| """MIT: read an explicitly selected canonical split from original text shards. |
| |
| No training on the all-documents view by accident: split is mandatory. |
| """ |
| from itertools import zip_longest |
| import hashlib |
| import json |
| from pathlib import Path |
| import pyarrow.parquet as pq |
|
|
|
|
| def document_id(inventory_id,source,original_file,row): |
| encoded=json.dumps(['release-doc-id-v1',inventory_id,source,original_file,row], |
| ensure_ascii=False,separators=(',',':')).encode('utf8') |
| return hashlib.sha256(encoded).hexdigest() |
|
|
|
|
| def iter_split(text_path,assignment_path,*,split,source,original_file,inventory_id,batch_size=8192): |
| if split=='validation':split='eval' |
| if split not in ('train','eval'):raise ValueError('Explicit train or validation split required') |
| text=pq.ParquetFile(text_path);assignment=pq.ParquetFile(assignment_path) |
| if text.metadata.num_rows!=assignment.metadata.num_rows:raise ValueError('Text/assignment coverage mismatch') |
| metadata=assignment.schema_arrow.metadata or {} |
| if metadata.get(b'original_final_file')!=original_file.encode():raise ValueError('Assignment belongs to another shard') |
| row_number=0 |
| for tb,ab in zip_longest(text.iter_batches(batch_size=batch_size,use_threads=False), |
| assignment.iter_batches(batch_size=batch_size,use_threads=False)): |
| if tb is None or ab is None or tb.num_rows!=ab.num_rows:raise ValueError('Batch coverage mismatch') |
| for row,assigned in zip(tb.to_pylist(),ab.to_pylist()): |
| if assigned['row']!=row_number:raise ValueError('Assignment row order mismatch') |
| if hashlib.sha256(row['text'].encode('utf8')).digest()!=assigned['content_sha256']: |
| raise ValueError('Document body differs from canonical split') |
| if assigned['split']==split: |
| yield {**row,'doc_id':document_id(inventory_id,source,original_file,row_number), |
| 'source':source,'original_row':row_number,'split':split,'group_id':assigned['group_id']} |
| elif assigned['split'] not in ('train','eval'):raise ValueError('Unknown canonical split') |
| row_number+=1 |
|
|
|
|
| def download_and_iter(repo_id,*,revision,split,max_documents=None): |
| """Authenticated downloads are unnecessary for the public release.""" |
| from huggingface_hub import hf_hub_download |
| def fetch(path):return hf_hub_download(repo_id,path,repo_type='dataset',revision=revision,token=False) |
| manifest=json.loads(Path(fetch('manifest.json')).read_text());done=0 |
| if not manifest.get('source_documents'):raise ValueError('No text shards listed') |
| for item in manifest['source_documents']: |
| tp=fetch(item['text_path']);ap=fetch(item['assignment_path']) |
| |
| for p,wanted in [(tp,item['text_sha256']),(ap,item['assignment_sha256'])]: |
| h=hashlib.sha256() |
| with open(p,'rb') as f: |
| while block:=f.read(8*1024**2):h.update(block) |
| if h.hexdigest()!=wanted:raise ValueError('Downloaded file SHA mismatch') |
| for row in iter_split(tp,ap,split=split,source=manifest['source'],original_file=item['original_final_file'], |
| inventory_id=manifest['content_inventory_id']): |
| yield row;done+=1 |
| if max_documents is not None and done>=max_documents:return |
|
|
