metadata

license: apache-2.0
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity

HAI - HelpingAI Semantic Similarity Model

This is a custom Sentence Transformer model fine-tuned from sentence-transformers/all-MiniLM-L6-v2. Designed as part of the HelpingAI ecosystem, it enhances semantic similarity and contextual understanding, with an emphasis on emotionally intelligent responses.

Model Highlights

Base Model: sentence-transformers/all-MiniLM-L6-v2

Model Details

Features:

Input Dimensionality: Handles up to 256 tokens per input.
Output Dimensionality: 384-dimensional dense embeddings.

Full Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) 
  (1): Pooling({'pooling_mode_mean_tokens': True})
  (2): Normalize()
)

Training Overview

Dataset:

Size: 75897 samples
Structure: <sentence_0, sentence_1, similarity_score>
Labels: Float values between 0 (no similarity) and 1 (high similarity).

Training Method:

Loss Function: Cosine Similarity Loss
Batch Size: 16
Epochs: 20
Optimization: AdamW optimizer with a learning rate of 5e-5.

Getting Started

Installation

Ensure you have the sentence-transformers library installed:

pip install -U sentence-transformers

Quick Start

Load and use the model in your Python environment:

from sentence_transformers import SentenceTransformer

# Load the HelpingAI semantic similarity model
model = SentenceTransformer("HelpingAI/HAI")

# Encode sentences
sentences = [
    "A woman is slicing a pepper.",
    "A girl is styling her hair.",
    "The sun is shining brightly today."
]
embeddings = model.encode(sentences)
print(embeddings.shape)  # Output: (3, 384)

# Calculate similarity
from sklearn.metrics.pairwise import cosine_similarity
similarity_scores = cosine_similarity([embeddings[0]], embeddings[1:])
print(similarity_scores)

high accuracy in sentiment-informed response tests.

Citation

If you use the HAI model, please cite the original Sentence-BERT paper:

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}