| """Pinned legal relevance, clinical inference and sexism sources; native labels only.""" |
| import ast |
| from collections import Counter, defaultdict |
| import csv |
| import hashlib |
| import io |
| import json |
| from pathlib import Path |
| import tarfile |
| import urllib.request |
| import zipfile |
|
|
| from huggingface_hub import hf_hub_download |
|
|
| from .relevance import text_key, write_mirror |
|
|
| ACORD_REVISION = '7ba1c6f2e0baaa32e78c78c2b02e9c57b102af4e' |
| EDOS_REVISION = 'b1f1fbcca9d42de0395432ada8fcfb5c35f68c77' |
| EVIDENCE_URL = 'https://evidence-inference.ebm-nlp.com/v2.0.tar.gz' |
| EVIDENCE_SHA256 = '6abe0d4ec0d331834981c0171c3c79d47515761867f82f1dc6066e43863a1586' |
|
|
|
|
| def acord(): |
| """ACORD attorney-annotated query/clause relevance, preserving native query splits. |
| |
| Join only explicit qrels against the official queries/corpus. Unjudged pairs |
| are never converted to negatives. Grades 0–4 correspond to 1–5 attorney stars. |
| Original dataset terms: CC BY 4.0. Archive, source revision and counts are |
| recorded in provenance.json; metadata retains query and clause identities. |
| """ |
| archive = hf_hub_download('theatticusproject/acord','ACORD Dataset & ReadMe.zip', |
| repo_type='dataset',revision=ACORD_REVISION) |
| splits, counts, groups = {}, Counter(), {} |
| with zipfile.ZipFile(archive) as source: |
| prefix = next(n.rsplit('/',1)[0]+'/' for n in source.namelist() if n.endswith('/queries.jsonl')) |
| queries = {r['_id']:r['text'] for r in map(json.loads,source.read(prefix+'queries.jsonl').splitlines())} |
| clauses = {r['_id']:r['text'] for r in map(json.loads,source.read(prefix+'corpus.jsonl').splitlines())} |
| for split,native in [('train','train'),('validation','valid'),('test','test')]: |
| records = {} |
| for row in csv.DictReader(io.StringIO(source.read(prefix+f'qrels/{native}.tsv').decode()),delimiter='\t'): |
| q,c,grade = row['query-id'],row['corpus-id'],int(row['score']) |
| if q not in queries or c not in clauses or grade not in range(5): |
| raise ValueError('Invalid ACORD qrel') |
| key=(q,c) |
| if key in records: |
| if records[key]['labels'] != str(grade): |
| raise ValueError('Conflicting ACORD qrels') |
| counts['duplicate_qrels'] += 1 |
| continue |
| meta=dict(id=text_key(q)+':'+c,query_id=q,clause_id=c,split_group_id=text_key(queries[q])) |
| records[key]=dict(sentence1=queries[q],sentence2=clauses[c],labels=str(grade), |
| metadata=json.dumps(meta,sort_keys=True)) |
| counts[f'{split}/grade/{grade}'] += 1 |
| splits[split]=list(records.values()) |
| groups[split]={text_key(queries[q]) for q,c in records} |
| assert all(not groups[a]&groups[b] for a in groups for b in groups if a!=b), 'ACORD query overlap' |
| return write_mirror('acord',splits,'string',dict(conversion_sha256=hashlib.sha256(Path(__file__).read_bytes()).hexdigest(),source='theatticusproject/acord',revision=ACORD_REVISION, |
| archive_sha256=hashlib.sha256(Path(archive).read_bytes()).hexdigest(),counts=dict(counts), |
| query_counts={s:len(g) for s,g in groups.items()},split_policy='native query partitions',license='cc-by-4.0')) |
|
|
|
|
| def edos(): |
| """EDOS native binary sexism judgments and original train/dev/test assignments. |
| |
| Original source: https://github.com/rewire-online/edos (CC0-1.0). |
| Reads the aggregated annotation CSV, retaining all native labels, identities |
| and split assignments. Category/vector labels remain in metadata; no generated |
| explanations enter inputs. See provenance.json for the pinned source/hash. |
| """ |
| url=f'https://raw.githubusercontent.com/rewire-online/edos/{EDOS_REVISION}/data/edos_labelled_aggregated.csv' |
| raw=urllib.request.urlopen(url).read() |
| splits={s:[] for s in ('train','validation','test')} |
| for row in csv.DictReader(io.StringIO(raw.decode())): |
| if row['label_sexist'] not in ('not sexist','sexist'): |
| raise ValueError('Unknown EDOS label') |
| split={'train':'train','dev':'validation','test':'test'}[row['split']] |
| meta=dict(id=row['rewire_id'],split_group_id=text_key(row['text']), |
| category=row['label_category'],vector=row['label_vector'],source_revision=EDOS_REVISION) |
| splits[split].append(dict(sentence1=row['text'],sentence2='',labels=row['label_sexist'], |
| metadata=json.dumps(meta,sort_keys=True))) |
| |
| heldout={text_key(r['sentence1']) for s in ('validation','test') for r in splits[s]} |
| before=len(splits['train']) |
| splits['train']=[r for r in splits['train'] if text_key(r['sentence1']) not in heldout] |
| return write_mirror('edos',splits,'string',dict(conversion_sha256=hashlib.sha256(Path(__file__).read_bytes()).hexdigest(),source_url=url,revision=EDOS_REVISION, |
| source_sha256=hashlib.sha256(raw).hexdigest(),excluded_train_overlap=before-len(splits['train']), |
| split_policy='native partitions; training copies of evaluation text removed',license='cc0-1.0')) |
|
|
|
|
| def evidence_inference(): |
| """Evidence Inference v2: full article + intervention/comparator/outcome → effect direction. |
| |
| Join annotations by PromptID AND PMCID, never by article alone. Use verified |
| labels, exclude tied votes and upstream known-bad prompts. Evidence spans in |
| metadata refer only to the same prompt and winning label. Full article text |
| stays in the input so isolated snippets need not identify treatment arms. |
| Native article train/validation/test splits are preserved. The upstream code |
| and annotations have MIT terms; article-text rights vary and are not relabeled |
| MIT. Mirror text/data licensing remains unspecified. |
| """ |
| raw=urllib.request.urlopen(EVIDENCE_URL).read() |
| digest=hashlib.sha256(raw).hexdigest() |
| if digest != EVIDENCE_SHA256: |
| raise ValueError('Evidence Inference archive changed; review before updating the pin') |
| script=Path(hf_hub_download('bigbio/evidence_inference','evidence_inference.py',repo_type='dataset', |
| revision='35dce6aba1b3eb9eb9af9bdc38ebeda73dad15b9')).read_text() |
| skip=set() |
| for node in ast.parse(script).body: |
| if isinstance(node,ast.Assign) and isinstance(node.targets[0],ast.Name) and node.targets[0].id in { |
| 'INCORRECT_PROMPT_IDS','QUESTIONABLE_PROMPT_IDS','SOMEWHAT_MALFORMED_PROMPT_IDS'}: |
| skip.update(ast.literal_eval(node.value.args[0])) |
| splits={s:[] for s in ('train','validation','test')}; counts=Counter();annotations=defaultdict(list) |
| with tarfile.open(fileobj=io.BytesIO(raw)) as compressed: |
| payload = {member.name:compressed.extractfile(member).read() for member in compressed if member.isfile()} |
| with tarfile.open(fileobj=io.BytesIO(raw)) as archive: |
| def read(name): |
| return payload[name].decode('utf-8-sig') |
| membership={} |
| for split in splits: |
| ids=set(read(f'splits/{split}_article_ids.txt').split()) |
| assert not ids.intersection(membership), 'Evidence Inference article overlap' |
| membership.update({article:split for article in ids}) |
| for row in csv.DictReader(io.StringIO(read('annotations_merged.csv'))): |
| if row['Valid Label'].lower() in ('true','1'): |
| annotations[(row['PromptID'],row['PMCID'])].append(row) |
| articles={} |
| for prompt in csv.DictReader(io.StringIO(read('prompts_merged.csv'))): |
| pid,article=prompt['PromptID'],prompt['PMCID'] |
| votes=Counter(a['Label Code'] for a in annotations[(pid,article)]) |
| if int(pid) in skip or not votes or article not in membership: |
| counts['excluded/invalid_or_unassigned_prompt'] += 1 |
| continue |
| ranked=votes.most_common() |
| if ranked[0][0] not in ('-1','0','1') or len(ranked)>1 and ranked[0][1]==ranked[1][1]: |
| counts['excluded/tied_or_invalid_labels'] += 1 |
| continue |
| label=ranked[0][0] |
| if article not in articles: |
| articles[article]=read(f'txt_files/PMC{article}.txt') |
| text=articles[article];spans=set() |
| for annotation in annotations[(pid,article)]: |
| start,end=int(annotation['Evidence Start']),int(annotation['Evidence End']) |
| if annotation['Label Code']==label and annotation['Valid Reasoning'].lower() in ('true','1') and 0<=start<end<=len(text): |
| spans.add((start,end)) |
| question=f"Intervention: {prompt['Intervention']}\nComparator: {prompt['Comparator']}\nOutcome: {prompt['Outcome']}" |
| meta=dict(id=pid,split_group_id=article,pmcid=article,prompt_id=pid,votes=dict(votes), |
| evidence_spans=[dict(start=a,end=b,text=text[a:b]) for a,b in sorted(spans)]) |
| splits[membership[article]].append(dict(sentence1=text,sentence2=question,labels=label, |
| metadata=json.dumps(meta,sort_keys=True))) |
| counts['retained/'+label] += 1 |
| return write_mirror('evidence-inference',splits,'string',dict(conversion_sha256=hashlib.sha256(Path(__file__).read_bytes()).hexdigest(),source_url=EVIDENCE_URL, |
| archive_sha256=digest,reviewed_exclusions_revision='35dce6aba1b3eb9eb9af9bdc38ebeda73dad15b9', |
| known_bad_prompt_ids=sorted(skip),counts=dict(counts),split_policy='native article partitions', |
| label_policy='majority verified labels; ties excluded',license='unspecified')) |
|
|
