Datasets:

CCB
/

cis5300-language-models

@@ -1,155 +1,47 @@
----
-language:
-- en
-- af
-- zh
-- de
-- fi
-- fr
-- hi
-- fa
-- ur
-- zu
-license: cc-by-4.0
-task_categories:
-- text-classification
-- text-generation
-task_ids:
-- language-modeling
-- multi-class-classification
-pretty_name: Language Models and City Classification
-size_categories:
-- 10K<n<100K
-tags:
-- n-gram
-- language-model
-- city-classification
-- nlp-course
-configs:
-- config_name: cities
-  data_files:
-  - split: train
-    path: cities/train-*
-  - split: validation
-    path: cities/validation-*
-  - split: test
-    path: cities/test-*
-dataset_info:
-  config_name: cities
-  features:
-  - name: city
-    dtype: string
-  - name: country
-    dtype: string
-  - name: country_name
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 368187
-    num_examples: 12392
-  - name: validation
-    num_bytes: 45988
-    num_examples: 1548
-  - name: test
-    num_bytes: 33400
-    num_examples: 1554
-  download_size: 175270
-  dataset_size: 447575
----
-# Language Models and City Classification (CIS 5300)
-## Dataset Description
-This dataset supports two tasks for learning about **n-gram language models**:
-### 1. Shakespeare Text Generation
-Raw text from Shakespeare's collected works (~4.5 MB, 167K lines) for training character-level n-gram language models. Students build models of increasing order (n=1 through n=7) and generate Shakespeare-style text.
-### 2. City Name Classification
-Classify city names by country of origin using character-level language models. The idea: city names from different countries have distinctive character patterns (e.g., German cities often end in "-burg" or "-stadt", Chinese cities have different character distributions).
-**Countries**: Afghanistan, China, Germany, Finland, France, India, Iran, Pakistan, South Africa
-## Usage
-### City Classification Task
 ```python
 from datasets import load_dataset
-# Load city name classification data
 cities = load_dataset("CCB/cis5300-language-models", "cities")
-print(cities)
-# DatasetDict({
-#     train: Dataset({features: ['city_name', 'country_code', 'country'], num_rows: 12392})
-#     validation: Dataset({features: [...], num_rows: 1548})
-#     test: Dataset({features: [...], num_rows: 1554})
-# })
-print(cities["train"][0])
-# {'city_name': 'shewah', 'country_code': 'af', 'country': 'Afghanistan'}
-```
-### Shakespeare Corpus
-```python
-from huggingface_hub import hf_hub_download
-# Download Shakespeare text for language modeling
-path = hf_hub_download("CCB/cis5300-language-models", "shakespeare.txt", repo_type="dataset")
-with open(path) as f:
-    text = f.read()
-print(f"Shakespeare corpus: {len(text):,} characters")
 ```
-## Dataset Structure
-### Cities Config
-| Split | Examples | Description |
-|-------|----------|-------------|
-| train | 12,392 | City names with country labels for training |
-| validation | 1,548 | Labeled validation set |
-| test | 1,554 | Labeled test set for evaluation |
-**Fields:**
-| Field | Type | Description |
-|-------|------|-------------|
-| `city_name` | string | Name of the city |
-| `country_code` | string | Two-letter country code (af, cn, de, fi, fr, in, ir, pk, za) |
-| `country` | ClassLabel | Full country name |
-### Supplementary Files
 | File | Size | Description |
 |------|------|-------------|
-| `shakespeare.txt` | 4.5 MB | Complete Shakespeare works for training language models |
-| `shakespeare_sonnets.txt` | 9 KB | Shakespeare's sonnets (additional training data) |
-| `nytimes_article.txt` | 5 KB | News article for cross-domain perplexity evaluation |
-| `cities_test_unlabeled.csv` | 20 KB | Unlabeled test cities (for student predictions) |
-## Source
-City name data sourced from [GeoNames](https://www.geonames.org/) (CC BY 4.0). Shakespeare text from Project Gutenberg (public domain).
-## Intended Use
-This dataset is used for **Homework 3** in [CIS 5300: Natural Language Processing](https://www.seas.upenn.edu/~cis5300/) at the University of Pennsylvania. Students:
-1. Build character-level n-gram language models on Shakespeare
-2. Implement smoothing (add-k) and evaluate with perplexity
-3. Use interpolation to combine models of different orders
-4. Apply language models to classify city names by country of origin
-## Citation
-```bibtex
-@misc{cis5300-language-models,
-    title = {CIS 5300 Language Models Dataset},
-    author = {Callison-Burch, Chris},
-    year = {2026},
-    publisher = {University of Pennsylvania},
-}
-```

+# CIS 5300 Language Models Dataset
+Dataset for Homework 3 of CIS 5300 (Natural Language Processing) at Penn.
+## Cities config
+Country-of-origin classification over short city-name strings, drawn from
+nine countries (Afghanistan, China, Germany, Finland, France, India, Iran,
+Pakistan, South Africa).
 ```python
 from datasets import load_dataset
 cities = load_dataset("CCB/cis5300-language-models", "cities")
 ```
+| Split        | Rows   | Has labels? |
+|--------------|--------|-------------|
+| `train`      | 12,392 | yes         |
+| `validation` |  1,548 | yes         |
+| `test`       |  1,554 | **no** (students predict) |
+Each row has `city`, `country` (ISO code), and `country_name`. For the
+`test` split, `country` and `country_name` are empty strings -- ground
+truth is held back so students must submit predictions for grading.
+## Raw corpus files
+For character-level language modeling and cross-domain perplexity work:
 | File | Size | Description |
 |------|------|-------------|
+| `shakespeare_input.txt`     | 4.5 MB | Complete Shakespeare works |
+| `shakespeare_sonnets.txt`   |   9 KB | Held-out Shakespeare sonnets |
+| `modern_english_sample.txt` | 4.4 KB | Modern-English prose for cross-domain perplexity |
+Fetch with `huggingface_hub.hf_hub_download(..., repo_type="dataset")`.
+## Licensing and attribution
+- `shakespeare_input.txt` and `shakespeare_sonnets.txt` are in the public domain.
+- `modern_english_sample.txt` is an excerpt from the English Wikipedia article
+  "Natural language processing" (https://en.wikipedia.org/wiki/Natural_language_processing),
+  used under the Creative Commons Attribution-ShareAlike 4.0 license
+  (https://creativecommons.org/licenses/by-sa/4.0/). The excerpt covers the
+  article's introduction and history sections, with Wikipedia section headers
+  removed so the text reads as continuous prose. See ATTRIBUTIONS.md for details.
+- The `cities` config is released under CC BY 4.0.