BERT_Sentiment_Twitter – AI Model by tti440 | AlphaNeural AI

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tti440/
BERT_Sentiment_Twitter

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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BERT_SentimetAnalysis_on Twitter

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: 0.1995
Accuracy: 0.9674
F1: 0.9674
Precision: 0.9674
Recall: 0.9674
Label0: Negative, Label1: Positive, Label2: Neutral
Dataset: https://www.kaggle.com/datasets/jp797498e/twitter-entity-sentiment-analysis?select=twitter_training.csv

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.1176	1.0	1698	0.3122	0.9336	0.9333	0.9352	0.9336
0.1254	2.0	3396	0.2247	0.9524	0.9525	0.9531	0.9524
0.0824	3.0	5094	0.1715	0.9637	0.9636	0.9637	0.9637
0.0551	4.0	6792	0.1900	0.9624	0.9624	0.9631	0.9624
0.0408	5.0	8490	0.1995	0.9674	0.9674	0.9674	0.9674

Framework versions

Transformers 4.38.1
Pytorch 2.1.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2