---
license: apache-2.0
language:
- en
base_model:
- google-t5/t5-small
pipeline_tag: text2text-generation
library_name: transformers
datasets:
- rajpurkar/squad
tags:
- t5
- encoder-decoder
- question-answering
- extractive-qa
- squad
- cross-attention-only
- parameter-efficient
- small-model
metrics:
- exact_match
- f1
model-index:
- name: t5-small-xa-only-squad
  results:
  - task:
      type: question-answering
      name: Extractive Question Answering
    dataset:
      name: SQuAD (validation, 256-example eval subset)
      type: rajpurkar/squad
      split: validation
    metrics:
    - type: exact_match
      value: 0.5293
      name: Exact Match
    - type: f1
      value: 0.7075
      name: Token F1
---

# T5-small — Cross-Attention-Only fine-tune on SQuAD

Encoder–decoder QA model fine-tuned from `google-t5/t5-small` (60.5M) by training **only the
decoder cross-attention** (`EncDecAttention`) blocks plus the decoder final layer norm —
**~6.3M trainable params (10.4%)**. The encoder, decoder self-attention, and feed-forward
weights are frozen.

**TL;DR:** a **60M-param** encoder–decoder, tuning just **6.3M** of those weights, reaches
**F1 0.7075 / EM 0.5293** on SQuAD — **outperforming a fully fine-tuned GPT-2-large (774M,
F1 0.5041)** that is 13× larger. The strongest evidence in this study that **architecture
beats scale** for context-grounded tasks.

## Results (SQuAD validation)

| Model | Architecture | Trainable / Total | EM | Token F1 |
| --- | --- | ---: | ---: | ---: |
| GPT-2-large | decoder-only | 774M / 774M | 0.3516 | 0.5041 |
| **T5-small XA-only (this model)** | enc-dec | **6.3M / 60.5M** | **0.5293** | **0.7075** |
| T5-large XA-only | enc-dec | 100.7M / 737M | 0.6406 | 0.8128 |
| T5-large LoRA r=8 | enc-dec | 2.4M / 740M | 0.6445 | 0.8152 |

Validation loss 0.4208, perplexity 1.52. Trained on only 3,000 SQuAD examples.

> Eval note: 256-example SQuAD-validation generation subset, 4-beam search.

## How to use

Trained with the input prefix `answer question: ` prepended to a
`question: ... context: ...` source string — match it exactly:

```python
from transformers import T5ForConditionalGeneration, AutoTokenizer

repo = "medelharchaoui/t5-small-xa-only-squad"
tok = AutoTokenizer.from_pretrained(repo)
model = T5ForConditionalGeneration.from_pretrained(repo)

question = "What culture do 'bairn' and 'hyem' originate from?"
context = ("'bairn' and 'hyem' are geordie words with origins in scandinavia; barn and hjem "
           "are the corresponding modern norwegian and danish words.")
text = f"answer question: question: {question} context: {context}"
ids = tok(text, return_tensors="pt", truncation=True, max_length=384).input_ids
print(tok.decode(model.generate(ids, num_beams=4, max_new_tokens=16)[0], skip_special_tokens=True))
```

## Training

| Setting | Value |
| --- | --- |
| Base model | `google-t5/t5-small` (60.5M) |
| Trainable params | `*.EncDecAttention.*` + `decoder.final_layer_norm` (~6.3M) |
| Dataset | `rajpurkar/squad`, 3,000 train examples |
| Precision | bf16 |
| Optimizer steps | 1,500 (batch 4 × grad-accum 8 = eff. batch 32) |
| LR / warmup | 2e-4, 150 warmup, weight decay 0.01 |
| Source / target max len | 384 / 32 |
| Seed | 37 |
| Hardware | 1× NVIDIA RTX 3060 (12 GB), local |

## Limitations

- English SQuAD-style extractive QA only; short answer spans grounded in the supplied context.
- Small model trained on only 3,000 examples — strong for its size on SQuAD, but not a
  general-purpose QA system.
- Evaluated on a held-out validation subset, not the official SQuAD test server.

## Citation

Part of an encoder–decoder vs decoder-only paradigm study (OptimiAI, 2026).