Sentiment_Analysis_PEFT_on_DistilBert

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results

This model is a fine-tuned version of distilbert-base-uncased on the sentiment140 (https://huggingface.co/datasets/sentiment140) dataset. Due to time and computational limits, I <b>used only 10% of the dataset</b> with a data augmentation. Despite minimal training, model's accuracy <b>improved by 59.5%</b> in comparison with non-fine tuned DistilBert LLM!<br>

It achieves the following results on the evaluation set:

• Loss: 0.4732
• Accuracy: 0.7753

<b>Classification Scores:</b><br>

• Fine Tuned DistilBert LLM with LoRa: Accuracy = 0.78, Precision = 0.77, Recall = 0.78, F1 Score = 0.78

• Non-Fine Tuned DistilBert LLM: Accuracy = 0.49, Precision = 0.45, Recall = 0.14, F1 Score = 0.22

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 8

Training results

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2