bert-base-uncased-amazon_polarity Open-source Text Classification Model - Accurately Analyze the Sentiment of Amazon Product Reviews

Bert Base Uncased Amazon Polarity

Developed by fabriceyhc

A text classification model fine-tuned on the Amazon product review sentiment analysis dataset based on the BERT base model, achieving 94.65% accuracy

Text Classification

Transformers

Open Source License:Apache-2.0 #E-commerce sentiment analysis #High-accuracy text classification #BERT fine-tuned model

Downloads 339

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of bert-base-uncased on the amazon_polarity dataset, specifically designed for sentiment analysis tasks (positive/negative classification) of product reviews.

Model Features

High accuracy

Achieves 94.65% classification accuracy on the Amazon product review test set

Comprehensive evaluation metrics

Provides multi-dimensional evaluation results including accuracy, precision, recall, F1 score, AUC, etc.

Stable training process

The training process shows stable performance improvement with training steps, ultimately converging well

Model Capabilities

Text sentiment analysis

Product review classification

Binary classification task processing

Use Cases

E-commerce analysis

Product review sentiment analysis

Automatically determines the sentiment tendency (positive/negative) of Amazon product reviews

94.65% accuracy

Quality monitoring

Product issue detection

Identifies potentially problematic products through negative reviews

🚀 bert-base-uncased-amazon_polarity

This model is a fine - tuned version of [bert - base - uncased](https://huggingface.co/bert - base - uncased) on the amazon_polarity dataset. It's designed for text classification tasks and achieves high accuracy on the evaluation set, providing a reliable solution for sentiment analysis of Amazon reviews.

🚀 Quick Start

This model is a fine - tuned version of [bert - base - uncased](https://huggingface.co/bert - base - uncased) on the amazon_polarity dataset. It achieves the following results on the evaluation set:

Loss: 0.2945
Accuracy: 0.9465

📚 Documentation

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: 1
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1782000
training_steps: 17820000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7155	0.0	2000	0.7060	0.4622
0.7054	0.0	4000	0.6925	0.5165
0.6842	0.0	6000	0.6653	0.6116
0.6375	0.0	8000	0.5721	0.7909
0.4671	0.0	10000	0.3238	0.8770
0.3403	0.0	12000	0.3692	0.8861
0.4162	0.0	14000	0.4560	0.8908
0.4728	0.0	16000	0.5071	0.8980
0.5111	0.01	18000	0.5204	0.9015
0.4792	0.01	20000	0.5193	0.9076
0.544	0.01	22000	0.4835	0.9133
0.4745	0.01	24000	0.4689	0.9170
0.4403	0.01	26000	0.4778	0.9177
0.4405	0.01	28000	0.4754	0.9163
0.4375	0.01	30000	0.4808	0.9175
0.4628	0.01	32000	0.4340	0.9244
0.4488	0.01	34000	0.4162	0.9265
0.4608	0.01	36000	0.4031	0.9271
0.4478	0.01	38000	0.4502	0.9253
0.4237	0.01	40000	0.4087	0.9279
0.4601	0.01	42000	0.4133	0.9269
0.4153	0.01	44000	0.4230	0.9306
0.4096	0.01	46000	0.4108	0.9301
0.4348	0.01	48000	0.4138	0.9309
0.3787	0.01	50000	0.4066	0.9324
0.4172	0.01	52000	0.4812	0.9206
0.3897	0.02	54000	0.4013	0.9325
0.3787	0.02	56000	0.3837	0.9344
0.4253	0.02	58000	0.3925	0.9347
0.3959	0.02	60000	0.3907	0.9353
0.4402	0.02	62000	0.3708	0.9341
0.4115	0.02	64000	0.3477	0.9361
0.3876	0.02	66000	0.3634	0.9373
0.4286	0.02	68000	0.3778	0.9378
0.422	0.02	70000	0.3540	0.9361
0.3732	0.02	72000	0.3853	0.9378
0.3641	0.02	74000	0.3951	0.9386
0.3701	0.02	76000	0.3582	0.9388
0.4498	0.02	78000	0.3268	0.9375
0.3587	0.02	80000	0.3825	0.9401
0.4474	0.02	82000	0.3155	0.9391
0.3598	0.02	84000	0.3666	0.9388
0.389	0.02	86000	0.3745	0.9377
0.3625	0.02	88000	0.3776	0.9387
0.3511	0.03	90000	0.4275	0.9336
0.3428	0.03	92000	0.4301	0.9336
0.4042	0.03	94000	0.3547	0.9359
0.3583	0.03	96000	0.3763	0.9396
0.3887	0.03	98000	0.3213	0.9412
0.3915	0.03	100000	0.3557	0.9409
0.3378	0.03	102000	0.3627	0.9418
0.349	0.03	104000	0.3614	0.9402
0.3596	0.03	106000	0.3834	0.9381
0.3519	0.03	108000	0.3560	0.9421
0.3598	0.03	110000	0.3485	0.9419
0.3642	0.03	112000	0.3754	0.9395
0.3477	0.03	114000	0.3634	0.9426
0.4202	0.03	116000	0.3071	0.9427
0.3656	0.03	118000	0.3155	0.9441
0.3709	0.03	120000	0.2923	0.9433
0.374	0.03	122000	0.3272	0.9441
0.3142	0.03	124000	0.3348	0.9444
0.3452	0.04	126000	0.3603	0.9436
0.3365	0.04	128000	0.3339	0.9434
0.3353	0.04	130000	0.3471	0.9450
0.343	0.04	132000	0.3508	0.9418
0.3174	0.04	134000	0.3753	0.9436
0.3009	0.04	136000	0.3687	0.9422
0.3785	0.04	138000	0.3818	0.9396
0.3199	0.04	140000	0.3291	0.9438
0.4049	0.04	142000	0.3372	0.9454
0.3435	0.04	144000	0.3315	0.9459
0.3814	0.04	146000	0.3462	0.9401
0.359	0.04	148000	0.3981	0.9361
0.3552	0.04	150000	0.3226	0.9469
0.345	0.04	152000	0.3731	0.9384
0.3228	0.04	154000	0.2956	0.9471
0.3637	0.04	156000	0.2869	0.9477
0.349	0.04	158000	0.3331	0.9430
0.3374	0.04	160000	0.4159	0.9340
0.3718	0.05	162000	0.3241	0.9459
0.315	0.05	164000	0.3544	0.9391
0.3215	0.05	166000	0.3311	0.9451
0.3464	0.05	168000	0.3682	0.9453
0.3495	0.05	170000	0.3193	0.9469
0.305	0.05	172000	0.4132	0.9389
0.3479	0.05	174000	0.3465	0.947
0.3537	0.05	176000	0.3277	0.9449

Framework versions

Transformers 4.10.2
Pytorch 1.7.1
Datasets 1.12.1
Tokenizers 0.10.3

📄 License

This model is licensed under the Apache - 2.0 license.

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