| --- |
| license: agpl-3.0 |
| language: |
| - it |
| - fr |
| - ja |
| - pt |
| - es |
| tags: |
| - frequency-list |
| - multilingual |
| - lemma |
| - netflix |
| pretty_name: Multilingual Frequency Lists |
| size_categories: |
| - 10K<n<100K |
|
|
| configs: |
| - config_name: fr |
| data_files: |
| - split: train |
| path: fr/freq_list.tsv |
| - config_name: ja |
| data_files: |
| - split: train |
| path: ja/freq_list.tsv |
| - config_name: es |
| data_files: |
| - split: train |
| path: es/freq_list.tsv |
| - config_name: it |
| data_files: |
| - split: train |
| path: it/freq_list.tsv |
| - config_name: pt |
| data_files: |
| - split: train |
| path: pt/freq_list.tsv |
| --- |
| |
| # Multilingual Frequency Lists |
|
|
| This dataset contains multiple word-frequency lists in various languages such as French, Japanese, Spanish, Italian and Portuguese. |
| Specifically, these are frequency lists of lemmas, meaning, for example, that words like 'run', 'runs' and 'running' are counted together as occurences of the same lemma 'run'. |
|
|
| These frequency lists were generated from ~1GB of subtitles scraped from a variety of Netflix shows and films and parsed using relevant [spacy](https://spacy.io/) models for each language. |
| Each frequency list contains between 15k and 20k of the top most frequent words. |
|
|
| ## Quickstart |
|
|
| ```python |
| from datasets import load_dataset |
| from pprint import pprint |
| |
| language = "ja" # japanese |
| dataset = load_dataset("joshdavham/multilingual-frequency-lists", language) |
| |
| pprint(dataset["train"][:5]) |
| # > {'count': [181501, 97107, 88598, 81836, 70747], |
| # 'lemma': ['ない', 'です', 'てる', 'ます', 'から'], |
| # 'rank': [1, 2, 3, 4, 5]} |
| ``` |
|
|
| ## License |
|
|
| This dataset is licensed under the AGPL. |
