Buckets:
| import argparse,json | |
| from pathlib import Path | |
| import pandas as pd,numpy as np,yaml | |
| from transformers import AutoTokenizer | |
| from representations.pooling import encode_response | |
| from representations.cache import file_hash | |
| def main(): | |
| p=argparse.ArgumentParser(); p.add_argument('--backbone',default='32b'); p.add_argument('--input',default='data/processed/responses.parquet'); p.add_argument('--output'); p.add_argument('--report-suffix'); args=p.parse_args() | |
| cfg=yaml.safe_load(Path(f'configs/backbone_{args.backbone}.yaml').read_text()) | |
| tok=AutoTokenizer.from_pretrained(cfg['model'],revision=cfg['revision'],local_files_only=True) | |
| ex=pd.read_parquet(args.input); out=Path(args.output or f'data/processed/tokenized_{args.backbone}.parquet') | |
| records=[] | |
| for i,r in enumerate(ex.to_dict('records')): | |
| e=encode_response(tok,r['prompt'],r['response'],cfg['max_length']); e.update({k:r[k] for k in ['example_id','response_id','prompt_id','feature_index']}); records.append(e) | |
| if (i+1)%2000==0: print(f'tokenized {i+1}/{len(ex)}',flush=True) | |
| df=pd.DataFrame(records); df.to_parquet(out,index=False) | |
| out.with_suffix('.provenance.json').write_text(json.dumps({'source_path':str(args.input),'source_sha256':file_hash(args.input),'tokens_sha256':file_hash(out),'model':cfg['model'],'revision':cfg['revision'],'pooling_sha256':file_hash('representations/pooling.py')},indent=2)) | |
| stats={k:{str(q):float(np.percentile(df[k],q)) for q in [50,75,90,95,99,100]} for k in ['prompt_tokens','response_tokens','original_sequence_length']} | |
| info={'model':cfg['model'],'revision':cfg['revision'],'max_length':cfg['max_length'],'examples':len(df),'truncated_fraction':float(df.truncated.mean()),'prompt_truncated_fraction':float((df.removed_prompt_tokens>0).mean()),'percentiles':stats} | |
| suffix=args.report_suffix if args.report_suffix is not None else ('' if Path(args.input).stem=='responses' else '_'+Path(args.input).stem) | |
| info.update(input=str(args.input),output=str(out)) | |
| Path(f'outputs/token_lengths_{args.backbone}{suffix}.json').write_text(json.dumps(info,indent=2)) | |
| report=f'# Token boundaries and lengths: {args.backbone}\n\n'+pd.DataFrame(stats).to_markdown()+'\n\n'+json.dumps({k:v for k,v in info.items() if k!='percentiles'},indent=2)+'\n\n' | |
| report+='Official chat template, non-thinking completed assistant turn. HelpSteer role separators are converted to chat turns. Pool only tokens fully contained in the final response character span; exclude template, prompt and padding. End token is exclusive. Overlength examples preserve all prompt tokens up to half the sequence budget; longer prompts retain beginning and end. Response truncation keeps the beginning and records every removed token. No response content is silently discarded.\n' | |
| Path(f'reports/token_lengths_{args.backbone}{suffix}.md').write_text(report); print(json.dumps(info,indent=2),flush=True) | |
| if __name__=='__main__':main() | |
Xet Storage Details
- Size:
- 2.96 kB
- Xet hash:
- 32533682317e30dfe43bd3d731d4200a5f2e19ee44ac2fc76e2f4b7561c2aaea
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.