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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-ner-thesis-dseb
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-roberta-base-finetuned-ner-thesis-dseb
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: 0.1345
- Precision: 0.1786
- Recall: 0.1351
- F1: 0.1538
- Accuracy: 0.9563
## 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: 1e-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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8175 | 1.0 | 12 | 0.3119 | 0.0 | 0.0 | 0.0 | 0.9593 |
| 0.2019 | 2.0 | 24 | 0.2414 | 0.0 | 0.0 | 0.0 | 0.9593 |
| 0.1156 | 3.0 | 36 | 0.2105 | 0.0 | 0.0 | 0.0 | 0.9593 |
| 0.0913 | 4.0 | 48 | 0.1831 | 0.0 | 0.0 | 0.0 | 0.9593 |
| 0.0987 | 5.0 | 60 | 0.1695 | 0.0 | 0.0 | 0.0 | 0.9593 |
| 0.0697 | 6.0 | 72 | 0.1727 | 0.0 | 0.0 | 0.0 | 0.9593 |
| 0.0528 | 7.0 | 84 | 0.1462 | 0.0 | 0.0 | 0.0 | 0.9593 |
| 0.0538 | 8.0 | 96 | 0.1441 | 0.0 | 0.0 | 0.0 | 0.9593 |
| 0.0504 | 9.0 | 108 | 0.1854 | 0.0 | 0.0 | 0.0 | 0.9605 |
| 0.0359 | 10.0 | 120 | 0.1516 | 0.0476 | 0.0312 | 0.0377 | 0.9641 |
| 0.031 | 11.0 | 132 | 0.1836 | 0.0 | 0.0 | 0.0 | 0.9621 |
| 0.038 | 12.0 | 144 | 0.1581 | 0.1579 | 0.0938 | 0.1176 | 0.9627 |
| 0.0349 | 13.0 | 156 | 0.1901 | 0.0 | 0.0 | 0.0 | 0.9625 |
| 0.0226 | 14.0 | 168 | 0.1740 | 0.0667 | 0.0312 | 0.0426 | 0.9648 |
| 0.0198 | 15.0 | 180 | 0.1729 | 0.125 | 0.0625 | 0.0833 | 0.9639 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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