andrea.pedrotti
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gfun_multimodal
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updates

This commit is contained in:
Andrea Pedrotti 2023-06-29 11:41:37 +02:00
parent 86fbd90bd4
commit 317fb93da6
2 changed files with 56 additions and 24 deletions
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68
hf_trainer.py
View File

@ -1,3 +1,5 @@
from os.path import expanduser
import torch
from transformers import (
AutoModelForSequenceClassification,
@ -15,6 +17,9 @@ import evaluate
transformers.logging.set_verbosity_error()
IWSLT_D_COLUMNS = ["text", "category", "rating", "summary", "title"]
RAI_D_COLUMNS = ["id", "lang", "provider", "date", "title", "text", "str_label", "label"]
def init_callbacks(patience=-1, nosave=False):
callbacks = []
@ -23,7 +28,7 @@ def init_callbacks(patience=-1, nosave=False):
return callbacks
def init_model(model_name):
def init_model(model_name, nlabels):
if model_name == "mbert":
hf_name = "bert-base-multilingual-cased"
elif model_name == "xlm-roberta":
@ -31,21 +36,35 @@ def init_model(model_name):
else:
raise NotImplementedError
tokenizer = AutoTokenizer.from_pretrained(hf_name)
model = AutoModelForSequenceClassification.from_pretrained(hf_name, num_labels=3)
model = AutoModelForSequenceClassification.from_pretrained(hf_name, num_labels=nlabels)
return tokenizer, model
def main(args):
tokenizer, model = init_model(args.model)
tokenizer, model = init_model(args.model, args.nlabels)
# data = load_dataset(
# "json",
# data_files={
# "train": "local_datasets/webis-cls/all-domains/train.json",
# "test": "local_datasets/webis-cls/all-domains/test.json",
# },
# )
data = load_dataset(
"json",
data_files={
"train": "local_datasets/webis-cls/all-domains/train.json",
"test": "local_datasets/webis-cls/all-domains/test.json",
},
"csv",
data_files = {
# "train": expanduser("~/datasets/rai/csv/rai-no-it-train.csv"),
# "test": expanduser("~/datasets/rai/csv/rai-no-it-test.csv")
# "train": expanduser("~/datasets/rai/csv/rai-train.csv"),
# "test": expanduser("~/datasets/rai/csv/rai-test-ita-labeled.csv")
"train": expanduser("~/datasets/rai/csv/train-split-rai.csv"),
"test": expanduser("~/datasets/rai/csv/test-split-rai-labeled.csv")
}
)
def process_sample(sample):
def process_sample_iwslt(sample):
inputs = sample["text"]
ratings = [r - 1 for r in sample["rating"]]
targets = torch.zeros((len(inputs), 3), dtype=float)
@ -56,17 +75,26 @@ def main(args):
for i, r in enumerate(ratings):
targets[i][r - 1] = 1
model_inputs = tokenizer(inputs, max_length=512, truncation=True)
model_inputs = tokenizer(inputs, max_length=128, truncation=True)
model_inputs["labels"] = targets
model_inputs["lang_ids"] = torch.tensor(lang_ids)
return model_inputs
def process_sample_rai(sample):
inputs = [f"{title}. {text}" for title, text in zip(sample["title"], sample["text"])]
labels = sample["label"]
model_inputs = tokenizer(inputs, max_length=512, truncation=True) # TODO pre-process text cause there's a lot of noise in there...
model_inputs["labels"] = labels
return model_inputs
data = data.map(
process_sample,
process_sample_rai,
batched=True,
num_proc=4,
load_from_cache_file=True,
remove_columns=["text", "category", "rating", "summary", "title"],
remove_columns=RAI_D_COLUMNS,
)
train_val_splits = data["train"].train_test_split(test_size=0.2, seed=42)
data.set_format("torch")
@ -87,7 +115,7 @@ def main(args):
recall_metric = evaluate.load("recall")
training_args = TrainingArguments(
output_dir=f"{args.model}-sentiment",
output_dir=f"{args.model}-rai-final",
do_train=True,
evaluation_strategy="steps",
per_device_train_batch_size=args.batch,
@ -99,7 +127,8 @@ def main(args):
max_grad_norm=5.0,
num_train_epochs=args.epochs,
lr_scheduler_type=args.scheduler,
warmup_steps=1000,
# warmup_ratio=0.1,
warmup_ratio=1500,
logging_strategy="steps",
logging_first_step=True,
logging_steps=args.steplog,
@ -109,7 +138,7 @@ def main(args):
save_strategy="no" if args.nosave else "steps",
save_total_limit=3,
eval_steps=args.stepeval,
run_name=f"{args.model}-sentiment-run",
run_name=f"{args.model}-rai-stratified",
disable_tqdm=False,
log_level="warning",
report_to=["wandb"] if args.wandb else "none",
@ -119,7 +148,8 @@ def main(args):
def compute_metrics(eval_preds):
preds = eval_preds.predictions.argmax(-1)
targets = eval_preds.label_ids.argmax(-1)
# targets = eval_preds.label_ids.argmax(-1)
targets = eval_preds.label_ids
setting = "macro"
f1_score_macro = f1_metric.compute(
predictions=preds, references=targets, average="macro"
@ -146,7 +176,7 @@ def main(args):
if args.wandb:
import wandb
wandb.init(entity="andreapdr", project=f"gfun-senti-hf", name="mbert-sent", config=vars(args))
wandb.init(entity="andreapdr", project=f"gfun-rai-hf", name="mbert-sent", config=vars(args))
trainer = Trainer(
model=model,
@ -162,7 +192,6 @@ def main(args):
print("- Training:")
trainer.train()
print("- Testing:")
test_results = trainer.evaluate(eval_dataset=data["test"])
print(test_results)
@ -174,13 +203,14 @@ if __name__ == "__main__":
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument("--model", type=str, metavar="", default="mbert")
parser.add_argument("--nlabels", type=int, metavar="", default=3)
parser.add_argument("--lr", type=float, metavar="", default=1e-5, help="Set learning rate",)
parser.add_argument("--scheduler", type=str, metavar="", default="linear", help="Accepted: [\"cosine\", \"cosine-reset\", \"cosine-warmup\", \"cosine-warmup-reset\", \"constant\"]")
parser.add_argument("--batch", type=int, metavar="", default=16, help="Set batch size")
parser.add_argument("--gradacc", type=int, metavar="", default=1, help="Gradient accumulation steps")
parser.add_argument("--epochs", type=int, metavar="", default=100, help="Set epochs")
parser.add_argument("--stepeval", type=int, metavar="", default=50, help="Run evaluation every n steps")
parser.add_argument("--steplog", type=int, metavar="", default=100, help="Log training every n steps")
parser.add_argument("--steplog", type=int, metavar="", default=50, help="Log training every n steps")
parser.add_argument("--patience", type=int, metavar="", default=10, help="EarlyStopper patience")
parser.add_argument("--fp16", action="store_true", help="Use fp16 precision")
parser.add_argument("--wandb", action="store_true", help="Log to wandb")

12
main.py
View File

@ -1,5 +1,3 @@
import wandb
from argparse import ArgumentParser
from time import time
@ -11,7 +9,6 @@ from gfun.generalizedFunnelling import GeneralizedFunnelling
TODO:
- General:
[!] zero-shot setup
- CLS dataset is loading only "books" domain data
- Docs:
- add documentations sphinx
"""
@ -96,7 +93,9 @@ def main(args):
config = gfun.get_config()
wandb.init(project="gfun", name=f"gFun-{get_config_name(args)}", config=config)
if args.wandb:
import wandb
wandb.init(project="gfun", name=f"gFun-{get_config_name(args)}", config=config)
gfun.fit(lX, lY)
@ -139,7 +138,8 @@ def main(args):
)
wandb.log(gfun_res)
log_barplot_wandb(lang_metrics_gfun, title_affix="per language")
if args.wandb:
log_barplot_wandb(lang_metrics_gfun, title_affix="per language")
if __name__ == "__main__":
@ -178,6 +178,8 @@ if __name__ == "__main__":
# Visual Transformer parameters --------------
parser.add_argument("--visual_trf_name", type=str, default="vit")
parser.add_argument("--visual_lr", type=float, default=1e-4)
# logging
parser.add_argument("--wandb", action="store_true")
args = parser.parse_args()