---
license: apache-2.0
language:
  - ms
task_categories:
  - question-answering
  - text-generation
tags:
  - aws
  - malay
  - bahasa-melayu
  - synthetic-paraphrase
  - instruction-tuning
size_categories:
  - 10K<n<100K
---

# teach_aws — AWS Q&A in Bahasa Melayu (paraphrase-augmented)

Instruction-tuning data for answering AWS questions in Bahasa Melayu. Each row is a
`(question, answer)` chat pair ready for SFT (TRL/axolotl-compatible `messages` format).

Built from [PixelSpaceAI/aws-malay-qa](https://huggingface.co/datasets/PixelSpaceAI/aws-malay-qa)
(Apache-2.0):

- **Answers are verbatim** from the source dataset — nothing was rewritten.
- **Rows are paraphrases only.** Questions were paraphrased with a large language model
  in two passes:
  - `para_v1` — neutral paraphrases of each source question.
  - `para_v2_A` … `para_v2_D` — typed paraphrases per source question covering
    syntactic rewording, consequence/inverse framing, terse informal register, and
    practical scenario framing.
- The **original Q/A pairs are not included** — they are withheld as our private held-out
  evaluation set for on-going work, and are available from the source dataset anyway.

## Row format

```json
{"messages": [{"role": "user", "content": "..."},
              {"role": "assistant", "content": "..."}],
 "service": "sagemaker",
 "surface": "para_v2_B",
 "src_idx": 123,
 "src": "PixelSpaceAI/aws-malay-qa"}
```

`service` is the AWS service area as tagged in the source dataset (`general`, `sagemaker`,
`vpc`, ...). `surface` identifies which variant of the question this row is.

## Generation scope and controls

Every source question in the dataset was paraphrased; surfaces too close to another
question were deduplicated by similarity. The original Q/A pairs are withheld as a
private evaluation set and are available from the source dataset.

## Why the methodology is trusted

**Answer fidelity.** Every assistant response is copied verbatim from the source
dataset. Paraphrasing applies to questions only — answers were never generated,
rewritten, or summarized, so the dataset cannot inject hallucinated technical content.

**Paraphrase quality controls.** Each paraphrase was required to preserve all
technical terms (service names, feature names, formats) and the same information
request. Two automatic filters applied to every generated surface: rejection of
paraphrases too close to their source question (token-set Jaccard >= 0.75), and
near-duplicate removal across questions.

**No-leakage training methodology.** This published pool covers every source
question, including paraphrases of questions related to our held-out evaluation set.
For our own fine-tuning we therefore use a filtered subset of the same generation
output: only paraphrases of train-split questions, after screening every candidate
surface — original or paraphrase — against the private held-out evaluation set with a
token-set Jaccard firewall at 0.7 (any surface at or above the threshold is dropped).
An independent verification step re-checks the final training file after every build;
no training surface is a held-out question, a paraphrase of one, or near-duplicate of
one (maximum retained similarity to date: 0.667, below threshold).

**Generalization evidence.** Fine-tuning on the decontaminated training subset
improved accuracy on held-out questions over the base model, indicating the
paraphrase augmentation teaches surface-invariance rather than memorization of
evaluation items.

## Statistics

| Surface | Rows |
|---|---|
| para_v1 | 7,980 |
| para_v2_A | 2,537 |
| para_v2_B | 2,638 |
| para_v2_C | 2,640 |
| para_v2_D | 2,660 |
| **total** | 18,455 |

Rows per AWS service (`service` field):

| Service | Rows |
|---|---|
| s3 | 2,210 |
| cloudfront | 1,275 |
| rds | 1,132 |
| route53 | 984 |
| cloudwatch | 952 |
| vpc | 882 |
| sns | 809 |
| api-gateway | 784 |
| lambda | 770 |
| dynamodb | 731 |
| guardduty | 714 |
| sqs | 549 |
| kms | 520 |
| redshift | 514 |
| eventbridge | 490 |
| cloudtrail | 483 |
| glue | 451 |
| waf | 451 |
| kinesis | 418 |
| athena | 369 |
| eks | 348 |
| snowball | 319 |
| step-functions | 315 |
| ecs | 277 |
| cognito | 258 |
| cloudformation | 228 |
| amplify | 190 |
| fargate | 188 |
| iam | 161 |
| ecr | 161 |
| sagemaker | 153 |
| appsync | 145 |
| secrets-manager | 108 |
| general | 95 |
| sam | 7 |
| aurora | 7 |
| x-ray | 7 |

## Attribution

- Base questions and all answers: [PixelSpaceAI/aws-malay-qa](https://huggingface.co/datasets/PixelSpaceAI/aws-malay-qa), Apache-2.0.
- Paraphrases machine-generated from the source questions; answers unchanged.

## Intended use / limitations

Malay-language AWS Q&A assistants and instruction tuning. Answers reflect the AWS FAQ
content the source dataset was built from and may lag current AWS behavior; verify
anything safety- or cost-critical against official AWS documentation.