfer only)

**Source configuration** (which languages the task adapter was trained on)
- `C1`: all other BRIGHTER languages
- `C2`: languages in the same top-level family as the target
- `C3`: languages in the same sub-family as the target

Some combinations are missing on purpose. When a family or sub-family pool was too small to form a valid training set, that run was not created rather than filled with an invented pool.

## How to use

```python
# pip install adapters==1.3.0 transformers torch huggingface_hub
import torch
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer
from adapters import AutoAdapterModel
import adapters.composition as ac

REPO = "MominaMahmood/All-Emotion-Adapters"
RUN = "adapters_xlmr_weighted/rus_track_a_C1"
BASE = "xlm-roberta-large"            # use "UBC-NLP/serengeti-E250" for adapters_serengeti runs
LABELS = ["anger", "disgust", "fear", "joy", "sadness", "surprise"]

path = snapshot_download(REPO, allow_patterns=[f"{RUN}/*"])
run = f"{path}/{RUN}"

tok = AutoTokenizer.from_pretrained(BASE)
model = AutoAdapterModel.from_pretrained(BASE)
model.load_adapter(f"{run}/lang", load_as="lang")
model.load_adapter(f"{run}/task", load_as="emotion_task")
model.load_head(f"{run}/head")
model.set_active_adapters(ac.Stack("lang", "emotion_task"))
model.eval()

texts = ["Я так рада тебя видеть!"]
enc = tok(texts, padding=True, truncation=True, max_length=256, return_tensors="pt")
with torch.no_grad():
    probs = torch.sigmoid(model(**enc).logits)[0].tolist()
print({e: round(p, 3) for e, p in zip(LABELS, probs)})
print("labels:", [e for e, p in zip(LABELS, probs) if p >= 0.5])
```

Use a threshold of **0.5** per label, as in training and evaluation.

## Training details

| Setting | Value |
|---|---|
| Architecture | MAD-X: language adapter + task adapter stacked on a frozen backbone |
| Backbones | `xlm-roberta-large`, `UBC-NLP/serengeti-E250` |
| Adapter configs | `SeqBnInvConfig` (language), `SeqBnConfig` (task), reduction factor 16 |
| Loss | Binary cross-entropy with per-label positive class weights ("weighted") |
| Learning rate | 5e-5 |
| Batch size | 8 |
| Max epochs | 10, early stopping with patience 3 on the dev split |
| Max sequence length | 256 |
| Library | `adapters` 1.3.0 |
| LAPT | Extra masked-language-model training of the language adapter for Emakhuwa (Emakhuwa text) and Sundanese (Javanese and Indonesian text) |

Model selection used the validation split only. Full results, significance tests and per-language analysis are in the dissertation repo linked above.

## Limitations

- **English has no disgust labels** in the BRIGHTER training data, so English runs cannot learn that emotion. Treat English disgust scores as meaningless.
- **Single random seed.** Results do not capture variation between training runs.
- **Emotion labels are dataset annotations**, not ground truth about how a writer feels.
- Performance on the low-resource languages (especially Emakhuwa and Nigerian Pidgin) is well below the high-resource languages.
- A main finding of the dissertation is that **sub-family transfer (C3) did not beat broader source pools**. For most languages, pick C1 or C2 first.
- **Not for high-stakes decisions about individuals** (for example health, hiring or policing) without expert human review.

## Citation

If you use these adapters, please cite the dissertation:

```
Siddiq, M. M. (2026). Multilingual Models Have Feelings Too? Culturally Aware
Sub-Family Transfer for Emotion Detection. MSc dissertation, University of Huddersfield.
```

Please also cite the BRIGHTER dataset and SemEval-2025 Task 11 papers.