metadata/code/text_classification/README.md

Text classification: reassembling pool checkpoints

Each text-classification cell <arch>__<dataset> has

file	content
zoo/<dataset>/<arch>/<model_id>.pth	base checkpoint (full model_state_dict, training config, val metric; word-level models also store the vocabulary stoi)
ckpt/text_classification/<arch>__<dataset>/<model_id>/collect_000.pth ... collect_199.pth	200 pool checkpoints
metadata/configs/text_classification/<cell>/<model_id>/config.json	base training config
metadata/configs/text_classification/<cell>/<model_id>/collect_config.json	collection protocol
metadata/registry/text_classification.json	one record per cell: base training info, collect config, per-checkpoint logs, pool statistics

The token embedding is not in the pool files

The pool is collected by continuing training from the converged base model with a fresh AdamW (small learning rate, collect_lr in collect_config.json) and saving one checkpoint every save_every steps. During collection the vocabulary token-embedding matrix is frozen, so it is identical in all 200 pool members and in the base model. It is therefore stored only once, in the base checkpoint, and left out of every pool file.

A pool file is a dict with

• model_state_dict: every tensor of the model except the frozen keys (float32);
• frozen_keys: the omitted keys, e.g. ["bert.embeddings.word_embeddings.weight"]; for t5small the three tied copies transformer.shared.weight, transformer.encoder.embed_tokens.weight, transformer.decoder.embed_tokens.weight;
• frozen_source: the local path of the base checkpoint at collection time (on the Hub it is the ckpt_path of the registry record, zoo/<dataset>/<arch>/<model_id>.pth);
• collect_step, train_step, val_metric, val_loss.

The parameters a generator has to produce are generated_params in the registry record (total minus frozen_params).

Reassembling a full model

from assemble import assemble
sd = assemble("zoo/agnews/bertbase/bertbase_010.pth",
              "ckpt/text_classification/bertbase__agnews/bertbase_010/collect_000.pth")
model.load_state_dict(sd, strict=True)

assemble(base_ckpt_path, pool_ckpt_path) copies each key in frozen_keys from the base's model_state_dict into the pool model_state_dict and returns the full state_dict. The same function applies to generated weights: fill the generated tensors, then add the frozen keys from the base checkpoint. For every cell the collection script checked that the reassembled collect_000.pth reproduces its logged validation metric (training_info.assemble_check in the registry).