Sentiment-Analysis-on-Twitter-BCS – AI Model by manasvinid | AlphaNeural AI

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Sentiment-Analysis-on-Twitter-BCS

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text-classification

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autotrain_compatible

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Sentiment-Analysis-on-Twitter-BCS

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: 0.1303
Accuracy: 0.9615
Precision: 0.7730
Recall: 0.6384
F1: 0.6993
Roc Auc: 0.9701

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Roc Auc
0.211	1.0	1798	0.1622	0.9515	0.6769	0.5893	0.6301	0.9417
0.1369	2.0	3596	0.1568	0.9568	0.7009	0.6696	0.6849	0.9646
0.1118	3.0	5394	0.1303	0.9615	0.7730	0.6384	0.6993	0.9701
0.0887	4.0	7192	0.1532	0.9631	0.8011	0.6295	0.7050	0.9708

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.0
Tokenizers 0.13.3