📊🙂😐😞 tanaos-sentiment-analysis-v1: A small but performant sentiment analysis model

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

This is a multilingual base sentiment analysis model (it supports 15+ languages) 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

Via the Artifex library (pip install artifex)

from artifex import Artifex

sa = Artifex().sentiment_analysis

very_positive_label = sa("It was exceptional. One of the best meals I have ever had.")
very_negative_label = sa("The movie was just awful and painfully predictable.")

print(very_positive_label)
print(very_negative_label)

# >>> [{'label': 'very_positive', 'score': 0.9976}]
# >>> [{'label': 'very_negative', 'score': 0.9981}]

Via the Transformers library

from transformers import pipeline

clf = pipeline("text-classification", model="tanaos/tanaos-sentiment-analysis-v1")

very_positive_label = clf("It was exceptional. One of the best meals I have ever had.")
very_negative_label = clf("The movie was just awful and painfully predictable.")

print(very_positive_label)
print(very_negative_label)

# >>> [{'label': 'very_positive', 'score': 0.9976}]
# >>> [{'label': 'very_negative', 'score': 0.9981}]

🧠 Model Description

• Base model: microsoft/Multilingual-MiniLM-L12-H384
• Task: Text classification (sentiment analysis)
• Languages: Multilingual (15+ languages)
• 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.
• Support 15+ languages.
• Provide a lightweight alternative for sentiment analysis tasks.

Not intended for:

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