Attempting to locate this file is a frustrating and risky experience:
The World Atlas of Language Structures (WALS) is a large database of structural (phonological, grammatical, lexical) properties of languages gathered from descriptive materials by a team of 55 authors. The "136 features" specification refers to a curated subset of features often used in NLP tasks because they have the widest coverage across languages. These features include attributes like:
Researchers have used RoBERTa + WALS to: wals roberta sets 136zip full
A typical pipeline:
You do not need a single “full sets 136zip” file for this. Attempting to locate this file is a frustrating
import wals
# Fetch language features
data = wals.get_language('eng') # English
print(data.genus, data.family)
Or download the entire CSV from wals.info/download.
The term "136zip" suggests a compressed archive containing pre-processed data sets. In the context of NLP pipelines, this archive typically contains: A typical pipeline:
trainer.train()
model.save_pretrained("./wals_roberta_finetuned")
Your final model will be a folder with a few files (no ZIPs needed).
WALS is a database of structural properties of languages (e.g., word order, phoneme inventories). It is not an NLP model but a linguistic dataset. It can be used to fine-tune RoBERTa for typological tasks.
Searching for such a string reveals a deeper trend in computational linguistics: the desire to combine classic typological databases (WALS) with modern neural architectures (Roberta) in a reproducible, self-contained manner. Official WALS access is via an interactive web interface or a relatively clean CSV download (from cldf-datasets/wals). But that doesn’t include Roberta-specific formatting, tokenization, or experiment splits.
Thus, "wals roberta sets 136zip full" is a researcher’s or engineer’s shorthand for: “I want the complete WALS dataset, already partitioned into 136 predefined sets (likely folds or feature groups), packaged with the Roberta model files, all zipped for easy download.” The number 136 might come from a specific publication’s experimental setup (e.g., 136 typological features used in a probing task).