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bert-finetuned-ner-v4.001 – AI Model by terzimert | AlphaNeural AI
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terzimert
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bert-finetuned-ner-v4.001
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transformers
pytorch
tensorboard
bert
token-classification
generated_from_trainer
caner
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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bert-finetuned-ner-v4.001
This model is a fine-tuned version of
bert-base-multilingual-cased
on the caner dataset. It achieves the following results on the evaluation set:
Loss: 0.4995
Precision: 0.8814
Recall: 0.8208
F1: 0.8500
Accuracy: 0.9327
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
0.2518
1.0
4842
0.5403
0.8354
0.7712
0.8020
0.9178
0.1364
2.0
9684
0.4746
0.8728
0.8016
0.8357
0.9287
0.0915
3.0
14526
0.4995
0.8814
0.8208
0.8500
0.9327
Framework versions
Transformers 4.27.4
Pytorch 1.13.1+cu116
Datasets 2.11.0
Tokenizers 0.13.2
