This model is a fine-tuned version of LiYuan/amazon-review-sentiment-analysis on yelp_review_full dataset with 650,000 Traning rows and 50,000 test rows. It achieves the following results on the evaluation set:

Loss: 0.8765
Accuracy: 0.6316
F1: 0.6294

Model description

This a bert-base-multilingual-uncased model fine tuned for sentiment analysis on product reviews. It predicts the sentiment of the review as a number of stars (between 1 and 5). This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above, or for further finetuning on related sentiment analysis tasks.

Intended uses & limitations

Bert-base is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification, or question answering. This fine-tuned version of BERT-base is used to predict review rating star given the review.

The limitations are this trained model focuses on reviews and products on Amazon. If you apply this model to other domains, it may perform poorly.

Training and evaluation data

The Training and evaluation data can be found here.

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: 200
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.9292	1.0	625	0.8798	0.6142	0.6190
0.7543	2.0	1250	0.8765	0.6316	0.6294

Framework versions

Transformers 4.42.3
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

harmanpreet-kaur/yelp-review-sentiment-analysis-model-1

yelp-review-sentiment-analysis-model-1