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xlmr-en-de-all_shuffled-2020-test1000 – AI Model by patpizio | AlphaNeural AI
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patpizio
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xlmr-en-de-all_shuffled-2020-test1000
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transformers
pytorch
xlm-roberta
text-classification
generated_from_trainer
FacebookAI/xlm-roberta-base
finetune
mit
autotrain_compatible
endpoints_compatible
us
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xlmr-en-de-all_shuffled-2020-test1000
This model is a fine-tuned version of
xlm-roberta-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.6763
R Squared: 0.0302
Mae: 0.5158
Pearson R: 0.1799
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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 2020
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
R Squared
Mae
Pearson R
No log
1.0
438
0.6969
0.0007
0.5295
0.1955
0.6601
2.0
876
0.6884
0.0129
0.5224
0.2002
0.663
3.0
1314
0.6763
0.0302
0.5158
0.1799
Framework versions
Transformers 4.34.1
Pytorch 2.0.1+cu117
Datasets 2.14.6
Tokenizers 0.14.1
