docs: update README

README.md

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-license: mit
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+# BanglaNostalgia Dataset
+
+A labeled Bangla text dataset for nostalgia detection from user comments. BanglaNostalgia is a 20K-row Bengali text dataset for binary nostalgia detection. It includes raw and cleaned text, timestamp, and labels. Suitable for Bangla NLP benchmarking, emotion analysis, and low-resource text classification research.
+
+## Dataset Snapshot
+
+- Total rows: `20,000`
+- Main file columns: `5`
+- Missing values: `0` in all columns
+- Label distribution:
+  - `label = 1`: `16,997` rows (`84.98%`)
+  - `label = 0`: `3,003` rows (`15.02%`)
+- Language: Bangla (Bengali)
+
+## Files
+
+- `bengali_nostalgia_labeled.csv`
+  - Full dataset in one file.
+  - Columns: `id`, `raw_text`, `label`, `text`, `reference_time`
+- `splits/bengali_nostalgia_labeled_part001.csv` ... `splits/bengali_nostalgia_labeled_part020.csv`
+  - 20 split files, each with `1,000` rows.
+  - Columns: `id`, `text`, `label`, `clean_text`, `reference_time`
+
+## Important Column Mapping
+
+Between the main file and split files:
+
+- Split `text` = Main `raw_text`
+- Split `clean_text` = Main `text`
+
+Use this mapping to avoid confusion when switching between the full file and part files.
+
+## Column Dictionary
+
+- `id`: Unique comment identifier.
+- `raw_text`: Original raw Bangla text.
+- `text`: Cleaned/normalized version of the text.
+- `clean_text`: Same semantic role as `text` (used in split files).
+- `label`: Binary target label for nostalgia classification.
+- `reference_time`: Timestamp string (ISO-like format) associated with the comment.
+
+## Label Definition
+
+Current labeling is binary:
+
+- `1`: Nostalgia-related expression
+- `0`: Non-nostalgia / other expression
+
+If your labeling policy differs, update this section before publishing the Kaggle description.
+
+## Recommended Use Cases
+
+- Bangla nostalgia detection
+- Bangla emotion/affect classification research
+- Low-resource NLP benchmarking
+- Text preprocessing and normalization studies for Bangla social text
+
+## Quick Start (Python)
+
+```python
+import pandas as pd
+
+df = pd.read_csv("bengali_nostalgia_labeled.csv")
+
+X = df["text"]          # cleaned text
+y = df["label"]         # binary label
+```
+
+## Data Quality Notes
+
+- No duplicate `id` values in the main file.
+- No exact duplicate rows in the main file.
+- Class imbalance is significant (`~85/15`), so use stratified splits and class-aware metrics.
+- `reference_time` can support temporal analysis, but verify whether it should be used as a model feature for your task.
+
+## Suggested Evaluation Metrics
+
+Because of class imbalance, prioritize:
+
+- Macro F1
+- Class-wise Precision/Recall
+- PR-AUC (for positive class)
+
+Avoid reporting only accuracy.
+
+## Limitations
+
+- Potential label subjectivity (nostalgia can be context-dependent).
+- Source-domain bias (platform and audience effects).
+- Temporal/cultural bias across years and events.
+
+## Ethical and Responsible Use
+
+- Do not attempt user re-identification.
+- Use for research/benchmarking, not for sensitive user profiling.
+- Respect platform terms if data originates from public comments.
+
+
+### 4) File Information
+
+
+- `bengali_nostalgia_labeled.csv`:
+  `Main file (20,000 rows). Columns: id, raw_text (original), label (0/1), text (cleaned), reference_time (timestamp).`
+
+- `splits/bengali_nostalgia_labeled_part001.csv` to `splits/bengali_nostalgia_labeled_part020.csv`:
+  `Split part file (1,000 rows each). Columns: id, text (same as main raw_text), label (0/1), clean_text (same as main text), reference_time.`