Datasets:

compling
/

cs489-f26-a3

pretty_name: CS 489 F26 A3 Constituent Labels
language:
  - en
tags:
  - constituent-labeling
  - representation-probing
  - cs489
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.parquet
      - split: dev
        path: dev.parquet

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 integer start and end fields and a string label.

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