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lombard-LMO-ITA-mmbert – AI Model by emmabedna | AlphaNeural AI
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emmabedna
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lombard-LMO-ITA-mmbert
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transformers
safetensors
modernbert
token-classification
generated_from_trainer
jhu-clsp/mmBERT-base
finetune
mit
endpoints_compatible
us
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lombard-LMO-ITA-mmbert
This model is a fine-tuned version of
jhu-clsp/mmBERT-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0375
Precision: 0.8334
Recall: 0.8365
F1: 0.8350
Accuracy: 0.9908
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: 42
optimizer: Use adamw_torch_fused 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
Precision
Recall
F1
Accuracy
0.0679
1.0
616
0.0357
0.7664
0.7909
0.7785
0.9882
0.0187
2.0
1232
0.0352
0.8227
0.8242
0.8234
0.9902
0.0072
3.0
1848
0.0375
0.8334
0.8365
0.8350
0.9908
Framework versions
Transformers 4.57.3
Pytorch 2.9.1+cu128
Datasets 4.4.2
Tokenizers 0.22.1
