neuroapps/sentiments_classifier

neuroapps_sentiment_classifier

This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:

• Loss: 0.2332
• Accuracy: 0.9319

Model description

This model outputs the sentiment value, either positive or negative from the sentence or phrase.

Intended uses & limitations

This model could be used for extracting sentiments from product reviews, product feedback, or general conversational text.

Training and evaluation data

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

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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2335	1.0	1563	0.1892	0.9277
0.1487	2.0	3126	0.2332	0.9319

Framework versions

• Transformers 4.27.3
• Pytorch 1.13.1+cu116
• Datasets 2.10.1
• Tokenizers 0.13.2