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bert-finetuned-ner-v4.002 – AI Model by terzimert | AlphaNeural AI
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terzimert
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bert-finetuned-ner-v4.002
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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-v4.002
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: 0.2863
Precision: 0.8476
Recall: 0.8993
F1: 0.8727
Accuracy: 0.9513
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
0.3262
1.0
3022
0.3082
0.8324
0.8667
0.8492
0.9380
0.2304
2.0
6044
0.2884
0.8410
0.8851
0.8625
0.9459
0.1601
3.0
9066
0.2863
0.8476
0.8993
0.8727
0.9513
Framework versions
Transformers 4.27.4
Pytorch 1.13.1+cu116
Datasets 2.11.0
Tokenizers 0.13.2
