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README.md

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----
-language:
-  - bn
-license: cc-by-4.0
-task_categories:
-  - text-classification
-task_ids:
-  - multi-class-classification
-pretty_name: Bangla Emergency Posts
-size_categories:
-  - 1K<n<10K
-tags:
-  - bangla
-  - bengali
-  - emergency
-  - social-media
-  - crisis-informatics
-configs:
-  - config_name: default
-    data_files:
-      - split: train
-        path: data/train.csv
-      - split: validation
-        path: data/validation.csv
-      - split: test
-        path: data/test.csv
-  - config_name: full
-    data_files:
-      - split: full
-        path: data/bangla_emergency_post.csv
----
-
-# Bangla Emergency Posts
-
-5,836 Bangla social media posts, hand-labelled into nine emergency categories.
-Built for *Bangla Emergency Post Classification on Social Media using Transformer
-Based BERT Models* (EICT 2023).
-
-Emergency text classification in Bangla is scarce despite the language having
-hundreds of millions of speakers. This dataset exists so that Bangla-speaking
-people can report emergencies in their own language and have those reports
-routed automatically.
-
-## Usage
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("NightRaven/bangla-emergency-posts")
-print(ds["train"][0])
-# {'content': 'ভূমিকম্পের পর স্কুলবাড়ি ভেঙে পড়ে। ...', 'label': 'natural_disaster'}
-```
-
-## Fields
-
-| field | type | description |
-|---|---|---|
-| `content` | string | the post text, Bangla with occasional English words |
-| `label` | string | one of the nine categories below |
-
-Labels are stored as strings. Where an integer id is needed, the models trained
-on this data use alphabetical order:
-
-| id | label | id | label | id | label |
-|----|-------|----|-------|----|-------|
-| 0 | accident | 3 | fire | 6 | suicide |
-| 1 | blood | 4 | natural_disaster | 7 | war |
-| 2 | crime | 5 | pandemic | 8 | weather |
-
-`blood` covers urgent blood-donation appeals, a common and distinct category of
-emergency post in Bangla social media.
-
-## Splits
-
-Stratified 56 / 14 / 30.
-
-| label | train | validation | test | total |
-|---|---:|---:|---:|---:|
-| accident | 579 | 123 | 301 | 1,003 |
-| blood | 123 | 31 | 66 | 220 |
-| crime | 1,355 | 372 | 765 | 2,492 |
-| fire | 295 | 82 | 122 | 499 |
-| natural_disaster | 277 | 64 | 153 | 494 |
-| pandemic | 83 | 24 | 40 | 147 |
-| suicide | 114 | 33 | 61 | 208 |
-| war | 104 | 20 | 51 | 175 |
-| weather | 337 | 70 | 191 | 598 |
-| **total** | **3,267** | **819** | **1,750** | **5,836** |
-
-## Collection and labelling
-
-Posts were gathered from Facebook, Twitter and Bangladeshi daily newspapers,
-covering events in Bangladesh and neighbouring India. Labelling was done by hand
-by native Bangla speakers.
-
-Collecting structured Bangla social media text is difficult — stemming rules are
-hard to pin down and stopword lists are inconsistent — so the data was labelled
-manually rather than filtered heuristically.
-
-
-
-
-
-## Citation
-
-```bibtex
-@inproceedings{nabil2023bangla,
-  title     = {Bangla Emergency Post Classification on Social Media using
-               Transformer Based BERT Models},
-  author    = {Nabil, Alvi Ahmmed and Arifeen, Shamsul and Das, Dola and
-               Salim, Md. Shahidul and Fattah, H. M. Abdul},
-  booktitle = {6th International Conference on Electrical Information and
-               Communication Technology (EICT)},
-  address   = {Khulna, Bangladesh},
-  year      = {2023}
-}
-```
-
-Models trained on this dataset:
-[NightRaven/bangla-emergency-post-classification](https://huggingface.co/NightRaven/bangla-emergency-post-classification)
+---
+language:
+  - bn
+license: cc-by-4.0
+task_categories:
+  - text-classification
+task_ids:
+  - multi-class-classification
+pretty_name: Bangla Emergency Posts
+size_categories:
+  - 1K<n<10K
+tags:
+  - bangla
+  - bengali
+  - emergency
+  - social-media
+  - crisis-informatics
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/train.csv
+      - split: validation
+        path: data/validation.csv
+      - split: test
+        path: data/test.csv
+  - config_name: full
+    data_files:
+      - split: full
+        path: data/bangla_emergency_posts.csv
+---
+
+# Bangla Emergency Posts
+
+5,836 Bangla social media posts, hand-labelled into nine emergency categories.
+Built for *Bangla Emergency Post Classification on Social Media using Transformer
+Based BERT Models* (EICT 2023).
+
+Emergency text classification in Bangla is scarce despite the language having
+hundreds of millions of speakers. This dataset exists so that Bangla-speaking
+people can report emergencies in their own language and have those reports
+routed automatically.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("NightRaven/bangla-emergency-posts")
+print(ds["train"][0])
+# {'content': 'ভূমিকম্পের পর স্কুলবাড়ি ভেঙে পড়ে। ...', 'label': 'natural_disaster'}
+```
+
+## Fields
+
+| field | type | description |
+|---|---|---|
+| `content` | string | the post text, Bangla with occasional English words |
+| `label` | string | one of the nine categories below |
+
+Labels are stored as strings. Where an integer id is needed, the models trained
+on this data use alphabetical order:
+
+| id | label | id | label | id | label |
+|----|-------|----|-------|----|-------|
+| 0 | accident | 3 | fire | 6 | suicide |
+| 1 | blood | 4 | natural_disaster | 7 | war |
+| 2 | crime | 5 | pandemic | 8 | weather |
+
+`blood` covers urgent blood-donation appeals, a common and distinct category of
+emergency post in Bangla social media.
+
+## Splits
+
+Stratified 56 / 14 / 30.
+
+| label | train | validation | test | total |
+|---|---:|---:|---:|---:|
+| accident | 579 | 123 | 301 | 1,003 |
+| blood | 123 | 31 | 66 | 220 |
+| crime | 1,355 | 372 | 765 | 2,492 |
+| fire | 295 | 82 | 122 | 499 |
+| natural_disaster | 277 | 64 | 153 | 494 |
+| pandemic | 83 | 24 | 40 | 147 |
+| suicide | 114 | 33 | 61 | 208 |
+| war | 104 | 20 | 51 | 175 |
+| weather | 337 | 70 | 191 | 598 |
+| **total** | **3,267** | **819** | **1,750** | **5,836** |
+
+## Collection and labelling
+
+Posts were gathered from Facebook, Twitter and Bangladeshi daily newspapers,
+covering events in Bangladesh and neighbouring India. Labelling was done by hand
+by native Bangla speakers.
+
+Collecting structured Bangla social media text is difficult — stemming rules are
+hard to pin down and stopword lists are inconsistent — so the data was labelled
+manually rather than filtered heuristically.
+
+
+
+
+
+## Citation
+
+```bibtex
+@inproceedings{nabil2023bangla,
+  title     = {Bangla Emergency Post Classification on Social Media using
+               Transformer Based BERT Models},
+  author    = {Nabil, Alvi Ahmmed and Arifeen, Shamsul and Das, Dola and
+               Salim, Md. Shahidul and Fattah, H. M. Abdul},
+  booktitle = {6th International Conference on Electrical Information and
+               Communication Technology (EICT)},
+  address   = {Khulna, Bangladesh},
+  year      = {2023}
+}
+```
+
+Models trained on this dataset:
+[NightRaven/bangla-emergency-post-classification](https://huggingface.co/NightRaven/bangla-emergency-post-classification)