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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: 0.1457
- Precision: 0.7916
- Recall: 0.9119
- F1: 0.8475
- Accuracy: 0.9591

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.5582        | 1.0   | 31   | 0.2963          | 0.8550    | 0.7538 | 0.8012 | 0.9207   |
| 0.1379        | 2.0   | 62   | 0.0478          | 0.9710    | 0.9579 | 0.9644 | 0.9923   |
| 0.0619        | 3.0   | 93   | 0.0372          | 0.9712    | 0.9658 | 0.9685 | 0.9929   |
| 0.0421        | 4.0   | 124  | 0.0385          | 0.9736    | 0.9651 | 0.9693 | 0.9924   |
| 0.0367        | 5.0   | 155  | 0.0408          | 0.9364    | 0.9587 | 0.9474 | 0.9898   |
| 0.0261        | 6.0   | 186  | 0.0421          | 0.9371    | 0.9579 | 0.9474 | 0.9895   |
| 0.0312        | 7.0   | 217  | 0.0457          | 0.9371    | 0.9579 | 0.9474 | 0.9895   |
| 0.0245        | 8.0   | 248  | 0.0427          | 0.9423    | 0.9603 | 0.9512 | 0.9901   |
| 0.0265        | 9.0   | 279  | 0.0406          | 0.9521    | 0.9635 | 0.9578 | 0.9911   |
| 0.0231        | 10.0  | 310  | 0.0409          | 0.9529    | 0.9635 | 0.9581 | 0.9912   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-ner-thesis-dseb", "results": []}]}
transformers tensorboard safetensors xlm-roberta token-classification generated_from_trainer base_model:xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us