bert_large_finetuned_ontonotes – AI Model by filippo-baglini | AlphaNeural AI

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filippo-baglini/
bert_large_finetuned_ontonotes

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token-classification

generated_from_trainer

google-bert/bert-large-cased

autotrain_compatible

endpoints_compatible

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bert_large_finetuned_ontonotes

This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0693

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: 16
eval_batch_size: 16
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
0.0887	1.0	893	0.0781
0.0864	2.0	1786	0.0732
0.0726	3.0	2679	0.0693

Framework versions

Transformers 4.51.1
Pytorch 2.5.1+cu124
Datasets 3.5.0
Tokenizers 0.21.0