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norbert2_sentiment_norec_to_gpu_3000_rader – AI Model by NTCAL | AlphaNeural AI
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norbert2_sentiment_norec_to_gpu_3000_rader
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transformers
pytorch
bert
text-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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norbert2_sentiment_norec_en_gpu_3000_rader
This model is a fine-tuned version of
bert-large-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 1.4721
Compute Metrics: :
Accuracy: 0.69
Balanced Accuracy: 0.5
F1 Score: 0.8166
Recall: 1.0
Precision: 0.69
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: 5e-05
train_batch_size: 1
eval_batch_size: 1
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
Compute Metrics
Accuracy
Balanced Accuracy
F1 Score
Recall
Precision
1.5689
1.0
3000
1.4457
:
0.69
0.5
0.8166
1.0
0.69
1.6177
2.0
6000
1.4721
:
0.69
0.5
0.8166
1.0
0.69
Framework versions
Transformers 4.26.0
Pytorch 1.13.1+cu117
Datasets 2.9.0
Tokenizers 0.13.2
