Long text token classification using LongFormer

Related tags

Text Data & NLP long-text-token-classification
Overview

Long text token classification using LongFormer

The data comes from: https://www.kaggle.com/c/feedback-prize-2021/

To train the model for 5 folds, you can run:

python train.py --fold 0 --model allenai/longformer-large-4096 --lr 1e-5 --epochs 10 --max_len 1536 --batch_size 4 --valid_batch_size 4
python train.py --fold 1 --model allenai/longformer-large-4096 --lr 1e-5 --epochs 10 --max_len 1536 --batch_size 4 --valid_batch_size 4
python train.py --fold 2 --model allenai/longformer-large-4096 --lr 1e-5 --epochs 10 --max_len 1536 --batch_size 4 --valid_batch_size 4
python train.py --fold 3 --model allenai/longformer-large-4096 --lr 1e-5 --epochs 10 --max_len 1536 --batch_size 4 --valid_batch_size 4
python train.py --fold 4 --model allenai/longformer-large-4096 --lr 1e-5 --epochs 10 --max_len 1536 --batch_size 4 --valid_batch_size 4

Note that you need have kfold column in training data.

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Comments
  • Why use 5 dropouts?

    Why use 5 dropouts?

    Hi ya,

    Thanks for your great repo. Can I ask something about the forward method of the model?

    In the forward method, I can see that you are passing the transformer output through the classification head with 5 different dropout layer and averaging the logits & the loss to get the final output. Can I ask why?

    I also tried using only 1 dropout without these auxiliary loss but the result is substantially worse. Can I ask if you have any idea why this is the case?

    Thank you

    opened by minhhieutran2112 2
  • Running on XLA :Too slow

    Running on XLA :Too slow

    Model too slow with XLA TPU any suggestions please @abhishekkrthakur

    def _mp_fn(index):
        device = xm.xla_device()
        # We wrap this 
        model = WRAPPED_MODEL.to(device)
        print('call fit')
        model.fit(
        train_dataset,
        train_bs=args.batch_size,
        device=device,
        epochs=args.epochs,
        callbacks=[es],
        fp16=False,
        accumulation_steps=args.accumulation_steps,
        )
        #train_nli(model)
    print('x')
    xmp.spawn(_mp_fn, start_method="fork")
    opened by jaideep11061982 0
  • the difference between btez-fb-large and fblongformerlarge1536

    the difference between btez-fb-large and fblongformerlarge1536

    thanks to you share, i want to konw the model btez-fb-large is train by what also use in this model or other things if also train by the model, what the difference between them

    opened by YaoSiYing 0
  • How to train with multiple gpus?

    How to train with multiple gpus?

    It will raise an error when I tried to train tez model on multiple gpus. model = nn.DataParallel(model, device_ids=[0,1,2,3]) AttributeError: 'DataParallel' object has no attribute 'fit'

    How can I do this? Thank you so much

    opened by Furyboyy 5
Owner
abhishek thakur
Kaggle: www.kaggle.com/abhishek
abhishek thakur
GitHub
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