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Sentiment_Analysis_using_BERT_With_10000_Samples – AI Model by Satyajithchary | AlphaNeural AI

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Satyajithchary/
Sentiment_Analysis_using_BERT_With_10000_Samples

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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Sentiment_Analysis_using_BERT_With_10000_Samples

This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.

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
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Framework versions

Transformers 4.34.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1