RobBERT-v2-nl-qa

The used dataset raalst/squad_v2_dutch was kindly provided by Henryk Borzymowski. It is a translated version of SQuAD V2. I converted it from json to jsonl format. it contains train and validation splits, no test split. I declared 20% of Train to be used as Testset in my finetuning run. That testset is what the evaluation is based on.

when using raalst/squad_v2_dutch, be sure to clean up quotes and double quotes in the contexts

The pretrained model was pdelobelle/robbert-v2-dutch-base, a dutch RoBERTa model

results obtained in training are :

metric = load("evaluate-metric/squad_v2" if squad_v2 else "evaluate-metric/squad")

{'exact': 61.75389109958193,
 'f1': 66.89717170237417,
 'total': 19853,
 'HasAns_exact': 48.967182330322814,
 'HasAns_f1': 58.09796564493008,
 'HasAns_total': 11183,
 'NoAns_exact': 78.24682814302192,
 'NoAns_f1': 78.24682814302192,
 'NoAns_total': 8670,
 'best_exact': 61.75389109958193,
 'best_exact_thresh': 0.0,
 'best_f1': 66.89717170237276,
 'best_f1_thresh': 0.0}

This seems mediocre to me.

settings (until I figured out how to report them properly):

DatasetDict({
  train: Dataset({
    features: ['id', 'title', 'context', 'question', 'answers'],
    num_rows: 79412
})
test: Dataset({
    features: ['id', 'title', 'context', 'question', 'answers'],
    num_rows: 19853
})
validation: Dataset({
    features: ['id', 'title', 'context', 'question', 'answers'],
    num_rows: 9669
})
})

tokenizer = AutoTokenizer.from_pretrained("pdelobelle/robbert-v2-dutch-base")

from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer

model = AutoModelForQuestionAnswering.from_pretrained("pdelobelle/robbert-v2-dutch-base")
training_args = TrainingArguments(
  output_dir="./qa_model",
  evaluation_strategy="epoch",
  learning_rate=2e-5,
  per_device_train_batch_size=16,
  per_device_eval_batch_size=16,
  num_train_epochs=3,
  weight_decay=0.01,
  push_to_hub=False,
)

trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_squad["train"],
eval_dataset=tokenized_squad["validation"],
tokenizer=tokenizer,
data_collator=data_collator,
)

trainer.train()

[15198/15198 2:57:03, Epoch 3/3]
Epoch 	Training Loss 	Validation Loss
1 	1.380700 	1.177431
2 	1.093000 	1.052601
3 	0.849700 	1.143632

TrainOutput(global_step=15198, training_loss=1.1917077029499668, metrics={'train_runtime': 10623.9565, 
'train_samples_per_second': 22.886, 'train_steps_per_second': 1.431, 'total_flos': 4.764955396486349e+16, 
'train_loss': 1.1917077029499668, 'epoch': 3.0})

Trained on Ubuntu with 1080Ti