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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
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