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sentiment-model – AI Model by Beedagani | AlphaNeural AI
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Beedagani
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sentiment-model
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transformers
safetensors
distilbert
text-classification
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
endpoints_compatible
us
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sentiment-model
This model is a fine-tuned version of
distilbert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.7455
Accuracy: 0.6621
F1 Weighted: 0.6509
F1 Macro: 0.6509
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: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1 Weighted
F1 Macro
1.0494
1.0
58
0.8751
0.6019
0.5478
0.5478
0.8304
2.0
116
0.7230
0.6944
0.6854
0.6854
0.6779
3.0
174
0.7129
0.6852
0.6764
0.6764
Framework versions
Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 3.1.0
Tokenizers 0.22.2
