DTAI-KULeuven/robbert-2023-dutch-large

29 days ago

Hi!

Is it possible to fine-tune this model specifically for summarization tasks? I've attempted this, but I consistently encounter errors regarding invalid training data.

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Seq2SeqTrainer, Seq2SeqTrainingArguments
tokenizer = AutoTokenizer.from_pretrained("DTAI-KULeuven/robbert-2023-dutch-large")
model = AutoModelForSequenceClassification.from_pretrained("DTAI-KULeuven/robbert-2023-dutch-large")

from datasets import load_dataset
train_dataset = load_dataset("json", data_files="data.json", split="train")
eval_dataset = load_dataset("json", data_files="data_eval.json")

training_args = Seq2SeqTrainingArguments(
    output_dir="/var/tmp/output",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=4,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=3,
    predict_with_generate=True,
)

trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    tokenizer=tokenizer
)

trainer.train()

I've tried to types of datasets:

#version 1 [{'input_text':'a', 'target_text':'1'},  {'input_text:'b', 'target_text':'2'}]
#version 2 {'input_text':['a', 'b'], 'target_text':['1','2']}]

pdelobelle

DTAI Research Group, KU Leuven org 22 days ago

Hi, the task you provide is a sequence-to-sequence task. These models are not made for sequence generation, as they are only encoder models, so you need a decoder as well.

You can look into extractive summarization or using our model as the encoder: https://huggingface.co/docs/transformers/model_doc/encoder-decoder

pdelobelle changed discussion status to closed 22 days ago

DTAI-KULeuven
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robbert-2023-dutch-large

Summarization