Absa 10 Domains Lora Base Parameters and Internals
Model Type
text generation
Use Cases
Areas:
e-commerce, review sentiment analysis
Applications:
Identifying aspects, opinions, and polarities in text reviews
Primary Use Cases:
Aspect-based sentiment analysis of product reviews
Additional Notes
The model uses a paraphrasing technique to extract sentiment-related features from reviews. It requires sufficient computational resources to handle data inputs efficiently.
Training Details
Data Sources:
10k reviews from an e-commerce site across 10 domains
Data Volume:
13513 reviews in total
Methodology:
Trained using a paraphrasing approach. The model was given example sentences and instructed to rewrite them in the same format, emphasizing identified aspects, opinions, and polarities.
Hardware Used:
VM in GCP with configurations to handle the memory constraints
Model Architecture:
Based on the google/flan-t5-base model with LORA configuration for training
Input Output
Input Format:
Follow example format given during training.
Accepted Modalities:
text
Output Format:
Paraphrased text highlighting aspects, opinions, and polarities.
Note: green Score (e.g. "73.2") means that the model is better than SilvioLima/absa_10_domains_lora_base.
Rank the Absa 10 Domains Lora Base Capabilities
๐ Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! ๐
Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation
What open-source LLMs or SLMs are you in search of? 52721 in total.