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longformer_sciq – AI Model by Shaier | AlphaNeural AI
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Shaier
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longformer_sciq
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transformers
pytorch
longformer
multiple-choice
generated_from_trainer
sciq
endpoints_compatible
us
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longformer_sciq
This model is a fine-tuned version of
allenai/longformer-base-4096
on the sciq dataset. It achieves the following results on the evaluation set:
Loss: 0.1479
Accuracy: 0.932
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 25
total_train_batch_size: 50
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
No log
1.0
233
0.1650
0.934
No log
2.0
466
0.1479
0.932
Framework versions
Transformers 4.21.3
Pytorch 1.12.1
Datasets 2.5.1
Tokenizers 0.11.0
