multilingual e5 small aligned sentiment EU AI Act Compliance Profile

multilingual-e5-small-aligned-sentiment

This model is a fine-tuned version of agentlans/multilingual-e5-small-aligned designed for assessing text sentiment across multiple languages.

Key Features

• Multilingual support
• Sentiment assessment for text
• Based on E5 small model architecture

Intended Uses & Limitations

This model is intended for:

• Assessing the sentiment of multilingual text
• Filtering multilingual content
• Comparative analysis of corpus text sentiment across different languages

Limitations:

• Performance may vary for languages not well-represented in the training data
• Should not be used as the sole criterion for sentiment assessment

Usage Example

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/multilingual-e5-small-aligned-sentiment"

# Initialize tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

def sentiment(text):
    """Assess the sentiment of the input text."""
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
    with torch.no_grad():
        logits = model(**inputs).logits.squeeze().cpu()
    return logits.tolist()

# Example usage
score = sentiment("Your text here.")
print(f"Sentiment score: {score}")

Performance Results

The model was evaluated on a diverse set of multilingual text samples:

• 10 English text samples of varying sentiment were translated into Arabic, Chinese, French, Russian, and Spanish.
• The model demonstrated consistent sentiment assessment across different languages for the same text.

| Text | English | French | **Spanish

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Base Model