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initial-dq-model – AI Model by lucafrost | AlphaNeural AI
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lucafrost
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initial-dq-model
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transformers
pytorch
distilbert
token-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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initial-dq-model
This model is a fine-tuned version of
distilbert-base-cased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1677
Precision: 0.7763
Recall: 0.9380
F1: 0.8495
Accuracy: 0.9423
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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
0.2251
1.0
1220
0.1768
0.7481
0.9264
0.8277
0.9378
0.186
2.0
2440
0.1677
0.7763
0.9380
0.8495
0.9423
Framework versions
Transformers 4.25.1
Pytorch 1.10.2+cu113
Datasets 2.8.0
Tokenizers 0.13.2
