Update README.md

README.md

@@ -1,134 +1,127 @@
 ---
-base_model: togethercomputer/gpt-oss-20b-bf16
-library_name: peft
-license: cc-by-4.0
 language:
 - ha
 - yo
 - sw
+license: cc-by-4.0
+task_categories:
+- text-classification
 tags:
 - sentiment-analysis
 - african-languages
-- lora
-- peft
+- hausa
+- yoruba
+- swahili
 - autoscientist-challenge
+size_categories:
+- 10K<n<100K
 ---
 
-# African Languages Sentiment Classifier (Hausa, Yorùbá, Swahili)
+# African Languages Sentiment Dataset (Hausa, Yorùbá, Swahili)
 
-> ## ⚠️ Read before using this model
-> This is a **LoRA adapter produced by an automated fine-tuning/data-evolution
-> pipeline (Adaption Labs' AutoScientist)**, released as a submission for the
-> [AutoScientist Challenge](https://adaptionlabs.ai/blog/autoscientist-challenge).
-> It is a **research artifact, not a vetted production model**. Before you use
-> it, please understand what it actually is:
->
-> - It was fine-tuned on a **machine-evolved version of the training data**
->   (AutoScientist's "Adaptive Data" step rewrote/expanded the original
->   labeled examples into `enhanced_prompt` / `enhanced_completion` pairs
->   with synthetic reasoning traces). It was **not** trained directly on the
->   raw human-annotated dataset as-is.
-> - The **evaluation numbers below come from AutoScientist's internal
->   judge-based quality and win-rate metrics**, not standard classification
->   accuracy, precision, recall, or F1. Treat "Grade" and "Win Rate" as
->   AutoScientist's own scoring rubric, not an external, independently
->   reproducible benchmark.
-> - **General-capability win rate slightly regressed** after adaptation
->   (52 → 48, see below) — this model appears to trade a small amount of
->   general capability for task-specific sentiment performance. That's a
->   normal fine-tuning tradeoff, not a bug, but you should know it's there.
-> - This adapter has **not been independently evaluated against the
->   `Davlan/afrisenti-twitter-sentiment-afroxlmr-large` baseline** on a
->   standard accuracy/macro-F1 metric. If you need that comparison, run it
->   yourself before drawing conclusions about relative performance.
+A stitched multi-source sentiment classification dataset combining three
+independently collected sentiment corpora for Hausa, Yorùbá, and Swahili,
+built for the [Adaption Labs AutoScientist Challenge](https://adaptionlabs.ai/blog/autoscientist-challenge)
+(Language category).
+
+> **Companion model**: fine-tuned weights trained on the adapted version of this dataset via
+> AutoScientist are released separately at
+> `gospelgit/African-Languages-Sentiment-Classifier`((https://huggingface.co/gospelgit/African-Languages-Sentiment-Classifier).
 >
-> In short: understand the pipeline behind this artifact before you plug it
-> into anything downstream.
-
-## 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-token/word completion (average prompt length 15.56 words, completion length 1 word) — i.e. the model is trained to generate the sentiment label as its next-token completion, not as a traditional classification head
-- **Languages:** Hausa, Yorùbá, Swahili
-- **License:** CC-BY-4.0
-- **Produced via:** [Adaption Labs AutoScientist](https://adaptionlabs.ai/blog/autoscientist-challenge) (Language category submission)
-
-## Training Data
-
-Trained on an AutoScientist-evolved version of the companion dataset
-[`gospelgit/African-Languages_Sentiments`](https://huggingface.co/datasets/gospelgit/African-Languages_Sentiments),
-which itself combines AfriSenti (Twitter), NollySenti (Nollywood movie
-reviews), and the Neurotech-HQ Swahili sentiment dataset (social
-media/product reviews).
-
-- **Adapted dataset size:** 15,280 rows (AutoScientist's "Adaptive Data" step
-  rewrote and filtered the original ~46,725-row combined corpus into
-  enhanced prompt/completion pairs — see disclaimer above)
-- Labels: `positive`, `negative`, `neutral`
-
-## 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))
-```
+> **File guide for this repo:**
+> - `train.csv` / `validation.csv` / `test.csv` — the **original, clean,
+>   human-labeled dataset** described in this card (46,725 rows, 70/15/15
+>   split). **Use these files** if you want the source data for your own
+>   training pipeline.
+> - `ha_yo_sw_sentimentAnalysis.csv` — the **AutoScientist-adapted version**,
+>   produced by AutoScientist's data-evolution step during the AutoScientist
+>   Challenge submission process. This is **not** the primary dataset and is
+>   included for transparency/reproducibility of that specific challenge
+>   submission, not as a recommended starting point.
+
+## Why this dataset
+
+Existing sentiment resources for these languages are dominated by a single
+domain (Twitter). This dataset combines **three different sources across
+three different domains** to reduce domain overfitting and give a more
+robust sentiment signal:
+
+| Source | Domain | Languages | Rows |
+|---|---|---|---|
+| [AfriSenti](https://github.com/afrisenti-semeval/afrisent-semeval-2023) | Twitter | Hausa, Yorùbá, Swahili | 40,290 |
+| [NollySenti](https://github.com/IyanuSh/NollySenti) | Nollywood movie reviews (human-translated) | Hausa, Yorùbá | 2,510 |
+| [Neurotech-HQ Swahili](https://github.com/Neurotech-HQ/swahili-sentiment-analysis-dataset) | Social media / product reviews (back-translated) | Swahili | 3,925 |
+
+## Dataset structure
+
+- `text`: the raw text (tweet, movie review, or social media comment)
+- `label`: one of `positive`, `negative`, `neutral` (single unified label column)
+- `language`: `hausa`, `yoruba`, or `swahili`
+- `source`: which of the three original datasets the row came from
+- `domain`: `twitter`, `movie_review`, or `social_media_reviews`
+- `split`: `train`, `validation`, or `test`
+
+## Splits
+
+All three languages use an identical **70 / 15 / 15** train/validation/test
+split, stratified by label, computed after pooling all sources per language
+(not the original per-source splits — see Limitations).
+
+| Language | Total | Train | Validation | Test |
+|---|---|---|---|---|
+| Hausa | 23,162 | 16,213 | 3,474 | 3,475 |
+| Yorùbá | 16,627 | 11,639 | 2,494 | 2,494 |
+| Swahili | 6,936 | 4,855 | 1,040 | 1,041 |
+
+## Intended use
+
+Training and evaluating sentiment classification models for Hausa, Yorùbá,
+and Swahili — particularly for benchmarking multilingual co-optimized
+training approaches (e.g. AutoScientist) against single-source baselines
+like `Davlan/afrisenti-twitter-sentiment-afroxlmr-large`.
 
 ## 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.
+- **Label imbalance across sources**: NollySenti and the Neurotech Swahili
+  set are binary (positive/negative only). `neutral` examples come
+  exclusively from AfriSenti, so neutral coverage is thinner relative to
+  positive/negative for all three languages.
+- **Re-split, not original splits**: because splits were recomputed by
+  pooling sources, this is not directly comparable row-for-row to
+  benchmarks trained on the original AfriSenti or NollySenti splits. Use
+  as a fresh baseline, not a drop-in replacement.
+- **Neurotech Swahili has no official original split** — an 80/10/10 split
+  was assigned with a fixed seed (42) before being pooled and re-split here.
+- **Swahili domain composition differs** from Hausa/Yorùbá: it is roughly
+  half Twitter, half social-media/product reviews, while Hausa and Yorùbá
+  are Twitter-dominant with a smaller movie-review slice.
 
 ## 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).
+If you use this combined dataset, please cite all three original sources:
+
+```
+@inproceedings{muhammad2023afrisenti,
+  title={AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages},
+  author={Muhammad, Shamsuddeen Hassan and others},
+  year={2023}
+}
+
+@inproceedings{shode2023nollysenti,
+  title={NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification},
+  author={Shode, Iyanuoluwa and Adelani, David Ifeoluwa and Peng, Jing and Feldman, Anna},
+  year={2023}
+}
+
+@misc{neurotech2021swahili,
+  title={Swahili Sentiment Analysis Dataset},
+  author={Neurotech-HQ},
+  year={2021},
+  howpublished={\\url{https://github.com/Neurotech-HQ/swahili-sentiment-analysis-dataset}}
+}
+```
+
+## Submission context
+
+Built for the Adaption Labs AutoScientist Challenge (Language category),
+targeting Hausa, Yorùbá, and Swahili as officially supported languages.