---
library_name: transformers
license: mit
base_model: agentlans/multilingual-e5-small-aligned-v2
tags:
- generated_from_trainer
model-index:
- name: multilingual-e5-small-aligned-v2-text-quality-v3
  results: []
language:
- multilingual
datasets:
- agentlans/en-translations-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

```python
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
- train_batch_size: 8
- 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: 10.0

### Training results

| Training Loss | Epoch | Step    | Validation Loss | Mse    | Combined Score | Input Tokens Seen |
|:-------------:|:-----:|:-------:|:---------------:|:------:|:--------------:|:-----------------:|
| 0.0725        | 1.0   | 108381  | 0.0727          | 0.0727 | 0.0727         | 110981376         |
| 0.0603        | 2.0   | 216762  | 0.0675          | 0.0675 | 0.0675         | 221962752         |
| 0.0559        | 3.0   | 325143  | 0.0703          | 0.0703 | 0.0703         | 332944128         |
| 0.0387        | 4.0   | 433524  | 0.0675          | 0.0675 | 0.0675         | 443925504         |
| 0.0325        | 5.0   | 541905  | 0.0704          | 0.0704 | 0.0704         | 554906880         |
| 0.0276        | 6.0   | 650286  | 0.0672          | 0.0672 | 0.0672         | 665888256         |
| 0.025         | 7.0   | 758667  | 0.0641          | 0.0641 | 0.0641         | 776869632         |
| 0.0182        | 8.0   | 867048  | 0.0676          | 0.0676 | 0.0676         | 887851008         |
| 0.0154        | 9.0   | 975429  | 0.0647          | 0.0647 | 0.0647         | 998832384         |
| 0.0133        | 10.0  | 1083810 | 0.0643          | 0.0643 | 0.0643         | 1109813760        |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0