bert-base-uncased-yelp_polarity Open-source Text Classification Model - Efficiently Classify Text with 95.16% Accuracy

Bert Base Uncased Yelp Polarity

Developed by fabriceyhc

This model is a fine-tuned version of bert-base-uncased on the yelp_polarity dataset for text classification tasks, achieving an accuracy of 95.16%.

Text Classification

Transformers

Open Source License:Apache-2.0 #High-precision sentiment analysis #Yelp review classification #BERT fine-tuning

Downloads 76

Release Time : 3/2/2022

Model Overview

A text classification model based on the BERT architecture, specifically optimized for sentiment analysis (positive/negative) of Yelp reviews.

Model Features

High accuracy

Achieves 95.16% accuracy on Yelp review sentiment analysis tasks

BERT architecture

Fine-tuned based on the powerful BERT-base-uncased model

Sentiment analysis

Specifically optimized for binary classification (positive/negative) of review sentiments

Model Capabilities

Text classification

Sentiment analysis

Review rating prediction

Use Cases

Business analysis

Restaurant review analysis

Analyze sentiment tendencies of restaurant reviews on platforms like Yelp

Accurately distinguishes over 95% of positive/negative reviews

Customer feedback analysis

Product review classification

Automatically classify sentiment of product reviews on e-commerce platforms

🚀 bert-base-uncased-yelp_polarity

This model is a fine - tuned version of [bert - base - uncased](https://huggingface.co/bert - base - uncased) on the yelp_polarity dataset. It can effectively perform text classification tasks on the yelp_polarity dataset, achieving high accuracy.

🚀 Quick Start

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

Loss: 0.3222
Accuracy: 0.9516

✨ Features

Fine - tuned: Based on the pre - trained bert - base - uncased model, fine - tuned on the yelp_polarity dataset.
High Accuracy: Achieves an accuracy of 0.9516 on the evaluation set.

🔧 Technical Details

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: 277200
training_steps: 2772000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8067	0.0	2000	0.8241	0.4975
0.5482	0.01	4000	0.3507	0.8591
0.3427	0.01	6000	0.3750	0.9139
0.4133	0.01	8000	0.5520	0.9016
0.4301	0.02	10000	0.3803	0.9304
0.3716	0.02	12000	0.4168	0.9337
0.4076	0.03	14000	0.5042	0.9170
0.3674	0.03	16000	0.4806	0.9268
0.3813	0.03	18000	0.4227	0.9261
0.3723	0.04	20000	0.3360	0.9418
0.3876	0.04	22000	0.3255	0.9407
0.3351	0.04	24000	0.3283	0.9404
0.34	0.05	26000	0.3489	0.9430
0.3006	0.05	28000	0.3302	0.9464
0.349	0.05	30000	0.3853	0.9375
0.3696	0.06	32000	0.2992	0.9454
0.3301	0.06	34000	0.3484	0.9464
0.3151	0.06	36000	0.3529	0.9455
0.3682	0.07	38000	0.3052	0.9420
0.3184	0.07	40000	0.3323	0.9466
0.3207	0.08	42000	0.3133	0.9532
0.3346	0.08	44000	0.3826	0.9414
0.3008	0.08	46000	0.3059	0.9484
0.3306	0.09	48000	0.3089	0.9475
0.342	0.09	50000	0.3611	0.9486
0.3424	0.09	52000	0.3227	0.9445
0.3044	0.1	54000	0.3130	0.9489
0.3278	0.1	56000	0.3827	0.9368
0.288	0.1	58000	0.3080	0.9504
0.3342	0.11	60000	0.3252	0.9471
0.3737	0.11	62000	0.4250	0.9343

Framework versions

Transformers 4.10.2
Pytorch 1.7.1
Datasets 1.6.1
Tokenizers 0.10.3

📄 License

This model is released under the Apache - 2.0 license.

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