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bert-finetuned-ner4invoice12 – AI Model by jgaertner | AlphaNeural AI
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jgaertner
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bert-finetuned-ner4invoice12
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transformers
tensorboard
safetensors
bert
token-classification
generated_from_trainer
google-bert/bert-base-multilingual-cased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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bert-finetuned-ner4invoice12
This model is a fine-tuned version of
bert-base-multilingual-cased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2330
Precision: 0.1905
Recall: 0.2609
F1: 0.2202
Accuracy: 0.9241
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: 8
eval_batch_size: 8
seed: 42
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
Precision
Recall
F1
Accuracy
No log
1.0
14
0.5105
0.0
0.0
0.0
0.8892
No log
2.0
28
0.2856
0.0566
0.0652
0.0606
0.9234
No log
3.0
42
0.2330
0.1905
0.2609
0.2202
0.9241
Framework versions
Transformers 4.42.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1
