---
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: 0.1007
- Precision: 0.5789
- Recall: 0.7857
- F1: 0.6667
- Accuracy: 0.9871

## 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: 5e-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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0117        | 1.0   | 50   | 0.2576          | 0.3256    | 0.35   | 0.3373 | 0.9523   |
| 0.0454        | 2.0   | 100  | 0.2768          | 0.5714    | 0.3    | 0.3934 | 0.9590   |
| 0.0188        | 3.0   | 150  | 0.1758          | 0.6429    | 0.45   | 0.5294 | 0.9746   |
| 0.0144        | 4.0   | 200  | 0.3266          | 0.5714    | 0.2    | 0.2963 | 0.9601   |
| 0.0134        | 5.0   | 250  | 0.2405          | 0.7143    | 0.375  | 0.4918 | 0.9667   |
| 0.0038        | 6.0   | 300  | 0.1727          | 0.5660    | 0.75   | 0.6452 | 0.9759   |
| 0.0036        | 7.0   | 350  | 0.1335          | 0.7561    | 0.775  | 0.7654 | 0.9835   |
| 0.0047        | 8.0   | 400  | 0.1240          | 0.7111    | 0.8    | 0.7529 | 0.9836   |
| 0.0013        | 9.0   | 450  | 0.1468          | 0.8       | 0.7    | 0.7467 | 0.9782   |
| 0.0001        | 10.0  | 500  | 0.1222          | 0.7368    | 0.7    | 0.7179 | 0.9811   |
| 0.0           | 11.0  | 550  | 0.1261          | 0.7368    | 0.7    | 0.7179 | 0.9817   |
| 0.0           | 12.0  | 600  | 0.1273          | 0.7368    | 0.7    | 0.7179 | 0.9817   |
| 0.0           | 13.0  | 650  | 0.1293          | 0.7368    | 0.7    | 0.7179 | 0.9809   |
| 0.0001        | 14.0  | 700  | 0.1367          | 0.7838    | 0.725  | 0.7532 | 0.9809   |
| 0.0003        | 15.0  | 750  | 0.1383          | 0.8056    | 0.725  | 0.7632 | 0.9808   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1