| """Small original-source conversions that avoid audio downloads or legacy loaders.""" |
| from collections import Counter |
| import hashlib |
| import io |
| import json |
| from pathlib import Path |
| import urllib.request |
| import xml.etree.ElementTree as ET |
| import zipfile |
|
|
| from datasets import Dataset, DatasetDict |
|
|
| from .relevance import text_key, write_mirror |
|
|
| SLURP_REVISION = '8eb16545762be97ace75334109d73824217311f1' |
| MCSCRIPT_URL = 'https://fedora.clarin-d.uni-saarland.de/sfb1102/MCScript-2.0.zip' |
| MCSCRIPT_SHA256 = '50037b08123a4660e2e00ad07bcfa2a7ddc1d666b05b198d359cd2cda8f5e88c' |
|
|
|
|
| def slurp(): |
| """SLURP original human transcripts and intent labels, without bundled audio. |
| |
| Native real train/devel/test partitions; synthetic speech is not included. |
| Identical transcripts with inconsistent intents are excluded, and duplicate |
| transcripts stay in their highest-priority native holdout (test, dev, train). |
| One- and two-word transcripts are excluded as insufficient intent context. |
| Original terms: CC BY 4.0. All removals and source hashes are recorded. |
| """ |
| splits={}; hashes={}; labels={};counts=Counter() |
| for split,native in [('train','train'),('validation','devel'),('test','test')]: |
| url=f'https://raw.githubusercontent.com/pswietojanski/slurp/{SLURP_REVISION}/dataset/slurp/{native}.jsonl' |
| raw=urllib.request.urlopen(url).read();hashes[native]=hashlib.sha256(raw).hexdigest() |
| splits[split]=[json.loads(line) for line in raw.splitlines() if line.strip()] |
| for row in splits[split]:labels.setdefault(text_key(row['sentence']),set()).add(row['intent']) |
| output={};seen=set() |
| for split in ('test','validation','train'): |
| output[split]=[] |
| for row in splits[split]: |
| key=text_key(row['sentence']) |
| reason='ambiguous_intent' if len(labels[key])!=1 else 'short_transcript' if len(row['sentence'].split())<3 else 'duplicate_transcript' if key in seen else None |
| if reason:counts[split+'/'+reason]+=1;continue |
| seen.add(key) |
| meta=dict(id=str(row['slurp_id']),split_group_id=key,scenario=row['scenario'],entities=row['entities'],source_revision=SLURP_REVISION) |
| output[split].append(dict(sentence1=row['sentence'],sentence2='',labels=row['intent'],metadata=json.dumps(meta,sort_keys=True))) |
| return write_mirror('slurp',output,'string',dict(source='https://github.com/pswietojanski/slurp',revision=SLURP_REVISION, |
| source_hashes=hashes,excluded=dict(counts),split_policy='native splits; exact transcript conflicts and overlap removed', |
| conversion_sha256=hashlib.sha256(Path(__file__).read_bytes()).hexdigest(),license='cc-by-4.0')) |
|
|
|
|
| def mcscript2(): |
| """MCScript2.0 native two-answer reading comprehension, original XML partitions. |
| |
| Preserve stories, questions and native choices; exactly one gold answer and |
| two distinct non-empty options are required. Story hashes prevent train/dev/ |
| test copies. Source URL/archive hash and per-split counts are recorded. |
| Dataset terms: unspecified until original distribution terms are verified. |
| """ |
| raw=urllib.request.urlopen(MCSCRIPT_URL).read() |
| if hashlib.sha256(raw).hexdigest()!=MCSCRIPT_SHA256:raise ValueError('MCScript archive changed') |
| output={};groups={};counts=Counter() |
| with zipfile.ZipFile(io.BytesIO(raw)) as archive: |
| for split,native in [('train','train'),('validation','dev'),('test','test')]: |
| output[split]=[];groups[split]=set() |
| for story in ET.fromstring(archive.read(native+'-data.xml')): |
| text=story.findtext('text');group=text_key(text);groups[split].add(group) |
| for question in story.findall('./questions/question'): |
| choices=[a.attrib['text'] for a in question.findall('answer')] |
| gold=[i for i,a in enumerate(question.findall('answer')) if a.attrib['correct'].lower()=='true'] |
| if len(choices)!=2 or len(set(choices))!=2 or not all(c.strip() for c in choices) or len(gold)!=1: |
| counts[split+'/invalid_choices']+=1;continue |
| meta=dict(id=story.attrib['id']+':'+question.attrib['id'],split_group_id=group, |
| scenario=story.attrib['scenario'],question_type=question.attrib['type']) |
| output[split].append(dict(inputs=f"Story: {text}\nQuestion: {question.attrib['text']}", |
| choices_list=choices,labels=gold[0],metadata=json.dumps(meta,sort_keys=True))) |
| assert all(not groups[a]&groups[b] for a in groups for b in groups if a!=b),'MCScript story overlap' |
| data=DatasetDict({s:Dataset.from_list(r) for s,r in output.items()}) |
| directory=Path('build/mcscript2-release');directory.mkdir(parents=True,exist_ok=True) |
| for split,rows in data.items():rows.to_parquet(str(directory/(split+'.parquet'))) |
| provenance=dict(source_url=MCSCRIPT_URL,archive_sha256=MCSCRIPT_SHA256,excluded=dict(counts), |
| splits={s:len(r) for s,r in data.items()},split_policy='native story partitions', |
| conversion_sha256=hashlib.sha256(Path(__file__).read_bytes()).hexdigest(),license='unspecified') |
| (directory/'provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') |
| (directory/'excluded-questions.jsonl').write_text('') |
| return data |
|
|
