tanaos-sentiment-analysis-v1: A small but performant sentiment analysis model

Looking for a custom Sentiment Analysis model, or any other task-specific Small Language Model fine-tuned to your specific needs? We will do it for you! https://tanaos.com/#try-it-out

This model was created by Tanaos with the Artifex Python library.

This is a sentiment analysis model based on microsoft/Multilingual-MiniLM-L12-H384 and fine-tuned on a synthetic dataset to classify text as one of the following labels:

• very_negative
• negative
• neutral
• positive
• very_positive

neutral is the default label for text that is either factual or does not express a clear sentiment.

This model can be used to classify text belonging to any domain, including but not limited to:

• Product reviews
• Movie reviews
• Social media posts
• Customer feedback

How to Use

Use this model for free via the Tanaos API in 3 simple steps:

1. Sign up for a free account at https://platform.tanaos.com/
2. Create a free API Key from the API Keys section
3. Replace <YOUR_API_KEY> in the code below with your API Key and use this snippet:

import requests

session = requests.Session()

sa_out = session.post(
    "https://slm.tanaos.com/models/sentiment-analysis",
    headers={
        "X-API-Key": "<YOUR_API_KEY>",
    },
    json={
        "text": "The movie was just awful and painfully predictable."
    }
)

print(sa_out.json()["data"])
# >>> [{'label': 'very_negative', 'score': 0.9981}]

Model Description

• Base model: microsoft/Multilingual-MiniLM-L12-H384
• Task: Text classification (sentiment analysis)
• Languages: English
• Fine-tuning data: A synthetic, custom dataset of passages labeled with one of the following sentiments: very_negative, negative, neutral, positive, very_positive.

Training Details

This model was trained using the Artifex Python library

pip install artifex

by providing the following instructions and generating 10,000 synthetic training samples:

from artifex import Artifex


sa = Artifex().sentiment_analysis

sa.train(
    domain="general",
    num_samples=10000
)

Intended Uses

This model is intended to:

• Classify sentiment in text from various domains, including product reviews, social media posts, customer feedback and more.
• Provide a lightweight alternative for sentiment analysis tasks.

Not intended for:

• Analyzing highly specialized or technical text without further fine-tuning.