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---
library_name: transformers
license: mit
base_model: intfloat/multilingual-e5-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: owm-math-scorer-multilingual-e5-small
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. -->
# owm-math-scorer-multilingual-e5-small
This model is a fine-tuned version of [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4478
- Precision: 0.8771
- Recall: 0.8769
- F1 Macro: 0.8770
- Accuracy: 0.8770
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 256
- eval_batch_size: 128
- seed: 0
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 1024
- total_eval_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| No log | 0 | 0 | 8.2909 | 0.2546 | 0.5 | 0.3374 | 0.5091 |
| 0.5633 | 0.2844 | 250 | 0.5661 | 0.8608 | 0.8596 | 0.8588 | 0.8589 |
| 0.5443 | 0.5688 | 500 | 0.5192 | 0.8655 | 0.8652 | 0.8653 | 0.8654 |
| 0.5395 | 0.8532 | 750 | 0.5461 | 0.8651 | 0.8636 | 0.8628 | 0.8629 |
| 0.5144 | 1.1377 | 1000 | 0.4992 | 0.8691 | 0.8692 | 0.8691 | 0.8691 |
| 0.5278 | 1.4221 | 1250 | 0.5322 | 0.8675 | 0.8613 | 0.8616 | 0.8624 |
| 0.501 | 1.7065 | 1500 | 0.4942 | 0.8708 | 0.8690 | 0.8692 | 0.8695 |
| 0.4942 | 1.9909 | 1750 | 0.4934 | 0.8697 | 0.8696 | 0.8693 | 0.8693 |
| 0.492 | 2.2753 | 2000 | 0.4873 | 0.8710 | 0.8711 | 0.8711 | 0.8711 |
| 0.4984 | 2.5597 | 2250 | 0.5061 | 0.8701 | 0.8694 | 0.8688 | 0.8688 |
| 0.4809 | 2.8441 | 2500 | 0.4995 | 0.8719 | 0.8673 | 0.8677 | 0.8682 |
| 0.4744 | 3.1286 | 2750 | 0.4783 | 0.8721 | 0.8722 | 0.8721 | 0.8721 |
| 0.4817 | 3.4130 | 3000 | 0.4715 | 0.8737 | 0.8738 | 0.8738 | 0.8738 |
| 0.4748 | 3.6974 | 3250 | 0.4734 | 0.8743 | 0.8725 | 0.8728 | 0.8731 |
| 0.4725 | 3.9818 | 3500 | 0.4703 | 0.8738 | 0.8736 | 0.8737 | 0.8738 |
| 0.4684 | 4.2662 | 3750 | 0.4693 | 0.8739 | 0.8734 | 0.8735 | 0.8737 |
| 0.4796 | 4.5506 | 4000 | 0.4697 | 0.8746 | 0.8727 | 0.8729 | 0.8732 |
| 0.4666 | 4.8350 | 4250 | 0.4715 | 0.8737 | 0.8738 | 0.8735 | 0.8735 |
| 0.4697 | 5.1195 | 4500 | 0.4853 | 0.8736 | 0.8692 | 0.8695 | 0.8700 |
| 0.466 | 5.4039 | 4750 | 0.4782 | 0.8734 | 0.8713 | 0.8716 | 0.8719 |
| 0.4663 | 5.6883 | 5000 | 0.4653 | 0.8746 | 0.8747 | 0.8746 | 0.8746 |
| 0.4677 | 5.9727 | 5250 | 0.4656 | 0.8749 | 0.8734 | 0.8737 | 0.8739 |
| 0.4615 | 6.2571 | 5500 | 0.4631 | 0.8753 | 0.8739 | 0.8741 | 0.8743 |
| 0.4689 | 6.5415 | 5750 | 0.4610 | 0.8759 | 0.8754 | 0.8756 | 0.8757 |
| 0.4643 | 6.8259 | 6000 | 0.4601 | 0.8753 | 0.8747 | 0.8749 | 0.8750 |
| 0.4591 | 7.1104 | 6250 | 0.4598 | 0.8748 | 0.8745 | 0.8746 | 0.8747 |
| 0.4628 | 7.3948 | 6500 | 0.4592 | 0.8759 | 0.8749 | 0.8751 | 0.8753 |
| 0.4589 | 7.6792 | 6750 | 0.4613 | 0.8759 | 0.8744 | 0.8747 | 0.8749 |
| 0.4626 | 7.9636 | 7000 | 0.4566 | 0.8758 | 0.8753 | 0.8754 | 0.8756 |
| 0.4632 | 8.2480 | 7250 | 0.4623 | 0.8746 | 0.8727 | 0.8730 | 0.8732 |
| 0.4545 | 8.5324 | 7500 | 0.4554 | 0.8766 | 0.8759 | 0.8760 | 0.8762 |
| 0.4596 | 8.8168 | 7750 | 0.4581 | 0.8755 | 0.8755 | 0.8755 | 0.8755 |
| 0.4571 | 9.1013 | 8000 | 0.4595 | 0.8759 | 0.8737 | 0.8740 | 0.8743 |
| 0.4585 | 9.3857 | 8250 | 0.4561 | 0.8760 | 0.8750 | 0.8752 | 0.8754 |
| 0.4541 | 9.6701 | 8500 | 0.4548 | 0.8756 | 0.8750 | 0.8751 | 0.8752 |
| 0.4576 | 9.9545 | 8750 | 0.4541 | 0.8757 | 0.8754 | 0.8755 | 0.8756 |
| 0.449 | 10.2389 | 9000 | 0.4554 | 0.8754 | 0.8752 | 0.8752 | 0.8753 |
| 0.4507 | 10.5233 | 9250 | 0.4535 | 0.8763 | 0.8763 | 0.8763 | 0.8763 |
| 0.4545 | 10.8077 | 9500 | 0.4543 | 0.8759 | 0.8758 | 0.8758 | 0.8759 |
| 0.4462 | 11.0922 | 9750 | 0.4529 | 0.8764 | 0.8756 | 0.8758 | 0.8759 |
| 0.4505 | 11.3766 | 10000 | 0.4538 | 0.8762 | 0.8751 | 0.8753 | 0.8755 |
| 0.4576 | 11.6610 | 10250 | 0.4714 | 0.8751 | 0.8714 | 0.8717 | 0.8722 |
| 0.4509 | 11.9454 | 10500 | 0.4613 | 0.8759 | 0.8760 | 0.8758 | 0.8758 |
| 0.4557 | 12.2298 | 10750 | 0.4538 | 0.8764 | 0.8753 | 0.8755 | 0.8757 |
| 0.4539 | 12.5142 | 11000 | 0.4523 | 0.8765 | 0.8758 | 0.8760 | 0.8761 |
| 0.4534 | 12.7986 | 11250 | 0.4515 | 0.8766 | 0.8767 | 0.8766 | 0.8767 |
| 0.4532 | 13.0830 | 11500 | 0.4509 | 0.8768 | 0.8763 | 0.8765 | 0.8766 |
| 0.4501 | 13.3675 | 11750 | 0.4517 | 0.8765 | 0.8762 | 0.8763 | 0.8763 |
| 0.4493 | 13.6519 | 12000 | 0.4527 | 0.8767 | 0.8768 | 0.8768 | 0.8768 |
| 0.4528 | 13.9363 | 12250 | 0.4499 | 0.8766 | 0.8765 | 0.8765 | 0.8766 |
| 0.4491 | 14.2207 | 12500 | 0.4519 | 0.8766 | 0.8755 | 0.8757 | 0.8759 |
| 0.4495 | 14.5051 | 12750 | 0.4594 | 0.8768 | 0.8769 | 0.8767 | 0.8767 |
| 0.4443 | 14.7895 | 13000 | 0.4519 | 0.8766 | 0.8764 | 0.8765 | 0.8766 |
| 0.4476 | 15.0739 | 13250 | 0.4509 | 0.8769 | 0.8766 | 0.8767 | 0.8768 |
| 0.4466 | 15.3584 | 13500 | 0.4494 | 0.8773 | 0.8769 | 0.8770 | 0.8771 |
| 0.4456 | 15.6428 | 13750 | 0.4489 | 0.8768 | 0.8765 | 0.8766 | 0.8767 |
| 0.4447 | 15.9272 | 14000 | 0.4552 | 0.8765 | 0.8751 | 0.8754 | 0.8756 |
| 0.4471 | 16.2116 | 14250 | 0.4520 | 0.8763 | 0.8763 | 0.8763 | 0.8763 |
| 0.4521 | 16.4960 | 14500 | 0.4509 | 0.8770 | 0.8756 | 0.8758 | 0.8760 |
| 0.4419 | 16.7804 | 14750 | 0.4533 | 0.8767 | 0.8768 | 0.8767 | 0.8768 |
| 0.4485 | 17.0648 | 15000 | 0.4483 | 0.8770 | 0.8768 | 0.8769 | 0.8769 |
| 0.4424 | 17.3493 | 15250 | 0.4490 | 0.8770 | 0.8769 | 0.8769 | 0.8770 |
| 0.4441 | 17.6337 | 15500 | 0.4502 | 0.8770 | 0.8769 | 0.8770 | 0.8770 |
| 0.4487 | 17.9181 | 15750 | 0.4480 | 0.8769 | 0.8763 | 0.8765 | 0.8766 |
| 0.4487 | 18.2025 | 16000 | 0.4500 | 0.8771 | 0.8772 | 0.8772 | 0.8772 |
| 0.4375 | 18.4869 | 16250 | 0.4483 | 0.8769 | 0.8766 | 0.8767 | 0.8768 |
| 0.4491 | 18.7713 | 16500 | 0.4515 | 0.8768 | 0.8769 | 0.8768 | 0.8768 |
| 0.4433 | 19.0557 | 16750 | 0.4477 | 0.8773 | 0.8769 | 0.8770 | 0.8771 |
| 0.4432 | 19.3402 | 17000 | 0.4480 | 0.8771 | 0.8769 | 0.8770 | 0.8771 |
| 0.442 | 19.6246 | 17250 | 0.4480 | 0.8770 | 0.8768 | 0.8769 | 0.8770 |
| 0.4407 | 19.9090 | 17500 | 0.4478 | 0.8771 | 0.8769 | 0.8770 | 0.8770 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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