| --- |
| 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](https://huggingface.co/datasets/compling/cs489-f26-a3). |
| Collection: [cs489-f26](https://huggingface.co/collections/compling/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 |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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: |
|
|
| ```json |
| {"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 | |
|
|
