def run_eval(
model,
vocab,
samples,
device="cpu",
projective=False,
multiroot=True,
batch_size=32,
):
runner = Runner()
runner.on(
Event.BATCH,
[
batch2tensors(device, vocab),
set_train_mode(model, training=False),
compute_total_arc_type_scores(model, vocab),
predict_batch(projective, multiroot),
evaluate_batch(),
get_n_items(),
],
)
n_tokens = sum(len(s["words"]) for s in samples)
ProgressBar(leave=False, total=n_tokens, unit="tok").attach_on(runner)
SumReducer("counts").attach_on(runner)
with torch.no_grad():
runner.run(
BucketIterator(samples, lambda s: len(s["words"]), batch_size))
return runner.state
def run_eval(
model,
vocab,
samples,
compute_loss=True,
device="cpu",
projective=False,
multiroot=True,
batch_size=32,
):
runner = Runner()
runner.on(
Event.BATCH,
[
batch2tensors(device, vocab),
set_train_mode(model, training=False),
compute_total_arc_type_scores(model, vocab),
],
)
@runner.on(Event.BATCH)
def maybe_compute_loss(state):
if not compute_loss:
return
ppt_loss = compute_aatrn_loss(
state["total_arc_type_scores"],
state["batch"]["ppt_mask"].bool(),
projective=projective,
multiroot=multiroot,
)
state["ppt_loss"] = ppt_loss.item()
state["size"] = state["batch"]["words"].size(0)
runner.on(Event.BATCH, [
predict_batch(projective, multiroot),
evaluate_batch(),
get_n_items()
])
n_tokens = sum(len(s["words"]) for s in samples)
ProgressBar(leave=False, total=n_tokens, unit="tok").attach_on(runner)
SumReducer("counts").attach_on(runner)
if compute_loss:
MeanReducer("mean_ppt_loss", value="ppt_loss").attach_on(runner)
with torch.no_grad():
runner.run(
BucketIterator(samples, lambda s: len(s["words"]), batch_size))
return runner.state
def finetune(
_log,
_run,
_rnd,
max_length=None,
artifacts_dir="ft_artifacts",
overwrite=False,
load_from="artifacts",
load_params="model.pth",
device="cpu",
word_emb_path="wiki.id.vec",
freeze=False,
thresh=0.95,
projective=False,
multiroot=True,
batch_size=32,
lr=1e-5,
l2_coef=1.0,
max_epoch=5,
):
"""Finetune a trained model with PPT."""
if max_length is None:
max_length = {}
artifacts_dir = Path(artifacts_dir)
_log.info("Creating artifacts directory %s", artifacts_dir)
artifacts_dir.mkdir(exist_ok=overwrite)
samples = {
wh: list(read_samples(which=wh, max_length=max_length.get(wh)))
for wh in ["train", "dev", "test"]
}
for wh in samples:
n_toks = sum(len(s["words"]) for s in samples[wh])
_log.info("Read %d %s samples and %d tokens", len(samples[wh]), wh,
n_toks)
path = Path(load_from) / "vocab.yml"
_log.info("Loading vocabulary from %s", path)
vocab = load(path.read_text(encoding="utf8"))
for name in vocab:
_log.info("Found %d %s", len(vocab[name]), name)
_log.info("Extending vocabulary with target words")
vocab.extend(chain(*samples.values()), ["words"])
_log.info("Found %d words now", len(vocab["words"]))
path = artifacts_dir / "vocab.yml"
_log.info("Saving vocabulary to %s", path)
path.write_text(dump(vocab), encoding="utf8")
samples = {wh: list(vocab.stoi(samples[wh])) for wh in samples}
path = Path(load_from) / "model.yml"
_log.info("Loading model from metadata %s", path)
model = load(path.read_text(encoding="utf8"))
path = Path(load_from) / load_params
_log.info("Loading model parameters from %s", path)
model.load_state_dict(torch.load(path, "cpu"))
_log.info("Creating extended word embedding layer")
kv = KeyedVectors.load_word2vec_format(word_emb_path)
assert model.word_emb.embedding_dim == kv.vector_size
with torch.no_grad():
model.word_emb = torch.nn.Embedding.from_pretrained(
extend_word_embedding(model.word_emb.weight, vocab["words"], kv))
path = artifacts_dir / "model.yml"
_log.info("Saving model metadata to %s", path)
path.write_text(dump(model), encoding="utf8")
model.word_emb.requires_grad_(not freeze)
model.tag_emb.requires_grad_(not freeze)
model.to(device)
for wh in ["train", "dev"]:
for i, s in enumerate(samples[wh]):
s["_id"] = i
runner = Runner()
runner.state.update({"ppt_masks": [], "_ids": []})
runner.on(
Event.BATCH,
[
batch2tensors(device, vocab),
set_train_mode(model, training=False),
compute_total_arc_type_scores(model, vocab),
],
)
@runner.on(Event.BATCH)
def compute_ppt_ambiguous_arcs_mask(state):
assert state["batch"]["mask"].all()
scores = state["total_arc_type_scores"]
ppt_mask = compute_ambiguous_arcs_mask(scores, thresh, projective,
multiroot)
state["ppt_masks"].extend(ppt_mask.tolist())
state["_ids"].extend(state["batch"]["_id"].tolist())
state["n_items"] = state["batch"]["words"].numel()
n_toks = sum(len(s["words"]) for s in samples[wh])
ProgressBar(total=n_toks, unit="tok").attach_on(runner)
_log.info("Computing PPT ambiguous arcs mask for %s set", wh)
with torch.no_grad():
runner.run(
BucketIterator(samples[wh], lambda s: len(s["words"]),
batch_size))
assert len(runner.state["ppt_masks"]) == len(samples[wh])
assert len(runner.state["_ids"]) == len(samples[wh])
for i, ppt_mask in zip(runner.state["_ids"],
runner.state["ppt_masks"]):
samples[wh][i]["ppt_mask"] = ppt_mask
_log.info("Computing (log) number of trees stats on %s set", wh)
report_log_ntrees_stats(samples[wh], "ppt_mask", batch_size,
projective, multiroot)
_log.info("Creating optimizer")
opt = torch.optim.Adam(model.parameters(), lr=lr)
finetuner = Runner()
origin_params = {
name: p.clone().detach()
for name, p in model.named_parameters()
}
finetuner.on(
Event.BATCH,
[
batch2tensors(device, vocab),
set_train_mode(model),
compute_l2_loss(model, origin_params),
compute_total_arc_type_scores(model, vocab),
],
)
@finetuner.on(Event.BATCH)
def compute_loss(state):
mask = state["batch"]["mask"]
ppt_mask = state["batch"]["ppt_mask"].bool()
scores = state["total_arc_type_scores"]
ppt_loss = compute_aatrn_loss(scores, ppt_mask, mask, projective,
multiroot)
ppt_loss /= mask.size(0)
loss = ppt_loss + l2_coef * state["l2_loss"]
state["loss"] = loss
state["stats"] = {
"ppt_loss": ppt_loss.item(),
"l2_loss": state["l2_loss"].item(),
}
state["extra_stats"] = {"loss": loss.item()}
state["n_items"] = mask.long().sum().item()
finetuner.on(Event.BATCH,
[update_params(opt),
log_grads(_run, model),
log_stats(_run)])
@finetuner.on(Event.EPOCH_FINISHED)
def eval_on_dev(state):
_log.info("Evaluating on dev")
eval_state = run_eval(model, vocab, samples["dev"])
accs = eval_state["counts"].accs
print_accs(accs, run=_run, step=state["n_iters"])
ppt_loss = eval_state["mean_ppt_loss"]
_log.info("dev_ppt_loss: %.4f", ppt_loss)
_run.log_scalar("dev_ppt_loss", ppt_loss, step=state["n_iters"])
state["dev_accs"] = accs
@finetuner.on(Event.EPOCH_FINISHED)
def maybe_eval_on_test(state):
if state["epoch"] != max_epoch:
return
_log.info("Evaluating on test")
eval_state = run_eval(model,
vocab,
samples["test"],
compute_loss=False)
print_accs(eval_state["counts"].accs,
on="test",
run=_run,
step=state["n_iters"])
finetuner.on(Event.EPOCH_FINISHED,
save_state_dict("model", model, under=artifacts_dir))
EpochTimer().attach_on(finetuner)
n_tokens = sum(len(s["words"]) for s in samples["train"])
ProgressBar(stats="stats", total=n_tokens, unit="tok").attach_on(finetuner)
bucket_key = lambda s: (len(s["words"]) - 1) // 10
trn_iter = ShuffleIterator(
BucketIterator(samples["train"],
bucket_key,
batch_size,
shuffle_bucket=True,
rng=_rnd),
rng=_rnd,
)
_log.info("Starting finetuning")
try:
finetuner.run(trn_iter, max_epoch)
except KeyboardInterrupt:
_log.info("Interrupt detected, training will abort")
else:
return finetuner.state["dev_accs"]["las_nopunct"]
def finetune(
_log,
_run,
_rnd,
max_length=None,
artifacts_dir="ft_artifacts",
overwrite=False,
load_from="artifacts",
load_params="model.pth",
device="cpu",
word_emb_path="wiki.id.vec",
freeze=False,
projective=False,
multiroot=True,
batch_size=32,
lr=1e-5,
l2_coef=1.0,
max_epoch=5,
):
"""Finetune a trained model with self-training."""
if max_length is None:
max_length = {}
artifacts_dir = Path(artifacts_dir)
_log.info("Creating artifacts directory %s", artifacts_dir)
artifacts_dir.mkdir(exist_ok=overwrite)
samples = {
wh: list(read_samples(which=wh, max_length=max_length.get(wh)))
for wh in ["train", "dev", "test"]
}
for wh in samples:
n_toks = sum(len(s["words"]) for s in samples[wh])
_log.info("Read %d %s samples and %d tokens", len(samples[wh]), wh,
n_toks)
path = Path(load_from) / "vocab.yml"
_log.info("Loading vocabulary from %s", path)
vocab = load(path.read_text(encoding="utf8"))
for name in vocab:
_log.info("Found %d %s", len(vocab[name]), name)
_log.info("Extending vocabulary with target words")
vocab.extend(chain(*samples.values()), ["words"])
_log.info("Found %d words now", len(vocab["words"]))
path = artifacts_dir / "vocab.yml"
_log.info("Saving vocabulary to %s", path)
path.write_text(dump(vocab), encoding="utf8")
samples = {wh: list(vocab.stoi(samples[wh])) for wh in samples}
path = Path(load_from) / "model.yml"
_log.info("Loading model from metadata %s", path)
model = load(path.read_text(encoding="utf8"))
path = Path(load_from) / load_params
_log.info("Loading model parameters from %s", path)
model.load_state_dict(torch.load(path, "cpu"))
_log.info("Creating extended word embedding layer")
kv = KeyedVectors.load_word2vec_format(word_emb_path)
assert model.word_emb.embedding_dim == kv.vector_size
with torch.no_grad():
model.word_emb = torch.nn.Embedding.from_pretrained(
extend_word_embedding(model.word_emb.weight, vocab["words"], kv))
path = artifacts_dir / "model.yml"
_log.info("Saving model metadata to %s", path)
path.write_text(dump(model), encoding="utf8")
model.word_emb.requires_grad_(not freeze)
model.tag_emb.requires_grad_(not freeze)
model.to(device)
for wh in ["train"]:
for i, s in enumerate(samples[wh]):
s["_id"] = i
runner = Runner()
runner.state.update({"st_heads": [], "st_types": [], "_ids": []})
runner.on(
Event.BATCH,
[
batch2tensors(device, vocab),
set_train_mode(model, training=False),
compute_total_arc_type_scores(model, vocab),
predict_batch(projective, multiroot),
],
)
@runner.on(Event.BATCH)
def save_st_trees(state):
state["st_heads"].extend(state["pred_heads"].tolist())
state["st_types"].extend(state["pred_types"].tolist())
state["_ids"].extend(state["batch"]["_id"].tolist())
state["n_items"] = state["batch"]["words"].numel()
n_toks = sum(len(s["words"]) for s in samples[wh])
ProgressBar(total=n_toks, unit="tok").attach_on(runner)
_log.info("Computing ST trees for %s set", wh)
with torch.no_grad():
runner.run(
BucketIterator(samples[wh], lambda s: len(s["words"]),
batch_size))
assert len(runner.state["st_heads"]) == len(samples[wh])
assert len(runner.state["st_types"]) == len(samples[wh])
assert len(runner.state["_ids"]) == len(samples[wh])
for i, st_heads, st_types in zip(runner.state["_ids"],
runner.state["st_heads"],
runner.state["st_types"]):
assert len(samples[wh][i]["words"]) == len(st_heads)
assert len(samples[wh][i]["words"]) == len(st_types)
samples[wh][i]["st_heads"] = st_heads
samples[wh][i]["st_types"] = st_types
_log.info("Creating optimizer")
opt = torch.optim.Adam(model.parameters(), lr=lr)
finetuner = Runner()
origin_params = {
name: p.clone().detach()
for name, p in model.named_parameters()
}
finetuner.on(
Event.BATCH,
[
batch2tensors(device, vocab),
set_train_mode(model),
compute_l2_loss(model, origin_params),
],
)
@finetuner.on(Event.BATCH)
def compute_loss(state):
bat = state["batch"]
words, tags, heads, types = bat["words"], bat["tags"], bat[
"st_heads"], bat["st_types"]
mask = bat["mask"]
arc_scores, type_scores = model(words, tags, mask, heads)
arc_scores = arc_scores.masked_fill(~mask.unsqueeze(2),
-1e9) # mask padding heads
type_scores[..., vocab["types"].index(vocab.PAD_TOKEN)] = -1e9
# remove root
arc_scores, type_scores = arc_scores[:, :, 1:], type_scores[:, 1:]
heads, types, mask = heads[:, 1:], types[:, 1:], mask[:, 1:]
arc_scores = rearrange(arc_scores,
"bsz slen1 slen2 -> (bsz slen2) slen1")
heads = heads.reshape(-1)
arc_loss = torch.nn.functional.cross_entropy(arc_scores,
heads,
reduction="none")
type_scores = rearrange(type_scores,
"bsz slen ntypes -> (bsz slen) ntypes")
types = types.reshape(-1)
type_loss = torch.nn.functional.cross_entropy(type_scores,
types,
reduction="none")
arc_loss = arc_loss.masked_select(mask.reshape(-1)).mean()
type_loss = type_loss.masked_select(mask.reshape(-1)).mean()
loss = arc_loss + type_loss + l2_coef * state["l2_loss"]
state["loss"] = loss
state["stats"] = {
"arc_ppl": arc_loss.exp().item(),
"type_ppl": type_loss.exp().item(),
"l2_loss": state["l2_loss"].item(),
}
state["extra_stats"] = {
"arc_loss": arc_loss.item(),
"type_loss": type_loss.item()
}
finetuner.on(
Event.BATCH,
[
get_n_items(),
update_params(opt),
log_grads(_run, model),
log_stats(_run)
],
)
@finetuner.on(Event.EPOCH_FINISHED)
def eval_on_dev(state):
_log.info("Evaluating on dev")
eval_state = run_eval(model, vocab, samples["dev"])
accs = eval_state["counts"].accs
print_accs(accs, run=_run, step=state["n_iters"])
state["dev_accs"] = accs
@finetuner.on(Event.EPOCH_FINISHED)
def maybe_eval_on_test(state):
if state["epoch"] != max_epoch:
return
_log.info("Evaluating on test")
eval_state = run_eval(model, vocab, samples["test"])
print_accs(eval_state["counts"].accs,
on="test",
run=_run,
step=state["n_iters"])
finetuner.on(Event.EPOCH_FINISHED,
save_state_dict("model", model, under=artifacts_dir))
EpochTimer().attach_on(finetuner)
n_tokens = sum(len(s["words"]) for s in samples["train"])
ProgressBar(stats="stats", total=n_tokens, unit="tok").attach_on(finetuner)
bucket_key = lambda s: (len(s["words"]) - 1) // 10
trn_iter = ShuffleIterator(
BucketIterator(samples["train"],
bucket_key,
batch_size,
shuffle_bucket=True,
rng=_rnd),
rng=_rnd,
)
_log.info("Starting finetuning")
try:
finetuner.run(trn_iter, max_epoch)
except KeyboardInterrupt:
_log.info("Interrupt detected, training will abort")
else:
return finetuner.state["dev_accs"]["las_nopunct"]
def finetune(
corpus,
_log,
_run,
_rnd,
max_length=None,
artifacts_dir="ft_artifacts",
load_samples_from=None,
overwrite=False,
load_src=None,
src_key_as_lang=False,
main_src=None,
device="cpu",
word_emb_path="wiki.id.vec",
freeze=False,
thresh=0.95,
projective=False,
multiroot=True,
batch_size=32,
save_samples=False,
lr=1e-5,
l2_coef=1.0,
max_epoch=5,
):
"""Finetune a trained model with PPTX."""
if max_length is None:
max_length = {}
if load_src is None:
load_src = {"src": ("artifacts", "model.pth")}
main_src = "src"
elif main_src not in load_src:
raise ValueError(f"{main_src} not found in load_src")
artifacts_dir = Path(artifacts_dir)
_log.info("Creating artifacts directory %s", artifacts_dir)
artifacts_dir.mkdir(exist_ok=overwrite)
if load_samples_from:
_log.info("Loading samples from %s", load_samples_from)
with open(load_samples_from, "rb") as f:
samples = pickle.load(f)
else:
samples = {
wh: list(read_samples(which=wh, max_length=max_length.get(wh)))
for wh in ["train", "dev", "test"]
}
for wh in samples:
n_toks = sum(len(s["words"]) for s in samples[wh])
_log.info("Read %d %s samples and %d tokens", len(samples[wh]), wh,
n_toks)
kv = KeyedVectors.load_word2vec_format(word_emb_path)
if load_samples_from:
_log.info(
"Skipping non-main src because samples are processed and loaded")
srcs = []
else:
srcs = [src for src in load_src if src != main_src]
if src_key_as_lang and corpus["lang"] in srcs:
_log.info("Removing %s from src parsers because it's the tgt",
corpus["lang"])
srcs.remove(corpus["lang"])
srcs.append(main_src)
for src_i, src in enumerate(srcs):
_log.info("Processing src %s [%d/%d]", src, src_i + 1, len(srcs))
load_from, load_params = load_src[src]
path = Path(load_from) / "vocab.yml"
_log.info("Loading %s vocabulary from %s", src, path)
vocab = load(path.read_text(encoding="utf8"))
for name in vocab:
_log.info("Found %d %s", len(vocab[name]), name)
_log.info("Extending %s vocabulary with target words", src)
vocab.extend(chain(*samples.values()), ["words"])
_log.info("Found %d words now", len(vocab["words"]))
samples_ = {wh: list(vocab.stoi(samples[wh])) for wh in samples}
path = Path(load_from) / "model.yml"
_log.info("Loading %s model from metadata %s", src, path)
model = load(path.read_text(encoding="utf8"))
path = Path(load_from) / load_params
_log.info("Loading %s model parameters from %s", src, path)
model.load_state_dict(torch.load(path, "cpu"))
_log.info("Creating %s extended word embedding layer", src)
assert model.word_emb.embedding_dim == kv.vector_size
with torch.no_grad():
model.word_emb = torch.nn.Embedding.from_pretrained(
extend_word_embedding(model.word_emb.weight, vocab["words"],
kv))
model.to(device)
for wh in ["train", "dev"]:
if load_samples_from:
assert all("pptx_mask" in s for s in samples[wh])
continue
for i, s in enumerate(samples_[wh]):
s["_id"] = i
runner = Runner()
runner.state.update({"pptx_masks": [], "_ids": []})
runner.on(
Event.BATCH,
[
batch2tensors(device, vocab),
set_train_mode(model, training=False),
compute_total_arc_type_scores(model, vocab),
],
)
@runner.on(Event.BATCH)
def compute_pptx_ambiguous_arcs_mask(state):
assert state["batch"]["mask"].all()
scores = state["total_arc_type_scores"]
pptx_mask = compute_ambiguous_arcs_mask(
scores, thresh, projective, multiroot)
state["pptx_masks"].extend(pptx_mask)
state["_ids"].extend(state["batch"]["_id"].tolist())
state["n_items"] = state["batch"]["words"].numel()
n_toks = sum(len(s["words"]) for s in samples_[wh])
ProgressBar(total=n_toks, unit="tok").attach_on(runner)
_log.info(
"Computing PPTX ambiguous arcs mask for %s set with source %s",
wh, src)
with torch.no_grad():
runner.run(
BucketIterator(samples_[wh], lambda s: len(s["words"]),
batch_size))
assert len(runner.state["pptx_masks"]) == len(samples_[wh])
assert len(runner.state["_ids"]) == len(samples_[wh])
for i, pptx_mask in zip(runner.state["_ids"],
runner.state["pptx_masks"]):
samples_[wh][i]["pptx_mask"] = pptx_mask.tolist()
_log.info("Computing (log) number of trees stats on %s set", wh)
report_log_ntrees_stats(samples_[wh], "pptx_mask", batch_size,
projective, multiroot)
_log.info("Combining the ambiguous arcs mask")
assert len(samples_[wh]) == len(samples[wh])
for i in range(len(samples_[wh])):
pptx_mask = torch.tensor(samples_[wh][i]["pptx_mask"])
assert pptx_mask.dim() == 3
if "pptx_mask" in samples[wh][i]:
old_mask = torch.tensor(samples[wh][i]["pptx_mask"])
else:
old_mask = torch.zeros(1, 1, 1).bool()
samples[wh][i]["pptx_mask"] = (old_mask | pptx_mask).tolist()
assert src == main_src
_log.info("Main source is %s", src)
path = artifacts_dir / "vocab.yml"
_log.info("Saving vocabulary to %s", path)
path.write_text(dump(vocab), encoding="utf8")
path = artifacts_dir / "model.yml"
_log.info("Saving model metadata to %s", path)
path.write_text(dump(model), encoding="utf8")
if save_samples:
path = artifacts_dir / "samples.pkl"
_log.info("Saving samples to %s", path)
with open(path, "wb") as f:
pickle.dump(samples, f)
samples = {wh: list(vocab.stoi(samples[wh])) for wh in samples}
for wh in ["train", "dev"]:
_log.info("Computing (log) number of trees stats on %s set", wh)
report_log_ntrees_stats(samples[wh], "pptx_mask", batch_size,
projective, multiroot)
model.word_emb.requires_grad_(not freeze)
model.tag_emb.requires_grad_(not freeze)
_log.info("Creating optimizer")
opt = torch.optim.Adam(model.parameters(), lr=lr)
finetuner = Runner()
origin_params = {
name: p.clone().detach()
for name, p in model.named_parameters()
}
finetuner.on(
Event.BATCH,
[
batch2tensors(device, vocab),
set_train_mode(model),
compute_l2_loss(model, origin_params),
compute_total_arc_type_scores(model, vocab),
],
)
@finetuner.on(Event.BATCH)
def compute_loss(state):
mask = state["batch"]["mask"]
pptx_mask = state["batch"]["pptx_mask"].bool()
scores = state["total_arc_type_scores"]
pptx_loss = compute_aatrn_loss(scores, pptx_mask, mask, projective,
multiroot)
pptx_loss /= mask.size(0)
loss = pptx_loss + l2_coef * state["l2_loss"]
state["loss"] = loss
state["stats"] = {
"pptx_loss": pptx_loss.item(),
"l2_loss": state["l2_loss"].item(),
}
state["extra_stats"] = {"loss": loss.item()}
state["n_items"] = mask.long().sum().item()
finetuner.on(Event.BATCH,
[update_params(opt),
log_grads(_run, model),
log_stats(_run)])
@finetuner.on(Event.EPOCH_FINISHED)
def eval_on_dev(state):
_log.info("Evaluating on dev")
eval_state = run_eval(model, vocab, samples["dev"])
accs = eval_state["counts"].accs
print_accs(accs, run=_run, step=state["n_iters"])
pptx_loss = eval_state["mean_pptx_loss"]
_log.info("dev_pptx_loss: %.4f", pptx_loss)
_run.log_scalar("dev_pptx_loss", pptx_loss, step=state["n_iters"])
state["dev_accs"] = accs
@finetuner.on(Event.EPOCH_FINISHED)
def maybe_eval_on_test(state):
if state["epoch"] != max_epoch:
return
_log.info("Evaluating on test")
eval_state = run_eval(model,
vocab,
samples["test"],
compute_loss=False)
print_accs(eval_state["counts"].accs,
on="test",
run=_run,
step=state["n_iters"])
finetuner.on(Event.EPOCH_FINISHED,
save_state_dict("model", model, under=artifacts_dir))
EpochTimer().attach_on(finetuner)
n_tokens = sum(len(s["words"]) for s in samples["train"])
ProgressBar(stats="stats", total=n_tokens, unit="tok").attach_on(finetuner)
bucket_key = lambda s: (len(s["words"]) - 1) // 10
trn_iter = ShuffleIterator(
BucketIterator(samples["train"],
bucket_key,
batch_size,
shuffle_bucket=True,
rng=_rnd),
rng=_rnd,
)
_log.info("Starting finetuning")
try:
finetuner.run(trn_iter, max_epoch)
except KeyboardInterrupt:
_log.info("Interrupt detected, training will abort")
else:
return finetuner.state["dev_accs"]["las_nopunct"]