---
library_name: transformers
license: mit
base_model: intfloat/multilingual-e5-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: alignment-score-model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# alignment-score-model

This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the alignment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0610
- Precision: 0.9802
- Recall: 0.9786
- F1 Macro: 0.9790
- Accuracy: 0.9790

Training script is available here: https://github.com/lapa-llm/lapa-llm/blob/main/pretraining/quality-classifiers/alignment_score.py

## Model description

This model measure how likely the given text is a disinformation or unaligned to Ukrainian context.

## Intended uses & limitations

Data filtering and evaluation of pretraining data at scale

## Training and evaluation data

Take a look into https://github.com/lapa-llm/lapa-llm/blob/main/pretraining/quality-classifiers/alignment_score.py

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 32
- eval_batch_size: 128
- seed: 0
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 1024
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 40
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| No log        | 0     | 0    | 0.3688          | 0.2550    | 0.5    | 0.3377   | 0.5100   |
| No log        | 1.0   | 31   | 0.2516          | 0.7558    | 0.5033 | 0.3450   | 0.5132   |
| No log        | 2.0   | 62   | 0.1391          | 0.8851    | 0.8467 | 0.8454   | 0.8498   |
| No log        | 3.0   | 93   | 0.1016          | 0.9340    | 0.9209 | 0.9217   | 0.9225   |
| 0.1646        | 4.0   | 124  | 0.0770          | 0.9693    | 0.9659 | 0.9665   | 0.9666   |
| 0.1646        | 5.0   | 155  | 0.0648          | 0.9778    | 0.9758 | 0.9763   | 0.9763   |
| 0.1646        | 6.0   | 186  | 0.0610          | 0.9802    | 0.9786 | 0.9790   | 0.9790   |


### Framework versions

- Transformers 4.56.1
- Pytorch 2.6.0a0+ecf3bae40a.nv25.01
- Datasets 4.0.0
- Tokenizers 0.22.0