README.md

---
language:
- en
license: apache-2.0
task_categories:
- text-classification
task_ids:
- sentiment-analysis
tags:
- mlops
- devops
- sentiment
- domain-specific
size_categories:
- n<1K
---

# MLOps & DevOps Sentiment Dataset

## Dataset description
A domain-specific sentiment dataset containing real-world MLOps and DevOps 
scenarios labeled as POSITIVE or NEGATIVE. Built to fine-tune sentiment 
classifiers for technical operations contexts where general-purpose models 
(trained on movie reviews) underperform.

## Why this dataset exists
General sentiment models misclassify technical sentences. For example:
- "The pipeline failed silently" → general models often miss the negativity
- "Terraform rollback was effortless" → domain context needed for high confidence

## Dataset structure
| Split | Examples |
|-------|----------|
| Train | 24 |
| Test  | 6  |

### Fields
- `text` — sentence describing an MLOps/DevOps scenario
- `label` — 0 (NEGATIVE) or 1 (POSITIVE)
- `domain` — `mlops` or `devops`
- `text_length` — word count (added during preprocessing)

## Source
Manually curated by [@atulkrs](https://huggingface.co/atulkrs) based on 
real-world MLOps and DevOps engineering experience.

## Usage
\`\`\`python
from datasets import load_dataset
ds = load_dataset("atulkrs/mlops-devops-sentiment")
print(ds["train"][0])
\`\`\`

## Intended use
- Fine-tuning sentiment classifiers for MLOps/DevOps tooling feedback
- Benchmarking domain adaptation of general NLP models
- Curriculum data for MLOps-aware LLM fine-tuning