|
|
--- |
|
|
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 |
|
|
|
