cs489-f26
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CS 489 (Fall 2026, UWaterloo) Assignment 1: seven-language Wikipedia corpus for data cleaning and LM training. • 3 items • Updated
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End of preview. Expand in Data Studio
A3 constituent-label data
English sentences with labeled constituent spans for CS 489 Assignment 3 at the University of Waterloo (Fall 2026). Each example provides word tokens and gold span boundaries; the assignment predicts the constituent label of each span using frozen BERT representations.
Dataset: compling/cs489-f26-a3. Collection: cs489-f26.
| Split | Sentences | Labeled spans |
|---|---|---|
train |
39,832 | 701,738 |
dev |
1,700 | 29,034 |
The label vocabulary contains 26 constituent labels. The TA holds a separate test split for final evaluation.
Load with Hugging Face Datasets
from datasets import load_dataset
corpus = load_dataset("compling/cs489-f26-a3")
train = corpus["train"]
dev = corpus["dev"]
sentence = train[0]
tokens = sentence["tokens"]
spans = sentence["constituents"]
Download the assignment files
The starter reads the original JSONL files and label list from data/.
Run this from the starter project root:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="compling/cs489-f26-a3",
repo_type="dataset",
allow_patterns=["train.jsonl", "dev.jsonl", "labels.json", "manifest.json"],
local_dir="data",
)
Sentence format
Each UTF-8 JSONL line is one sentence with three fields:
id: a unique string identifier for the record.tokens: the ordered list of word tokens, preserving capitalization and punctuation.constituents: a list of objects with integerstartandendfields and a stringlabel.
A hand-written example:
{"id":"toy-0001","tokens":["The","cat","sleeps","."],"constituents":[{"start":0,"end":2,"label":"NP"},{"start":0,"end":4,"label":"S"},{"start":2,"end":3,"label":"VP"}]}
Boundaries use zero-based word-token indices with an exclusive end: tokens[start:end].
They satisfy 0 <= start < end <= len(tokens).
Each span has one label, and the spans are sorted by (start, end) with unique boundary pairs within each sentence.
labels.json is a JSON list of 26 label strings.
A label's zero-based index in this list is its class ID.
Use the supplied order for every split, probe, and saved artifact.
Use the complete train/dev files as supplied and count every listed span during training and evaluation.
Files
| File | Contents |
|---|---|
train.jsonl, dev.jsonl |
Sentence records in the format above, one record per line |
labels.json |
Ordered label vocabulary |
manifest.json |
Dataset metadata, counts, and SHA-256 checksums for the assignment files |
train.parquet, dev.parquet |
Equivalent typed tables used by load_dataset, preserving every field and row |
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