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tanaos/tanaos-sentiment-analysis-v1

sourceHugging Facemitupdated 6mo agoView on Hugging Face
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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 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 through the Artifex library:

install Artifex with

bash
pip install artifex

use the model with

python
from artifex import Artifex

sentiment_analysis = Artifex().sentiment_analysis()

label = sentiment_analysis("While the battery life is average, the camera quality is good.")
print(label)

# >>> [{'label': 'neutral', 'score': 0.9254}]

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

bash
pip install artifex

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

python
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.