README.md

---
base_model: togethercomputer/gpt-oss-20b-bf16
library_name: peft
license: cc-by-4.0
language:
- ha
- yo
- sw
tags:
- sentiment-analysis
- african-languages
- lora
- peft
- autoscientist-challenge
---

# African Languages Sentiment Classifier (Hausa, Yorùbá, Swahili)

A LoRA-adapted sentiment classifier for Hausa, Yorùbá, and Swahili, fine-tuned
on a combined dataset of **46,725 rows** stitched from three independent
sources across three different domains, built to reduce the single-domain
(Twitter-only) bias common in existing African-language sentiment resources.

## Model Details

- **Base model:** `togethercomputer/gpt-oss-20b-bf16`
- **Adapter type:** LoRA (PEFT), rank 64, alpha 128, target modules `q_proj`/`k_proj`/`v_proj`/`o_proj`
- **Task formulation:** causal LM, prompt → single-word completion (the model generates the sentiment label as its next-token completion)
- **Languages:** Hausa, Yorùbá, Swahili
- **License:** CC-BY-4.0
- **Produced via:** [Adaption Labs AutoScientist](https://adaptionlabs.ai/blog/autoscientist-challenge) (Language category submission)
- **AutoScientist training run ID:** `adaption_gpt_oss_20b_ha_yo_sw_sentiment_1eb424c7`

## Training Data

This model was trained on a combined dataset built from three sources:

| Source | Domain | Languages | Rows |
|---|---|---|---|
| AfriSenti | Twitter | Hausa, Yorùbá, Swahili | 40,290 |
| NollySenti | Nollywood movie reviews (human-translated) | Hausa, Yorùbá | 2,510 |
| Neurotech-HQ Swahili | Social media / product reviews (back-translated) | Swahili | 3,925 |

3-class labels (`positive` / `negative` / `neutral`), 70/15/15 train/dev/test
split per language, stratified by label.

> **Note on training data adaptation**: this adapter was trained on a
> version of this data that was adapted once via [Adaption Labs'
> AutoScientist](https://adaptionlabs.ai/blog/autoscientist-challenge) —
> its "Adaptive Data" step rewrote the original rows into
> `enhanced_prompt`/`enhanced_completion` pairs (15,280 rows after this
> process) as part of its data-and-recipe co-optimization loop. **The
> dataset used to produce this result is included in this repo, alongside
> the model weights** (see Files and versions).

## Training Procedure

- 5 epochs, 585 total steps
- Train/eval loss decreased steadily across all 5 epochs (eval loss:
  0.828 → 0.787 → 0.769 → 0.760 → 0.757)
- Learning rate: warm-up then decay schedule
- Framework: PEFT 0.15.1

## Evaluation (AutoScientist internal metrics)

These are AutoScientist's own judge-based scores comparing the base model
against the fine-tuned ("adapted") model — **not standard accuracy/F1**:

| Metric | Before (base) | After (adapted) |
|---|---|---|
| Quality score (0–10 scale) | 3.0 | 6.9 (+130% relative) |
| Grade | E | C |
| Percentile | 1.3 | 8.4 |
| Win rate — on this dataset | 44 | 57 |
| Win rate — general category (all tasks) | 52 | 48 |

**Read this table carefully**: task-specific quality improved substantially
(grade E→C, +130% relative quality score), but the general-category win rate
slightly *dropped* (52→48), meaning the adaptation traded a small amount of
general-purpose capability for sentiment-task performance. This is disclosed
deliberately — don't assume "adapted" is strictly better in every dimension.

## Intended Use

Sentiment classification (positive/negative/neutral) for short-form text in
Hausa, Yorùbá, or Swahili, primarily for research and benchmarking purposes
within the AutoScientist Challenge. Not validated for production deployment.

## How to Use

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("togethercomputer/gpt-oss-20b-bf16")
model = PeftModel.from_pretrained(base_model, "gospelgit/African-Languages-Sentiment-Classifier")
tokenizer = AutoTokenizer.from_pretrained("gospelgit/African-Languages-Sentiment-Classifier")

prompt = "Classify the sentiment of this text as positive, negative, or neutral: <your text here>"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=5)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Limitations

- Evaluated via AutoScientist's internal judge/win-rate system, not an
  external, reproducible benchmark — independent verification is
  recommended before relying on these numbers.
- Trained on an evolved/rewritten version of the source data, not the raw
  human-annotated labels directly.
- Slight general-capability regression observed post-adaptation (see table
  above).
- Swahili has less underlying data than Hausa/Yorùbá — performance may be
  less stable for that language.

## Citation

If you use this model, please also cite the original dataset sources
listed in the [dataset card](https://huggingface.co/datasets/gospelgit/African-Languages_Sentiments).