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---
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerel-bio-rubert-base
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-rubert-base
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6122
- Precision: 0.7873
- Recall: 0.7882
- F1: 0.7878
- Accuracy: 0.8601
## 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: 6
- eval_batch_size: 6
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 102 | 1.1211 | 0.6196 | 0.5809 | 0.5996 | 0.7125 |
| No log | 2.0 | 204 | 0.6800 | 0.7333 | 0.7165 | 0.7248 | 0.8137 |
| No log | 3.0 | 306 | 0.5985 | 0.7445 | 0.7488 | 0.7466 | 0.8303 |
| No log | 4.0 | 408 | 0.5673 | 0.7608 | 0.7622 | 0.7615 | 0.8402 |
| 0.7954 | 5.0 | 510 | 0.5665 | 0.7751 | 0.7702 | 0.7726 | 0.8485 |
| 0.7954 | 6.0 | 612 | 0.5934 | 0.7826 | 0.7742 | 0.7784 | 0.8544 |
| 0.7954 | 7.0 | 714 | 0.5804 | 0.7795 | 0.7751 | 0.7773 | 0.8527 |
| 0.7954 | 8.0 | 816 | 0.6075 | 0.7839 | 0.7878 | 0.7858 | 0.8577 |
| 0.7954 | 9.0 | 918 | 0.6139 | 0.7887 | 0.7889 | 0.7888 | 0.8614 |
| 0.1024 | 10.0 | 1020 | 0.6122 | 0.7873 | 0.7882 | 0.7878 | 0.8601 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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