sentiment_classification – AI Model by Lesllie | AlphaNeural AI | AlphaNeural AI

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sentiment_classification

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meta-llama/Meta-Llama-3-8B

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sentiment_classification

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2402
Balanced Accuracy: 0.7473
Accuracy: 0.7310

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: 0.0001
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
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Balanced Accuracy	Accuracy
No log	1.0	109	3.0101	0.2994	0.1966
No log	2.0	218	1.8856	0.6310	0.5276
No log	3.0	327	1.4658	0.6790	0.6379
No log	4.0	436	1.3075	0.7057	0.6966
1.4667	5.0	545	1.2760	0.7747	0.7310
1.4667	6.0	654	1.3011	0.7332	0.7172
1.4667	7.0	763	1.2458	0.7380	0.7241
1.4667	8.0	872	1.2393	0.7460	0.7310
1.4667	9.0	981	1.2405	0.7473	0.7310
0.0095	10.0	1090	1.2402	0.7473	0.7310

Framework versions

PEFT 0.10.0
Transformers 4.40.0
Pytorch 2.2.2+cu121
Datasets 2.18.0
Tokenizers 0.19.1