# E-commerce review analysis

A sentiment analysis model based on DistilBERT for classifying product reviews as positive, negative, or neutral

LENS-8000 is a transformer-based text embedding model focused on feature extraction and sentence similarity tasks, excelling in multiple classification and retrieval tasks.

Giga Embeddings Instruct is a high-performance embedding model focused on text classification and retrieval tasks, excelling in multiple benchmark tests.

A 7B-parameter sentence embedding model based on the Qwen2 architecture, specializing in sentence similarity tasks with outstanding performance on the MTEB benchmark.

Stella 400M is a medium-scale English text processing model focused on classification and information retrieval tasks.

NV-Embed-v2 is an embedding model based on sentence-transformers, excelling in multiple MTEB benchmarks and suitable for various natural language processing tasks.

Snowflake Arctic Embed XS is a lightweight sentence embedding model focused on sentence similarity and feature extraction tasks.

e5-base-4k is a model that performs excellently in multiple natural language processing tasks, especially in tasks such as text classification, retrieval, clustering, and semantic text similarity.

SGPT-Bloom-7b1-msmarco is a sentence transformer model based on the BLOOM architecture, primarily used for sentence similarity calculation and feature extraction tasks.

A post-trained BERT model based on review corpora, focusing on understanding sentiment, options, and various e-commerce-related dimensions

This model is a fine-tuned DistilBERT text classification model designed to predict the helpfulness of Amazon reviews.

A post-trained model based on BERT-base-uncased, optimized for understanding sentiments and options in e-commerce reviews

BERT model post-trained on laptop review corpus, specializing in understanding sentiment orientation and e-commerce domain features