sentiment-model – AI Model by lalitbansal3681 | AlphaNeural AI

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lalitbansal3681/
sentiment-model

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

endpoints_compatible

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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: 0.7407
Accuracy: 0.6701
F1 Weighted: 0.6680
F1 Macro: 0.6680

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: 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: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Weighted	F1 Macro
0.8972	1.0	115	0.7663	0.6481	0.6354	0.6354
0.6316	2.0	230	0.6893	0.7006	0.7019	0.7019
0.4222	3.0	345	0.7400	0.7006	0.6981	0.6981
0.3065	4.0	460	0.8219	0.7037	0.6971	0.6971
0.2053	5.0	575	0.8725	0.7006	0.7004	0.7004
0.1547	6.0	690	0.9039	0.6883	0.6901	0.6901

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 3.1.0
Tokenizers 0.22.2