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README.md

@@ -1,28 +1,57 @@
 ---
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype: int64
-  - name: domain
-    dtype: string
-  - name: text_length
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 2454.4
-    num_examples: 24
-  - name: test
-    num_bytes: 613.6
-    num_examples: 6
-  download_size: 6208
-  dataset_size: 3068.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+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