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kikwaib/
mt5-base-kenswquad-abstractive

Name: mt5-base-kenswquad-abstractive
Rating: 2.5 (8 reviews)

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mT5-base-KenSwQuAD-Abstractive

The final model in a 3-stage Hierarchical Curriculum Learning pipeline for Swahili Question Answering.

This model generates abstractive answers to questions about Swahili text, using Context Scaffolding technique with anchor tokens to improve answer localization.

Model Description

Property	Value
Base Architecture	mT5-base (Google)
Parameters	582.4M
Vocabulary	250,150 tokens (50 custom anchor tokens)
Languages	Swahili (sw), English (en)
Task	Abstractive Question Answering
License	Apache 2.0

Pipeline Position

This model is Stage 3 of a curriculum learning pipeline:

Stage	Model	Task	Status
1	mt5-base-squad-transfer	English SQuAD Transfer	✅
2	mt5-base-kenswquad-extractive	Swahili Extractive QA	✅
3	mt5-base-kenswquad-abstractive	Swahili Abstractive QA	✅

Performance

Evaluation Results

Metric	Value	Description
BLEU	15.52	N-gram overlap with reference
BERTScore F1	0.7721 (77.21%)	Semantic similarity
Eval Loss	2.4184	Cross-entropy loss

Training Progress

Epoch	Train Loss	Val Loss	BLEU	BERTScore F1
1	3.033	2.596	13.37	0.757
2	2.614	2.485	13.36	0.769
3	2.363	2.452	15.68	0.768
4	2.094	2.455	14.72	0.767
5	1.917	2.418	15.52	0.772 ⭐
6	2.046	2.398	15.03	0.770
7	1.881	2.435	14.95	0.770
8	1.728	2.400	16.25	0.769

Best model saved at Epoch 5 (highest BERTScore F1)

Usage

Basic Usage

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer (MUST be from same checkpoint!)
tokenizer = AutoTokenizer.from_pretrained("kikwaib/mt5-base-kenswquad-abstractive")
model = AutoModelForSeq2SeqLM.from_pretrained("kikwaib/mt5-base-kenswquad-abstractive")

# Simple question answering
question = "Nani aliandika kitabu hiki?"
context = "Kitabu hiki kiliandikwa na Ngugi wa Thiong'o. Kilitolewa mwaka 1967."

input_text = f"question: {question} context: {context}"
inputs = tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True)
outputs = model.generate(**inputs, max_length=128)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(answer)  # "Ngugi wa Thiong'o"

With Context Scaffolding (Recommended)

For optimal performance, use anchor tokens to mark paragraph boundaries:

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("kikwaib/mt5-base-kenswquad-abstractive")
model = AutoModelForSeq2SeqLM.from_pretrained("kikwaib/mt5-base-kenswquad-abstractive")

# Apply scaffolding with <p1>, <p2>, ... tokens
question = "Nani aliandika kitabu hiki?"
context = "<p1> Kitabu hiki kiliandikwa na Ngugi wa Thiong'o. <p2> Kilitolewa mwaka 1967."

# Add pointer hint if you know which paragraph contains the answer
input_text = f"question: {question} context: {context} <pointer> <p1>"

inputs = tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True)
outputs = model.generate(**inputs, max_length=128)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(answer)  # "Ngugi wa Thiong'o"

Scaffolding Helper Function

python

import re

def apply_scaffolding(context, question, paragraph_pointer=None):
    """
    Transform raw context into scaffolded format with anchor tokens.
    
    Args:
        context: Raw text (paragraphs separated by newlines)
        question: The question to answer
        paragraph_pointer: Optional paragraph number (1-indexed) containing the answer
    
    Returns:
        Scaffolded input string
    """
    # Split into paragraphs
    paragraphs = [p.strip() for p in re.split(r'\n+', context) if p.strip()]
    
    # Add anchor tokens
    scaffolded = ""
    for i, para in enumerate(paragraphs):
        p_num = i + 1
        if p_num < 50:
            scaffolded += f"<p{p_num}> {para} "
        else:
            scaffolded += f"{para} "
    
    # Add pointer hint
    hint = ""
    if paragraph_pointer and 1 <= int(paragraph_pointer) < 50:
        hint = f" <pointer> <p{paragraph_pointer}>"
    
    return f"question: {question} context: {scaffolded}{hint}"

# Usage
input_text = apply_scaffolding(
    context="First paragraph.\nSecond paragraph with answer.",
    question="What is in the second paragraph?",
    paragraph_pointer=2
)

Pipeline Usage

python

from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="kikwaib/mt5-base-kenswquad-abstractive",
    tokenizer="kikwaib/mt5-base-kenswquad-abstractive"
)

result = qa_pipeline("question: Nani aliandika kitabu? context: <p1> Ngugi aliandika kitabu hiki.")
print(result[0]['generated_text'])

Context Scaffolding

What is Context Scaffolding?

Context Scaffolding is a technique that structures input text with special anchor tokens:

Paragraph Anchors (<p1>, <p2>, ..., <p49>): Mark paragraph boundaries
Pointer Token (<pointer>): Provides a late contextual hint about which paragraph contains relevant information

Why Use Scaffolding?

Helps the model locate information in long contexts
Provides attention guidance to relevant paragraphs
Improves performance on multi-paragraph documents

Example Transformation

Without Scaffolding:

question: What is the capital? context: Kenya is in East Africa. Nairobi is the capital city.

With Scaffolding:

question: What is the capital? context: <p1> Kenya is in East Africa. <p2> Nairobi is the capital city. <pointer> <p2>

Training Details

Training Data

Dataset	Samples	Description
KenSwQuAD (Abstractive)	2,428	QA pairs where answer requires reasoning
Train Split	2,185 (90%)	Training samples
Test Split	243 (10%)	Evaluation samples

Hyperparameters

Parameter	Value
Learning Rate	5e-5
Batch Size	8
Max Epochs	15
Early Stopping Patience	3
Max Input Length	1024 tokens
Max Target Length	128 tokens
Weight Decay	0.01
Max Gradient Norm	1.0
Optimizer	AdamW
Best Model Metric	BERTScore F1

Training Infrastructure

Component	Specification
GPU	NVIDIA A100-SXM4-40GB
GPU Memory	42.5 GB
Platform	Google Colab
Training Time	29.7 minutes
Epochs Completed	8 (early stopped)

Framework Versions

Package	Version
Transformers	4.57.3
Datasets	4.0.0
PyTorch	2.9.0+cu126
CUDA	12.6

Sample Predictions

Question	Ground Truth	Prediction	Analysis
Ni vipi uwekezaji utaimarishwa	Kwa mikopo na nafasi ya kufanyia kazi	Kwa kuwapa wawekezaji mikopo na nafasi za ajira	✅ Semantically equivalent
Wahispania walikuja na ugonjwa gani	nduli	ndui	⚠️ Close variant
Kipi kinachangia wanandoa kutalakiana	Kukosa kusajili ndoa zao rasmi	Kwa kukosa kusajili ndoa zao	✅ Core meaning preserved

Limitations

Tokenizer Dependency: Must load tokenizer from this checkpoint (has 50 custom tokens)
Context Length: Inputs longer than 1024 tokens are truncated
Scaffolding Preference: Best performance requires anchor token formatting
Dataset Size: Trained on relatively small abstractive dataset (2,428 samples)
Swahili Variants: May struggle with dialectal differences

Ethical Considerations

Intended Use: Research and educational applications for Swahili NLP
Not Intended: Production systems without additional validation
Bias: May reflect biases present in KenSwQuAD training data
Verification: Answers should be verified for critical applications

Citation

If you use this model, please cite:

bibtex

@misc{mt5-kenswquad-abstractive,
  author = {Kikwai, B.},
  title = {mT5-base-KenSwQuAD-Abstractive: Hierarchical Curriculum Learning for Swahili QA},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/kikwaib/mt5-base-kenswquad-abstractive}
}

Related Models

Model	Task	Link
mt5-base-squad-transfer	Stage 1: English QA Transfer	🔗
mt5-base-kenswquad-extractive	Stage 2: Swahili Extractive QA	🔗
mt5-base-kenswquad-abstractive	Stage 3: Swahili Abstractive QA	This model

Acknowledgments

KenSwQuAD Dataset: For providing Swahili QA pairs
Google: For the mT5 base model
Hugging Face: For the Transformers library and model hosting

Model trained: December 20, 2025 | Last updated: December 20, 2025