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test_twowayloss_implementation – AI Model by bdpc | AlphaNeural AI | AlphaNeural AI
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test_twowayloss_implementation
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transformers
pytorch
bert
text-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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test_twowayloss_implementation
This model is a fine-tuned version of
bert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 8.9001
Accuracy: 0.5659
Precision: 0.0114
Recall: 0.5082
F1: 0.0223
Hamming: 0.4341
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
lr_scheduler_warmup_ratio: 0.1
training_steps: 10
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Precision
Recall
F1
Hamming
8.8818
0.0
5
8.9210
0.5632
0.0110
0.4947
0.0216
0.4368
8.124
0.0
10
8.9001
0.5659
0.0114
0.5082
0.0223
0.4341
Framework versions
Transformers 4.35.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.7.1
Tokenizers 0.14.1
