agentlans/multilingual-e5-small-quality-v3

Multilingual Text Quality Model

This model rates the quality of non-English text for AI learning. Input a text string, and it outputs a numeric quality score reflecting overall informativeness and usefulness.

Performance

On the evaluation set, it achieved:

• —Loss: 0.0641
• —MSE: 0.0641
• —Combined Score: 0.0641
• —Tokens processed during training: 1,109,813,760

Usage Example

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "agentlans/multilingual-e5-small-quality-v3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")

# Higher scores indicate higher text quality.
# The sign of the score has no particular meaning.
# For example, a negative score doesn't necessarily mean that the text is low quality.
def quality(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
    with torch.no_grad():
        score = model(**inputs).logits.squeeze().cpu().item()
    return score

print(quality("Your text here."))

Limitations

• —Works best on non-fiction and general-purpose texts.
• —Scores give an overall quality estimate but don’t explain why.
• —Unlike the other quality-v3 models, this model is only trained on short non-English sentences.
• —Check for biases and suitability before use.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 5e-05
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Use adamwtorch with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: linear
• —num_epochs: 10.0

Training results

Framework versions

• —Transformers 4.51.3
• —Pytorch 2.6.0+cu124
• —Datasets 3.2.0
• —Tokenizers 0.21.0