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distilbert-base-uncased-finetune – AI Model by rdchambers | AlphaNeural AI
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rdchambers
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distilbert-base-uncased-finetune
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transformers
pytorch
distilbert
token-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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distilbert-base-uncased-finetune
This model is a fine-tuned version of
distilbert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0149
Precision: 0.8458
Recall: 0.8060
F1: 0.8255
Accuracy: 0.9954
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
48
0.0556
0.5372
0.1902
0.2809
0.9838
No log
2.0
96
0.0171
0.8320
0.8023
0.8169
0.9951
No log
3.0
144
0.0149
0.8458
0.8060
0.8255
0.9954
Framework versions
Transformers 4.18.0
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1
