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test-ner – AI Model by kSaluja | AlphaNeural AI
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kSaluja
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test-ner
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transformers
pytorch
tensorboard
bert
token-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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Model card
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test-ner
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.1014
Precision: 0.9609
Recall: 0.9574
F1: 0.9591
Accuracy: 0.9732
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
151
0.1848
0.9060
0.9184
0.9122
0.9490
No log
2.0
302
0.1137
0.9548
0.9529
0.9538
0.9705
No log
3.0
453
0.1014
0.9609
0.9574
0.9591
0.9732
Framework versions
Transformers 4.18.0
Pytorch 1.11.0+cu113
Datasets 2.1.0
Tokenizers 0.12.1
