license: mit language:

• multilingual
• af
• am
• ar
• as
• az
• be
• bg
• bn
• br
• bs
• ca
• cs
• cy
• da
• de
• el
• en
• eo
• es
• et
• eu
• fa
• fi
• fr
• fy
• ga
• gd
• gl
• gu
• ha
• he
• hi
• hr
• hu
• hy
• id
• is
• it
• ja
• jv
• ka
• kk
• km
• kn
• ko
• ku
• ky
• la
• lo
• lt
• lv
• mg
• mk
• ml
• mn
• mr
• ms
• my
• ne
• nl
• 'no'
• om
• or
• pa
• pl
• ps
• pt
• ro
• ru
• sa
• sd
• si
• sk
• sl
• so
• sq
• sr
• su
• sv
• sw
• ta
• te
• th
• tl
• tr
• ug
• uk
• ur
• uz
• vi
• xh
• yi
• zh datasets:
• agentlans/en-translations base_model:
• agentlans/multilingual-e5-small-aligned pipeline_tag: text-classification tags:
• multilingual
• sentiment-assessment

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.

Training Data

The model was trained on the Multilingual Parallel Sentences dataset, which includes:

• Parallel sentences in English and various other languages
• Semantic similarity scores calculated using LaBSE
• Additional sentiment metrics
• Sources: JW300, Europarl, TED Talks, OPUS-100, Tatoeba, Global Voices, and News Commentary

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 128
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.46.3
• Pytorch 2.5.1+cu124
• Datasets 3.1.0
• Tokenizers 0.20.3