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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-finetuned-ner-without-data-sort
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-finetuned-ner-without-data-sort
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0420
- Precision: 0.9914
- Recall: 0.9909
- F1: 0.9912
- Accuracy: 0.9920
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.1879 | 0.9378 | 0.9414 | 0.9396 | 0.9493 |
| No log | 2.0 | 426 | 0.1038 | 0.9725 | 0.9750 | 0.9737 | 0.9751 |
| 0.4424 | 3.0 | 639 | 0.0701 | 0.9861 | 0.9851 | 0.9856 | 0.9863 |
| 0.4424 | 4.0 | 852 | 0.0637 | 0.9882 | 0.9880 | 0.9881 | 0.9880 |
| 0.0675 | 5.0 | 1065 | 0.0546 | 0.9851 | 0.9878 | 0.9865 | 0.9879 |
| 0.0675 | 6.0 | 1278 | 0.0480 | 0.9894 | 0.9904 | 0.9899 | 0.9901 |
| 0.0675 | 7.0 | 1491 | 0.0473 | 0.9919 | 0.9904 | 0.9912 | 0.9911 |
| 0.0426 | 8.0 | 1704 | 0.0441 | 0.9921 | 0.9916 | 0.9919 | 0.9921 |
| 0.0426 | 9.0 | 1917 | 0.0426 | 0.9921 | 0.9916 | 0.9919 | 0.9922 |
| 0.033 | 10.0 | 2130 | 0.0420 | 0.9914 | 0.9909 | 0.9912 | 0.9920 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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