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---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-ner
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. -->
# roberta-base-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5055
- Precision: 0.8737
- Recall: 0.8677
- F1: 0.8707
- Accuracy: 0.8449
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.19 | 50 | 0.5675 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| No log | 0.37 | 100 | 0.5571 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| No log | 0.56 | 150 | 0.5541 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| No log | 0.75 | 200 | 0.5682 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| No log | 0.93 | 250 | 0.5845 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| No log | 1.12 | 300 | 0.5533 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| No log | 1.31 | 350 | 0.5940 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| No log | 1.49 | 400 | 0.5553 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| No log | 1.68 | 450 | 0.5661 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| 0.6392 | 1.87 | 500 | 0.5435 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| 0.6392 | 2.05 | 550 | 0.5300 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| 0.6392 | 2.24 | 600 | 0.5522 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| 0.6392 | 2.43 | 650 | 0.5155 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| 0.6392 | 2.61 | 700 | 0.5037 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| 0.6392 | 2.8 | 750 | 0.4923 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| 0.6392 | 2.99 | 800 | 0.4897 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| 0.6392 | 3.17 | 850 | 0.5021 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| 0.6392 | 3.36 | 900 | 0.5122 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
| 0.6392 | 3.54 | 950 | 0.4987 | 0.8575 | 0.9004 | 0.8784 | 0.8560 |
| 0.5724 | 3.73 | 1000 | 0.4861 | 0.8587 | 0.8971 | 0.8775 | 0.8541 |
| 0.5724 | 3.92 | 1050 | 0.4788 | 0.8607 | 0.9019 | 0.8808 | 0.8580 |
| 0.5724 | 4.1 | 1100 | 0.4989 | 0.8634 | 0.8826 | 0.8729 | 0.8459 |
| 0.5724 | 4.29 | 1150 | 0.4760 | 0.8653 | 0.8976 | 0.8812 | 0.8572 |
| 0.5724 | 4.48 | 1200 | 0.4699 | 0.8659 | 0.8835 | 0.8746 | 0.8482 |
| 0.5724 | 4.66 | 1250 | 0.4865 | 0.8729 | 0.8822 | 0.8775 | 0.8519 |
| 0.5724 | 4.85 | 1300 | 0.4763 | 0.8626 | 0.9023 | 0.8820 | 0.8586 |
| 0.5724 | 5.04 | 1350 | 0.4676 | 0.8653 | 0.8941 | 0.8794 | 0.8564 |
| 0.5724 | 5.22 | 1400 | 0.4979 | 0.8672 | 0.8850 | 0.8760 | 0.8494 |
| 0.5724 | 5.41 | 1450 | 0.4749 | 0.8648 | 0.8965 | 0.8804 | 0.8566 |
| 0.5092 | 5.6 | 1500 | 0.5003 | 0.8686 | 0.8720 | 0.8703 | 0.8410 |
| 0.5092 | 5.78 | 1550 | 0.4635 | 0.8713 | 0.8872 | 0.8792 | 0.8547 |
| 0.5092 | 5.97 | 1600 | 0.4615 | 0.8653 | 0.8928 | 0.8788 | 0.8543 |
| 0.5092 | 6.16 | 1650 | 0.4785 | 0.8677 | 0.8937 | 0.8805 | 0.8556 |
| 0.5092 | 6.34 | 1700 | 0.4856 | 0.8728 | 0.8813 | 0.8771 | 0.8535 |
| 0.5092 | 6.53 | 1750 | 0.4681 | 0.8695 | 0.8917 | 0.8805 | 0.8574 |
| 0.5092 | 6.72 | 1800 | 0.4633 | 0.8683 | 0.8950 | 0.8814 | 0.8586 |
| 0.5092 | 6.9 | 1850 | 0.4887 | 0.8787 | 0.8655 | 0.8720 | 0.8432 |
| 0.5092 | 7.09 | 1900 | 0.4807 | 0.8706 | 0.8759 | 0.8733 | 0.8476 |
| 0.5092 | 7.28 | 1950 | 0.4613 | 0.8723 | 0.8935 | 0.8828 | 0.8607 |
| 0.4572 | 7.46 | 2000 | 0.4582 | 0.8729 | 0.8861 | 0.8794 | 0.8545 |
| 0.4572 | 7.65 | 2050 | 0.4784 | 0.8794 | 0.8681 | 0.8737 | 0.8476 |
| 0.4572 | 7.84 | 2100 | 0.4749 | 0.8710 | 0.8798 | 0.8754 | 0.8504 |
| 0.4572 | 8.02 | 2150 | 0.4755 | 0.8721 | 0.8828 | 0.8774 | 0.8531 |
| 0.4572 | 8.21 | 2200 | 0.4875 | 0.8736 | 0.8668 | 0.8702 | 0.8463 |
| 0.4572 | 8.4 | 2250 | 0.4763 | 0.8807 | 0.8664 | 0.8735 | 0.8480 |
| 0.4572 | 8.58 | 2300 | 0.4795 | 0.8745 | 0.8644 | 0.8694 | 0.8445 |
| 0.4572 | 8.77 | 2350 | 0.4822 | 0.8739 | 0.8616 | 0.8677 | 0.8385 |
| 0.4572 | 8.96 | 2400 | 0.4824 | 0.8761 | 0.8774 | 0.8768 | 0.8510 |
| 0.4572 | 9.14 | 2450 | 0.4818 | 0.8748 | 0.8608 | 0.8677 | 0.8400 |
| 0.4061 | 9.33 | 2500 | 0.4814 | 0.8795 | 0.8712 | 0.8753 | 0.8488 |
| 0.4061 | 9.51 | 2550 | 0.4846 | 0.8754 | 0.8796 | 0.8775 | 0.8510 |
| 0.4061 | 9.7 | 2600 | 0.5112 | 0.8758 | 0.8718 | 0.8738 | 0.8461 |
| 0.4061 | 9.89 | 2650 | 0.5002 | 0.8689 | 0.8701 | 0.8695 | 0.8461 |
| 0.4061 | 10.07 | 2700 | 0.5163 | 0.8769 | 0.8605 | 0.8686 | 0.8391 |
| 0.4061 | 10.26 | 2750 | 0.4947 | 0.8733 | 0.8774 | 0.8754 | 0.8510 |
| 0.4061 | 10.45 | 2800 | 0.4895 | 0.8795 | 0.8850 | 0.8822 | 0.8599 |
| 0.4061 | 10.63 | 2850 | 0.4984 | 0.8737 | 0.8705 | 0.8721 | 0.8457 |
| 0.4061 | 10.82 | 2900 | 0.4952 | 0.8733 | 0.8779 | 0.8756 | 0.8521 |
| 0.4061 | 11.01 | 2950 | 0.5012 | 0.8720 | 0.8644 | 0.8682 | 0.8422 |
| 0.3677 | 11.19 | 3000 | 0.4994 | 0.8717 | 0.8751 | 0.8734 | 0.8486 |
| 0.3677 | 11.38 | 3050 | 0.5002 | 0.875 | 0.8777 | 0.8763 | 0.8529 |
| 0.3677 | 11.57 | 3100 | 0.5039 | 0.8724 | 0.8735 | 0.8730 | 0.8490 |
| 0.3677 | 11.75 | 3150 | 0.5094 | 0.8729 | 0.8642 | 0.8686 | 0.8416 |
| 0.3677 | 11.94 | 3200 | 0.5059 | 0.8731 | 0.8673 | 0.8702 | 0.8443 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
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