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mmbert-xnli – AI Model by Enkhmanlai | AlphaNeural AI
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Enkhmanlai
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mmbert-xnli
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transformers
tensorboard
safetensors
modernbert
text-classification
generated_from_trainer
jhu-clsp/mmBERT-small
finetune
mit
endpoints_compatible
us
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mmbert-xnli
This model is a fine-tuned version of
jhu-clsp/mmBERT-small
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.3736
Accuracy: 0.8863
F1: 0.8863
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: 3e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
No log
1.0
337
0.4502
0.8600
0.8587
0.6169
2.0
674
0.3983
0.8796
0.8786
0.2926
3.0
1011
0.3693
0.8904
0.8907
Framework versions
Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2
