Include reproducible source conversion

repackage_dataset/specialized_text.py

@@ -0,0 +1,154 @@
+"""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)))
+    # Preserve evaluation membership; discard training copies of evaluation texts.
+    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'))