---
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
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# 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