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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test-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. -->
# test-ner
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: 0.2327
- Precision: 0.9133
- Recall: 0.9225
- F1: 0.9179
- Accuracy: 0.9687
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3488 | 1.0 | 625 | 0.1874 | 0.8414 | 0.8560 | 0.8487 | 0.9486 |
| 0.1914 | 2.0 | 1250 | 0.1857 | 0.8674 | 0.8794 | 0.8734 | 0.9552 |
| 0.1418 | 3.0 | 1875 | 0.1618 | 0.8752 | 0.8906 | 0.8828 | 0.9596 |
| 0.0883 | 4.0 | 2500 | 0.1701 | 0.8952 | 0.9011 | 0.8982 | 0.9631 |
| 0.0582 | 5.0 | 3125 | 0.1873 | 0.8774 | 0.9149 | 0.8958 | 0.9620 |
| 0.0453 | 6.0 | 3750 | 0.1902 | 0.9008 | 0.9131 | 0.9069 | 0.9641 |
| 0.0353 | 7.0 | 4375 | 0.2059 | 0.8992 | 0.9067 | 0.9029 | 0.9654 |
| 0.015 | 8.0 | 5000 | 0.2231 | 0.9031 | 0.9183 | 0.9106 | 0.9659 |
| 0.0114 | 9.0 | 5625 | 0.2234 | 0.9120 | 0.9198 | 0.9159 | 0.9677 |
| 0.0066 | 10.0 | 6250 | 0.2327 | 0.9133 | 0.9225 | 0.9179 | 0.9687 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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