sentiment_model_14mar

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sentiment_model_14mar

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: 0.2916
• Y True: [1 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 1 1 0 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 0 0 0 1 1 1 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
• Y Pred: [0 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 1 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 0 1 0 1 1 1 0 1 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
• Accuracy: 0.9204
• F1: 0.9202
• Precision: 0.9227
• Recall: 0.9204
• Confusion Matrix: [[142 6] [ 17 124]]

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
• lr_scheduler_warmup_steps: 50
• num_epochs: 5

Training results

Framework versions

• Transformers 4.26.1
• Pytorch 1.12.1+cu113
• Datasets 2.9.0
• Tokenizers 0.13.2