Distilbert Base Uncased Sst2 Train 16 4

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Distilbert Base Uncased Sst2 Train 16 4

Developed by SetFit

A text classification model fine-tuned on the SST2 sentiment analysis dataset based on the DistilBERT base model

Text Classification

Open Source License:Apache-2.0 #Sentiment Analysis #Lightweight BERT #Text Classification

Downloads 15

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of DistilBERT, specifically designed for sentiment analysis tasks, capable of classifying text into positive or negative sentiments.

Model Features

Efficient and Lightweight

Based on the DistilBERT architecture, it is smaller and faster than standard BERT models while maintaining high performance.

Sentiment Analysis

Optimized specifically for text sentiment classification tasks.

Transfer Learning

Fine-tuned after pre-training on large-scale corpora, suitable for specific tasks.

Model Capabilities

Text Classification

Sentiment Analysis

English Text Processing

Use Cases

Social Media Analysis

Comment Sentiment Analysis

Analyze the sentiment tendencies of user comments

Accuracy 63.87%

Market Research

Product Feedback Analysis

Evaluate customer satisfaction with products

🚀 distilbert-base-uncasedsst2train-16-4

This model is a fine - tuned version of distilbert-base-uncased on the None dataset. It offers valuable performance in relevant tasks, achieving specific results on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.1501
Accuracy: 0.6387

📚 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: 2e - 05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7043	1.0	7	0.7139	0.2857
0.68	2.0	14	0.7398	0.2857
0.641	3.0	21	0.7723	0.2857
0.5424	4.0	28	0.8391	0.2857
0.5988	5.0	35	0.7761	0.2857
0.3698	6.0	42	0.7707	0.4286
0.3204	7.0	49	0.8290	0.4286
0.2882	8.0	56	0.6551	0.5714
0.1512	9.0	63	0.5652	0.5714
0.1302	10.0	70	0.5278	0.5714
0.1043	11.0	77	0.4987	0.7143
0.0272	12.0	84	0.5278	0.5714
0.0201	13.0	91	0.5307	0.5714
0.0129	14.0	98	0.5382	0.5714
0.0117	15.0	105	0.5227	0.5714
0.0094	16.0	112	0.5066	0.7143
0.0104	17.0	119	0.4869	0.7143
0.0069	18.0	126	0.4786	0.7143
0.0062	19.0	133	0.4707	0.7143
0.0065	20.0	140	0.4669	0.7143
0.0051	21.0	147	0.4686	0.7143
0.0049	22.0	154	0.4784	0.7143
0.0046	23.0	161	0.4839	0.7143
0.0039	24.0	168	0.4823	0.7143
0.0044	25.0	175	0.4791	0.7143
0.0037	26.0	182	0.4778	0.7143
0.0038	27.0	189	0.4770	0.7143
0.0036	28.0	196	0.4750	0.7143
0.0031	29.0	203	0.4766	0.7143
0.0031	30.0	210	0.4754	0.7143

Framework versions

Transformers 4.15.0
Pytorch 1.10.2+cu102
Datasets 1.18.2
Tokenizers 0.10.3

📄 License

This model is licensed under the Apache - 2.0 license.

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