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---
base_model: alexyalunin/RuBioBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerel-bio-RuBioBERT-al
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. -->
# nerel-bio-RuBioBERT-al
This model is a fine-tuned version of [alexyalunin/RuBioBERT](https://huggingface.co./alexyalunin/RuBioBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9899
- Precision: 0.7839
- Recall: 0.7903
- F1: 0.7871
- Accuracy: 0.8572
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.044 | 1.0 | 153 | 0.8960 | 0.7760 | 0.7814 | 0.7787 | 0.8499 |
| 0.0126 | 2.0 | 306 | 0.9091 | 0.7808 | 0.7810 | 0.7809 | 0.8505 |
| 0.0264 | 3.0 | 459 | 0.8231 | 0.7871 | 0.7896 | 0.7883 | 0.8572 |
| 0.007 | 4.0 | 612 | 0.9504 | 0.7825 | 0.7873 | 0.7849 | 0.8552 |
| 0.0027 | 5.0 | 765 | 0.9055 | 0.7838 | 0.7915 | 0.7876 | 0.8581 |
| 0.0066 | 6.0 | 918 | 0.9222 | 0.7829 | 0.7859 | 0.7844 | 0.8577 |
| 0.0005 | 7.0 | 1071 | 0.9561 | 0.7805 | 0.7903 | 0.7854 | 0.8564 |
| 0.0003 | 8.0 | 1224 | 0.9858 | 0.7816 | 0.7889 | 0.7852 | 0.8567 |
| 0.0004 | 9.0 | 1377 | 0.9904 | 0.7834 | 0.7899 | 0.7866 | 0.8572 |
| 0.0003 | 10.0 | 1530 | 0.9899 | 0.7839 | 0.7903 | 0.7871 | 0.8572 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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