bert-finetuned-twitter_sentiment_analysis – AI Model by rahulk98 | AlphaNeural AI

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rahulk98/
bert-finetuned-twitter_sentiment_analysis

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-finetuned-twitter_sentiment_analysis

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: 0.4113
F1: 0.7556
Roc Auc: 0.8165
Accuracy: 0.7454

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: 3e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
No log	1.0	197	0.3564	0.7485	0.8072	0.6981
No log	2.0	394	0.3285	0.7686	0.8197	0.7010
0.3302	3.0	591	0.3463	0.7810	0.8315	0.7425
0.3302	4.0	788	0.3806	0.7730	0.8276	0.7496
0.3302	5.0	985	0.4113	0.7556	0.8165	0.7454

Framework versions

Transformers 4.46.3
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.20.3