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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLM-AgloBERTa-eu-hu-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. -->
# XLM-AgloBERTa-eu-hu-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2582
- Precision: 0.9039
- Recall: 0.9209
- F1: 0.9123
- Accuracy: 0.9661
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2501 | 1.0 | 1250 | 0.1919 | 0.8343 | 0.8634 | 0.8486 | 0.9456 |
| 0.1718 | 2.0 | 2500 | 0.1631 | 0.8662 | 0.8793 | 0.8727 | 0.9540 |
| 0.1269 | 3.0 | 3750 | 0.1743 | 0.8748 | 0.8913 | 0.8830 | 0.9571 |
| 0.0899 | 4.0 | 5000 | 0.1642 | 0.8734 | 0.9083 | 0.8905 | 0.9587 |
| 0.0578 | 5.0 | 6250 | 0.1958 | 0.8867 | 0.9000 | 0.8933 | 0.9599 |
| 0.0474 | 6.0 | 7500 | 0.1823 | 0.9062 | 0.9069 | 0.9065 | 0.9647 |
| 0.031 | 7.0 | 8750 | 0.1928 | 0.9007 | 0.9137 | 0.9071 | 0.9643 |
| 0.0168 | 8.0 | 10000 | 0.2168 | 0.9042 | 0.9113 | 0.9077 | 0.9644 |
| 0.0109 | 9.0 | 11250 | 0.2423 | 0.9028 | 0.9191 | 0.9108 | 0.9658 |
| 0.0057 | 10.0 | 12500 | 0.2582 | 0.9039 | 0.9209 | 0.9123 | 0.9661 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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