S

Sentiment Roberta Large English

Developed by siebert
A binary sentiment analysis model for English text fine-tuned on RoBERTa-large, capable of predicting positive or negative sentiment
Downloads 127.23k
Release Time : 3/2/2022

Model Overview

This model is fine-tuned on 15 diverse datasets, enabling reliable sentiment analysis across various English text types, with particular expertise in handling diverse text formats like reviews and tweets.

Model Features

Multi-domain Generalization
Trained on 15 diverse datasets, outperforms single-domain models across various text types (reviews, tweets, etc.)
High-performance Benchmark
Achieves an average accuracy of 93.2%, 15 percentage points higher than DistilBERT models trained solely on SST-2
Ready-to-use Interface
Provides Hugging Face pipeline integration and Colab example scripts for quick deployment

Model Capabilities

English text sentiment analysis
Binary sentiment classification (positive/negative)
Multi-domain text processing

Use Cases

Social Media Analysis
Tweet Sentiment Analysis
Analyzes user sentiment trends on social media platforms like Twitter
Achieved 88.5% accuracy on Nakov et al. (2013) dataset
Product Review Analysis
E-commerce Review Analysis
Analyzes user sentiment on platforms like Yelp and Amazon
Achieved 96.5% accuracy on Yelp dataset and 98% on McAuley review dataset
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
Š 2025AIbase